“My friend got 15 LPA by literally cheating on everything. The night before, he was searching for projects on GitHub to add to his resume. He cheated on the OA round and faked everything.”

That line from a Reddit thread is sad, but it sums up what’s happening in India’s most celebrated engineering colleges. Once seen as the sure-shot reward for years of grinding through exams and coaching, campus placements are losing credibility. And it’s not just because of a few students lying on their resumes. The system itself is showing cracks.

Placements are supposed to be the final validation of an institute’s quality. They are bannered on websites, in NIRF rankings, and in brochures. But the numbers are sliding, and no amount of spin is hiding it.

Government data presented to Parliament earlier this year revealed that 22 of the 23 IITs saw a drop in BTech placements between 2021-22 and 2023-24, with 15 of them seeing double-digit declines. Placements at IIT Delhi slipped from nearly 88% to 73%, at IIT Dharwad from about 90% to 66%, and IIT Jammu saw the figure crash from 92% to 70%. 

Even the top names — Bombay, Madras, Kanpur — all saw 10-13 percentage point drops.

The story is the same at the NITs. Of the 31 institutions, 27 reported falling average salary packages last year compared to the previous year. The number of NIT students placed dropped from 18,957 in 2022-23 to 16,915 in 2023-24, a fall of almost 11%. This isn’t a blip. It’s a trend.

The Ones Placed Are Also Average

The sense of disillusionment is widespread among students. Another Redditor put it sharply: “Most people cheat the OAs or have shit-ton of referrals. There are just some 20% who study properly and get into good companies. The rest are just getting average companies by faking projects or references.”

That bitterness is telling. Students are working the system, but employers are catching on. Cheating an online assessment may land you an interview, but it doesn’t get you through a live coding round or keep you afloat once you’re on the job.

The economy isn’t helping either. Global headwinds, layoffs in tech, cautious hiring. Even companies that used to hire in bulk from campuses are cutting numbers. For example, Indian IT aims to hire 1 lakh freshers this year. But at the same time, they are also laying off massively. 

This gap between perception and reality feeds cynicism. As one Reddit post put it: “The numbers shown by colleges are mostly inflated. For the majority, it’s service-based companies at 5–8 LPA. If you remove the handful of top offers, the average collapses.”

Some roles are being automated, others are being offshored or moved to contract work. For example, in a recent interaction with AIM, Hexaware CTO Satyajith Mundakkal said the young generation is excessively reliant on AI coding tools. 

He noted that resistance to AI tools is inevitable. “If I take GitHub Copilot away, 90% of my young population will struggle to code. They use these as coaches and guides. Our more senior population looks at it with a critical eye. They validate the work. That’s their role.”

Read: Hexaware CTO Says Without AI, 90% of Young Coders Would Struggle

The market has shifted, but students and colleges are still clinging to the old model where everyone expected a placement and a fat package at the end of four years, and now they are too reliant on AI for it. 

Eduard Ruzga, a staff engineer at Prezi, earlier told AIM that he doesn’t rely on GitHub activity as a hiring signal. “I would look at PRs if I wanted to learn about a potential candidate. There are other things that spike activity without real work,” he noted. What matters, according to him, is the reasoning and code quality.

Read: Are Developers Faking it on GitHub Using AI Coding Tools?

Everyone is Cheating

The hype around packages distorts the picture further. Every year, headlines trumpet crore-plus offers at IITs. What doesn’t get said is that those are rare, a handful of international postings, often including the cost of relocation, taxes, and stock components that may never vest.

The new changes to the H-1B visa policy with the $100,000 fee is going to make even those scarce. For the majority, the salaries are modest, sometimes lower than what family and friends believe based on the hype.

So placements are not just declining in percentage terms, but also losing out in value. The distribution is skewed with only a few getting stellar offers, most settle for less, some get nothing. Yet the brochures and media coverage keep selling the dream.

Companies, for their part, are moving away from trusting resumes or college brands. They’re running their own assessments, hiring through competitions, poaching through referrals. The old campus model — one visit, mass hiring — is shrinking. That leaves many students stranded.

The Reddit voices sound harsh, but they capture the mood on campuses better than any official statement. One user wrote: “Placements are just luck now. You either get a good referral, or you rot. Skills don’t matter unless you can show them in the exact format companies want.” 

Another said: “We’re all playing a game where everyone is cheating. The ones who don’t get left behind.”

The post Indian College Placements are a Joke appeared first on Analytics India Magazine.

Artificial intelligence may not be replacing developers yet, but it seems the fear of AI-led layoffs is already turning true. The best approach to retain the job you are in seems to be upskilling with the recommended AI tools.

The tables seem to have turned now as the companies, which until a year or two ago were asking employees to avoid AI tools, are now instructing them to adapt to AI, or find another employer. 

Those who resist AI are facing severe consequences. Coinbase CEO Brian Armstrong recently admitted that he fired engineers who refused to sign up for the company’s AI coding tools. Armstrong said on John Collison’s podcast ‘Cheeky Pint’ that he was stunned when some managers warned adoption would be slow even after Coinbase bought enterprise licenses for GitHub Copilot and Cursor.

“I went rogue,” he said, about the mandate he dropped into the company’s engineering Slack: “AI is important. We need you all to learn it and at least onboard. You don’t have to use it every day yet until we do some training, but at least onboard by the end of the week. And if not, I’m hosting a meeting on Saturday with everybody who hasn’t done it and I’d like to meet with you to understand why.”

Coders to ‘Code Enablers’?

That Saturday meeting led to layoffs. “Some of them had a good reason, because they were just getting back from a trip or something, and some of them didn’t [have a good reason]. And they got fired,” Armstrong said. 

He admitted it was a “heavy-handed approach” that not everyone liked, but he wanted to make the message clear: AI is not optional. 

GitHub CEO Thomas Dohmke has been just as blunt. “Either you embrace AI, or get out of this career,” he wrote on X earlier this month while sharing his blog post on the subject. 

Dohmke argued that developers are shifting from being coders to “code enablers,” managing AI agents, prompts, and outputs. He predicted that up to 90% of code writing could be automated within five years. “The software developer role is set on a path of significant change. Not everyone will want to make the change.”

Microsoft, Google, Salesforce, and several AI startups are increasingly expecting AI to write most of the code for them, and the same is the case with Indian IT majors and startups. 

At IgniteTech, CEO Eric Vaughan went even further, tearing down his workforce when he realised resistance was stronger than adoption. By early 2024, nearly 80% of staff were gone. “Changing minds was harder than adding skills,” he said. 

Every Monday was declared “AI Monday.” Staff were told they could not work on anything else. Those who refused were out. “Every company is facing an existential threat by this transformation,” Vaughan said. “This is not a tech change. It is a cultural change, and it is a business change.”

Enabling Employees?

The debate is no longer whether AI will reshape coding, but how much control developers will retain. For Armstrong, Dohmke, and Vaughan, resistance is grounds for firing. 

Some CEOs are pretty clear that they want everyone to know that the firing and layoffs are because of AI. Microsoft CEO Satya Nadella, in a memo on July 24, said that the layoff of 15,000 people over the course of the previous 12 months had been “weighing heavily” on him despite the aggressive AI push. 

Similarly, Google CEO Sundar Pichai told employees amidst the 12,000 TCS layoffs that employees basically have to use more AI tools. “I think we have to accomplish more,” Pichai said. “The world is looking to Google for leadership and responsible innovation.” But it does not just stop here. 

According to the report, Pichai further highlighted that the firm needs to optimise internal processes and actively reduce redundancy, while maximising team output. Overall, being more AI-savvy.  

AI advisor and investor Allie K Miller, while posting on LinkedIn last year a list of jobs that are paying big to people working with AI, said that AI will not replace jobs but actually enable employees. One of the startups she invested in told her: “We’d rather hire one software engineer who knows how to use AI than five who don’t, even if it’s the same cost.”

Read: 1 Developer Who Knows AI is Better Than 5 Who Don’t

The Fork in the Road

Beyond the mandates and firings, adoption numbers suggest the shift is already underway. At Y Combinator, partner Jared Friedman revealed that a quarter of founders admitted that over “95% of their codebase was AI-generated.” These were highly skilled founders who, just a year earlier, would have built everything themselves.

The cultural shift is visible even in smaller startups. “We’ve been using Cursor in our organisation for a while now, and it’s definitely boosted productivity,” Abhishek Upperwal, founder of Soket AI had earlier told AIM. But he warned that over-reliance wastes time when tools fail. “They work really well for common tasks like web development but tend to fall short with more complex challenges.” 

Salesforce CEO Marc Benioff had earlier gone a step further when he announced the company would not hire any more software engineers this year as AI is just better. 

What’s certain is that coding as a skill has evolved. It involves prompt design, AI orchestration, auditing, and debugging at scale. Some call it “vibe coding,” others say this is the end of software engineering as we know it. 

The fork in the road for developers is clear: either learn to work with AI or risk being left behind. And this time, CEOs are not waiting around.

The post CEOs are Firing Developers for Not Using AI appeared first on Analytics India Magazine.

Data and AI professionals thrive when they can directly see the impact of their work. Whether they are building predictive algorithms to optimise operations or creating models that drive strategic decision-making, a clear link between work and business impact fuels motivation. 

A strong data culture empowers employees across all levels to make informed decisions. Leading companies promote open data access and prioritise decisions driven by evidence, not intuition.

Great workplaces for data and AI professionals operate this way by default—using usage analytics, behavioural data, and employee feedback to foster transparency, continuous improvement, and personalised experiences that boost productivity and well-being.

Collaboration and Ethics are Non-Negotiable

Modern AI teams do not work in isolation. The best environments foster deep collaboration between data scientists, engineers, analysts, and business stakeholders. Whether through cross-functional squads or paired workflows, this collaboration ensures AI solutions are aligned to business needs and implemented responsibly.

Workplaces that prioritise responsible AI practices, such as conducting ethical reviews, fairness checks, and transparent development processes, build long-term trust. Leaders in the field increasingly recognise the need for ethical frameworks that go beyond compliance and into active oversight. 

Moreover, organisations must support ongoing education via courses, conferences, internal training, and expert communities. Equally crucial are soft skills like communication, advocacy, ethics, and empathy. These skills ensure AI is responsibly designed and effectively communicated.

Great workplaces offer data professionals the chance to tackle complex, meaningful problems. Structured challenge keeps work exciting, and recognition for both small wins and big projects sustains engagement. 

Moreover, varied opportunities, from rotations and cross-functional hackathons to conferences and certifications, keep skills sharp and careers advancing.

Recognition, Balance, and External Validation Matter

AI professionals work best when they are recognised, not only for innovation, but also for maintaining quality, consistency, and fairness. Performance review systems must reflect the complexity of the work, especially where experimentation is core to the process. 

Work-life balance also plays a role. Flexible work policies, mental health support, and sustainable workloads reduce attrition and burnout, both of which are common in high-pressure AI roles.

A growing number of companies are pursuing external recognition to validate their people-first approach. For instance, AIM’s Best Firm certification is based on internal employee sentiment and independent benchmarks. 

Firms that receive this recognition demonstrate that they offer the kind of environment top talent seeks—where data work is supported, valued, and embedded in core business success.

In India, TheMathCompany earned this certification after 91% of its data scientists endorsed its learning opportunities, well-being initiatives, and inclusive policies.

Others, including CEAT, AB InBev, HDFC, Hansa Cequity, MiQ, Wipro and Rakuten, have also received the badge, highlighting strong support systems, merit-based evaluations, and a commitment to diversity.AIM’s Best Firm certification signals workplaces that genuinely empower their technical teams.

The post What Makes a Workplace Great for Data & AI Professionals? appeared first on Analytics India Magazine.

In collaboration with MachineHack, IIT Gandhinagar (IITGN) presents two exciting hackathons. ML Decode and Data Sage have been designed to challenge knowledge, enhance problem-solving skills, and push the boundaries of technical abilities.

IIT Gandhinagar is a centre for cutting-edge research, which fosters interdisciplinary collaborations to tackle key challenges across science, technology, design and society. By partnering with leading global institutions and industries, the institute is driving innovation in areas such as energy, healthcare, AI, sustainability, and materials science.

With world-class research facilities, a strong emphasis on translational impact, and a flexible academic structure, the institute provides an environment that supports innovation, industry collaboration, and international research engagement.

The hackathon tests practical data science skills, including statistics, probability, data visualisation, and decision-making. Key topics include central tendency, outliers, skewness, basic probability, interpreting charts, A/B testing, and evaluation metrics like precision and recall. 

Success depends on your ability to understand data in real-world contexts—think visually, spot patterns, and make informed decisions based on the insights gained.

The Challenge

Put your knowledge to the test in a fast-paced MCQ round!

• Format: 10 questions | 10 minutes
  
• Live leaderboard: Track your real-time ranking
  
• Duration: 20 days (single attempt only)
  
• Scoring: Based on accuracy + time efficiency

Be a Part of the Hackathon

Shortlisting & Certification

Stand out and get recognised for your performance!

• Participation certificate from MachineHack
  
• Top rankers’ certificate from IIT Gandhinagar
  

Masterclass (Invite-Only)

An exclusive opportunity for the top 1,000 participants

• Live masterclass by the IITGN faculty
  
• Hands-on sessions on real-world ML/DS use cases
  
• Insights into advanced courses and academic pathways

 Why Participate?

Top performers in the hackathon will receive a prestigious certificate from IIT Gandhinagar, while the top 1,000 participants will gain exclusive access to a live masterclass led by IITGN faculty. This is a single-attempt, time-bound challenge designed to test your skills under pressure, with scores based on a combination of accuracy and time efficiency—making every second count.

Register for the Hackathon

Hackathon Tracks & Eligibility

ML Decode

Designed for students and professionals with a keen interest in machine learning and AI.

Eligibility:

• UG/PG in Tech, Science, or Commerce
  
• Minimum 60% or 6.0 CGPA
  
• Mandatory Grade 12 Mathematics
  
• No GATE required
  
• Deadline: July 24
  

Data Sage

Tailored for candidates with a strong technical foundation looking to prove their data science acumen.

Eligibility:

• BTech/MSc/MCA/BS-MS or equivalent
  
• Minimum 55% or 5.5 CGPA
  
• Background in Engineering or Technology
  
• Deadline: July 24

Whether you’re looking to kickstart your career or level up in your current role, this is a not-to-be-missed opportunity to validate your skills, earn prestigious recognition, and learn from the best in the field.

The post MachineHack announces ML Decode & Data Sage Hackathon with IIT Gandhinagar appeared first on Analytics India Magazine.

Code is just a part of the larger software cycle. However, it is the code that creates a lot of friction in the team. Ask most developers, and they’ll tell you that code reviews, meant to ensure quality, can sometimes feel like heated debates rather than helpful feedback sessions. 

Aravind Putrevu, director of developer GTM at CodeRabbit, believes that AI is about to soften the edges of this process. “The way I see it, AI brings impartiality to code reviews,” he said. “It doesn’t have an ego. It’s not there to nitpick or win an argument. It’s there to help.”

Putrevu pointed out that developers are often introverted by nature. Apart from team standups or collaborative debugging, the one place where they’re forced into meaningful conversations is during code reviews. “That’s where opinions clash, egos get bruised, and things can sometimes get ugly,” he said. “You’re defending your code while someone else pokes holes in it.”

AI tools like CodeRabbit are trying to change that dynamic by stepping in as the “first line of defence”. They catch the obvious mistakes, enforce style guidelines, and suggest improvements before a human reviewer takes over. “We’re not removing the human from the loop,” he clarified. “The developer still makes the final call, but AI can take care of the silly stuff.”

Click here to check out CodeRabbit on VSCode.

This new workflow—dubbed ‘vibe checking’—mirrors the vibe shift in coding. Earlier, developers wrote everything by hand. Now, with tools like GitHub Copilot, Cursor, and Lovable, AI helps generate significant chunks of code. The review process is just catching up.

The Rise (and Risks) of Vibe Coding

Another big trend Putrevu noted is the rise of “vibe coding” tools—applications like Lovable or Replit that let non-coders build apps using natural language prompts. 

On the surface, this seems like a revolution. Guritfaq Singh, co-founder at CodeRabbit, offered a word of caution. “It’s like learning to drive but not knowing that petrol is flammable,” he said. “You can go fast, but you’re missing the fundamentals. You don’t understand version control, code maintenance, or why code behaves a certain way.”

He recounted a recent interaction with a popular educator experimenting with an AI tool. “He didn’t know what YAML was. And this person was teaching others how to use these tools!” Singh laughed. It’s not his fault, really—it just shows that we need better guidance and more senior developers mentoring these new users.

And that’s where CodeRabbit comes in. “It acts like a senior engineer sitting next to you,” he says. “It guides you, reviews your pull request, and helps you avoid costly mistakes.” Compared to other tools, he said that CodeRabbit generates roughly 5 billion tokens per day. This is mainly for reviewing.

Scaling AI and the Growing Role of Developers

There’s no doubt AI is generating a lot of code. Cursor reportedly outputs close to a billion lines of code per day. Google recently said over 50% of the code in its repositories is now AI-generated, and YC Startups are generating 90% of their code with AI.

Alarm bells went off when Anthropic CEO Dario Amodei predicted that in less than six months, AI would handle 90% of coding. This is similar to what Sridhar Vembu, founder of Zoho, thinks. He recently said that 90% of what programmers write today is ‘boilerplate’.

But that doesn’t mean we need fewer engineers. Quite the opposite. There’s a whole new generation of developers coming in—many from non-traditional backgrounds. The demand is only going to grow.

However, there’s a catch. Developers who resist learning these tools could be left behind. “It’s like going from a hand-pulled rickshaw to an engine-powered one,” he said. “You’re still the driver, but if you don’t learn how to use the machine, you might become obsolete.”

On social media, there’s been chatter about AI tools replacing entire developer teams. Posts have gone viral claiming that a combination of Cursor and Claude could function as a $20/month senior engineer.

Putrevu laughed this off, saying that you can’t replace the experience and judgment of a seasoned dev with a few prompts. What you can do, though, is give every junior dev a powerful companion. 

In essence, AI tools today are not the new team—they’re the new teammate.

In the past, developers leaned heavily on Stack Overflow and IDEs for instant code assistance—whether debugging a cryptic error, understanding a library, or finding the right syntax. Putrevu says, “These tools helped individual productivity, not team collaboration.” 

While Stack Overflow served as a lifeline for lone developers, it rarely addressed issues like code consistency, review quality, or contextual feedback within a team setting. Similarly, IDEs were optimised for writing code, not understanding how that code fits into a broader system. 

This is where tools like CodeRabbit step in, not to replace Stack Overflow or IDEs, but to evolve the development experience into a more collaborative, review-driven process—embedding intelligent context and feedback right where the developer works.

Quality Still Matters

Despite all the promises, there are risks. Too much automation can result in what Putrevu calls “comment overload.” He jokes about a meme where a 10-line PR gets 500 comments, while a 200-line PR gets a casual ‘LGTM’. That’s the danger—when AI-generated code is accepted blindly.

Good code still needs good judgment. That’s why the human reviewer isn’t going away any time soon.

As for the future of vibe reviewing? “It’s not about replacing conversations,” he says. “It’s about making those conversations more meaningful. AI doesn’t shut down the vibe. It sets the tone.”

It remains clear that while the world is vibing to code, there is still no such thing as vibe engineering. While vibe coding can accelerate prototyping, it doesn’t replace software engineering. 

Real engineering involves long-term system design, reliability, scalability, and maintainability—concerns that current AI-generated code cannot handle. So, despite the hype, there’s no “vibe engineering”. It’s still just engineering, with or without the code typing.

The post We are Entering the Era of Vibe Engineering appeared first on Analytics India Magazine.

As India experiences a surge in AI job opportunities, graduates entering the job market in 2025 will need to master a strong set of skills to stay ahead of the competition. While speculations and discussions among developers on Reddit suggest a 5-6 month grind for building skills, the right direction to follow remains unclear.

The demand for skilled AI and software engineers is set to soar, considering that India’s tech industry anticipates a 9% growth in 2025, driven by sectors like IT, retail, telecom, and BFSI.

Based on current trends, here are the top skills for landing a job in India as a 2025 graduate starting from scratch:

Core Programming Skills

1. Python:

The demand for Python remains high due to its versatility and extensive use in web development, data science, automation, and AI.

Python, the language that became the most used language in 2024, is the top choice for job seekers who want to pursue any career in AI. Its simplicity and versatility have strengthened its status as the go-to language for AI and machine learning development. While C++ is still taught in universities, getting into the industry and building AI products requires the knowledge of Python.

From startups to tech giants, companies across India are actively seeking Python-proficient developers to drive their AI initiatives. Learning the core language, however, is just not enough.

Apart from being proficient in handling APIs, engineers also need to be well-versed in libraries such as TensorFlow, Keras, and PyTorch. These, along with pandas, NumPy, and Matplotlib for data science and Django and Flask for web development, are equally important.

2. JavaScript ecosystem

Tools and libraries such as Node.js, React, Angular, and the MERN stack (MongoDB, Express.js, React, Node.js) continue to dominate web development.

JavaScript’s role extends beyond web development; it has become increasingly important in AI, particularly for deploying machine learning models in web applications. Frameworks like TensorFlow.js allow developers to run AI models directly in the browser, enhancing user experiences without server-side computations. 

Why MERN Stack?

The MERN stack is a popular framework for building dynamic web applications. Its relevance extends to AI when developing platforms that require real-time data interaction and user engagement. 

Companies favour candidates with MERN stack experience to create scalable and AI-integrated web solutions that enhance user experiences. MERN is getting increasingly competitive, so staying ahead of the curve requires extensive practice and training. 

SQL and MongoDB

SQL remains critical for structured data management, while MongoDB caters to NoSQL database needs, which is essential for modern and flexible data applications.

Most Sought-After Skills

1. Data Structures and Algorithms (DSA):

• Why: Fundamental for clearing coding interviews across software development roles.
• Languages: Java and Python are popular choices for practising DSA. Java is often preferred for deeper understanding in Indian hiring scenarios.

2. Backend Development:

• Tech Stack: Java full stack (Spring Boot) or Python (Django or Flask)
• Why: Java continues to dominate backend applications, and Python is growing in demand.
• Entry Point: It is ideal for starting with MNCs, as Java remains the backbone of many enterprise applications.

3. Frontend Development:

• Tech Stack: React.js (part of MERN) or Angular
• Why: Frontend roles are abundant but competitive, and React is a market favourite.

4. Full Stack Development:

• Tech Stack: MERN
• Why: Many companies look for developers capable of handling both frontend and backend. However, the competition is high.

5. Data Analysis and Transition to Machine Learning:

• Skills: Python, SQL, Excel, Tableau and Power BI are relevant skills for entry-level data analysis roles.
• Next Steps: Transition into data engineering (PySpark, ETL) or machine learning (TensorFlow, PyTorch).

6. Cloud Computing:

• Platforms: Amazon Web Services (AWS), Azure, Google Cloud
• Skills: Docker, Kubernetes, and basic DevOps tools must be learnt to enhance employability.

7. Industry-Relevant Projects:

• Key Technologies: React, AWS, Docker, Spring Boot
• Why: Companies prioritise candidates with practical experience in modern tools over academic projects.

8. Text Editors

Master editors like VS Code, Sublime Text, or Atom boost productivity with features like syntax highlighting and code completion.

9. Integrated Development Environments (IDE)

Tools like PyCharm, Visual Studio, and IntelliJ IDEA streamline development with error highlighting and automation.

10. Object-Oriented Design (OOD)

• Apply principles like inheritance, encapsulation, and polymorphism.
• Design modular, scalable, and maintainable software architectures.

11. Cross-Platform Development

• Build apps for multiple platforms using Flutter, React Native, or Xamarin.
• Ensure seamless user experience across devices and operating systems.

12. Prioritisation Based on Scenarios:

• If interested in data, focus on Python, SQL, and Excel for Data Analysis.
• If aiming for development, start with Java and DSA and move to backend or full-stack development.
• If undecided, begin with DSA and a versatile language like Python, which can later transition into ML or web development.

13. Data Tools

Excel remains a fundamental tool for basic analysis, while Tableau, Power BI, Qlik Sense and QlikView offer advanced visualisation and business intelligence capabilities.

Must-Have Skills for Data Engineers:

• Cloud Platforms: Expertise in AWS, Azure, and Google Cloud Platform (GCP) is vital for managing and deploying cloud-based data infrastructure.
• Database Management: It is crucial to have knowledge of both relational (e.g., MySQL, PostgreSQL) and non-relational (e.g., MongoDB, Cassandra) databases.
• Data Pipelines and Orchestration: Familiarity with tools like Airflow (workflow orchestration), Kafka (real-time data processing), and ETL pipelines is critical for creating efficient data workflows.
• Snowflake: Increasingly recognised as a powerful data platform for storage and analytics, learning Snowflake is a must for data professionals.

The post Top Technical Skills You Must Have as a Developer in 2025 appeared first on Analytics India Magazine.

As the global AI landscape evolves rapidly, conferences have become essential gathering points for professionals, researchers, founders and enthusiasts. These events offer a platform to exchange ideas, unveil breakthroughs and foster collaborations that drive the future of AI.

For executives, engineers, or entrepreneurs alike, these top five global AI conferences in 2025 are where cutting-edge research meets real-world application. From the data-rich hubs of the US to the innovation ecosystems of India, Singapore, and the UK, these summits will set the tone for the AI evolution.

Cypher 2025 – Bengaluru, India

Dates: September 17–19, 2025
Venue: KTPO, Whitefield, Bengaluru

Celebrating its 10th edition, Cypher is India’s largest AI summit, attracting over 5,000 attendees daily. Organised by AIM, the event brings together industry leaders, AI pioneers, enterprises, startups, policymakers, and data practitioners to discuss the future of AI. The summit features keynotes, workshops, and exhibitions covering machine learning, data science, cybersecurity, and more.
Register here

SuperAI 2025 – Singapore

Dates: June 18–19, 2025
Location: Marina Bay Sands, Singapore

SuperAI is Asia’s largest AI event, bringing together over 7,000 attendees, more than 150 speakers, and over 1,000 AI companies. The conference features keynotes, panel discussions, and exhibitions showcasing the latest advancements in AI. Key highlights include the Genesis Startup Competition and the NEXT Hackathon, which offer opportunities for startups and innovators to showcase their ideas.
Register here

Data + AI Summit 2025 – San Francisco, USA & Online

Dates: June 9–12, 2025
Location: Moscone Centre, San Francisco, California, USA (Hybrid)
Hosted by Databricks, the Data + AI Summit is the world’s largest data, analytics, and AI conference, witnessing over 20,000 attendees. The four-day event offers over 700 sessions, keynotes, and training workshops on topics such as data engineering, machine learning, and large language models. Attendees can choose between in-person and virtual attendance options for the summit.
Register here

The AI Summit London 2025 – London, UK

Dates: June 11–12, 2025
Location: Tobacco Dock, London, UK
As the headline AI event of London Tech Week, The AI Summit London unites the most innovative technologists and business leaders in the UK and Europe to explore AI’s transformative real-world applications. The two-day conference features cutting-edge insights, high-value connections, and strategies to accelerate AI-driven growth across various industries.
Register here

Ai4 2025 – Las Vegas, USA

Dates: August 11–13, 2025
Location: MGM Grand, Las Vegas, Nevada, USA
Ai4 is North America’s largest AI industry event, bringing together business leaders and data practitioners to facilitate the adoption of artificial intelligence and machine learning technology. The conference features a range of tracks covering various industries, providing insights into AI applications and innovations.
Register here 

The post World’s Top 5 AI Conferences in 2025 To Watch Out For appeared first on Analytics India Magazine.

Artificial Intelligence is now everywhere and is advancing at lightning speed. If you’re looking to stay ahead of the curve, there’s no better way than diving into India’s top AI conferences. Whether you’re a data scientist, a CXO, a researcher or just AI-curious, these conferences offer networking, insights and exposure to the future of technology.

Here’s your guide to the five best AI conferences in India in 2025. 

1. Cypher 2025 

Bengaluru | September 17–19, 2025

If there’s one AI conference in India that brings everyone from startup founders to Fortune 500 executives under one roof, it’s Cypher. Organised by Analytics India Magazine, this mega-event attracts over 5,000 attendees daily and features 100+ speakers across three days.

Expect powerful keynotes, practical breakout sessions, live demos and even workshops tailored for engineers and decision-makers alike. The energy, scale and diversity of content make Cypher a must-attend for anyone serious about AI.

Who should attend: Enterprise leaders, AI practitioners, data engineers, and tech entrepreneurs.

Register here 

2. MachineCon GCC Summit 2025 

Bengaluru | June 19–20, 2025

Tailored for leaders in Global Capability Centres (GCCs), MachineCon focuses on the strategic use of AI at scale, especially within large enterprises. Expect high-level discussions on how generative AI is reshaping operational models, people management, and business strategy.

The event features a mix of CXO talks, closed-door sessions and real-world use case presentations, all with a laser focus on solving problems at scale in enterprise ecosystems.

Who should attend: Digital transformation leaders and heads of GCCs or innovation hubs.

Register here

3. Action AI Conference 2025 

 New Delhi | September 28, 2025

A one-day, power-packed event designed for practical AI implementation. Action AI brings together professionals solving actual business and societal problems using data and automation.

Speakers include industry innovators and thought leaders who aren’t just theorising but actually building. You’ll walk away with case studies, tools, frameworks, and ideas you can apply immediately.

Who should attend: Professionals, product managers and AI consultants.

Register here

4. AI Visionaries Summit 2025 – Empowering the Future with AI Insights 

New Delhi | May 30–31, 2025

Organised by Magnivel International, this summit brings together corporate leaders from diverse sectors to discuss strategies, challenges, and solutions in the rapidly evolving field of artificial intelligence. The event focuses on practical applications of AI, including generative AI, sustainable AI, and AI-powered decision-making across industries

Who should attend: Business strategists and professionals interested in leveraging AI for organisational growth and innovation.

Register here 

5. Vertical AI Summit 2025 

 Bengaluru | July 10–11, 2025

The Vertical AI Summit focuses on domain-specific AI applications that deliver measurable business outcomes. It brings together over 500 professionals, including founders, operators, and investors, to explore real-world AI solutions in sectors like healthcare, manufacturing, logistics, and finance.

Who should attend: Business leaders, industry-specific AI practitioners, investors, and decision-makers looking to implement or invest in specialised AI solutions.

Register here

The post Top 5 AI Conferences in India That You Can’t Afford to Miss in 2025 appeared first on Analytics India Magazine.

For a quick overview of a subject or a breakdown of concepts, SlideShare serves as a go-to platform for many. The recapitulations found in many of the presentations are both concise and informative.

The most popular presentations topics are the ones that have received the most number of likes and have been viewed more than the other presentations in a particular category.

AIM brings you the 15 most popular ppt topics on Artificial Intelligence, Machine Learning. Deep Learning and everything else in between.

1) Artificial Intelligence and Law Overview

People who are not aware of what artificial intelligence is will find the topic presented in a very simple manner here.

Along with the explanation of what AI is, the two major approaches towards AI are discussed– logic and rules-based approach, and machine learning approach. Special emphasis on the machine learning approach can be seen in the slides devoted to its detailed examination. The examination goes beyond the rudimentary explanation of what machine learning is and presents examples of proxies that seem like machine learning but are not.

The presentation lists examples of AI in the field of law and identifies some of the limitations of AI technology.

2)  What is Artificial Intelligence – Artificial Intelligence Tutorial For Beginners

For the uninitiated, this presentation offers an ideal rundown of AI. The question of AI being a threat is raised at the very beginning. However, as the presentation progresses, it discusses the basics necessary for understanding AI. The most basic question of what is artificial intelligence is answered.

A brief history of AI and the discussion on recent advances in the field of AI is also found. The various areas where AI currently sees practical application have been listed. Fascinating uses that AI can be put to in the future are also found in the presentation. The two approaches of achieving AI, machine learning and deep learning, is touched upon.

All in all, this presentation serves as a simple introduction to AI.

3) Why Social Media Chat Bots Are the Future of Communication

An exciting application of AI can be found in chatbots. Here, the limitless scope of chatbots is explored. The various milestones reached by leading players  in bot technology such as Facebook, Skype and KIK are enumerated.

The evolution of chatbots and its absorption of more AI in the future is also looked into. E-Commerce is touted as the biggest beneficiary of the advancement in chatbots and that bot technology will owe its rise to services and commerce.

Two tech giants, Facebook and Google, have been pitted against each other based on their ongoing developments in this area and the question of who will emerge as the best is raised.

4) AI and the Future of Work

This presentation talks about the far-fetching applicability of AI and ML,and the perils of that applicability. In order to derive a better understanding of this presentation, it is advisable to first watch the original talk.

During the course of the presentation, many examples of how machines can learn and perform any human task that is repetitive in nature are cited.

Other possibilities suggested include the creation of new unheard jobs for human beings as a result of aggressive use of AI and other allied technologies. Qualities that are characteristic only of human beings, may be the basis on which these jobs will be created is also suggested.

It concludes with a message- Ride the train, don’t jump in front of it.

5) AI and Machine Learning Demystified

In this presentation, Carol Smith establishes that AI cannot replace humans. Smith conveys that AI can serve the purpose of enabling human beings in making better decisions.

The slides talk about how the actions of AI are the result of the human inputs going into its programming. An AI’s bias is not its own, but the human bias with which it has been programmed, is emphasised on.

Other issues such as the need for regulations and other considerations within it that require deliberation are also touched upon. The presentation leaves you with a message – Don’t fear AI, Explore it.

6) Study: The Future of VR, AR and Self-Driving Car

Though no descriptive breakdown of topics related to AI is found, the presentation offers interesting numerical insights into many questions. Statistics on three main subjects – artificial intelligence, virtual reality and wearable technology, is provided here.

A variety of questions and the numerical representations of their responses are found under four main categories:

• Will you  purchase a self-driving car when they become available?
• Are you concerned with the rise of Artificial Intelligence?
• Is wearable technology part of your daily life?
• Do you own or intend to purchase a Virtual Reality headset in the next twelve months?

From consumer opinions to overall consensus of countries, the numbers show current trends and the possible trends in the future based on increasing development in the mentioned technologies.

7) Artificial Intelligence

There are many who have been introduced to AI only recently due to the buzz surrounding it and may not be aware of the early developments that led to its current status.

This presentation from 2009 offers a simple yet informative introduction to the rudiments of AI. AI’s history and a timeline of all the significant milestones in AI up to 2009 can be found. The presentation also provides an introduction to AI programming languages such as LISP and PROLOG.

For those who would like to have a crash course on the basics of AI in order to catch up with it current trends, this presentation serves the purpose.

8) Solve for X with AI: a VC view of the Machine Learning & AI landscape

While the concepts of  AI or ML are not spoken about, light is shed on other important aspects of it. The presentation discusses about how many known tech giants such as Google are bolstering their AI capabilities through mergers and acquisitions.

The role of venture capital(VC) in the landscape of AI and machine learning,and the involvement of VC in the firms that were acquired are mentioned.

Another point highlighted is how large companies are moving towards ML and re-configuring themselves around ML, and how it is not a US-centric phenomenon. Key points have been expressed in the form of self-explanatory graphical representations. Rounding off the presentation is the possible direction that ML can take and a few pointers on achieving success in ML.

9) Deep Learning – The Past, Present and Future of Artificial Intelligence

This presentation provides a comprehensive insight into deep learning. Beginning with a brief history of AI and introduction to basics of machine learning such as its classification, the focus shifts towards deep learning entirely.

Various kinds of networks such as recurrent neural nets and generative adversarial networks have been discussed at length. Emphasis has been given to important aspects of these networks and other mechanisms such as natural language processing (NLP).

Detailed examples of practical applications and the scope of deep learning are found throughout the presentation. However, this presentation may prove difficult for first time learner’s of AI to comprehend.

10) The Future Of Work & The Work Of The Future

The subject of self-learning of robots and machines is explored here. Talking about the fictional Babel fish, it is suggested that the advancements in technology leading to improved learning and translations by machines  made the Babel fish a near-real entity.

New ‘power’ values such as speed, networked governance, collaboration and transparency, among others, have been put forth and juxtaposed against older ones that are not fully technology  driven.

Going against the popular assumption that robots and machines will replace human beings, the presentation proposes that we are on the brink of the largest job creation period in humanity.

11) Asia’s Artificial Intelligence Agenda

This presentation is a briefing paper by the MIT Technological Review and talks about how the global adoption of AI is being sped up by Asian countries. It suggests that Asia will not only benefit greatly from the rise in AI technology, but will also define it.

The data collected for the review has been summarized in the form of simple info-graphics. They are a numerical reflection of the mood surrounding the adoption of AI across different industries and how it could possibly impact human capital.  The review also suggests that while there is awareness about AI in Asia, only a small percentage of companies are investing in it.

Pointers for business leaders in Asia to capitalize on AI is offered in the end along presentation with an info-graphic timeline of the history of AI.

Download review report in pdf

12) 10 Lessons Learned from Building Machine Learning Systems

While they are two separate presentations, they talk about the same subject- machine learning. The presentations are a summary of the analysis of machine learning adopted by two platforms, Netflix and Quora.

In case of Netflix, emphasis has been given to the choice of the right metric and the type of data used for testing and training. It also emphasises the need to understand the dependence between the data used and the models employed. The advice to optimize only areas that matter is offered.

The second presentation on Quora, talks about teaching machines only what is necessary. It stresses on the need the to focus on feature engineering and being thoughtful about the ML infrastructure. Another point it highlights is the combination of supervised and unsupervised being the key in ML application.

13) Design Ethics for Artificial Intelligence

With 135 slides, this presentation provides an exhaustive insight into the creation of an ethically sound AI. An introduction to the subject of User Experience(UX) design is followed by the rules that have to be considered during the designing process.

The chronological progression of UX, beginning with experience design and ending with intelligence design, and the direction in which this process is headed is also discussed.

Supported by powerful visuals, the presentation touches upon many essential considerations such as nature of intelligence, purpose of existence, awareness of self and the need for which the AI is created.

It raises a pertinent point that while creating AI, human beings are creating something that embodies qualities that they lack.

14) Artificial Intelligence

Made for a school competition in 2009, it provides many examples of cutting-edge applications of AI at the time.

Many of the examples, such as mind controlled prosthetic limbs, Ultra Hal Assistant and Dexter- the robot provide a trip down the AI memory lane where the applications of AI seemed like a page out of a sci-fi novel. It presents a list of areas where AI can assist human beings.

It concludes with  a series of questions, some of which, are still being debated. Such as machines replacing human beings’ and human unemployment due to the use of machines.

15) Artificial Intelligence (AI) – Impact on Different Sectors

The presentation provides an overview of Artificial Intelligence (AI), covering its definition, history, and applications. It explores various AI techniques such as machine learning, neural networks, and expert systems. The presentation discusses the impact of AI on different sectors, including healthcare, finance, and transportation. It also delves into the ethical considerations and potential risks associated with AI development. The future of AI is examined, highlighting emerging trends and potential advancements. The presentation concludes by emphasizing the importance of responsible AI development and its potential to revolutionize various aspects of human life, while also addressing concerns about job displacement and privacy.

The post 15 Best Presentations On Artificial Intelligence And Machine Learning in 2025 appeared first on Analytics India Magazine.

The volume of code written by AI coding tools has increased significantly since the emergence of ‘vibe coding’. Both developers and non-developers can now churn out code at an unprecedented rate using tools like GitHub Copilot, Cursor, and other AI-powered assistants. 

However, while code generation has accelerated, other aspects of the software development lifecycle—particularly code reviews—have not kept pace, potentially creating bottlenecks and quality concerns.

The Bottleneck in Code Reviews

“As AI generates more of our code, the bottleneck shifts from writing to reviewing. This new reality makes AI-powered code review valuable and essential for modern development teams,” Harjot Gill, CEO at CodeRabbit, said.

Traditionally, code reviews require manual scrutiny by peers, where senior engineers meticulously examine pull requests line by line. This process is both time-consuming and prone to human error. Missed bugs and overlooked inefficiencies can lead to costly business downtime and engineering distractions when these issues manifest in production.

“Engineering leaders often find that their senior engineers are stretched thin,” said Aravind Putrevu, Director of Developer Experience at CodeRabbit and an AI-driven code reviewer. “They not only write and maintain their code but also spend significant time reviewing junior engineers’ work. This becomes a bottleneck, slowing down the overall software development process.”

While AI has been instrumental in generating code, its unchecked proliferation raises concerns. More code does not necessarily mean better code. The sheer volume of AI-generated code necessitates a more efficient review process. 

“If AI code assistants like Cursor focus on helping developers write code, CodeRabbit acts as a reviewer on the other side,” Putrevu explained. “It serves as the first line of defense, eliminating obvious errors and ensuring that only refined code reaches human reviewers.”

CodeRabbit uses LLMs to automate reviews, identify potential issues, and provide actionable feedback. This process allows human reviewers to focus on architectural and business-critical decisions rather than spending time on trivial mistakes.

More Code, More Problems?

With AI enabling developers to generate and ship code faster, a key question emerges: Is quality being sacrificed in pursuit of speed?

“We’re seeing a new pattern emerge: developers using AI to write 80% of their code in minutes, then spending days debugging subtle integration issues and architectural misalignments. That’s why intelligent code review is becoming the critical path to deployment,” Gill further said.

“Earlier, a developer might spend 20% of their workday writing code. Now, with AI, they can generate significantly more in the same amount of time,” Putrevu noted. “But this increased output also means the review process becomes a much bigger bottleneck.”

With industry leaders claiming that AI can soon write 95% of the code, the problem of reviewing and debugging becomes even more prominent. This has also led to the birth of vibe debugging. 

Unchecked AI-generated code can lead to maintenance hazards. If poor-quality code makes its way into production, organisations could find themselves dealing with bloated, inefficient, and difficult-to-maintain systems. Simply having more code does not always equate to better software—it must be reviewed, refined, and optimised.

Should you be worried about AI reviewing your code?

Just like people have been sceptical about using AI coding tools within their organisations, they are also now sceptical about AI code reviewers for similar reasons. But CodeRabbit is emerging as a viable solution to this challenge as it ensures that code is not only syntactically correct but also adheres to best practices and organisational guidelines.

“Organisations can set predefined rules and quality metrics that AI reviewers enforce,” Putrevu added. “Even if AI agents are generating the code, CodeRabbit ensures that it meets a certain quality threshold before merging into production.”

This approach provides a scalable solution to the problem. AI-driven reviews complement traditional tools like SonarQube and Codacy by offering real-time suggestions and generating fixes, reducing the need for manual intervention. “We’re not replacing human reviewers. We’re augmenting them, helping developers ship faster without compromising quality,” Putrevu clarified.

Why Companies are Turning to AI for Code Reviews

Organisations from Fortune 100 companies like Visa and Mastercard to digital-native enterprises like Flipkart and emerging Y Combinator startups are adopting AI code reviewers. These companies recognise the need to maintain high-quality standards while accelerating development cycles.

For startups, where senior engineers may be scarce, AI-assisted reviews ensure that code quality is not compromised despite limited resources. Indie developers also benefit from an automated second opinion that provides insights they might otherwise miss.

“Developers don’t want to rely solely on static analysis reports or post-mortem quality checks,” Putrevu emphasised. “They need real-time feedback that helps them iterate quickly and efficiently.”

AI-generated code has the potential to revolutionise software development, but it also introduces new challenges. Without robust code review processes, organisations risk shipping subpar code that is difficult to maintain.

“More AI-generated code doesn’t necessarily mean better software,” Putrevu concluded. “The key is ensuring that the review process keeps pace with the speed of development. AI-driven reviewers like CodeRabbit are stepping in to bridge this gap, ensuring that teams can move fast without breaking things.”

The post AI Can Write Code in Seconds—But Who is Checking for Bugs? appeared first on Analytics India Magazine.

In today’s competitive job market, where you are also competing with AI agents for positions, finding the right job has become increasingly challenging. From crafting the perfect resume to sending personalised cover letters and tracking numerous applications, the job search process can be overwhelming. 

However, candidates approach job hunting in interesting ways. According to JobScan, 99% of Fortune 500 companies now use applicant tracking systems (ATS) to screen resumes, making it essential for job seekers to leverage AI tools to stand out.

1. Careerflow: LinkedIn Optimisation and Application Tracking

Careerflow functions as a personalised CRM for job seekers, streamlining the entire application process while focusing particularly on LinkedIn profile optimisation.

Key Features:

• LinkedIn profile enhancement for better visibility
• Automated job tracking capabilities
• Skill insights and analytics
• AI-generated cover letters
• One-click job data import from Chrome

What makes Careerflow stand out is its comprehensive approach to LinkedIn optimisation, which helps increase profile views and visibility to recruiters. The platform offers a hiring search tool to find actively hiring companies using filters such as skills, locations, and keywords. 

Additionally, candidates can get their resumes reviewed by experts based on target companies and required skill sets, making it an invaluable resource for serious job seekers.

2. Autojob: Automated Job Applications

Autojob is an all-inclusive job application platform that uses automation and AI technology to increase your hiring chances by up to three times.

Key Features:

• One-click job applications
• Personalised email outreach to recruiters
• Email finder tools for company outreach
• Predefined filters for qualified job sorting
• CV optimisation with rating feedback

This platform allows you to upload your CV and select job preferences, after which the auto mode applies to relevant positions on your behalf. Autojob’s advanced filtering system sorts and searches for the most qualified positions, unlike some job search engines that suggest irrelevant positions based on search strings. 

The platform also excludes companies you’ve previously worked with or aren’t interested in joining, making your job search more efficient and targeted.

3. Sonara: Your Personal AI Recruiter

Sonara operates as your AI recruiter, streamlining the job search process through advanced algorithms and automation.

Key Features:

• Automated job applications
• Resume optimisation
• Real-time analytics on application status
• Built-in virtual assistant with guides and resources
• Personalised cover letter generation

This platform provides valuable insights and analytics into the number of applications sent, reviewed, shortlisted, and rejected. Sonara’s virtual assistant offers detailed guides, tips, and resources to navigate the job search process effectively. 

The platform sends real-time notifications when new opportunities matching your profile become available, ensuring you never miss relevant openings. Sonara also offers a trial period where you can add up to three jobs daily, allowing you to test its capabilities before committing.

4. Prepper: AI-Powered Interview Preparation

Prepper, created by UK-based job search engine Adsuna, is an AI interview coach to help candidates prepare for upcoming interviews.

Key Features:

• Job-specific interview questions based on job descriptions
• Answer evaluation and feedback
• Preparation for different interview formats
• Industry-specific question generation

The tool works by analysing the job description you provide and generating relevant interview questions you’re likely to face. You can practice answering these questions and receive AI-powered feedback on your responses, helping you refine your interview skills before the actual meeting. 

This preparation can significantly boost your confidence and performance during interviews, increasing your chances of success.

5. JobScan: Optimise Your Resume for ATS Systems

JobScan has established itself as one of the most popular resume optimisation platforms in the job application ecosystem. This AI-powered tool analyses your resume against specific job descriptions to determine compatibility with ATS systems.

Key Features:

• Resume optimisation and ATS alignment
• Job tracker board to monitor all applications from one place
• Match percentage indicator showing alignment between your resume and job requirements
• LinkedIn profile analysis

JobScan helps job seekers understand what recruiters are looking for by providing a match percentage that indicates whether your qualifications align with the position’s requirements. 

The platform also allows you to track ongoing applications and interview schedules, eliminating the need for separate spreadsheets. With JobScan, you can fine-tune your resume to ensure it passes through automated screening systems and reaches human recruiters.

The job search landscape has evolved significantly with the integration of AI technologies. These five tools—JobScan, Careerflow, Autojob, Sonara, and Prepper—address different aspects of the job search process, from resume optimisation to interview preparation. By leveraging these AI-powered solutions, you can save time, increase efficiency, and improve your chances of securing your next position.

The post Top 5 AI Platforms That Will Help You Get Your Next Job appeared first on Analytics India Magazine.

The capabilities of generative AI have prompted businesses to explore its potential in customer service, but the underlying technical blockers remain significant. Recently dubbed “The Giant Engineering Problem That Nobody Else on Earth Has Been Able to Solve”, Large Language Model “hallucinations” expose businesses deploying customer-facing AI to intolerable risks.

Hallucinations happen because, at their core, LLMs generate responses through a probabilistic, token-by-token, autoregressive process. The model continuously selects what it sees as the most likely tokens from an extensive “token vocabulary” that can span hundreds of thousands of tokens. For example, OpenAI’s GPT-4o has a vocabulary size of nearly 200,000 tokens.

This token selection process is inherently error-prone, as each probabilistic prediction relies solely on the preceding context. This often leads to many different types of hallucinations and deviations from critical service protocols. Such unpredictability poses a significant challenge in high-stakes environments where consistent behavior is non-negotiable.

Some try to address unpredictability in chatbots by employing traditional solutions to confine LLM responses through rigid flow charts, as seen in frameworks like LangFlow, LangGraph, or Rasa. These solutions guide interactions along linear paths, but this is already known to fail at managing real-world queries that may involve multiple intents and conversational paths that deviate from the flow designer’s vision.

Moreover, adjusting responses in these contexts frequently necessitates tedious manual edits to flows and fragile modifications of prompts, posing risks of protocol breaches and unintended consequences. But even after all this, critical hallucinations still occur at an unacceptable level.

For example, if you’ve managed to use such frameworks to increase accuracy and correctness to an unprecedented 99%, that still exposes a bank servicing 1 million daily conversations to 10,000 new customer-facing mistakes to deal with every day, many of which can be practically unlimited in scope and severity. This is why enterprises are still averse to deploying customer-facing GenAI. But with Parlant, a framework now embraced by some of the largest financial services companies in the world, this is finally starting to change.

Fixing an LLM’s Achilles Heel

Parlant adopts a fundamentally different approach by developing an open-source conversational AI engine that allows developers to take control of their user-facing AI agents. Parlant is built by Emcie, an up-and-coming startup with leading software engineers from Microsoft, EverC, Check Point, and Dynamic Yield, along with natural language processing (NLP) researchers from the Weizmann Institute of Science, in collaboration with world-class Conversation Design experts from the Conversation Design Institute.

Parlant enables an AI Conversation Modeling system that automatically tailors responses from a large and dynamically controlled selection of pre-approved “utterances.” Using these new conversation modeling paradigms, organisations can precisely control GenAI communications while maintaining the level of naturalness and flexibility expected of LLMs, as operators and designers can manage and refine utterances with adjustable freedom levels, and Parlant’s engine applies intelligently applies them at the right time based on situational awareness and guidelines that you can provide it.

To simplify creating these utterances while prototyping, Parlant offers a ‘Fluid Composition’ mode where AI generates natural responses. This mode allows conversational designers to extract and tweak these auto-suggested responses into approved utterances while experimenting with their AI agents iteratively during development. 

Once established, the system switches to the ‘strict’ mode, exclusively using pre-approved utterances to construct responses. This ensures predictability and control while preserving the AI’s ability to creatively address diverse inquiries by intelligently utilising a large set of approved utterances using an LLM’s natural capabilities to select the best responses precisely.

Parlant analyses the conversation context at runtime, determines the relevant set of utterance candidates, and dynamically applies them to produce a response. It also filters and selects guidelines based on the context, allowing the developer to achieve a high degree of behavioural control over their agents without sacrificing the ability to scale the agents’ complexity. This runtime filtering of guidelines enables developers to support more conversational use cases while maintaining focused behaviour from their LLM in many different situations. 

Moreover, Parlant lets you easily troubleshoot by tracing how and why each utterance was applied for any given response. This is made possible by using highly descriptive and explainable log outputs, produced by the LLM during the utterance selection process.

Parlant, an open-source project, is LLM-agnostic, meaning that it supports multiple LLM providers, including OpenAI, Google, Meta, and Anthropic, via multiple inference providers. 

Prompt-Level Innovations Improve LLM Instruction Following

What allows Parlant to ensure aligned and expected outcomes from LLMs lies in the team’s research focus on techniques to gain control over LLMs.

Emcie, the startup company behind Parlant, earlier this year published a research study titled ‘Attentive Reasoning Queries (ARQ): A Systematic Method for Optimising Instruction-Following in Large Language Models’. The study outlines methods to optimise instruction following in LLMs.

Unlike free-form reasoning approaches such as Chain-of-Thought (CoT), Attentive Reasoning Queries (ARQs) guide LLMs through systematic, targeted queries that reinforce critical information and instructions and prevent hallucinations and attention drift.

The research also revealed test results where ARQs achieved a 90.2% success rate in correctly interpreting and applying instructions, outperforming CoT reasoning and direct response generation. The study also revealed that ARQs have the potential to be more computationally efficient than free-form reasoning when carefully designed. 

The post Open-Source ‘Parlant’ Fixes Hallucinations in Enterprise GenAI Chatbots appeared first on Analytics India Magazine.

AI-powered debugging tools have changed how developers identify, diagnose, and fix code issues. These tools use machine learning and artificial intelligence to automate traditional debugging processes, saving time and improving code quality. Here are the six most popular AI debugging tools, including their key features, required skills, and comparative advantages.

What are AI Debugging Tools?

AI debugging tools signify a major advancement in software development workflows. They use artificial intelligence to identify anomalies, propose fixes, and even self-correct coding issues in real time. These tools can significantly decrease debugging time while enhancing overall code quality and developer productivity.

What Makes AI Debugging Tools Different from Traditional Tools?

Traditional debugging requires developers to inspect code manually, set breakpoints, and trace execution to identify issues. In contrast, AI-powered debugging can proactively identify potential bugs, automatically suggest fixes, and learn from patterns within codebases to continually enhance its recommendations. These tools can detect complex issues that might be overlooked by human reviewers or traditional linting tools.

Who can use AI Debugging Tools?

While AI debugging tools aim to simplify the debugging process, developers should have certain skills before effectively using these tools:

• General Programming Knowledge
  • Fundamental programming concepts in your target languages.
  • Understanding of software architecture principles.
  • Familiarity with standard debugging techniques.
• Tool-Specific Knowledge
  • Understanding of the specific AI tool’s interface and workflow.
  • Knowledge of how to interpret and implement AI-suggested fixes.
  • Ability to review and validate AI-generated solutions.
• Domain Expertise
  • For specialised tools like TensorBoard, knowledge of machine learning concepts.
  • For test automation tools, an understanding of testing methodologies.
  • For security-focused tools, familiarity with common security vulnerabilities.

Top AI Debugging Tools and Their Features

1. CodeRabbit AI

CodeRabbit is an AI-powered code reviewer that delivers context-aware feedback on pull requests within minutes. It enhances manual code reviews by identifying overlooked issues and offering direct interaction for code generation and refinement.

Key Features:

• Context-aware feedback on pull requests
• Real-time chat for dynamic discussions during code reviews
• Automated bug detection and documentation generation
• Seamless integration with GitHub and GitLab workflows
• Incremental review system for large codebases

Required Expertise:

• Familiarity with Git-based workflows (e.g., GitHub, GitLab)
• Basic programming knowledge in supported languages
• Understanding of code review processes

2. CodeAnt AI

CodeAnt AI is designed to identify and automatically repair flawed code. It detects anti-patterns, duplicate or dead code, overly complex functions, and security vulnerabilities while offering auto-fixes directly within IDEs and CI systems.

Key Features:

• Detection of anti-patterns, dead/duplicate code, and security vulnerabilities
• One-click fixes for code quality issues
• Application security scanning (SAST) and infrastructure misconfiguration detection (IaC)
• Documentation of the entire codebase for visibility into structure and quality
• Supports on-premise or Virtual Private Cloud (VPC) deployment

Required Expertise:

• Knowledge of software security principles (for SAST/IaC features)
• Familiarity with CI/CD systems like GitHub or BitBucket
• Basic programming skills in supported languages

3. GitHub Copilot

GitHub Copilot is an AI pair programmer that helps write and debug code by providing suggestions based on context.

Key Features:

• Code suggestions and completion
• Debugging assistance with context awareness
• Integration with development environments
• Error analysis and fix suggestions

Required Expertise:

• Familiarity with version control systems
• Basic programming skills in your target language
• Critical thinking to evaluate suggestions

4. Codeium

Codeium offers AI-powered code assistance with autocomplete functionality and refactoring capabilities across 70+ programming languages.

Key Features:

• Unlimited single and multi-line code completions
• IDE-integrated chat for refactoring and explaining code
• Support for 70+ languages including JavaScript, Python, TypeScript, PHP, Go, Java, C++
• Refactoring tool for code optimisation

Required Expertise:

• Basic programming knowledge in targeted languages
• Familiarity with IDEs and their integration features

5. DeepCode

DeepCode uses deep learning to analyse code for bugs and security vulnerabilities across multiple programming languages.

Key Features:

• Real-time code analysis for identifying potential errors
• Security vulnerability detection
• AI-powered quick fixes with high accuracy (80% success rate)
• Customised rule creation capabilities

Required Expertise:

• Basic programming knowledge in supported languages (Java, JavaScript, Python)
• Understanding of common coding patterns and antipatterns

6. Qodo

Qodo stands out for its test generation capabilities and precise code suggestions that enhance overall code quality.

Key Features:

• Precise code suggestions with docstrings and exception handling
• Code explanation with detailed descriptions
• Automated test generation
• Code behavior coverage
• Seamless Git integration for collaboration         

Required Expertise:

• Knowledge of software testing principles
• Experience with version control systems
• Understanding of code documentation practices

Comparison of AI Debugging Tools

Below is a comparative analysis of the top AI debugging tools, highlighting their supported languages and unique features:

AI Debugging Tools for Different Languages

Different programming languages need specialised debugging tools:

Best For Python:

• PyTorch Debugger (pdb) integrates with Python’s built-in debugger
• MLflow for managing the machine learning lifecycle.

Best For JavaScript/TypeScript:

• Tabnine for intelligent code completions.
• Chrome DevTools for web application debugging.

Best For Java/C++:

• CodeGuru for performance recommendations.
• IBM Rational Software Analyzer for early bug detection.

The post Top 6 AI Debugging Tools for Software Developers appeared first on Analytics India Magazine.

Landing a job in 2025 as a developer is difficult. The availability of hundreds of tools in the market makes it even more difficult, leaving one with no idea about what experts use. 

Based on current adoption rates and industry trends, it is important to be up-to-date. These tools represent the backbone of modern web development across frontend, backend, and database layers. 

Development Environments and Version Control

Visual Studio Code

Visual Studio Code (VS Code) dominates the code editor space with multi-language support, an integrated terminal, and an extensive extension ecosystem. It offers built-in Git integration, intelligent code completion, and debugging capabilities across various programming languages. 

The editor’s lightweight nature doesn’t compromise performance while handling large projects. VS Code’s live sharing feature enables real-time collaboration between developers. Its customisability allows for personalised workflows with themes and keyboard shortcuts. 

Git

Git provides distributed version control that tracks code changes, enables multiple developers to work concurrently, and facilitates branch-based development workflows. It maintains a complete history of all modifications while consuming minimal storage through efficient compression algorithms. 

Git enables seamless reversion to previous states, conflict resolution during merges, and secure remote repository hosting. Its branching model supports parallel development of features, bug fixes, and experiments without affecting the main codebase. 

Docker

Docker delivers application containerisation that ensures consistent environments across development, testing, and production. It packages applications with dependencies into isolated containers that run identically regardless of host infrastructure. 

Docker eliminates “works on my machine” problems by providing reproducible development environments. Its layered file system architecture optimises storage and speeds up deployment by reusing common components. 

Docker Compose simplifies multi-container application orchestration with declarative configuration files. Containers start in seconds, consume fewer resources than virtual machines, and scale horizontally with orchestration tools like Kubernetes.

Frontend Development Tools

React

React led frontend development with 39.5% adoption in 2024, enabling component-based UI construction with a virtual Document Object Model (DOM) for performance optimisation. Its one-way data flow architecture prevents unpredictable state changes and simplifies debugging. 

React’s JSX syntax combines HTML with JavaScript for more intuitive component authoring. The library’s focus on UI components allows integration with various state management solutions like Redux or Context API. 

React Native extends the framework to mobile development, sharing code between web and mobile applications. Facebook’s continued development ensures stability and regular feature updates.

Angular

Angular provides a comprehensive TypeScript-based framework powering enterprise applications like Gmail and Upwork. Its component architecture enforces code organisation while two-way data binding simplifies model-view synchronisation. 

It includes a Command Line Interface (CLI) that automates project scaffolding, component generation, and build optimisation. Built-in dependency injection facilitates testable code and service reusability across components. 

Angular Universal supports server-side rendering for improved SEO and initial load performance. The framework’s opinionated structure accelerates development for large teams.

Vue.js

Vue.js offers progressive frontend development with optional adoption levels, from simple script tags to full build systems. Its gentle learning curve allows developers to incrementally adopt features as needed. 

Vue combines React’s virtual DOM performance with Angular’s template syntax familiarity. The framework’s single-file components encapsulate template, logic, and styling in one file for better maintainability. Vue’s reactivity system automatically tracks dependencies and updates the DOM when data changes. 

The core library focuses on the view layer while official companion libraries handle routing and state management. Version 3 introduced the Composition API for better TypeScript support and code reuse.

Bootstrap

Bootstrap accelerates responsive web development with pre-built components, grid system, and utility classes. It ensures consistent UI across browsers while accommodating various screen sizes through its mobile-first approach. 

The framework includes extensively tested JavaScript components for common interface patterns like modal dialogues and carousels. Bootstrap’s utility classes enable rapid styling without writing custom CSS. The framework’s extensive documentation includes examples and code snippets for immediate implementation. 

Backend Development Tools

Node.js

Node.js leads server-side JavaScript with 40.8% adoption in 2024, powering platforms like Netflix with asynchronous, event-driven architecture. Its non-blocking Input/Output (I/O) model handles thousands of concurrent connections efficiently, making it ideal for real-time applications. 

NPM provides access to over a million packages, accelerating development through code reuse. Node.js enables JavaScript throughout the entire stack, eliminating context switching between languages. The V8 engine delivers fast code execution while regular releases maintain security and performance. 

Django

Django delivers a Python-based framework with “batteries included” philosophy, providing authentication, object-relational mapping (ORM), an admin interface, and out-of-the-box security features. Its MTV (Model-Template-View) architecture enforces clean separation of concerns. Django’s ORM abstracts database operations across different database engines without changing code. 

The automatic admin interface generates CRUD (Create, Read, Update, and Delete) operations for models, accelerating backend development. Built-in security features protect against common vulnerabilities like SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF) attacks. Django REST framework extends capabilities for API development with minimal additional code. 

Spring Boot

Spring Boot simplifies Java application development with auto-configuration, embedded servers, and production-ready features. It eliminates boilerplate code through convention-over-configuration approaches while maintaining flexibility for custom requirements. 

The framework’s starter dependencies bundle compatible libraries to solve common problems like database access or security. Spring Boot’s actuator module provides production-ready features including health checks, metrics, and monitoring endpoints. Its embedded server approach enables standalone application deployment without external containers. 

Express.js

Express.js provides minimalist Node.js web framework functionality with routing, middleware support, and template engine integration. Its unopinionated design offers flexibility in application structure and component selection. Express middleware creates a pipeline for request processing, enabling cross-cutting concerns like authentication or logging. 

The framework’s routing system handles different HTTP methods and URL patterns with parameter extraction capabilities. Express works with various view engines for server-side rendering while supporting JSON responses for API development. 

NestJS

NestJS combines TypeScript with object-oriented programming principles to create scalable Node.js server applications. It implements architectural patterns from Angular, promoting consistent structure across backend services. NestJS provides built-in support for dependency injection, making components testable and loosely coupled. 

The framework includes integrated support for GraphQL, WebSockets, and microservices alongside traditional REST APIs. Its modular architecture encourages encapsulation of related functionality into cohesive units. 

Database Technologies

PostgreSQL

PostgreSQL delivers enterprise-grade relational database capabilities with advanced data types, robust transaction support, and powerful indexing options. It supports complex queries through window functions, common table expressions, and recursive queries. 

PostgreSQL’s extensible architecture allows custom data types, operators, and procedural languages. The database ensures ACID (atomicity, consistency, isolation, and durability) compliance with multi-version concurrency control for high throughput without locking issues. Its built-in replication options support high availability and read scaling. 

PostgreSQL handles geospatial data through PostGIS extension and JSON operations for hybrid relational-document storage patterns. Regular security updates maintain protection against emerging threats.

MongoDB

MongoDB provides a document-oriented NoSQL database that stores data in flexible JSON-like documents without requiring predefined schemas. Its horizontal scaling through sharding distributes data across multiple servers for improved performance and storage capacity. 

MongoDB’s query language supports complex operations, including aggregation pipelines for data transformation and analysis. The database includes built-in replication for high availability and disaster recovery. Its document model maps naturally to object-oriented programming structures, reducing impedance mismatch. 

API Development

GraphQL

GraphQL revolutionises API development by enabling clients to request exactly the data they need, reducing over-fetching and under-fetching problems. Its strongly-typed schema serves as a contract between server and client, improving documentation and enabling better tooling. 

GraphQL resolvers provide flexibility in data sources, including databases, microservices, or third-party APIs. The single endpoint architecture simplifies API versioning and evolution without breaking clients. Real-time capabilities through subscriptions support event-driven applications. 

GraphQL’s introspection enables automatic documentation generation and self-discovery of API capabilities. Performance optimisation techniques like dataloader prevent the N+1 query problem common in nested relational data.

The full-stack development landscape continues to evolve rapidly, with tools like Node.js and React dominating with over 40% adoption rates in 2024. This comprehensive toolkit spans development environments, frontend frameworks, backend systems, databases, and DevOps solutions.

The post Top Tools to Use for Full Stack Developers in 2025 appeared first on Analytics India Magazine.

In 2025, AI tools are redefining creativity and productivity, offering solutions that range from coding assistance to artistic expression. Everyone has converted their photos into Ghibli art in the last week, and the internet is filled with those images.

We spent a week experimenting with some of the most talked-about AI tools— Sora, Cursor, NotebookLM, MidJourney, Ideogram, ElevenLabs, Perplexity, and Otter—to see how they could transform the workflow. 

The results were fascinating, showcasing both their strengths and limitations.

OpenAI’s Sora

Sora by OpenAI is a text-to-video generator that converts simple text prompts into dynamic video clips. When you have generated Ghibli-style art with ChatGPT, you can make it into videos, which takes the experience to a whole new level.

Pros:

• Streamlines video creation, saving time and resources.
• Produces high-quality, visually appealing videos.

Cons:

• Increases dependency on AI, potentially impacting traditional video production skills.
• Requires time to master prompt crafting for optimal results.

Sora is a game-changer for content creators, making high-quality video production accessible without expensive software or technical expertise. However, mastering the tool’s nuances is essential to achieving the best results.

While the tool offers a fun and engaging way to create art, it does raise questions about AI’s role in replicating traditional artistic expressions. It’s particularly useful for casual users looking to enhance their photos, but might not be suitable for professional artists who prefer manual creative control.

Cursor

Anysphere’s Cursor AI is an AI-powered coding assistant designed to enhance developer productivity. Andrej Karpathy, one of the proponents of the tool, noted that it has slowly become indispensable for developers looking to do “vibe coding”.

Pros:

• Provides smart code suggestions and autocompletions, accelerating coding processes 
• Context-aware recommendations improve workflow efficiency.

Cons:

• May lead to over-reliance on AI, potentially affecting coding skill development.
• Some integration challenges with different development environments.

Cursor is a fantastic tool for software engineers looking to speed up their development cycle. However, developers must balance AI assistance with problem-solving skills to maintain a strong coding foundation. Also, many people are also testing out other tools like Windsurf and Zed.

NotebookLM

NotebookLM is an experimental AI tool by Google Labs that acts as a virtual research assistant. But that’s just one thing. The tool lets you input your documents on it and create podcasts in a realistic voice, which you can listen to for hours for better understanding. It is indeed quite phenomenal.

Pros:

• Helps synthesise complex information from multiple sources.
• Streamlines note-taking by summarising and organising information.

Cons:

• Handling sensitive information may raise security concerns.
• As an experimental tool, it lacks some features and stability.

NotebookLM is particularly useful for researchers and students managing large amounts of information. More than being useful in the long run, it seems like more of an immersive entertainment tool which you will never get bored of listening to.

MidJourney

MidJourney is the OG AI art generation tool that creates images based on textual prompts. After OpenAI released DALL-E and the era of open source Stable Diffusion, people started loving the style of Midjourney and have been hooked to it ever since. And now, the quality of the images generated is simply phenomenal.

Pros:

• Produces vibrant, detailed visuals across various styles.
• Encourages creative exploration and experimentation.

Cons:

• Requires a paid subscription for full access.
• Output quality varies depending on prompt specificity.
• Alternatives are catching up quickly.

MidJourney remains a favorite among digital artists and designers. However, those unwilling to invest in a subscription might find free alternatives more appealing.

Ideogram

Ideogram AI specialises in integrating text within images, making it useful for logos, posters, and other graphics. More than Midjourney or Gemini’s image generator, it is a Canva-like tool for creating graphics and presentations.

Pros:

• Seamlessly combines textual content with visuals.
• User-friendly interface for design enthusiasts.

Cons:

• Has constraints regarding image dimensions.
• Offers fewer customisation options than traditional graphic design software.

Ideogram is an excellent tool for quick graphic design tasks, but professional designers may find its customisation options limiting compared to Adobe or Canva.

ElevenLabs

ElevenLabs is an AI voice generation platform that creates realistic and expressive speech. The recent podcast between Lex Fridman and PM Narendra Modi was also translated into different languages using the platform. 

Pros:

• Produces natural-sounding speech with emotional nuance.
• Offers voice cloning capabilities with user consent.

Cons:

• Potential misuse in generating unauthorised voice replicas.
• Some features require a paid plan.

ElevenLabs is an incredible tool for voiceovers and audiobook creation, though ethical concerns around voice cloning should be considered before use.

Perplexity

Perplexity AI is a conversational AI search engine designed to provide accurate and up-to-date information. The Deep Research features on the platforms is amazing as it spends minutes finding the relevant information and gives back responses and details with proper citation.

Pros:

• Accesses real-time data, ensuring response relevance.
• Provides citations for verification of information.

Cons:

• It may still produce incorrect answers and require user validation.
• Sometimes delivers overly complex responses.

Perplexity is a great tool for fact-checking and research, but users should cross-reference sources before relying on its outputs.

Otter

Otter.ai is a transcription service that converts speech to text in real-time. You can just let it record, and it will transcribe everything in real time with maximum accuracy depending on the sound quality of the input. 

Pros:

• Provides accurate and real-time transcriptions.
• Useful for meetings, interviews, and lectures.

Cons:

• Struggles with noisy backgrounds and heavy accents.
• Some features are locked behind a subscription with limited access

Otter is a must-have for journalists and students, making transcription tasks significantly easier, though background noise can sometimes impact accuracy.

After testing these AI tools for a week, it’s clear that each has its strengths and limitations. Some, like ChatGPT’s Ghibli-style generator and MidJourney, are great for artistic expression, while others, like Cursor and NotebookLM, enhance productivity. 

However, reliance on AI tools should be balanced with human oversight to ensure ethical use and maintain essential skills.

The post We Spent a Week Using the Top AI Tools—Here’s What Happened appeared first on Analytics India Magazine.

For many years, software development has merged logic, creativity, and expertise. While developers have established the foundation of today’s digital infrastructure, AI coding tools are now stepping into the spotlight in what is referred to as “vibe coding. ” 

CTOs and AI experts predict that AI will soon handle 95% of coding, rendering all junior developers obsolete. However, with AI assistants writing, debugging, and even optimising code, a significant question arises: Are human developers heading towards obsolescence?

These tools are not just assisting developers—they are automating tasks that once required years of experience. Here’s a look at some of the top AI-powered coding tools that are reshaping the industry.

GitHub Copilot – The AI Pair Programmer

GitHub Copilot, developed by Microsoft and OpenAI, is arguably the most popular AI coding assistant today. Integrated with VS Code, Copilot suggests entire lines and functions in real-time. By analysing context and developer intent, it generates code snippets that can significantly accelerate the development process. It’s like having a supercharged pair programmer who never sleeps.

Pros:

• Speeds up coding by suggesting relevant code snippets.
• Works across multiple programming languages.
• Improves with usage and adapts to coding styles.

Cons:

• Sometimes generates incorrect or insecure code.
• Can make developers overly reliant on AI suggestions.

Cursor by Anysphere

Cursor is an AI-powered code editor built as a fork of VS Code by the company Anysphere, is the one that started the ‘vibe coding’ phase of AI with Andrej Karpathy praising it for its capabilities. It provides real-time AI-driven code suggestions, intelligent debugging, and even automated refactoring supporting models from Anthropic, Google, and OpenAI. 

Pros:

• Native AI integration within a VS Code-like environment.
• Enhances debugging and refactoring.
• Streamlines AI-assisted coding workflows.

Cons:

• Requires internet access for AI-powered features.
• Limited to specific environments.

Codeium Windsurf

Codeium’s Windsurf is an AI-powered coding assistant that offers autocomplete, refactoring suggestions, and documentation support. Developers are transitioning from Cursor to Codeium’s Windsurf faster than ever, as they find that the platform is not only very similar to Cursor but also quicker and more precise.

Pros:

• Free for personal use with extensive features.
• Supports multiple IDEs and programming languages.
• Works offline in certain setups.

Cons:

• Not as polished or widely adopted as GitHub Copilot.
• Requires internet access for optimal functionality.

Replit Ghostwriter

Replit’s Ghostwriter is an AI-assisted coding tool integrated into Replit’s cloud-based development environment. It helps developers by generating and completing code snippets, providing documentation support, and enabling real-time collaboration, even running on your smartphone.

Pros:

• Ideal for collaborative coding and rapid prototyping.
• Supports multiple programming languages.
• Works in an entirely online environment.

Cons:

• Not as powerful as Copilot for large-scale projects.
• Requires a Replit account and internet access.

Tabnine

Tabnine is an AI-powered code completion tool focused on enterprise use cases. Unlike Copilot, which relies on public repositories, Tabnine allows organisations to train models on their private codebases, ensuring secure and context-aware assistance.

Pros:

• Privacy-focused with on-premise deployment options.
• Tailor suggestions based on company-specific codebases.
• Works across multiple IDEs.

Cons:

• Requires setup and training for best results.
• Subscription-based with higher pricing for enterprise use.

Will AI Replace Developers?

AI coding tools are undoubtedly changing the development landscape, but full developer obsolescence is unlikely. While AI can generate code, it lacks human intuition, problem-solving abilities, and the deep understanding of business logic required to build complex software systems. 

Instead, these tools will likely transform the role of developers, making them more focused on architecture, security, and creative problem-solving rather than repetitive coding tasks.

Rather than replacing developers, AI is enhancing their efficiency. The future of programming will likely feature a hybrid approach in which AI manages mundane tasks, allowing human developers to concentrate on innovation. Those who adapt and learn to leverage these AI tools will gain a competitive edge in the industry.

So, are developers becoming obsolete? Not yet. But the smartest developers are learning to work alongside AI, using it as an ally rather than fearing it as a replacement.

The post Are Developers Becoming Obsolete? These AI Tools Say Yes appeared first on Analytics India Magazine.

In the bustling tech hubs and corporate centres of India, a quiet revolution is taking place. A new generation of women leaders is rising – bold, visionary and ready to transform the future of global capability centres (GCCs). These women spearhead digital transformation, champion diversity, and strengthen global strategies, proving that leadership has no gender. 

Meet these exceptional Indian women who are not only leading their organisations but also paving the way for future generations of leaders.

1. Lalitha Indrakanti

CEO of Jaguar Land Rover Technology & Business Services India

Lalitha Indrakanti is a seasoned business leader with nearly three decades of global experience in consulting, advisory, IT services, and digital transformations. At JLR Technology & Business Services India, she guides the organisation’s strategic vision across various enterprise functions, including engineering, IT, and supply chain. 

Indrakanti is known for her resilience, driving innovation and fostering inclusive cultures within her organisation. Outside her day job, she is an active member of the wider business community and industry bodies. A former member of the NASSCOM GCC Council and ex-chairperson of its regional council for Karnataka, Indrakanti has consistently served on corporate boards for over a decade.

2. Sirisha Voruganti

CEO & MD of Lloyds Technology Centre India

Under Sirisha Voruganti’s guidance, the Lloyds Technology Centre in Hyderabad has become a hub of innovation. Her deep expertise in IT architecture, data engineering, and fintech, combined with experience in senior technology roles at global companies, helps her foster an environment where new ideas and solutions flourish, driving growth in the tech world.

Previously, she served as the MD and board member at JCPenney India. Notably, she was also the first woman to hold the position of managing director for JPMorgan Chase in Technology in India. Voruganti’s emphasis on diversity and gender equity has transformed the workplace culture, encouraging a more inclusive environment.

3. Uma Ratnam Krishnan

Managing director & CEO of Optum India

Uma Ratnam Krishnan is a key figure in the healthcare technology sector. She leads Optum India’s digital transformation and innovation and has over thirty years of experience in leadership roles across various industries. 

Krishnan started her career as a diplomat in the Indian Foreign Service, which she credits for teaching her about diversity and adaptability. She later transitioned into corporate roles, primarily in the banking sector, working with institutions like ANZ Grindlays Bank and HDFC Bank.

She also served as the co-CEO of Barclays Global Service Centre India. In her current role, she oversees the company’s operations in India, focusing on delivering transformational solutions to global employers and the government to improve the quality of care and help lower costs.

4. Mamatha Madireddy

Managing director & head of HSBC India Global Service Centres

With over 20 years at HSBC, Mamatha Madireddy has been at the heart of its growth and transformation in India. Now, as the MD and head of HSBC India Global Service Centres, she brings a leadership style that values inclusivity, innovation, and excellence. 

Operating from Hyderabad, she continues to shape the future of banking operations. She is also the chair of the NASSCOM Telangana GCC Council and a member of its National GCC Council.

5. Kalavathi GV

Executive director and head of the global development centre at Siemens Healthineers

Kalavathi GV believes that technology has the power to revolutionise healthcare. As the leader of Siemens Healthineers’ global development centre in India, she focuses on innovation that enhances patient care and redefines industry standards.

Before joining Siemens Healthineers, she spent over 14 years in leadership roles at Philips Healthcare, followed by a decade at GE Healthcare. She also has experience in leading global digital transformation efforts at major multinational companies and shaping the future of healthcare through technology and innovation.

6. Anuprita Bhattacharya

Head of Merck IT Centre and IT country head India

Anuprita Bhattacharya is a seasoned IT leader known for her expertise in digital transformation and operational excellence. She champions diversity and inclusion initiatives within Merck’s GCC in India, fostering a collaborative work environment. 

She held various HR roles at Merck Group from 2018 to 2022, including lead HR business partner and HR business partner. She spent nearly a decade at General Motors, starting as an assistant manager in HR and progressing to HR manager and deputy manager roles between 2007 and 2016. 

7. Sreema Nallasivam

CEO of Metro Business Solution Centre 

As CEO of Metro Business Solution Center, Sreema Nallasivam has helped the company grow globally and change the game when it comes to GCCs. She’s a big supporter of women leaders, encouraging empathy, patience, and collaboration in leadership. Nallasivam believes in putting the team first, shifting the focus from individual achievements to working together and driving innovation.

With over 13 years at Metro, Sreema has played a pivotal role in shaping Metro’s GCC, headquartered in Pune. She is also a board member of Metro Global Solution Center.

8. Dhanya Rajeswaran 

Global vice president and country managing director for India, Fluence

Dhanya Rajeswaran serves as the global vice president for Fluence, a leading global provider of energy storage products, services, and optimisation software for renewables and storage. In this capacity, she oversees Fluence’s global innovation centre in Bengaluru, which has become the company’s largest hub. Fluence was established in India in 2022 and under her leadership, the Global Innovation Center in Bangalore has rapidly scaled to become a powerhouse of innovation with nearly 2/3rds of the global teams having a strong presence here.

9. Daisy Chittilapilly

President of Cisco India and SAARC

Daisy Chittilapilly is the president of Cisco’s India and SAARC regions. She took on this role in August 2021, bringing a wealth of experience from her previous positions at Cisco. 

Throughout her career, Chittilapilly has been recognised for her leadership and ability to drive digital transformation across various industries in India, including agriculture, healthcare, and infrastructure. She is also a strong advocate for gender equality in STEM fields, actively addressing the gap between women’s graduation rates and their participation in the workforce. 

10. Madhurima Khandelwal

Vice President at American Express

Madhurima Khandelwal is a highly accomplished professional in the field of analytics and leadership, currently serving as the managing director for the Credit & Fraud Risk (CFR) India Center of Excellence (CoE) at American Express. 

In this role, she leads a team of over 1,700 colleagues, focusing on developing solutions for American Express’s global business. With a career spanning over 18 years at American Express, Khandelwal has held various strategic roles, including head of AI Labs, where she significantly enhanced the company’s machine learning and artificial intelligence capabilities.

The post Meet the Top 10 Women Driving Change in GCCs appeared first on Analytics India Magazine.

The rise of AI is putting immense strain on energy systems worldwide. As India emerges as a key player in AI, it must find smart and sustainable ways to meet this growing demand. 

Ann Dunkin, former chief information officer at the US energy department, spoke with AIM on the sidelines of the Invest Karnataka 2025 summit, sharing interesting insights into how India can balance AI growth with sustainable energy solutions.

A Multi-Faceted Energy Strategy

India’s AI future largely depends on a robust and sustainable energy framework. Dunkin believes that the right approach is to leverage all available renewable resources. “Look at India’s assets and where the country can monetise these to get clean power at the lowest cost.”

She spoke about the “All-of-the-above energy policy” that revolves around wind, geothermal, solar, clean hydrogen, and nuclear power. 

Interestingly, the term gained prominence during the 2012 US presidential election, particularly with President Barack Obama’s administration using it to balance fossil fuel development with investments in renewable energy.

While the political shift in the US has lately impacted climate policies, Dunkin remains optimistic about clean energy’s momentum. “Even if our president chooses not to, the market will drive clean energy forward.”

She added that, with abundant sunlight and significant wind energy potential, India is well-positioned to tap into renewable resources to support the power-hungry AI infrastructure.

According to the Economic Survey 2024-2025, India’s data centre market is expected to grow from $4.5 billion in 2023 to $11.6 billion by 2032 at a compound annual growth rate of 10.98%.

With coal generating nearly 75% of India’s electricity, the data centre industry’s reliance on fossil fuels raises sustainability challenges. However, businesses are increasingly turning to renewable energy sources such as solar and wind power. 

Hiranandani Group’s data centre venture, Yotta, plans to secure more than 80% of its energy from renewable sources within the next three to five years. Likewise, Hyderabad-based CtrlS is targeting full reliance on renewable power by 2030.

Meanwhile,  Reliance Group, led by billionaire Mukesh Ambani, is purchasing AI GPUs from NVIDIA to build a new data centre in Jamnagar, Gujarat. It is touted as the world’s largest data centre. With a projected capacity of three gigawatts, the facility will dwarf existing data centres, which currently operate below one gigawatt.

Jamnagar, home to Reliance’s oil refining and petrochemical operations, is key to the company’s renewable energy ambitions. Nearby, a 5,000-acre green energy complex, including solar, wind, and hydrogen energy projects, is under development. 

The Data Centre Dilemma

Major tech companies like AWS and Microsoft are investing heavily in Indian data centres, raising questions about their long-term sustainability. 

Dunkin pointed out that the focus should not just be on expansion but also on optimising energy and water usage. “Investing in data centres is good, but looking at how we’re going to reduce the cost, energy consumption, and water consumption is equally important.”

She added that AI’s energy footprint can be reduced through innovations in hardware and software. She used DeepSeek and other models to illustrate how customers will need less compute to run them. She also noted that smaller language models, which can operate directly on devices, are set to become more common.

Safety First

Beyond energy, Dunkin said that there is a need for AI governance in India. She stressed the importance of data security, regulatory safeguards, and ensuring AI models are built on diverse and unbiased datasets.

“If your model is trained by a bunch of American white men, you’re going to get a model that’s biased towards American white men. We need training data from all backgrounds.”

At the Paris AI Action Summit, Prime Minister Narendra Modi voiced similar concerns about biases in LLMs.  

“We must build quality datasets free from biases, democratise technology and create people-centric applications. We need to address concerns related to cybersecurity, disinformation, and other threats, and must ensure that technology is rooted in many ecosystems for it to be effective and useful,” said Modi. 

It becomes crucial for India to build AI frameworks that reflect its diverse linguistic and cultural landscape.

Agentic Systems 

Looking ahead, Dunkin sees agentic AI as a key productivity driver. However, she also cautioned against data privacy risks, advocating for personal AI models that do not share user data externally. 

Surprisingly, Dunkin doesn’t use much of the AI apps like ChatGPT due to privacy and security concerns. “I don’t use it a lot, in part because I don’t have a private model that I have access to, and so I don’t want my data out there,” she said. 

“Your personal model should live on your device or in your personal cloud. It should fetch external data without sending your private data out.”

This thought aligns with India’s push for digital sovereignty and greater control over data privacy in AI applications.

The post AI Growth in India May Be a ‘Power’ Struggle appeared first on Analytics India Magazine.

India’s developer community reached new heights in 2024, both in terms of growth and innovation. The number of developers on GitHub in India surpassed 17 million, which is more than the entire population of Bengaluru city, making it one of the fastest-growing communities on the web. Not just that, the contributions to public generative AI projects increased by 95%, while India’s developer community continued to embrace AI-developer tools, setting the stage for even greater breakthroughs in 2025.

With a surging developer community and the large-scale adoption of AI tools, here’s how India’s developer community will make its mark on the world in 2025.

Indian Devs Hold the Power for Large-Scale Transformation

The sheer size of India’s vibrant developer community signals incredible prosperity for 2025—after all, where developers thrive, economic growth follows.

Combined with the power of AI tools like GitHub Copilot, India’s developer community is poised to fuel a fresh wave of homegrown tech multinationals, a new generation of disruptive startups, and an empowered open-source community like never before. These communities will leverage AI developer tools to shape global innovation and create digital solutions that benefit society—much like the impactful work being done by Open Healthcare Network in India.

To turn this vision into reality, GitHub is empowering every Indian developer to unlock the potential of AI-powered software by offering GitHub Copilot Free in VS Code. With the community shipping software up to 55% faster, immense digital and economic progress lies ahead for India.

Imagine a country where each developer, equipped with AI tools like GitHub Copilot, contributes to the nation’s growth by solving problems for their society. They educate people about technology, contribute to open-source digital public goods, and create newer ways to drive large-scale transformation.

It’s a New Era of ABCD: AnyBody Can Develop!

AI has successfully created a bridge between humans and machine languages. This will empower children in India to start programming in their native languages even before learning English in schools.
Be it Hindi, Kannada or Marathi, aspiring developers can now write and understand code—traditionally a complex abstraction layer—in natural language.

With AI-powered tools such as GitHub Copilot, students can learn to code in their own language, using AI as a personal programming assistant, much like a calculator for coding. This will add millions more to India’s rising developer community, cementing its place as the largest developer hub in the world. More importantly, it will ensure that every Indian student can explore STEM careers and express their creativity through code without needing to learn English first.

Digital Diwali: Where Ideas Ignite with the Power of AI

AI is ready to empower anyone to turn their ideas into reality, all in natural language, leading to an explosion of creativity in India and across the globe. This has never been more possible than with GitHub Spark, an AI-powered tool for creating and sharing micro apps (“sparks”) tailored to individual needs and preferences. While still in technical preview, it’s going to build a world where anyone and everyone is empowered to create or adapt software for themselves.

If India continues to nurture its developer community and grow it at scale while simultaneously embracing the transformative power of AI, the nation will not only cement its place as a global AI leader but also extend the economic opportunity of building software to all its people. The promise of this new future is entirely possible—and 2025 will be a pivotal year in paving the way.

The post AI and India’s Developer Surge Will Spark Innovation in 2025 appeared first on Analytics India Magazine.

AIM organises some of the tech industry’s most impactful conferences to bring together experts and innovators from various fields. These events cover a broad spectrum of topics, from promoting diversity and inclusion in technology to delving into the latest breakthroughs in generative AI. 

Whether you’re a data engineer, AI startup founder, developer, or corporate leader, AIM’s conferences provide important opportunities to learn, connect, and stay at the forefront of the advancing tech landscape.

We have cherry-picked the top 10 talks from the 2024 AIM conferences, which offer exclusive insights into the future of AI, the challenges it presents, and how India is positioning itself as a global leader in the field.

1. ‘Is GenAI for Real’ by Zerodha CTO Kailash Nadh

At Cypher 2024, Zerodha CTO Kailash Nadh expressed his scepticism about the timeline for achieving Artificial General Intelligence (AGI), calling claims of its arrival in two to five years “unrealistic”. He attributed the definition of AGI to Western AI companies and pointed out that it has always been “five years away”. He dismissed such predictions as likely motivated by business or valuation reasons.

Nadh also highlighted the vital role of open-source technology in the generative AI boom. He noted that open-source tools and models often outperform proprietary ones and also mentioned that Zerodha is using large language models (LLMs) to automate certain tasks. On the topic of AI and jobs, Nadh reassured that Zerodha would not allow job losses due to AI. 

2.‘Lessons in Bravery, Integrity & Leadership for Tech Professionals’ by Kiran Bedi 

At Rising 2024, Kiran Bedi, who became India’s first woman Indian Police Service (IPS) officer, shared her journey and insights on women’s success in male-dominated fields. Bedi explained that her first priority was always her career and making herself self-reliant and self-sufficient. Despite the challenges of working in the IPS, where women were underrepresented and faced discrimination, Bedi said she never questioned her abilities and confidently pursued her goals.

Discussing the low representation of women in leadership roles, Bedi pointed out that lack of career prioritisation and family support were key barriers. She recalled how her parents supported her through difficult times. 

Bedi highlighted that women need to plan and manage their personal lives effectively and advised that motherhood and family life should be carefully managed to avoid conflicts with career goals.

3. ‘India Proves Sam Altman Wrong!’ by Former Tech Mahindra CEO CP Gurnani

At the MachineCon GCC Summit 2024, CP Gurnani, co-founder of AIonOS and former CEO of Tech Mahindra, challenged OpenAI CEO Sam Altman’s claim that India couldn’t develop its own LLMs. 

Gurnani revealed that Tech Mahindra developed an Indian LLM for local languages and over 37 dialects in just five months with a budget of under $5 million. 

Gurnani used companies like IndiGo and Airtel as examples of how they have competed with giants like Jio to discuss how India’s success hinges on “frugal innovation.” He also introduced his new venture, AIonOS, which aims to disrupt industries like travel and logistics through AI.

Tech Mahindra also launched Project Indus, an indigenous LLM focused on Indic languages and dialects to improve linguistic inclusivity in AI.

4. ‘Scaling AI for Billions: The Indian Perspective’ by Wadhwani AI CEO Shekar Sivasubramanian

At Cypher 2024, Wadhwani AI CEO Shekar Sivasubramanian discussed the concept of applied AI and the challenges of working in India’s vast and diverse ecosystem. 

He pointed out that applied AI lies in bridging the gap between the chaotic, unstructured AI ecosystem and the rigorous, systematic research world. In India, deployment must come before AI development with a focus on solving real-world problems rather than abstract ones. 

Sivasubramanian also highlighted the importance of a “market-of-one” approach, where AI solutions are tailored to specific needs, particularly in government and rural settings.

He outlined the differences between designing AI for ‘India’ – the urban, well-connected population – and ‘Bharat’ – where users often have limited experience with technology. He stressed that AI must be simple, intuitive, and practical for everyday users.

5. ‘Navigating Data Chaos: Using Gen AI to Extract Structured Insights from Unstructured Customer Data’ by NoBroker Data Sciences and Engineering Director Zaher Abdul Azeez

At AIM’s Data Engineering Summit 2024, Zaher Abdul Azeez, director of data sciences and engineering at NoBroker, discussed the potential of GenAI in transforming customer-facing services by extracting valuable insights from unstructured customer data, such as conversations.  

Azeez pointed out that customer conversations, which tend to be subjective and informal, are extremely valuable for businesses, particularly those centred around customer experience. Traditional methods of analysing these interactions are manual and labour-intensive. However, GenAI, especially LLMs, offers a more efficient solution by understanding and processing unstructured data from these conversations.

6. ‘Generative AI and the Road to Singularity’ by Tech Whisperer Founder Jaspreet Bindra

At MLDS 2024, Jaspreet Bindra, founder of Tech Whisperer and CEO of Ai&Beyond, opened his keynote by exploring the concept of singularity and its implications for AI. He raised the question of whether AI could surpass human intelligence to become self-sufficient and leave humans obsolete. 

Bindra delved into the varying predictions of AGI timelines. He cited experts like Sam Altman and Ray Kurzweil, who have different definitions and timelines for AGI’s arrival, ranging from 2026 to 2030.

7. ‘The Next Tech Superpower: How India Can Lead the World in AI Innovation’ by Former Infosys CFO Mohandas Pai

At Cypher 2024, former Infosys CFO and board member Mohandas Pai spoke about the growing technological partnership between India and the United States, positioning them as leading global digital powers. He contrasted this collaboration with China’s isolation due to its restrictive digital firewall, which limits outside influence and internal connectivity with the global tech ecosystem. 

Pai drew attention to the close ties between Bengaluru and Silicon Valley, as well as their shared innovation culture and extensive research collaborations. Although Bengaluru boasts the world’s largest talent pool of chip designers, testers, and embedded software professionals, with over 3.50 lakh experts, Pai noted that the city requires more capital and competitive investment to maximise its potential.

Despite political differences, Pai described the US and India as “connected at the hip” in technology, serving as a “force multiplier” for mutual growth. In contrast, he criticised Delhi for its lack of progress on domestic issues like pollution.

8. ‘Powering India’s AI-First Ambitions With Shakti Cloud’ by Yotta CEO Sunil Gupta

Sunil Gupta, co-founder, managing director and CEO of Yotta, spoke at AIM Cypher 2024 and shared key developments regarding the company’s advancements in AI infrastructure.

Yotta, backed by the Hiranandani Group, had made significant progress in acquiring GPUs to support the AI boom in India. Last year, the company announced plans to acquire 32,000 NVIDIA GPUs over the next two years and had already secured 16,000 NVIDIA H100 GPUs.

As an elite NVIDIA partner, Gupta shared that Yotta’s partnership had ensured access to the latest GPUs. This enabled the company to meet a wide range of AI use cases in India, from developing large-scale models to smaller ones. 

9. ‘Voice Based AI Agents’ by Sarvam AI Co-Founder Vivek Raghavan 

At Cypher 2024, Sarvam AI co-founder Vivek Raghavan discussed the company’s mission to develop voice-based AI solutions tailored to Indian languages and dialects. 

He highlighted that India’s culture of conversation drives its focus on voice-led models in local languages. Raghavan demonstrated Sarvam’s voice agents, which operate via telephone and WhatsApp, allowing users to interact in languages like Kannada and Hindi for tasks such as booking appointments and customer support. 

10. ‘Impact Investing in AI Merging Profit with Purpose’ by Ronnie Screwvala 

At Cypher 2024, upGrad co-founder Ronnie Screwvala talked about how AI can help India achieve its Viksit Bharat vision by 2047 by aiming for a GDP growth from $3.4 trillion to $30 trillion. 

He said that AI should be seen as a tool for enhancing capabilities rather than a threat. Screwvala stressed the importance of AI in maximising intellectual property creation and fostering innovation. 

Read: 6 Must-Attend Conferences for Developers by AIM in 2025

The post Top 10 Talks from AIM Conferences in 2024 appeared first on Analytics India Magazine.

Looks like the race to build large language models is winding down, with only a few clear winners. Among them, DeepSeek V3 has claimed the spotlight in 2024, leading the charge for Chinese open-source models. Competing head-to-head with closed-source giants like GPT-4 and Claude 3.5, DeepSeek V3 notched 45,499 downloads last month, standing tall alongside Meta’s Llama 3.1 (491,629 downloads) and Google’s Gemma 2 (377,651 downloads), according to Hugging Face.

But not all LLMs launched this year could ride the wave of success—some fell flat, failing to capture interest despite grand promises. Here’s a look at the models that couldn’t make their mark in 2024.

1. Databricks DBRX

Databricks launched DBRX, an open-source LLM with 132 billion parameters, in March 2024. It uses a fine-grained MoE architecture that activates four of 16 experts per input, with 36 billion active parameters. The company claimed that the model outperformed closed-source counterparts like GPT-3.5 and Gemini 1.5 Pro. 

However, since its launch, there has been little discussion about its adoption or whether enterprises find it suitable for building applications. The Mosaic team, acquired by Databricks in 2023 for $1.3 billion, led its development, and the company spent $10 million to build DBRX. But sadly, the model saw an abysmal 23 downloads on Hugging Face last month.

2. Falcon 2 

In May, the Technology Innovation Institute (TII), Abu Dhabi, released its next series of Falcon language models in two variants: Falcon-2-11B and Falcon-2-11B-VLM. The Falcon 2 models showed impressive benchmark performance, with Falcon-2-11B outperforming Meta’s Llama 3 8B and matching Google’s Gemma 7B, as independently verified by the Hugging Face leaderboard. 

However, later in the year, Meta released Llama 3.2 and Llama 3.3, leaving Falcon 2 behind. According to Hugging Face, Falcon-2-11B-VLM recorded just around 1,000 downloads last month.

3. Snowflake Arctic 

In April, Snowflake launched Arctic LLM, a model with 480B parameters and a dense MoE hybrid Transformer architecture using 128 experts. The company proudly stated that it spent just $2 million to train the model, outperforming DBRX in tasks like SQL generation. 

The company’s attention on DBRX suggested an effort to challenge Databricks. Meanwhile, Snowflake acknowledged that models like Llama 3 outperformed it on some benchmarks.

4. Stable LM 2 

Stability AI launched the Stable LM 2 series in January last year, featuring two variants: Stable LM 2 1.6B and Stable LM 2 12B. The 1.6B model, trained on 2 trillion tokens, supports seven languages, including Spanish, German, Italian, French, and Portuguese, and outperforms models like Microsoft’s Phi-1.5 and TinyLlama 1.1B in most tasks.

Stable LM 2 12B, launched in May, offers 12 billion parameters and is trained on 2 trillion tokens in seven languages. The company claimed that the model competes with larger ones like Mixtral, Llama 2, and Qwen 1.5, excelling in tool usage for RAG systems. However, the latest user statistics tell a different story, with just 444 downloads last month.

5. Nemotron-4 340B 

Nemotron-4-340B-Instruct is an LLM developed by NVIDIA for synthetic data generation and chat applications. Released in June 2024, it is part of the Nemotron-4 340B series, which also includes the Base and Reward variants. Despite its features, the model has seen minimal uptake, recording just around 101 downloads on Hugging Face in December, 2024.

6. Jamba 

AI21 Labs introduced Jamba in March 2024, an LLM that combines Mamba-based structured state space models (SSM) with traditional Transformer layers. The Jamba family includes multiple versions, such as Jamba-v0.1, Jamba 1.5 Mini, and Jamba 1.5 Large.

With its 256K token context window, Jamba can process much larger chunks of text than many competing models, sparking initial excitement. However, the model failed to capture much attention, garnering only around 7K downloads on Hugging Face last month.

7. AMD OLMo 

AMD entered the open-source AI arena in late 2024 with its OLMo series of Transformer-based, decoder-only language models. The OLMo series includes the base OLMo 1B, OLMo 1B SFT (Supervised Fine-Tuned), and OLMo 1B SFT DPO (aligned with human preferences via Direct Preference Optimisation). 

Trained on 16 AMD Instinct MI250 GPU-powered nodes, the models achieved a throughput of 12,200 tokens/sec/gpu. 

The flagship OLMo 1B model features 1.2 billion parameters, 16 layers, 16 heads, a hidden size of 2048, a context length of 2048 tokens, and a vocabulary size of 50,280, targeting developers, data scientists, and businesses. Despite this, the model failed to gain any traction in the community.

The post LLMs that Failed Miserably in 2024 appeared first on Analytics India Magazine.

NVIDIA is one of the most influential hardware giants in the world. Apart from its much sought-after GPUs, the company also provides free courses to help you understand more about generative AI, GPU, robotics, chips, and more. 

Most importantly, all of these are available free of cost and can be completed in less than a day. Let’s take a look at them.

1. Building RAG Agents for LLMs

Building RAG Agents for LLMs course is available for free for a limited time. It explores the revolutionary impact of large language models (LLMs), particularly retrieval-based systems, which are transforming productivity by enabling informed conversations through interaction with various tools and documents. Designed for individuals keen on harnessing these systems’ potential, the course emphasises practical deployment and efficient implementation to meet the demands of users and deep learning models. Participants will delve into advanced orchestration techniques, including internal reasoning, dialog management, and effective tooling strategies.

In this workshop you will learn to develop an LLM system that interacts predictably with users by utilising internal and external reasoning components.

Course link: https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-15+V1

2. Accelerating Data Science Workflows with Zero Code Changes

Efficient data management and analysis are crucial for companies in software, finance, and retail. Traditional CPU-driven workflows are often cumbersome, but GPUs enable faster insights, driving better business decisions. 

In this workshop, one will learn to build and execute end-to-end GPU-accelerated data science workflows for rapid data exploration and production deployment. Using RAPIDS-accelerated libraries, one can apply GPU-accelerated machine learning algorithms, including XGBoost, cuGraph’s single-source shortest path, and cuML’s KNN, DBSCAN, and logistic regression. 

More details on the course can be checked here – https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+T-DS-03+V1

3. Generative AI Explained

This self-paced, free online course introduces generative AI fundamentals, which involve creating new content based on different inputs. Through this course, participants will grasp the concepts, applications, challenges, and prospects of generative AI. 

Learning objectives include defining generative AI and its functioning, outlining diverse applications, and discussing the associated challenges and opportunities. All you need to participate is a basic understanding of machine learning and deep learning principles.

To learn the course and know more in detail check it out here – https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-NP-01+V1

4. Digital Fingerprinting with Morpheus

This one-hour course introduces participants to developing and deploying the NVIDIA digital fingerprinting AI workflow, providing complete data visibility and significantly reducing threat detection time. 

Participants will gain hands-on experience with the NVIDIA Morpheus AI Framework, designed to accelerate GPU-based AI applications for filtering, processing, and classifying large volumes of streaming cybersecurity data. 

Additionally, they will learn about the NVIDIA Triton Inference Server, an open-source tool that facilitates standardised deployment and execution of AI models across various workloads. No prerequisites are needed for this tutorial, although familiarity with defensive cybersecurity concepts and the Linux command line is beneficial.

To learn the course and know more in detail check it out here – https://courses.nvidia.com/courses/course-v1:DLI+T-DS-02+V2/

5. Building A Brain in 10 Minutes

This course delves into neural networks’ foundations, drawing from biological and psychological insights. Its objectives are to elucidate how neural networks employ data for learning and to grasp the mathematical principles underlying a neuron’s functioning. 

While anyone can execute the code provided to observe its operations, a solid grasp of fundamental Python 3 programming concepts—including functions, loops, dictionaries, and arrays—is advised. Additionally, familiarity with computing regression lines is also recommended.

To learn the course and know more in detail check it out here – https://courses.nvidia.com/courses/course-v1:DLI+T-FX-01+V1/

6. An  Introduction to CUDA

This course delves into the fundamentals of writing highly parallel CUDA kernels designed to execute on NVIDIA GPUs. 

One can gain proficiency in several key areas: launching massively parallel CUDA kernels on NVIDIA GPUs, orchestrating parallel thread execution for large dataset processing, effectively managing memory transfers between the CPU and GPU, and utilising profiling techniques to analyse and optimise the performance of CUDA code. 

Here is the link to know more about the course – https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+T-AC-01+V1

7. Augment your LLM Using RAG

Retrieval Augmented Generation (RAG), devised by Facebook AI Research in 2020, offers a method to enhance a LLM output by incorporating real-time, domain-specific data, eliminating the need for model retraining. RAG integrates an information retrieval module with a response generator, forming an end-to-end architecture. 

Drawing from NVIDIA’s internal practices, this introduction aims to provide a foundational understanding of RAG, including its retrieval mechanism and the essential components within NVIDIA’s AI Foundations framework. By grasping these fundamentals, you can initiate your exploration into LLM and RAG applications.

To learn the course and know more in detail check it out here – https://courses.nvidia.com/courses/course-v1:NVIDIA+S-FX-16+v1/

8. Getting Started with AI on Jetson Nano

The NVIDIA Jetson Nano Developer Kit empowers makers, self-taught developers, and embedded technology enthusiasts worldwide with the capabilities of AI. 

This user-friendly, yet powerful computer facilitates the execution of multiple neural networks simultaneously, enabling various applications such as image classification, object detection, segmentation, and speech processing. 

Throughout the course, participants will utilise Jupyter iPython notebooks on Jetson Nano to construct a deep learning classification project employing computer vision models. 

By the end of the course, individuals will possess the skills to develop their own deep learning classification and regression models leveraging the capabilities of the Jetson Nano.

Here is the link to know more about the course – https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-RX-02+V2

9. Building Video AI Applications at the Edge on Jetson Nano

This self-paced online course aims to equip learners with skills in AI-based video understanding using the NVIDIA Jetson Nano Developer Kit. Through practical exercises and Python application samples in JupyterLab notebooks, participants will explore intelligent video analytics (IVA) applications leveraging the NVIDIA DeepStream SDK. 

The course covers setting up the Jetson Nano, constructing end-to-end DeepStream pipelines for video analysis, integrating various input and output sources, configuring multiple video streams, and employing alternate inference engines like YOLO. 

Prerequisites include basic Linux command line familiarity and understanding Python 3 programming concepts. The course leverages tools like DeepStream, TensorRT, and requires specific hardware components like the Jetson Nano Developer Kit. Assessment is conducted through multiple-choice questions, and a certificate is provided upon completion. 

For this course, you will require hardware including the NVIDIA Jetson Nano Developer Kit or the 2GB version, along with compatible power supply, microSD card, USB data cable, and a USB webcam. 

To learn the course and know more in detail check it out here – https://courses.nvidia.com/courses/course-v1:DLI+S-IV-02+V2/

10. Build Custom 3D Scene Manipulator Tools on NVIDIA Omniverse

This course offers practical guidance on extending and enhancing 3D tools using the adaptable Omniverse platform. Taught by the Omniverse developer ecosystem team, participants will gain skills to develop advanced tools for creating physically accurate virtual worlds. 

Through self-paced exercises, learners will delve into Python coding to craft custom scene manipulator tools within Omniverse. Key learning objectives include launching Omniverse Code, installing/enabling extensions, navigating the USD stage hierarchy, and creating widget manipulators for scale control. 

The course also covers fixing broken manipulators and building specialised scale manipulators. Required tools include Omniverse Code, Visual Studio Code, and the Python Extension. Minimum hardware requirements comprise a desktop or laptop computer equipped with an Intel i7 Gen 5 or AMD Ryzen processor, along with an NVIDIA RTX Enabled GPU with 16GB of memory. 

To learn the course and know more in detail check it out here – https://courses.nvidia.com/courses/course-v1:DLI+S-OV-06+V1/

11. Getting Started with USD for Collaborative 3D Workflows

In this self-paced course, participants will delve into the creation of scenes using human-readable Universal Scene Description ASCII (USDA) files. 

The programme is divided into two sections: USD Fundamentals, introducing OpenUSD without programming, and Advanced USD, using Python to generate USD files. 

Participants will learn OpenUSD scene structures and gain hands-on experience with OpenUSD Composition Arcs, including overriding asset properties with Sublayers, combining assets with References, and creating diverse asset states using Variants.

To learn more about the details of the course, here is the link – https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-02+V1

12. Assemble a Simple Robot in Isaac Sim

This course offers a practical tutorial on assembling a basic two-wheel mobile robot using the ‘Assemble a Simple Robot’ guide within the Isaac Sim GPU platform. The tutorial spans around 30 minutes and covers key steps such as connecting a local streaming client to an Omniverse Isaac Sim server, loading a USD mock robot into the simulation environment, and configuring joint drives and properties for the robot’s movement. 

Additionally, participants will learn to add articulations to the robot. By the end of the course, attendees will gain familiarity with the Isaac Sim interface and documentation necessary to initiate their own robot simulation projects. 

The prerequisites for this course include a Windows or Linux computer capable of installing Omniverse Launcher and applications, along with adequate internet bandwidth for client/server streaming. The course is free of charge, with a duration of 30 minutes, focusing on Omniverse technology. 

To learn the course and know more in detail check it out here – https://courses.nvidia.com/courses/course-v1:DLI+T-OV-01+V1/

13. How to Build Open USD Applications for industrial twins

This course introduces the basics of the Omniverse development platform. One will learn how to get started building 3D applications and tools that deliver the functionality needed to support industrial use cases and workflows for aggregating and reviewing large facilities such as factories, warehouses, and more. 

The learning objectives include building an application from a kit template, customising the application via settings, creating and modifying extensions, and expanding extension functionality with new features. 

To learn the course and know more in detail check it out here – https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-OV-13+V1

14. Disaster Risk Monitoring Using Satellite Imagery

Created in collaboration with the United Nations Satellite Centre, the course focuses on disaster risk monitoring using satellite imagery, teaching participants to create and implement deep learning models for automated flood detection. The skills gained aim to reduce costs, enhance efficiency, and improve the effectiveness of disaster management efforts. 

Participants will learn to execute a machine learning workflow, process large satellite imagery data using hardware-accelerated tools, and apply transfer-learning for building cost-effective deep learning models. 

The course also covers deploying models for near real-time analysis and utilising deep learning-based inference for flood event detection and response. Prerequisites include proficiency in Python 3, a basic understanding of machine learning and deep learning concepts, and an interest in satellite imagery manipulation. 

To learn the course and know more in detail check it out here – https://courses.nvidia.com/courses/course-v1:DLI+S-ES-01+V1/

15. Introduction to AI in the Data Center

In this course, you will learn about AI use cases, machine learning, and deep learning workflows, as well as the architecture and history of GPUs.  With a beginner-friendly approach, the course also covers deployment considerations for AI workloads in data centres, including infrastructure planning and multi-system clusters. 

The course is tailored for IT professionals, system and network administrators, DevOps, and data centre professionals. 

To learn the course and know more in detail check it out here – https://www.coursera.org/learn/introduction-ai-data-center

16. Fundamentals of Working with Open USD

In this course, participants will explore the foundational concepts of Universal Scene Description (OpenUSD), an open framework for detailed 3D environment creation and collaboration. 

Participants will learn to use USD for non-destructive processes, efficient scene assembly with layers, and data separation for optimised 3D workflows across various industries. 

Also, the session will cover Layering and Composition essentials, model hierarchy principles for efficient scene structuring, and Scene Graph Instancing for improved scene performance and organisation.

To know more about the course check it out here – https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-OV-15+V1

17. Introduction to Physics-informed Machine Learning with Modulus 

High-fidelity simulations in science and engineering are hindered by computational expense and time constraints, limiting their iterative use in design and optimisation. 

NVIDIA Modulus, a physics machine learning platform, tackles these challenges by creating deep learning models that outperform traditional methods by up to 100,000 times, providing fast and accurate simulation results.

One will learn how Modulus integrates with the Omniverse Platform and how to use its API for data-driven and physics-driven problems, addressing challenges from deep learning to multi-physics simulations.

To learn the course and know more in detail check it out here – https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-OV-04+V1

18. Introduction to DOCA for DPUs

The DOCA Software Framework, in partnership with BlueField DPUs, enables rapid application development, transforming networking, security, and storage performance. 

This self-paced course covers DOCA fundamentals for accelerated data centre computing on DPUs, including visualising the framework paradigm, studying BlueField DPU specs, exploring sample applications, and identifying opportunities for DPU-accelerated computation. 

One gains introductory knowledge to kickstart application development for enhanced data centre services.

To learn the course and know more in detail check it out here – https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-NP-01+V1

The story was updated on 2nd Jan, 25 to reflect the latest courses and correct the URLs to them.

The post Top AI Courses by NVIDIA for Free in 2025 appeared first on Analytics India Magazine.

At AIM Media House, we believe in transforming complex data into visually captivating stories. A single image can convey what a thousand words often cannot, and hence, each one of our infographics is crafted to deliver unparalleled insights, empowering readers to make informed decisions and stay ahead of the curve. 

Whether it’s about emerging trends, cutting-edge technologies, or deep industry analyses, we aim to provide a visual delight that informs, inspires, and engages.

In the past year alone, AIM has created hundreds of infographics, each meticulously designed to simplify data and amplify understanding, reflecting our commitment to providing the best visual content in the industry.

Here, we present some of the most shared and celebrated infographics of 2024—pieces that sparked conversations, guided decisions, and brought clarity to complexity. 

Yeh Dil MMAANG (AI) More 

This infographic, which first appeared in The Belamy, our most-read weekly newsletter, on November 4, 2024, highlighted the meteoric rise of AI-driven growth across big tech and showcased the pivotal role AI plays in shaping their strategies and revenues.

The title, ‘Yeh Dil MMAANG (AI) More’, is inspired by Pepsi’s iconic slogan “Yeh Dil Maange More,” beautifully capturing the relentless pursuit of AI innovation. From Microsoft’s transformative Copilot empowering 70% of the Fortune 500 to Amazon’s explosive generative AI adoption and Apple’s innovative integration of ChatGPT, each company is redefining its future. 

Key Infrastructure Players in AI 

This infographic (The Belamy, September 16, 2024) provided a comprehensive overview of the key players shaping AI infrastructure today. From AMD’s dominance in GPUs and PCs, NVIDIA’s advancements in AI hardware, and Intel’s innovations to foundational chip manufacturers like TSMC and Arm, the ecosystem showcases a dynamic interplay of design and performance. Networking giants like Nokia and Cisco ensure seamless integration, while trailblazers like SambaNova and Cerebras push the boundaries in training and inference capabilities. 

10 Years of OpenAI

This infographic captured the transformative decade of OpenAI. From the debut of GPT-1 in 2018 to the revolutionary advancements of GPT-4, DALL·E, and ChatGPT Enterprise, OpenAI has consistently redefined AI applications across industries. Notable innovations like Canvas and ChatGPT Search have expanded their utility, while the introduction of cost-efficient models like GPT-4o Mini showcases its commitment to accessibility.

Daily Power Consumption by ChatGPT

This infographic featured in the AIM article ‘95% Less Energy Consumption in Neural Networks Can be Achieved. Here’s How’. It highlighted the massive energy requirements of ChatGPT, which consumes 500,000 kilowatt-hours daily, equivalent to powering 17,000 US households or 62,500 Indian households. This underscores the critical need for innovation in energy-efficient AI to balance performance and sustainability.

Energy Consumption Comparison: ChatGPT vs Google Search

Published in the same article, this comparison demonstrates the stark disparity in energy consumption between ChatGPT and Google Search. A single ChatGPT query uses 2.9 watt-hours, almost 10 times more than Google Search’s 0.3 watt-hours. The infographic calls attention to the urgency of developing energy-efficient techniques for AI systems to reduce their environmental impact while maintaining scalability.

OpenAI Mafia 

This infographic, featured in the article ‘The OpenAI Mafia Just Got Bigger’, throws light on the dynamic network of former OpenAI employees who have gone on to launch over 30 AI startups after leaving the organisation. With notable names like Andrej Karpathy, co-founder of Eureka Labs, and Ilya Sutskever, who now leads Safe Superintelligence, this thriving alumni network is shaping the AI landscape. OpenAI’s Mira Murati also quit recently to pursue her passion. 

Google’s AI Era Unleashed 

2024 marked the dawn of the Gemini ‘Thought’ Era, setting a new standard in AI reasoning and transparency. This infographic captured Google’s relentless advancements in AI, culminating in the launch of Gemini 2.0 Flash Thinking—a model that not only solves complex problems with lightning speed but also reveals its thought process for unmatched transparency.

Accenture’s $4.2 Bn GenAI Boom 

This infographic, featured in The Belamy (December 23, 2024), illustrated Accenture’s remarkable strides in generative AI, positioning itself as a trailblazer reshaping the IT landscape. With $1.2 billion in bookings and $500 million in revenue this quarter alone, Accenture’s cumulative $4.2 billion in GenAI bookings and $1.4 billion in sales since September 2023 underscore its critical role in driving large-scale transformations.

Top 25 GCC Heads India 2024

This infographic, featured in AIM’s ‘Top 25 GCC Heads India 2024’ on LinkedIn, celebrated the leaders transforming global capability centres (GCCs) into innovation powerhouses in India. These visionary executives are driving technological advancements, fostering efficiency, and shaping strategies that align with global goals while navigating India’s unique market dynamics.

Bengaluru: The GCC Capital 

This infographic highlighted Bengaluru’s dominant role as India’s global capability centre (GCC) hub. With 875 GCCs, accounting for 30% of India’s total, Bengaluru employs 6 lakh professionals and contributes $22.2 billion annually to the economy. The Karnataka government’s GCC policy 2024 aims to further this growth by attracting 500 new GCCs and creating 3.5 lakh jobs by 2029, targeting a total economic output of $50 billion.

The post Top 10 Infographics of 2024 by AIM Media House | Editor’s Choice appeared first on Analytics India Magazine.

In 2024, AIM Media House dominated the social media world. From Salesforce India’s historic revenue surge to Meesho’s open-source ML platform, viral posts showcased the impact of technology across industries. Whether it was celebrating leadership changes at Accenture or introducing the AIM 100 list of influential AI leaders, these moments sparked global conversations. 

On X, innovations like Cursor AI and the rise of young tech prodigies like Dhravya Shah captured the imagination of thousands. 

Here’s a roundup of the top social media moments by AIM that went viral.

LinkedIn:

Salesforce India Crosses the $1 Billion Milestone

Salesforce India achieved a remarkable 36% revenue surge, reaching the $1 billion mark for the first time. CEO Arundhati Bhattacharya celebrated the company’s exponential growth, expanding from 2,500 employees in 2020 to over 13,000 today. 

To further strengthen its presence, Salesforce announced plans to build its first Salesforce Tower in Bengaluru, which is slated to open in 2026. This will position the city as a future hub of innovation. Meanwhile, cricket legend Rahul Dravid joined Salesforce as the brand ambassador.

The Power of GCCs

This post highlighted how Global Capability Centers (GCCs) are reshaping India’s corporate landscape. These centres are not only operational hubs but are also encouraging innovations and employment across industries. The leaders behind these centres showcase how they are promoting efficiency, initiating technological growth, and contributing to India’s economy.

Karthik Narain Steps Into CTO Role at Accenture

Accenture’s appointment of Karthik Narain as the new chief technology officer was a major moment for the company. Narain succeeded the legendary Paul Daugherty, who retired after nearly four decades at the company. Narain, a seasoned tech leader, expressed his gratitude to Daugherty while acknowledging his contributions to Accenture’s tech journey. 

Meesho Goes Open-Source with its ML Platform

Meesho made waves at the NVIDIA Summit in Mumbai after it announced the open-sourcing of its ML platform. The announcement not only highlighted Meesho’s commitment to empowering developers globally but also opened up the tools that power one of India’s largest e-commerce platforms. With this move, Meesho aims to democratise AI and ML and enable developers and data scientists to access resources to accelerate innovation.

Top Computer Science Influencers You Need to Follow

AIM curated a list of India’s most influential computer science influencers who inspire the next generation of coders and tech enthusiasts. This list includes experts who offer a wealth of knowledge on AI, algorithms, and programming tips, making it a must-follow for anyone passionate about coding. From demystifying AI to offering no-nonsense career advice, these digital mentors are shaping the future of technology while keeping things entertaining.

X:

Cursor AI: A Game-Changer for Developers

Cursor AI from Anysphere took the internet by storm and empowered users of all ages to create impressive projects with ease. Whether it’s an eight-year-old child building a chatbot or someone creating a financial dashboard using voice commands, OpenAI-backed Cursor AI is revolutionising the accessibility of coding. 

With its seamless integration, customisation options, and GPT-4 assistance, the platform is making AI-driven development accessible to everyone, regardless of technical background.

Say hello to Cursor AI, the game-changer from Anysphere that's turning everyone into a developer! Whether it’s an 8-year-old building a chatbot in an hour or creating a financial dashboard using just voice commands, Cursor AI is making it all possible. With features like seamless… pic.twitter.com/kLZbVWoSXG

— AIM (@Analyticsindiam) September 2, 2024

Top AI Leaders Share Their Vision for the Future

A viral post on X showcased the insights of some of the most prominent AI experts, including Yann LeCun, François Chollet and Andrej Karpathy. This post provided a glimpse into the future of AI from the perspectives of these industry leaders, where everything from innovation to ethical challenges was discussed. The post was even shared by the experts themselves. 

Here's what top AI leaders and experts are saying about the future of AI@ylecun | @fchollet | @karpathy | @ClementDelangue | @elonmusk | @drfeifei | @sama | @geoffreyhinton pic.twitter.com/WneYzYg7m3

— AIM (@Analyticsindiam) August 6, 2024

The AIM 100

AIM unveiled the AIM 100, a list of the world’s most influential leaders in AI, in a post that quickly gained traction. Leaders like Elon Musk, Yann LeCun, Andrew Ng, Julie Sweet, CP Gurnani and Bhavish Aggarwal, among others, are pioneering AI development and bringing AI to every field possible. 

Introducing the AIM 100: The Most Influential Global Leaders in AI!

These trailblazers are shaping the future of AI, tackling everything from data privacy to algorithmic bias. Get inspired by their stories and see how they're guiding AI’s evolution with innovation AND ethics… pic.twitter.com/dETM2WX8XR

— AIM (@Analyticsindiam) October 1, 2024

Swaayatt Robots Secures $4 Million Funding

A Bhopal-based AI and robotics startup, Swaayatt Robots, raised $4 million at a valuation of $151 million from US-based investors. This news captured attention not only because of the success of the funding but also because the startup is set to raise an additional $11 million soon, which could propel its valuation to nearly $200 million. A viral post on X showcased the rising influence of Indian startups in the global tech ecosystem.

Bhopal-based Indian AI and Robotics startup Swaayatt Robots (स्वायत्त रोबोट्स) recently raised $4 million at a valuation of $151 million from US investors. The company will further raise $11M at a valuation of round $175M-$200M.

Read more – https://t.co/W3GVA3K3dg pic.twitter.com/bU27hliF7B

— AIM (@Analyticsindiam) June 9, 2024

Dhravya Shah: A 16-Year-Old Programming Prodigy

Dhravya Shah’s journey from programming at 16 to building an open-source alternative to Redis caught the eye of X users. Now 18, Shah’s ambitious project Radish, a database system inspired by Redis, is gaining attention for its unique approach and open-source model. His story of self-driven innovation became a viral inspiration for young developers, highlighting how age is no barrier to entrepreneurial success in the tech world.

@DhravyaShah was barely 16 when he started programming and game development. At 18, he is working towards building Radish, an open source alternative to @Redisinc.

“Redis is basically an in-memory database, which is a dictionary. Similarly, I started with a normal dictionary,… pic.twitter.com/MqryuYw3fV

— AIM (@Analyticsindiam) April 24, 2024

Instagram:

Yotta Welcomes 4,000 NVIDIA H100 GPUs

India’s AI community celebrated a major milestone with NVIDIA’s crucial contribution to providing the hardware. The spotlight was on Yotta, a leading data centre company that made waves by receiving its first shipment of 4,000 NVIDIA H100 GPUs. It marked a new era of innovation in the Indian tech landscape.

View this post on Instagram

A post shared by AIM Media House (@aim.media.house)

Gukesh Dommaraju Makes History

AI cameras captured the emotional moment when Gukesh Dommaraju became the youngest World Chess Champion. With two cameras strategically placed to focus on the players, AI technology enhanced the broadcast by tracking focus flow and delivering real-time data – a game-changing moment in sports broadcasting.

Lennart Ootes, part of the broadcast team for the final match between Dommaraju and Ding Liren, shared insights on how AI technology elevated the broadcast experience. For the first time, AI cameras provided live data about focus flow, a testament to how rapidly AI is shaping the future of sports media.

View this post on Instagram

A post shared by AIM Media House (@aim.media.house)

Fireside Chat at NVIDIA AI Summit 2024 between Jensen Huang & Mukesh Ambani

The fireside chat between NVIDIA CEO Jensen Huang and Reliance Industries chairman Mukesh Ambani at the Nvidia AI Summit 2024 highlighted key AI trends. The conversation covered pivotal advancements, touching on topics shaping the future of AI in India and beyond and how India will become the biggest intelligence market in the world. 

View this post on Instagram

A post shared by AIM Media House (@aim.media.house)

Vox Pop in Streets of Bengaluru after Appraisal Season

Our creative team went to the streets of Bengaluru to ask techies about their yearly appraisals. While most of them were very happy with the increase in salary, some were sceptical about answering as they left their companies shortly after.

View this post on Instagram

A post shared by AIM Media House (@aim.media.house)

Nithyananda’s AI Chatbot

Nithyananda, the Indian self-styled godman, launched his own AI chatbot. The Ask Nithyananda chatbot was aimed at solving all the users’ problems and was trained on 27 years of his teachings. AIM asked the chatbot the location of Kailasa, the country he founded, and it revealed its exact location.

View this post on Instagram

A post shared by AIM Media House (@aim.media.house)

The post AIM’s Top Social Media Posts That Went Viral in 2024 appeared first on Analytics India Magazine.

India is a bustling hub for AI, data science, and technology enthusiasts. And with its base in Bengaluru, AIM Media House is at the forefront of this tech transformation. With a mission to empower and connect professionals and organisations through knowledge and innovation, AIM organises some of the industry’s most influential conferences. 

From fostering diversity and inclusion in tech to exploring the latest advancements in generative AI, AIM’s conferences cater to a wide range of interests and expertise. Whether you’re a data engineer, AI startup founder, developer, or corporate leader, these events offer unparalleled opportunities to learn, network, and stay ahead in the fast-paced tech landscape. 

Here’s a quick look at AIM’s upcoming conferences next year, why you should attend them, and how they’re shaping the future of AI and technology in India.

MLDS 2025

MLDS is a haven for developers and data scientists looking to stay ahead in the ever-evolving world of artificial intelligence. Focused on the latest innovations in generative AI and software development, this three-day event offers something for everyone, from keynotes to hands-on workshops. 

With paper presentations and tech talks across three tracks, MLDS ensures you leave with actionable insights, whether you’re a beginner or an experienced AI practitioner.

The event’s emphasis on practical applications and industry case studies makes it a must-attend for professionals looking to integrate AI into their workflows. Moreover, the networking opportunities with AI leaders and like-minded peers are unparalleled. 

Register here

• Dates: February 5-7, 2025
• Venue: NIMHANS Convention Center, Bengaluru, India

The Rising 2025

As one of India’s most impactful conferences on diversity and inclusion in tech, The Rising addresses some of the most pressing issues in today’s workplaces. From practical strategies for fostering equity to inspiring success stories, this summit provides a deep dive into creating a culture of belonging. 

Leaders from top companies share their approaches to tackling DEI challenges, making it a rich learning ground for both individuals and organisations.

Whether you’re an HR professional, tech leader, or someone interested in creating equitable spaces, The Rising is the perfect platform to gain insights and actionable takeaways. 

Register here

• Dates: March 20-21, 2025
• Venue: J N Tata Auditorium, Bengaluru, India

Happy Llama 2025

The first edition of Happy Llama is not just a conference, it’s a celebration of India’s vibrant AI startup ecosystem. If you’re an entrepreneur or an investor, this is your chance to connect with the minds driving innovation in AI. The summit includes engaging pitch battles, insightful talks, and workshops tailored to startup needs, making it an unmissable opportunity for those seeking to learn and grow.

Whether you’re looking for funding, partnerships, or just some inspiration, Happy Llama provides the perfect platform for it all and more. With its dynamic format and energetic vibe, this one-day event at Bengaluru’s Radisson Blu is your gateway to networking with the best and brightest in AI startups.

Register here

• Date: April 25, 2025
• Venue: Hotel Radisson Blu, Bengaluru, India

Data Engineering Summit (DES) 2025

For professionals in data engineering, DES is the ultimate event to explore the latest tools, techniques, and trends shaping the field. As India’s first and only conference dedicated to data engineering, it delves into software deployment architectures, data frameworks, and scalable solutions for real-world problems.

This summit brings together top engineers and thought leaders to share their expertise, offering attendees a unique opportunity to enhance their skills and knowledge. Whether you work in analytics, machine learning, or cloud computing, DES is a must-attend event.

Register here

• Dates: May 15-16, 2025
• Venue: Taj Yeshwantpur, Bengaluru, India

MachineCon GCC Summit 2025

MachineCon GCC Summit is the perfect blend of vision and action, designed specifically for leaders in India’s global capability centres. Focused on the transformative potential of generative AI, the summit explores how GCCs can harness AI to drive operational excellence and innovation.

With a curated lineup of sessions featuring pioneers and experts, this two-day event offers strategic insights into the future of GCCs. 

Register here

• Dates: June 19-20, 2025
• Venue: Taj Yeshwantpur, Bengaluru, India

Cypher 2025

Saving the best for the last, we have Cypher, AIM’s flagship conference. Since its first edition in 2015, Cypher has grown exponentially to become not just India’s biggest AI summit but also the most impactful one. 

With over 5,000 attendees daily, the event brings together a diverse community of AI enthusiasts, professionals, and thought leaders. The agenda spans keynotes, panel discussions, and exhibitions, offering a comprehensive view of AI’s impact across industries.

Whether you’re a beginner curious about AI’s potential or an industry leader looking for the latest advancements, Cypher has something for everyone. 

Register here

• Dates: September 17-19, 2025
• Venue: KTPO @ Whitefield, Bengaluru, India

The post 6 Must-Attend Conferences for Developers by AIM in 2025 appeared first on Analytics India Magazine.

While 2024 has been a year of progress, it has also been a hotbed of bold claims and heated controversies within the world of AI. This article looks back at the most controversial figures in AI in 2024 – individuals who stirred debates, challenged norms and redefined what AI can and should do.

Jürgen Schmidhuber

German computer scientist Jürgen Schmidhuber, known for his work on recurrent neural networks, has often argued that he and other researchers have not received adequate recognition for their contributions to deep learning. Instead, he claimed, Geoffrey Hinton, Yann LeCun, and Yoshua Bengio have received disproportionate credit.

Most recently, he alleged that Hinton’s Nobel Prize is based on uncredited work. Schmidhuber alleged that Hinton and Hopfield’s contributions were heavily influenced by existing research without inadequate acknowledgement.

“This is a Nobel Prize for plagiarism,” Schmidhuber wrote on LinkedIn. He argued that methodologies developed by Alexey Ivakhnenko and Shun’ichi Amari in the 1960s and 1970s, respectively, formed the foundation of the laureates’ work. 

“They republished methodologies developed in Ukraine and Japan without citing the original papers. Even in later surveys, they didn’t credit the original inventors,” Schmidhuber said, suggesting that the omission may have been intentional.

Rosalind Picard

Rosalind Picard, a professor at MIT Media Lab, recently faced controversy over alleged discriminatory remarks made during a keynote speech at NeurIPS 2024 towards Chinese students.

During her presentation, Picard mentioned an incident involving a Chinese student who had been expelled. The act drew criticism for appearing to single out nationality and reinforce harmful stereotypes. This prompted apologies from both Picard and the NeurIPS organisers and sparked discussions about inclusivity and respect within the AI research community.

Bhavish Aggarwal

Bhavish Aggarwal, founder of Ola’s AI chatbot Krutrim, made several notable statements about AI this year that sparked discussions. 

Earlier this year, Aggarwal framed India’s AI development in terms of data sovereignty and criticised what he termed “techno-colonialism” to describe the exploitation of developing countries by global tech giants through technology.

“India generates the largest amount of digital data in the world, but all of it is sitting in the West…They take our data out, process it into AI and then bring it back and sell it in dollars to us. It’s the same East India Company all over again,” he said.

His remarks triggered controversy, as critics noted that much of Ola’s early funding had come from global investment firms.

Meanwhile, following an incident where LinkedIn’s AI tool referred to him using gender-neutral pronouns, Aggarwal announced that Ola would shift from Microsoft’s Azure cloud platform to its own Krutrim cloud. Moreover, he called on other Indian companies to follow suit, which some interpreted as promoting anti-Western sentiment in the tech industry.

Eric Schmidt

Former Google CEO Eric Schmidt recently made several notable statements about AI and its potential risks. In interviews with ABC News and PBS, Schmidt warned that AI systems could reach a “dangerous point” when they can self-improve, suggesting that we need to consider “unplugging” them at that stage.

He expressed concern about computers running autonomously and making their own decisions, calling for human oversight to maintain “meaningful control” over autonomous weapons.

Mira Murati 

Mira Murati, former chief technology officer of OpenAI, found herself at the centre of controversy in March regarding the training data for Sora, OpenAI’s new text-to-video AI model. 

During an interview with The Wall Street Journal, Murati was asked about the specific sources of data used to train Sora. She revealed that the model was trained on “publicly available and licensed data”. However, when asked whether content from platforms like YouTube, Instagram, or Facebook was used to train the model, she responded with uncertainty, saying, “I’m actually not sure about that. I’m not confident about it.”

Hoan Ton-That

This year, Hoan Ton-That, CEO and co-founder of Clearview AI, remained a controversial figure in the AI industry due to his company’s facial recognition technology and its practices. 

Clearview AI faced significant legal issues, including a €30.5 million fine from the Dutch Data Protection Authority for maintaining an “illegal database” of billions of facial images. The company was also warned of additional penalties of up to €5.1 million for failing to comply with EU data protection laws.

Despite these challenges, Ton-That defended the company as he asserted that it only uses publicly available online data and compared its approach to Google’s photo search. He argued that Clearview’s technology plays a crucial role in law enforcement, citing its use in investigations into the January 6 Capitol riots. 

Scarlett Johansson

Earlier this year, Scarlett Johansson became embroiled in a major controversy regarding the alleged unauthorised use of her voice by OpenAI in ChatGPT. The issue surfaced when OpenAI unveiled a new voice feature called ‘Sky’, which many users noticed sounded strikingly similar to Johansson’s voice from her role in the movie ‘Her’.

The situation escalated when Johansson publicly expressed her frustration after discovering that OpenAI had created a voice for the chatbot that she felt resembled hers too closely. This came despite her having declined an offer from OpenAI in September 2022 to lend her voice to the project. Upon hearing a demo of the new voice, Johansson was reportedly shocked and upset, leading her to demand that OpenAI halt the use of the voice.

Prabhakar Raghavan 

Earlier this year, Prabhakar Raghavan, Google’s chief technologist, faced criticism over the company’s Gemini AI image generation feature. The controversy stemmed from Gemini producing historically inaccurate and overly diverse images in response to prompts about specific historical figures and events. 

For example, when prompted to create depictions of the Founding Fathers of the United States, the AI-generated images included individuals from various ethnic backgrounds, which did not align with historical records.

Raghavan admitted that the feature had fallen short and issued an apology for the inaccuracies. He explained that the AI model had been adjusted to promote diversity in its outputs, which occasionally resulted in overcorrection.

Elon Musk

Elon Musk has a love-hate relationship with OpenAI. The tech billionaire recently filed a preliminary injunction to stop OpenAI from switching to a for-profit model. Musk, who co-founded OpenAI, accused the company of antitrust violations and betraying its founding principles. His lawsuit, which now includes Microsoft as a defendant, argues that OpenAI has moved away from its original nonprofit mission to use AI research to benefit humanity. In response, OpenAI released emails and documents from 2017 showing that Musk had supported a for-profit structure and even sought majority control of the company. OpenAI CEO Sam Altman had publicly called Musk “a clear bully”.

The post AI Personalities Who Sparked Controversy in 2024 appeared first on Analytics India Magazine.

AIM has a robust video content module known for producing over 100 curated videos annually, alongside content from our conferences and events. These videos offer insights into the latest advancements in AI, spotlight industry leaders, and showcase innovations shaping the future of technology. From enterprise strategies to quirky AI-driven experiments, here are the top 10 AIM videos of the year.

1. Redefining Mobility with AI | Ford Business Solutions

At Ford’s Business Solutions team in Chennai, AI is driving the future of mobility. This video dives into how nearly 1,000 professionals at Ford’s Global Data Insights & Analytics (GDIA) team are using AI and big data to transform the automotive industry. From connected cars to AI-powered logistics, Ford’s innovations extend far beyond vehicles, creating global impact.

Key Takeaway: Ford is at the forefront of AI-driven mobility, fostering a culture of innovation and inclusion.

2. Every Industry Will Transform Using AI | Ishit Vachhrajani, AWS

AWS Enterprise Strategist Ishit Vachhrajani takes center stage to discuss how generative AI is disrupting industries, from healthcare to manufacturing. This episode of Simulated Reality explores the role of AI-driven solutions in reshaping businesses globally.

Key Takeaway: AI will redefine industries at every level, creating transformative possibilities across the board.

3. Wipro: Best Firm for Data Scientists

Wipro’s recognition as the “Best Firm for Data Scientists” highlights their commitment to fostering talent and innovation. This video delves into how Wipro’s data scientists are contributing to real-world solutions, and why the company stands out as a leader in analytics and AI.

Key Takeaway: Wipro provides a stellar environment for data scientists, cementing its place as a top employer in AI and analytics.

4. AI: A Solution or a Problem? | Kailash Nadh, Zerodha

Zerodha CTO Kailash Nadh explores the intersection of AI, open-source culture, and fintech. This in-depth conversation reveals how Zerodha harnesses AI while staying grounded in practical, human-centric innovation.

Key Takeaway: AI is powerful but must align with real-world problems, not just hype.

5. Building AGI in India | Young Indic AI Developers

Three of India’s leading AI developers discuss their groundbreaking work on foundational models, AGI, and India’s AI ecosystem. This dynamic conversation explores the future of AI development in India.

Key Takeaway: India’s young AI talent is making significant strides toward AGI and foundational AI models.

6. Database Market in India | Raj Verma, Singlestore

In this engaging interview, Raj Verma, CEO of Singlestore, unpacks the $120 billion potential of India’s database market. Verma highlights Singlestore’s role in shaping real-time analytics and distributed SQL.

Key Takeaway: Real-time data and analytics are revolutionizing industries in India and beyond.

7. AI Meets the Kitchen | ChatGPT Recipe with Chef Nidhi Nahata

In this light-hearted video, chef Nidhi Nahata uses ChatGPT to create a unique recipe, blending AI with culinary arts. Will AI lead to a masterpiece or a kitchen disaster?

Key Takeaway: AI’s creative potential can extend to unexpected places, like the kitchen!

8. AI vs You: Who can write better Pick-Up lines? | Vox Pop

In this fun and interactive video, we explore AI’s impact on creativity. Can AI generate better pick-up lines than humans? This lighthearted experiment dives into AI’s role in shaping music, fashion, and humor.

Key Takeaway: AI’s creativity is expanding, challenging human ingenuity in unexpected ways.

9. OpenAI in India | Pragya Misra

Pragya Misra, Lead Public Policy at OpenAI India, discusses OpenAI’s progress towards AGI, hiring challenges in India, and Sam Altman’s vision for the region.

Key Takeaway: OpenAI is deeply invested in nurturing AI talent and innovation in India.

10. The ‘Woke’ Google Gemini Reaction

In this reaction video, AIM’s editorial team dissects Google’s controversial Gemini tool, highlighting the debate around AI ethics and representation.

Key Takeaway: AI ethics and accuracy remain central to AI development and deployment.

These top 10 videos showcase how AI is shaping industries, culture, and even our everyday lives. If you’d like to collaborate with us on video production, reach out to info@aimmediahouse.com.

Our video team is exceptional, delivering high-quality, engaging content that highlights the latest in AI innovation. Stay tuned for more engaging content in 2025!

The post Top 10 Videos of 2024 by AIM – Editors’ Pick appeared first on Analytics India Magazine.

As the conversation around AI for Bharat increased in 2024, the Indian Institutes of Technology (IITs) continued to solidify their position to make an impact using generative AI for the country.

From collaborations with industry giants like AMD, TCS, and Lightstorm to indigenous innovations such as IndicVoices and MedSumm, IITs showcased a remarkable blend of research, innovation, and real-world application.

These initiatives spanned a wide spectrum, including advancements in quantum imaging, semiconductor efficiency, multilingual AI capabilities, and Indic models and upskilling. These milestones reflect not just the technological prowess of IITs but their commitment to solving India’s pressing challenges through AI.

IIT Bombay’s MoU with Samsung R&D Institute

IIT Bombay collaborated with Samsung R&D to drive AI advancements and innovation in digital health and other key areas. The five-year partnership enables joint research projects, offering IIT Bombay students and faculty the chance to work closely with Samsung engineers. This collaboration fosters industry readiness among students while providing Samsung engineers with training and certification in cutting-edge technologies like AI and digital health.

Centre for Human-Centric AI at IIT Madras

The IIT Madras Pravartak Technologies Foundation launched the ‘Centre for Human-Centric Artificial Intelligence’ (CHAI), aiming to amplify human potential through AI. The Centre focuses on technology development, entrepreneurship, human resource enhancement, and fostering international collaborations, presenting a critical opportunity for India to lead in human-centric AI innovation.

AMD Partnered with IIT Bombay to Boost Semiconductor Startups

AMD partnered with the Society for Innovation and Entrepreneurship (SINE) at IIT Bombay to support semiconductor startups in India. As part of this collaboration, AMD provided grants to IIT Bombay-incubated startups working on energy-efficient Spiking Neural Network (SNN) chips. These startups focused on significantly reducing the energy consumption of conventional neural networks.

TCS Collaborated with IIT Bombay to Develop Quantum Diamond Microchip Imager

Tata Consultancy Services (TCS) formed a strategic partnership with IIT Bombay to create India’s first Quantum Diamond Microchip Imager. This advanced sensing tool aimed to enhance semiconductor chip precision, reduce failures, and improve the energy efficiency of electronic devices.

Over two years, TCS experts worked with Dr. Kasturi Saha, Associate Professor in IIT Bombay’s Department of Electrical Engineering, to develop the quantum imaging platform at the PQuest Lab. This platform promised better quality control for semiconductor chips, enhancing reliability and efficiency across industries.

Lightstorm and IIT Madras Launched Employment Skilling Initiative

Lightstorm, a leading connectivity infrastructure provider, signed an MoU with IIT Madras to launch an “Employment Skilling Initiative.” The program aimed to bridge skill gaps among underprivileged students and support youth, women, and job seekers from tier-2 and tier-3 cities.

In collaboration with IIT Madras’ Technology Innovation Hub, Lightstorm offered placement assistance for Arts, Science, and Commerce students, with high achievers securing internships. Dr. Mangala Sunder Krishnan, Professor Emeritus at IIT Madras, commended the initiative for promoting quality education and inclusive economic growth.

Sarvam AI and IIT Madras Released IndicVoices Dataset

Sarvam AI, AI4Bharat, and IIT Madras unveiled IndicVoices, a speech dataset encompassing 7,348 hours of natural and spontaneous speech from 16,237 speakers across 145 Indian districts and 22 languages. This diverse dataset included read (9%), extempore (74%), and conversational (17%) audio segments, with 1,639 hours already transcribed.

Using IndicVoices, they developed IndicASR, the first Automatic Speech Recognition (ASR) model supporting all 22 languages listed in the 8th Schedule of the Indian Constitution. Funded by BHASHINI (MeitY) and supported by grants from Nilekani Philanthropies and EkStep Foundation, the project set a global benchmark for multilingual data collection. All data, tools, and models were made publicly available.

IIT Patna Researchers Introduced MedSumm Framework

Researchers from IIT Patna presented MedSumm, a multimodal approach integrating Hindi-English codemixed medical queries with visual aids to enhance healthcare understanding. The MMCQS dataset, part of this work, included 3,015 multimodal medical queries with golden summaries in English, combining visual and textual data.

Meta’s Launch of Srijan and YuvAI at IIT Jodhpur

IIT Jodhpur’s “Srijan,” a pioneering Center for Generative AI developed with Meta and IndiaAI, has bolstered India’s AI ecosystem. Paired with the “YuvAI Initiative for Skilling and Capacity Building” in partnership with AICTE, these initiatives emphasise open-source AI, skill development, and research. Together, they aim to nurture talent capable of addressing national challenges through generative AI.

IAF-IIT Delhi Partnership

The Indian Air Force (IAF) and IIT Delhi joined forces through a MoU to innovate in aviation textiles. This collaboration focuses on obsolescence management, self-reliance, upgradation, and digitisation within aviation-grade textiles, promoting indigenisation and cutting-edge research.

NHA and IIT Kanpur’s Healthcare AI Initiative

IIT Kanpur and the National Health Authority (NHA) signed an MoU to advance AI-driven digital public goods for healthcare under the Ayushman Bharat Digital Mission (ABDM). This partnership includes developing a federated learning platform, open benchmarking tools for AI models, and a secure consent management system aiming to revolutionize India’s healthcare landscape.

Online AI Courses for School Students by IIT Madras

IIT Madras introduced two online certificate courses in data science, AI, and electronic systems for students in classes 11 and 12 as part of the IITM School Connect programme. These courses offer hands-on career exposure and prepare young learners for future opportunities in AI and technology.

IIT Delhi’s MoU with Honda on Cooperative Intelligence

IIT Delhi partnered with Honda Cars India Limited (HCIL) to advance Cooperative Intelligence (CI), an AI framework that enables seamless interaction between machines and humans. This collaboration focuses on enhancing mutual understanding through environmental awareness, scene recognition, and intent analysis to improve future mobility solutions.

The post Top 12 AI Developments from IITs in 2024 appeared first on Analytics India Magazine.

We’ve all been there – sitting at our desks, nervously awaiting feedback from our editor, wondering if they can tell we used ChatGPT to polish our article. Even when it’s just used to refine our writing, a doubt creeps in: will they think we cheated? This worry doesn’t just haunt writers; graphic designers, coders, and even those perfecting PowerPoint presentations feel the same.

But is this fear of being judged for using AI even justified?

AI adoption in workplaces is undoubtedly a hot topic, with business leaders everywhere exploring its potential. The reality, however, seems to be different. According to the latest Slack Workforce Index by Salesforce, AI adoption among workers in the US barely increased in the last three months, moving up from 32% to just 33%. Compared to the 8% leap seen a year ago, it now seems the enthusiasm might be fading.

The report further explores the reason behind the stagnation. Nearly half of global desk workers admit they are uncomfortable telling their manager they’ve used AI. It’s not about rejecting technology but the stigma attached to using AI. Employees worry they will be seen as lazy, incompetent, or a cheat. So, instead of feeling empowered and confident, they’re apprehensive about using AI in the workplace. 

Yet, there’s another side to the story. As per a report by The Washington Post, employees are embracing AI with open arms and witnessing rewards. These “super users” say AI has boosted their productivity and doubled their efficiency in tasks like strategic planning and project management. Some even use it to analyse data sets or screen job candidates, saving hours during their workday. 

However, it’s not all about optimised workflows. Experts warn that heavy reliance on AI comes with risks. Privacy concerns, inaccuracies, and even potential job losses loom large. And perhaps, most importantly, there’s the looming danger of workers losing touch with the very skills AI is meant to augment.

Further, the report highlighted that AI enablement is a factor in job searches. But is this trend particularly strong in India’s competitive job market?

In an exclusive interview with AIM, Christina Janzer, ​​SVP of Research and Analytics at Slack, said, “This trend is evident in the Indian market. In fact, according to the report, nine out of 10 desk workers in India consider a prospective employer’s ability to provide and support AI tools crucial in their job decisions. This shows the deep integration of AI into workforce expectations, driven by India’s rapidly evolving digital and tech ecosystem.”

India is emerging as a global leader in AI adoption. Notably, 61% of online Indian workers are already leveraging AI – a significant leap compared to the global average of 40%. This enthusiasm, coupled with 80% of Indian workers expressing excitement about AI, underscores the country’s progressive approach. 

Janzer attributed this to two key factors: India’s strong IT talent pool and a forward-thinking mindset that views AI as a collaborator rather than a replacement. This openness further aligns with India’s digital-first vision, where innovation and experimentation are highly valued. Indian companies are leveraging AI to boost productivity and enhance the employee experience, setting a blueprint for global businesses aiming to unlock AI’s potential.

So, this leaves companies to come up with guidelines for the use of AI at work.

Companies Who Don’t Like AI

With the rise of generative AI tools like ChatGPT, global companies have redefined the use of tech in the workplace. According to a 2023 study by BlackBerry, three out of four organisations worldwide have either considered or implemented bans on GenAI applications at work. The survey, which spanned over 2,000 IT decision-makers from the US, Canada, Europe, Japan, and Australia, highlighted growing concerns over the implications of these technologies. 

Interestingly, 61% of those enforcing such bans see this as long-term or permanent restrictions. 

For example, in the publishing sector, Medium.com took a stance in May 2024 by barring AI-generated content from its Partner Program. As of May 1, stories generated or edited by AI are ineligible for a paywall, emphasising the platform’s commitment to authenticity.

Meanwhile, Wired has declared a complete embargo on publishing AI-generated or AI-edited text. However, they don’t shy away from leveraging AI for brainstorming ideas, generating headlines, or conducting research.

Salesforce has gone a step further with a comprehensive Acceptable Use Policy for AI. Their guidelines explicitly prohibit AI use in areas like professional advice and critical decision-making, ensuring ethical boundaries remain intact.

Even in journalism, boundaries are being set. The BBC now enforces editorial policies restricting the use of generative AI in news production. AI is permitted only when it serves an illustrative purpose or becomes the subject of the content itself, emphasising human-driven storytelling in factual journalism.

At AIM, we stand at the intersection of tradition and innovation. While we embrace AI to enhance and fine-tune our work, we draw the line at allowing it to replace the human essence in content creation or imagery. For us, AI will always remain a tool, not a creator.

Indian companies like Zomato, Razorpay, Zepto, and Schbang are guiding employees on how to effectively use AI. Janzer noted that leaders play a pivotal role in accelerating AI adoption by setting clear expectations, creating opportunities for experimentation, and fostering a culture of shared learning.

What’s Next?

There is uncertainty around the usage of AI in the workplace, but workers look forward to upskilling. The Slack report mentioned that 76% of employees feel an urgency to become an AI expert, but 61% spent less than five hours learning the same, and 30% noted they have had no AI training. 

This highlights a need for employers to address the issue of bridging the gap in training and clarifying AI guidelines. This is the need of the hour, as current employees and new professionals entering the workforce have a better chance of surviving in a more supportive workplace environment.

The post AI Stigma Holds Employees Back appeared first on Analytics India Magazine.

With 271 deals finalised, 2023 was a good year for AI mergers and acquisitions. Building on that momentum, 2024 is projected to close with 326 AI deals, marking a 20% year-over-year increase. According to Aventis Advisors, the growth in AI deals for 2024 is expected to see 32% growth compared to 2023.

The trend in 2024 AI acquisitions centred on expanding cloud infrastructure, streamlining data management and optimisation, growing generative and sector-specific AI, attracting AI talent, and building cross-platform AI solutions.

Here’s a look at the top mergers and acquisitions that made headlines this year.

1. AMD x Silo AI 

AMD acquired Silo AI, Europe’s largest private AI lab, in a $665 million all-cash deal. Founded in 2017 and based in Helsinki, Finland, Silo AI specialises in creating AI models, platforms, and solutions for leading enterprises across industries. Its clients include Allianz, Philips, Rolls-Royce, and Unilever. The company has also played a key role in developing open-source multilingual LLMs like Poro and Viking, optimised for AMD platforms.

2. Databricks x Tabular 

Databricks announced its acquisition of Tabular on June 4, 2024, in a deal valued over $1 billion. Tabular, founded in 2021 by Ryan Blue, Daniel Weeks, and Jason Reid, is a data management company specialising in Apache Iceberg, an open-source table format for large analytics datasets. The acquisition brings together the creators of Apache Iceberg and Linux Foundation Delta Lake, the two leading open-source lakehouse formats.

In 2024, Databricks acquired several companies to expand its data and AI capabilities. In January, it acquired Einblick, a data science and AI startup. Lilac, an AI startup working on improving data quality for generative AI and LLMs, was acquired in the same month. 

Databricks also acquired Prodvana, a cloud-native infrastructure management startup, in July 2024 to strengthen its cloud capabilities.

3. NVIDIA x Run:ai 

In April 2024, NVIDIA acquired Run:ai, an Israeli firm specialising in Kubernetes-powered AI/ML workflow orchestration, in a deal valued at $700 million. Run:ai’s platform helps enterprise clients optimise and manage their compute infrastructure across on-premises, cloud, and hybrid environments. It supports multiple Kubernetes variants and integrates with third-party AI tools and frameworks. Run:ai serves large enterprises in various industries, using its platform to manage GPU clusters at a data-center scale. 

NVIDIA also acquired OctoAI, a Seattle-based generative AI startup, for $250 million, as well as Brev.dev, a San Francisco-based startup specialising in AI and machine learning development platforms.

4. Snowflake X Datavolo

On November 20, 2024, Snowflake confirmed its acquisition of Datavolo, a move to boost its data management and pipeline automation capabilities. Founded in 2023 by Joseph Witt and Luke Roquet, Datavolo automates multimodal data pipelines for AI, utilising Apache NiFi to optimise data flow between enterprise sources.

5.Canva X Leonardo.AI 

In July 2024, Canva acquired Leonardo.AI, an Australian generative AI startup founded in 2022. The company’s platform, which enables users to create images and videos, saw rapid growth, amassing 19 million users and generating over a billion images in just 18 months. While the deal’s terms weren’t disclosed, it surpassed Leonardo.AI’s prior $80 million valuation.

6.OpenAI X Rockset 

OpenAI acquired Rockset on June 21, 2024. Founded in 2016 by former Facebook engineers Venkat Venkataramani and Tudor Bosman, along with database architect Dhruba Borthakur, Rockset focuses on real-time analytics and search database technology. The company had raised over $117.5 million in funding before the acquisition.

This acquisition strengthens OpenAI’s retrieval infrastructure by incorporating Rockset’s technology to enhance data usage and provide real-time insights across AI products. The Rockset team has integrated into OpenAI, with current customers transitioning off the platform.

7. IBM x Hashicorp 

IBM announced its plan to acquire HashiCorp on April 24, 2024, for $35 per share in cash, valuing the deal at $6.4 billion. HashiCorp, a leader in multi-cloud infrastructure automation, provides solutions for infrastructure lifecycle management and security lifecycle management in hybrid and multi-cloud environments.

With HashiCorp’s tools like Terraform and Vault, IBM plans to enhance its hybrid cloud platform, improving infrastructure management and security, while positioning itself to capture a larger share of the $1.1 trillion cloud market.

8. Salesforce x Tenyx 

Salesforce announced its acquisition of Tenyx, an AI voice agent company based in California, on September 3, 2024. Founded in 2022, Tenyx serves industries like e-commerce and healthcare. 

9. Microsoft x Inflection

In March 2024, Microsoft struck a $620 million deal with Inflection AI, securing non-exclusive rights to offer Inflection’s AI model via Azure Cloud for several years. An additional $33 million was allocated to waive off claims tied to hiring Inflection employees, bringing the total value of the agreement—including executive compensation—to over $1 billion.

The deal also saw Microsoft hire Inflection’s co-founders, Mustafa Suleyman and Karén Simonyan, along with approximately 70 other employees. Suleyman assumed the role of CEO of Microsoft’s new consumer AI division, overseeing products such as Copilot, Bing, and Edge. 

10. Amazon x Adept 

In June 2024, Amazon reached a deal with AI startup Adept, bringing in key executives and licensing its technology. As part of the agreement, Amazon hired Adept’s co-founder and CEO, David Luan, along with other leaders and about two-thirds of Adept’s employees. Luan leads Amazon’s new AGI Autonomy division, reporting to Rohit Prasad, who oversees artificial general intelligence at Amazon.

11. Yotta x IndiQus Technologies

In November 2024, Yotta Data Services acquired IndiQus Technologies, the parent firm of Apiculus, to expand its cloud and AI capabilities.

Through this deal, Yotta expands its portfolio in cloud and AI services, laying the groundwork for an AI-focused cloud platform. IndiQus founders Sunando Bhattacharya and KB Shiv Kumar joined Yotta as chief revenue officer and chief innovation officer, respectively. 

12. Thomson Reuters x Materia 

Thomson Reuters acquired Materia, a US-based startup that develops AI assistants for tax, audit, and accounting professionals, on October 22, 2024. Founded in 2022, Materia’s platform automates research and workflows, simplifying tasks for accountants.

The acquisition aligns with Thomson Reuters’ AI strategy and will integrate Materia’s technology into its portfolio to deliver generative AI tools and assistants. Financial details of the deal were not disclosed.

13. HPE x Juniper 

On January 9, 2024, HPE acquired Juniper Networks in a deal worth $14 billion, paying $40 per share—32% more than Juniper’s stock price at the close of trading on January 8, 2024. This acquisition was set to accelerate HPE’s ambitions in the networking space, with the merger expected to boost the networking segment’s share of HPE’s total revenue from 18% to 31%. Even more striking, networking now contributes over 56% to HPE’s operating income.

14. AMD x ZT Systems 

AMD announced that it had acquired ZT Systems for $4.9 billion on August 19, 2024. The deal consisted of 75% cash and 25% stock, with a potential additional payment of up to $400 million based on performance targets.

Founded in 1994 and headquartered in Secaucus, New Jersey, ZT Systems specialises in compute design and infrastructure for AI, cloud, and general-purpose computing. With a strong track record of providing essential computing and storage solutions for major cloud providers, the company generates approximately $10 billion in annual revenue.

The post 2024’s Biggest AI Companies Mergers and Acquisitions appeared first on Analytics India Magazine.