Updated March 5, 2026
0:00 Welcome to Colaberry AI podcast brought to you by Colaberry AI Research Labs and Carl Foundation. Today, we're diving deep, really deep into something pretty big. How India is building its AI ecosystem. It's this mix of tech, national strategy, global ambition, fascinating stuff. Our mission, take the source material, a really interesting discussion, actually, and boil it down for you. 0:23 We wanna pull out the key insights, the important technical bits, maybe some surprising facts. Basically, give you shortcuts. You're totally up to speed on this. Yeah. And the main source we're digging into today is from the Corel Learning Labs EAP one video. 0:34 It just launched 05/16/2025. It featured Ramdhan Yadav Kadamiraja. He's cofounder of KHL, Knowledge House Labs, and also founder of Colaberry. What's really striking is how it shows this, convergence of education and innovation in AI, especially looking at India's strategy. Okay. 0:52 Let's unpack that. Starting with Colaberry itself, founded way back in 2012. Right? That's right. 2012. 0:57 And it began with supporting US veterans, helping them move into civilian careers. Exactly. But it grew, quite significantly from there. Into a global thing, I gather. Helping thousands over 45 countries get into data science analytics. 1:11 Yes. Precisely. And they've gotten noticed for it in cake. 5,000 Financial Times recognition. Fast growing. 1:18 And they have this AI platform, refactor.aiai, that's picked up awards from XPRIZE, MIT, Solve, GM. Correct. That platform is key. It really highlights their approach to skills and transformation using AI. It's this solid foundation that lets them aim so high with this new India initiative. 1:37 Right. So this CAROL Learning Lab series, it's more than just training sessions. Well, much more. They call it a movement specifically to democratize access to artificial intelligence. Yeah. 1:46 The whole idea is AI built for the people, by the people, with the people. A very inclusive vision. Definitely. And there's this strong belief that India has a, well, a historic opportunity to lead, not just in AI generally, but in responsible AI, inclusive AI, applied AI. And who are the key players making this happen? 2:02 You mentioned Ram Dhan. Yes. And also Ramon Maramasa, who's MD of Kolibari India and founder of KHL. Survey Singh, the DG of KHL, is crucial for aligning with national goals. Sarita Chamati moderated that first session and helped shape the education working group, and then you have the backing of Colaberry itself, T Hub, significant government support, plus key academics like doctor Ramji at CCMB, doctors Ratnakar and Rita Faba. 2:29 It's a real coalition. That paints a picture of serious national effort. But where does India actually stand now? Globally, I mean. How does it compare to the big AI players? 2:40 Well, if you look globally today, it's mainly The US and China setting the pace. The US is really dominant in foundational research. Think OpenAI, Meta, Google, Anthropic, NVIDIA pushing the limits. They've got the advanced infrastructure, and they attract a lot of global talent. Okay. 2:54 The research leaders. Then you have China. They excel at operationalizing AI at massive scale across whole cities, industries, public services. They lead in AI patents, actual deployment, and huge government initiatives, really efficient implementation. And India, where does it fit in that picture? 3:09 It's well, it's complex. India is the most populous nation, massive tech talent pool, no doubt. But currently, it's not quite in the top five AI nations. The data shows a lower share of global AI research papers, limited access to the kind of high performance computing you need for big AI models. The really heavy duty stuff. 3:27 Exactly. Plus challenges in getting practical, hands on AI education into all the colleges and making sure talented students from rural areas or tier two cities aren't left out. So there's a gap between the potential and the current reality. But things are changing fast. The source material really emphasizes momentum. 3:45 Absolutely. Huge acceleration, especially after something called deep sea. That's the data driven economy policy framework, a big step in India's digital strategy. Yeah. And just recently, the government launched the India AI mission. 3:57 Big commitment about 10,300 crore rupees, so roughly 1,250,000,000.00 US dollars. Wow. That's significant investment. What's it for specifically? It's targeted. 4:06 First, building serious compute platform, world class GPU clusters, clusters, accessible to start ups researchers. Second, launching India AI innovation centers for that cutting edge r and d. Third, a national AI dataset platform, crucial for getting quality, trusted data for model building. Data is always key. Always. 4:24 Then there are schemes like Future Skills India, startup funding, empowering people, and a really technical goal, creating safe, reliable India foundational models tailored for India's needs. SCRBAI is tasked with that, the big vision behind it all. AI is a public good. Accessible, safe, inclusive. And there are specific pillars for achieving this. 4:44 I think a KPMG report was mentioned. Yes. Five key pillars identified by KPMG. One, revolutionize AI education. Two, democratize data access. 4:53 Three, build global AI research hubs in India. Four, promote India first AI integration solutions for India specific context, languages, challenges. And five, balance AI for societal good. A comprehensive framework. It is. 5:07 But the key point they stress is this has to be a ground up movement, not just top down directives. Needs everyone involved. Which brings us back to Farrell. How do they connect this national strategy to the community level? The source calls it a people powered learning and innovation lab. 5:21 That's a great description. They have two main arms, an education working group and an applied AI working group. Okay. The Carell AI learning lab series falls under the education group. It's basically a weekly open virtual learning session. 5:35 The idea is to bring in actual AI practitioners, domain experts, people doing the work. They share real case studies, visionary ideas, do demos, hands on sessions, even kick start projects. Sounds very practical. Very. And importantly, every single topic gets looked at through the lens of fairness, privacy, impact, and inclusivity. 5:53 So the tech talk is always grounded in ethics and society. I like the name Learning Labs, inspired by Boston's innovation scene. Right? Cross generational, cross disciplinary. Exactly that vibe. 6:04 Dynamic, interactive. And the technical focus areas, they're chosen carefully, emerging tech, like generative AI, agentic AI, which is often where gen AI gets really applied, but also critical sectors, health care AI, agriculture, climate, finance, infrastructure, utilities, areas where AI can genuinely impact hundreds of millions of lives in India. Huge potential impact there. Massive. And it's all open sessions are recorded, shared freely. 6:31 They really want educators, professionals to jump in, use the material, spread it. Okay. Let's get practical. What does this actually mean for you, the listener? The labs are tackling real questions. 6:40 The q and a part of the source gave some great examples. Let's dig into those technical applications. Right. One big area was AI in clinical research and project management. Think about clinical trials, super complex. 6:51 Definitely. Lots of moving parts, tons of data. Exactly. So AI can help automate project management tasks, tracking timelines, monitoring progress, moving beyond manual spreadsheets, you know. But more powerfully, AI can speed up data processing dramatically. 7:07 And you can use visualization tools like Power BI, Spotfire, or other reporting tools to make sense of that data much faster for quicker, better decisions. Imagine spotting a trend in trial data almost instantly. That could be transformative for drug development. What about smaller businesses, SMEs? Good question. 7:26 Big companies often lead in enterprise AI. Sure. But the source points out innovation is really bubbling up in smaller firms too. For SMEs, the focus is often on things like generative AI, small language models, SLMs, which are more manageable, and conversational AI. Makes sense. 7:41 More accessible tech. And there's this concept of Genry BI or Gen b. Think business intelligence, but supercharged. And analysts can just ask a question in plain English like, why did sales dip last quarter in the North Region? And get a real answer back based on the data. 7:55 Exactly. The AI queries the data and gives you the insights. It really lowers the barrier for getting value from your data, especially for smaller teams without dedicated data scientists. Okay. Now agentic AI versus AI agents. 8:09 These terms get thrown around. Can you clarify the difference technically? Yeah. It's important. They're not the same. 8:15 Agenic AI refers to systems that have some agency, some ability to act on their own, but usually within limits. Yeah. They automate parts of a workflow that still involves humans. Like a smart assistant handling specific tasks within a larger process? Precisely. 8:30 It might extract data or categorize emails, but a human is still overseeing the whole thing. It has agency, but not full autonomy. AI agents, on the other hand, these are designed to be truly autonomous, like a digital employee or even a team. You give them a high level task, say, manage this marketing campaign, and they can plan, execute, adapt, make decisions independently. Wow. 8:50 So they operate much more like a human collaborator. That's the goal. They can orchestrate multiple steps, handle unexpected issues without needing constant human direction. Think of an AI agent managing customer support tickets from start to finish. And for either of these to work, data, data, data. 9:09 Absolutely critical. You can't have effective agents without robust, clean, accessible data. That's why you see massive investment everywhere in data engineering, streamlining data pipelines. It's the foundation. Makes sense. 9:21 And there's this interesting trend now. Synthetic data initiatives. Basically, training agents first on artificially generated data. Why do that? It lets you train and test them extensively, work out kinks before you let them loose on sensitive real world, maybe masked organizational data, faster iteration, safer deployment. 9:39 The projection and it sounds kinda sci fi, but it's coming, is that within maybe two years, we could see autonomous robots and AI agents actually being part of company org charts, working alongside human teams. Integrated teams of humans and AI agents, that's a paradigm shift. It really is. Yeah. Now related to that reliability, the problem of confident errors or hallucinations in generative AI. 10:03 This is a huge focus. Right. The AI making stuff up but sounding totally sure about it. Exactly. You have to remember, traditional software is deterministic. 10:10 You put x in, you get y out every time. Like a calculator Predictable. Generative AI generates. It's It's inherently probabilistic, creative, which is powerful, but it can lead to these plausible sounding inaccuracies. So how do you fix that or at least reduce it? 10:26 The main technical approach right now is using RRAID. That's retrieval augmented generation. Okay. R RRAID. How does that work? 10:33 Think of it as giving the generative AI a research assistant. Mhmm. Instead of just making things up based on its vast, sometimes messy training data, the RRAID system first retrieves relevant factual information from a trusted source, like your company's internal documents, a specific database, reliable websites. It fetches the facts first. Precisely. 10:52 Then the generative model uses that retrieved information and only that information to construct the answer. So it's augmenting the generation with factual retrieval. This makes the output way more accurate and grounded. That sounds much safer. Is human oversight still needed? 11:07 Oh, absolutely crucial, especially with a reg. Humans review the outputs. If it's good, maybe it gets cached for faster deterministic answers next time. If it's wrong or not quite right, the human corrects it. And that correction feeds back into the system, fine tuning it, making it better over time. 11:24 Human in the loop. Exactly. You absolutely need that feedback loop, especially in high stakes areas. Think health care AI. You can't afford hallucinations there. 11:32 Accuracy is paramount. So human validation is non negotiable. This really highlights the technical depth needed, which brings up a big question. Why is it so important for India to build its own AI capability? Why not just use systems built elsewhere? 11:47 That's a critical point. If India doesn't develop its own talent, its own research, its own ethical guidelines, it risks becoming just a consumer dependent on AI systems built elsewhere. And those systems might not understand India's diverse languages, reflect its cultural values, or really serve the specific needs of its communities, especially rural or underserved ones. You might import biases or solutions that just don't fit. So it's about technological sovereignty in a way and relevance. 12:18 Exactly. But the positive side, as the source strongly argues, is that India has everything it needs to lead. The people, the innovative spirit, the passion, now significant government investment, improving infrastructure. Yeah. All the ingredients are there to be a leader in, as they put it, AI for humanity. 12:34 Okay. So for you listening right now, how can you actually get involved? What's the call to action? It's pretty broad and inclusive. If you're a student, bring your questions, your curiosity, your ideas to learning lab. 12:43 If you're an academic Mhmm. Partner up, bring these real world insights into your classrooms, help shape that next generation. Makes sense. Industry folks. Share your stories, your experiences. 12:53 Yeah. Mentor the learners. Maybe even cocreate projects or products through this network. Real world application is key. And if you're in policy, use this network. 13:04 It's a way to hear directly from the ground level, see where innovation is happening, what challenges people face. So it's a collective effort? Yeah. Absolutely. The goal isn't just powerful AI. 13:13 It's purposeful AI. Establishing Indian leadership and AI thought, innovation, inclusion, creativity on the world stage. A powerful vision. Before we wrap up, you usually leave us with something to chew on, a provocative thought. Okay. 13:28 Here's one. We're seeing AI agents becoming part of our work, maybe even our lives, and they're increasingly trained on synthetic data. That's a huge shift. So the question for you is, what foundational changes do you need to make in your skills, how you think, how you collaborate to not just cope, but actually thrive in a future where humans and AI agents work as truly integrated teams? That's definitely something to think about. 13:49 How do we evolve alongside these agents? Thank you for listening in. Subscribe and follow Calabrio on social media links in the description, and check out our website www.calabrio.ai backslash podcast for more insights like this.
