“You guys are building the future here,” Yann LeCun, the chief scientist of Meta AI, remarked as he photographed the crowd with his Meta Ray-Ban smart glasses at the Meta Build with AI Summit held in Bangalore on Wednesday. He was referring to India’s talent pool working on open-source initiatives in India.
LeCun highlighted how diverse contributions enhance the development of AI, noting that it has significantly impacted open-source initiatives in India driven by companies like Sarvam AI, AI4Bharat, and others.
He said that while promoting open source serves Meta’s interest, he was fascinated by how people were developing products that even Meta hadn’t envisioned. “I see how people try to use the technology with good open-source data.” He acknowledged the work of AI4Bharat, a research lab at IIT Madras which works on developing open-source datasets to use AI for Indic languages.
“Going forward, India has an important role to play, not just in technology and product development, not just for local products, but for international products and also for research,” adding that India is brimming with talent.
He was quick to draw parallels to the establishment of FAIR Paris 10 years ago. “I think we can sort of use this blueprint to accelerate the progress of AI in India even further.”
The day before, LeCun visited IIT Madras and AI4Bharat, and was impressed by projects that focus on language translation and cultural preservation, such as IndicTrans2 and others – all thanks to Llama. “AI is going to help inspire,” he remarked, particularly in regions with many languages and cultural complexities.
Speaking at IIT Madras, LeCun highlighted that the current language models might seem like they are reasoning, but are, in fact, only carrying out intelligent retrieval, and are not the pathway to human-level intelligence.
LLMs are Not All We Need
To reach the next level of AI—what LeCun calls advanced machine intelligence or AMI, also known as “friend” in French—we need systems that can truly help people in their daily lives. This involves developing systems that can understand cause and effect and model the physical world.
LeCun used the example of a child who can figure out how to load a dishwasher on their first attempt—something no AI has yet been able to achieve. We need systems that can learn and adapt to new tasks with common sense. This has been LeCun’s vision for autonomous machine intelligence for the longest time.
Following up on his long debate about LLMs, one of the key challenges that LeCun highlighted about current AI models is that even the most powerful ones have seen less data than a human child absorbs in the first few years of their life.
“We need to train AI to understand the world by observing it, just like humans do. This requires new architectures that move beyond today’s models.” LeCun emphasised focusing on object-driven AI instead of prompt-based, as they would be able to solve problems that current AI models can’t.
“These systems will be able to plan actions and predict outcomes, and they’ll do so while adhering to built-in safety measures, or ‘guardrails’. This approach not only makes AI more powerful but also ensures it operates within safe boundaries,” said LeCun.
Local to Global and Decentralised
LeCun envisions AI as a shared infrastructure that democratises access to knowledge. He articulated a future where “the big frontier AI systems will not be produced by smaller companies… but will be trained in a distributed fashion”. This approach would allow for localised data processing while maintaining a global consensus.
“Open-source technology is crucial today and will become even more so in the future. This is because AI is increasingly becoming a shared infrastructure that can be used globally,” LeCun said and added that for AI to reach its full potential, it needs to serve as a repository of human knowledge.
While we’re attempting to achieve this now, we’re limited by a lack of diverse data in terms of languages, cultures, values, and interests.
Left to right: Tanuj Bhojwani, Yann LeCun, and Nandan Nilekani
While speaking at the panel with Infosys co-founder Nandan Nilekani and head of People+ai Tanuj Bhojwani, LeCun shared the idea of building India as the AI use case capital of the world. Nilekani said, “We think that we can build on top of digital public infrastructure.” He said this is providing the foundation to go to AI much more rapidly, a concept he previously referred to as “DPI to the power of AI’.
Centralising all this data isn’t feasible, nor would it fully represent the vast range of human knowledge. LeCun underscored the importance of collaboration among governments, industries, and researchers to establish the necessary infrastructure for this vision. “There’s a lot of work to do,” he acknowledged, stressing the need to empower individuals, especially in underserved areas.
Even if we could access the necessary data, fine-tuning these AI systems would require input from people with diverse cultural and linguistic backgrounds. No single organisation or country can do this alone; it must be a collective global effort. In this sense, AI development needs to be transparent and decentralised.
India will need to invest in infrastructure, expertise, and policies that support such a decentralised approach to AI. Rather than seeing it as a threat to sovereignty, this would enable true global sovereignty over knowledge and technology.
“I see a future where we’ll all have smart glasses or devices that allow us to interact with AI assistants,” said LeCun. These systems could eventually be smarter than us, but LeCun said that we shouldn’t see that as threatening. Instead, it’s empowering—like having a team of experts at our disposal.
Everyone, not just people in tech or academia, should have access to these tools. “Imagine rural communities in India being able to ask questions in their own languages, improving sectors like agriculture and healthcare. This kind of future would bring significant positive changes,” added LeCun.
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