Why Energy-Based Models Could Be the Next Big Shift in AI
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What are energy-based models and how do they differ from traditional AI?
My intelligence is not attached to any language in my brain. I think in an abstract way, not every AI has to be LLM-based. And there was a realization to me is like, not everything is related to language in this world. Like robotics is not attached to language. EBM is one part of the story. EBM attached to the LLM is another part of the story. This is the whole point of energy-based model. You never have to guess the next word. You don't really play in a guessing game anymore. You see your energy landscape, you know where the right answer is.
Welcome, humans, to the latest episode of the Neuron Podcast. I'm Corey Knowles, editor of the Neuron, and we're joined, as always, by Grant Harvey. How are you, Grant? Looks like some big news dropped recently here from a company called Logical Intelligence, eh?
Yeah, yeah, that's right. Logical intelligence. This is some pretty huge news. So logical intelligence just announced that Yann LeCun, we're talking the Turing Award winner, former chief AI scientist at Meta, and one of the godfathers of deep learning, just joined as the founding chair of their technical research board. And this company is building something completely different from ChatGPT and Claude. The founder, Yves Bodnia, has a wild background as well. She's a physicist with a PhD in quantum information and algebraic topology. She's published 22 papers on dark matter and quantum mechanics. And she's saying that her new Kona model represents the first credible science of AGI.
So today we're going to break down what energy-based models actually are, why they almost can't hallucinate, where they belong versus language models, and whether this is actually a path to AGI or just a really strong constraint solver. So today we're going to bring her on. Eve Bodnia, welcome to The Neuron. It's great to have you.
Thank you. Cheers. Cheers.
Excellent. Well, let's start with you. That's such an awesome background, thinking of dark matter and quantum physics. How did that background lead you to start Logical Intelligence?
Actually, this background is a little bit more complex than it sounds. I think since I was a kid, I was just naturally curious and I was trying to understand how this universe works. And I was trying to pick a field which has no limits to myself. I felt that I'm not going to be a good doctor or like an engineer because there's a level of things you can master and kind of like I was not good enough to go further. And on the theoretical science, I felt like I could just go up and up. And I was relatively okay with mathematics and theoretical physics. And I'm like, well, maybe I'm just going to start with physics and just throw myself into it and try to understand how things work. What's like, the fundamental laws of nature and become a professor and I just made the decision maybe when I was like around 11 years old and since then my whole life I was like trying to optimize for finding the best people the smartest people around me so I can learn from them and like the best resources available for this so I
Moved a lot with my family and ended up being in the Bay Area. I went to UC Berkeley for my undergrad and I met Professor Daniel McKinsey.
How do energy-based models reduce hallucinations in AI?
Hi, Dan. He was like so deep into dark matter, but also he was focused on just general like understanding how symmetries and how the symmetries work and how it's applied to describe the laws of nature. So it was not just dark matter, it was mainly the particle physics, which is one of the most fundamental areas. And I was attracted to mathematical foundations of it. And eventually, once you expose the different areas, you start seeing the patterns. And I was like, well, I kind of like understand a little bit how particle physics works and the same mathematical methodology can be applied to like how brain works. Well, there's some frameworks, like not every framework, but some frameworks can be applied how the brain works.
And once you start questioning how the brain works, you're naturally questioning what is intelligence and how it works.
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Chapters
8 chapters
1
What are energy-based models and how do they differ from traditional AI?
0:00–3:33
2
How do energy-based models reduce hallucinations in AI?
3:33–7:45
3
Why might energy-based models complement large language models?
7:45–11:27
4
What practical applications can energy-based models enhance?
11:27–14:30
5
How does Eve Bodnia's background influence her work in AI?
14:30–18:34
6
What challenges exist in scaling energy-based models?
18:34–22:42
7
How do energy-based models contribute to the future of AGI?
22:42–27:20
8
What insights can we gain about AI from the discussion on energy landscapes?
27:20–55:22
Speakers
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