Max Tegmark: AI and Physics | Lex Fridman Podcast #155

The following is a conversation with Max Tegmark, his second time on the podcast. In fact, the previous conversation was episode number one of this very podcast. He is a physicist and artificial intelligence researcher at mit, co founder of the Future of Life Institute, and Author of Life 3.0 Being Human in the Age of Artificial Intelligence. He's also the head of a bunch of other humans huge fascinating projects and has written a lot of different things that you should definitely check out. He has been one of the key humans who has been outspoken about long term existential risks of AI and also its exciting possibilities and solutions to real world problems, most recently at the intersection of AI and physics and also in re engineering the algorithms that divide us by controlling the information we see and thereby creating bubbles and all other kinds of complex social phenomena that we see today. In general, he's one of the most passionate and brilliant people I have the fortune of knowing. I hope to talk to him many more times on this podcast in the future. Quick mention of our sponsors The Jordan Harbinger Show, Four Sigmatic Mushroom Coffee, Better Help, Online Therapy and ExpressVPN. So the choice is wisdom, caffeine, sanity or privacy. Choose wisely my friends, and if you wish, click the sponsor links below to get a discount to support this podcast. As a side note, let me say that much of the researchers in the machine learning and artificial intelligence communities do not spend much time thinking deeply about existential risks of AI because our current algorithms are seen as useful but dumb. It's difficult to imagine how they may become destructive to the fabric of human civilization and in the foreseeable future. I understand this mindset, but it's very troublesome to me. This is both a dangerous and uninspiring perspective, reminiscent of a lobster sitting in a pot of lukewarm water that a minute ago was cold. I feel a kinship with this lobster. I believe that already the algorithms that drive our interaction on social media have an intelligence and power that far outstrip the intelligence and power of any one human being. Now really is the time to think about this, to define the trajectory of the interplay of technology and human beings in our society. I think that the future of human civilization very well may be at stake over this very question of the role of artificial intelligence in our society. If you enjoy this thing, subscribe on YouTube, review it on Apple Podcasts, follow on Spotify, support on Patreon, or connect with me on Twitter lexfriedman. And now, here's my conversation with Max Tagmark. So people might not know this, but you were Actually, episode number one of this podcast just a couple of years ago, and now we're back. And it so happens that a lot of exciting things happened in both physics and artificial intelligence, both fields that you're super passionate about. Can we try to catch up to some of the exciting things happening in artificial intelligence, especially in the context of the way it's cracking open the different problems of the sciences?

Yeah, I'd love to. Especially now as we start 2021 here, it's a really fun time to think about what were the biggest breakthroughs in AI, not the ones necessarily that media wrote about, but that really matter. And what does that mean for our ability to do better science? What does it mean for our ability to help people around the world? And what does it mean for new problems that they could cause if we're not smart enough to avoid them? So what do we learn basically from this?

Yes, absolutely. So one of the amazing things you're part of is the AI Institute for Artificial Intelligence and Fundamental Interactions. What's up with this institute? What are you working on? What are you thinking about?

The idea is something I'm very on fire with, which is basically AI meets physics. And it's been almost five years now since I shifted my own MIT research from physics to machine learning. And in the beginning, I noticed a lot of my colleagues, even though they were polite about it, were like kind of, what is Max doing? What is this weird stuff?

He's lost his mind.

But then gradually, I, together with some colleagues, we're able to persuade more and more of the other professors in our physics department to get interested in this. And now we got this amazing NSF center, so 20 million bucks for the next five years. MIT and a bunch of neighboring universities here also. And I noticed now those colleagues looking at be funny, have stopped asking what the point is of this, because it's becoming more clear. And I. I really believe that, of course, AI can help physics a lot to do better physics, but physics can also help AI a lot, both by building better hardware. My colleague Marin Solzacic, for example, is working on an optical chip for much faster machine learning, where the computation is done not by moving electrons around, but by moving photons around dramatically less energy use faster, better. We can also help AI a lot, I think, by having a different set of tools and a different, maybe more audacious attitude. AI has, to a significant extent, been an engineering discipline where you're just trying to make things that work and being more interested in maybe selling them than in figuring out exactly how they work and proving theorems about that they will always work. Contrast that with physics. When Elon Musk sends a rocket to the International Space Station, they didn't just train with machine learning. Oh, let's fire it a little bit more to the left, A bit more to the right. That also missed. Let's try here. No, we figured out Newton's laws of gravitation and got other things and got a really deep fundamental understanding. And that what gives us such confidence in rockets. And my vision is that in the future, all machine learning systems that actually have impact on people's lives will be understood at a really, really deep level. Right? So we trust them, not because some sales rep told us to, but because they've earned our trust. And really safety critical things even prove that they will always do what we expect them to do. That's very much the physics mindset. So it's interesting. If you look at big breakthroughs that have happened in machine learning, this year from dancing robots is pretty fantastic. Not just because it's cool, but if you just think about, not that many Years ago, this YouTube video at this DARPA challenge where the MIT robot comes out of the car and face plants, how far we've come in just a few years. Similarly, alpha fold 2 crushing the protein folding problem. We can talk more about implications for medical research and stuff, but hey, that's huge progress. You can look at GPT3 that can spout off English text, which sometimes.