Yann LeCun (Chief AI Scientist) 00:00.980
They didn't really find until the 1980s, nobody found really a good way to train those those multi-layer systems mostly because the neurons that that they had at the time were the wrong type. They had neurons that were binary. So neurons in the brain are binary. They they either
Yann LeCun (Chief AI Scientist) 00:19.220
fire or they don't fire and people wanted
Yann LeCun (Chief AI Scientist) 00:23.020
to reproduce that. So they they build simulating neurons that would either be active or inactive. And it turns out for the modern learning algorithms to work, we call them back we call it back propagation. You need to have neurons that have sort of graded responses. Um, and uh
Yann LeCun (Chief AI Scientist) 00:40.100
that only became practical possible or people realized it could work in the 1980s. People
Yann LeCun (Chief AI Scientist) 00:46.220
had the idea before, but they never could really make it work. And so that caused um a renewal of interest in neural nets in the 1980s. They had been largely abandoned in the late 60s. And then they came to the fore again in the mid-to-late 80s. That's when I started kind of my
Yann LeCun (Chief AI Scientist) 01:04.740
graduate school basically in 1983.
Yann LeCun (Chief AI Scientist) 01:07.500
And uh there was a wave of interest that lasted about 10 years. And then interest went again um in the mid-90s until the late 2000 when we rebranded it into deep learning. Neural net had kind of a bad rep. Um people in computer science and engineering thought neural nets were
Yann LeCun (Chief AI Scientist) 01:28.900
kind of a bad thing had a bad reputation.
Yann LeCun (Chief AI Scientist) 01:32.060
And so we we branded it into deep learning and sort of brought it back to the floor and then the results were were there in computer vision, in natural language understanding, speech recognition to really convince people that this was uh a good
Janna Levin (Professor of Physics and Astronomy) 01:46.100
thing. Now, Adam, you at at a very young age were interested in theoretical physics, not specifically computer science. And you're watching some of this unfold in some sense from afar. What's the catalyst that sweeps up so many people decades later. There's
Janna Levin (Professor of Physics and Astronomy) 02:03.460
There's this time where it's of great interest, there's great success in handwriting recognition or in uh visual recognition and these things, but it's not sweeping up the world. What What happens that brings us to this point where we're all now talking about large language
Janna Levin (Professor of Physics and Astronomy) 02:18.300
models.
Adam Brown (Research Scientist) 02:19.500
So, many physicists in the last years have pivoted, should we say, from working on physics to working on AI, and it really traces back to some of the work that Jan and others did to prove that it works.
Adam Brown (Research Scientist) 02:34.300
Like when it wasn't working, it was just this this thing that's over there in computer science and like of many things in the world that are not particularly uh may be interesting but not many physicists are paying attention to it. But then after
Adam Brown (Research Scientist) 02:46.580
you know Jan and some of the other pioneers of this field proved that it would work, it became a totally fascinating subject for physics. That you link up these neurons together in a certain way and suddenly you get emergent behaviour that didn't exist at the individual neuron
Adam Brown (Research Scientist) 03:02.740
level. That seems like a a subject
Adam Brown (Research Scientist) 03:05.300
that physicists who spend their life imagining how the sort of rich pageantry of the world could emerge from simple laws that immediately attracted the attention of many physicists. And nowadays it's a a a very common career path to do a PhD in physics and then apply it to a
Adam Brown (Research Scientist) 03:20.020
emergent system. But the emergent system is an emergent network of neurons that collectively give rise to intelligence. Mhm.
Janna Levin (Professor of Physics and Astronomy) 03:27.580
Now, mhm, let's do a lightning round cos you raised the dreaded word intelligence. Everybody in this room very likely has interacted with something that we're now calling an AI. These are all large language models and before I ask you to define those for us, I just want to kind
Janna Levin (Professor of Physics and Astronomy) 03:43.380
of do a lightning round of of what's your yes or no response to certain things. So Adam,
Janna Levin (Professor of Physics and Astronomy) 03:53.420
yes or no are these AIs, these large language models understanding the meaning of the conversation Yes.
Yann LeCun (Chief AI Scientist) 04:05.140
Sort of.
Janna Levin (Professor of Physics and Astronomy) 04:08.980
Perfect.
Janna Levin (Professor of Physics and Astronomy) 04:14.260
Right, exactly. It was my fault for giving you a binary choice. Okay, so that allows me to ask the next question because it's not a foregone conclusion. If you don't say yes to that, it's going to be interesting what you say to this. Are these AIs conscious?
Yann LeCun (Chief AI Scientist) 04:30.500
Absolutely not.
Janna Levin (Professor of Physics and Astronomy) 04:32.300
Adam.
Adam Brown (Research Scientist) 04:33.820
Probably not. Okay.
Janna Levin (Professor of Physics and Astronomy) 04:36.260
Um, will they soon be?
Adam Brown (Research Scientist) 04:39.660
I think they'll one day be conscious if if progress continues in the way that we're we're continuing. When is hard to say, but Mhm.
Yann LeCun (Chief AI Scientist) 04:49.060
John. Yes, for appropriate definitions of consciousness.
Janna Levin (Professor of Physics and Astronomy) 04:52.100
Yes, okay. Well, we do have some philosophers in the house and um we're we're not going to indulge in philosophical definitions of consciousness or there our hour would go. And we'd still be here. Oh, I just heard that groan I think from our friends up in the balcony.