WEBVTT 00:01.220 --> 00:04.460 I welcome to day four of our time together. 00:04.460 --> 00:06.650 This is a very important day. 00:06.650 --> 00:12.110 Today we're going to be looking at the rise of the transformer, the architecture that sits behind most 00:12.110 --> 00:14.240 of the LMS that we'll be working on in this course. 00:14.240 --> 00:18.830 We're going to be talking about stuff like Copilots and agents, and most importantly, we're going 00:18.860 --> 00:25.610 to be dealing with some of the basic foundational ingredients behind LMS that we'll be working on tokens, 00:25.610 --> 00:28.040 contacts, windows, parameters, API costs. 00:28.070 --> 00:32.240 Now, for some of you, this will be stuff that you're already quite familiar with, but I hope to go 00:32.240 --> 00:37.310 a bit deeper and show you some more insight, so this will still be a useful use of your time. 00:37.310 --> 00:42.830 So hang in there and if you're new to it, I hope to give you a really good foundation to this world. 00:43.340 --> 00:49.100 But first we have to reveal the winner of our leadership battle. 00:49.130 --> 00:54.590 Hopefully you remember that at the end of the last session, I left you with Alex versus Blake versus 00:54.590 --> 01:01.960 Charlie, GPT four zero, Claude three, Opus and Gemini and that they were battling together to vote 01:02.080 --> 01:04.990 for the leader of the pack. 01:05.110 --> 01:06.400 You remember their. 01:06.430 --> 01:08.830 Their pithy pitches to each other. 01:08.830 --> 01:11.020 And now I will reveal the outcome. 01:11.020 --> 01:15.160 So the first vote came in from Alex from GPT. 01:15.310 --> 01:19.870 GPT made the vote for Blake to be leader. 01:19.900 --> 01:23.620 Next up, the next vote coming in was Blake voting. 01:23.620 --> 01:27.250 And Blake voted for Charlie for Gemini. 01:27.280 --> 01:29.290 So Claude voted for Gemini. 01:29.320 --> 01:31.030 Blake voted for Charlie. 01:31.030 --> 01:34.180 And so it all comes down to Charlie's vote. 01:34.210 --> 01:35.200 Big drumroll. 01:35.200 --> 01:35.920 Make your bets. 01:35.920 --> 01:37.810 Decide who you think is going to be the winner. 01:37.840 --> 01:39.490 This is the winner. 01:39.520 --> 01:43.330 Charlie voted for Blake, and therefore Claude. 01:43.360 --> 01:51.040 Three opus Blake was the winner of our thoroughly unscientific, but quite fun challenge, and I hope 01:51.070 --> 01:54.970 that that is aligned with your expectations from those pictures. 01:55.180 --> 01:57.100 And you should try this yourself. 01:57.100 --> 02:01.530 This is as I say, I ran this a couple of months ago and I'm sure that the results will be different 02:01.560 --> 02:02.220 now. 02:02.850 --> 02:04.920 At least you get different different pictures for sure. 02:04.920 --> 02:07.320 So by all means give this a try yourself. 02:07.320 --> 02:12.060 You can copy my prompt or try something a bit different and see what you come up with. 02:12.060 --> 02:15.600 And in fact, I actually wrote a game based on this. 02:15.600 --> 02:22.020 If you go to my personal web page@edward.com, you'll see that I have a game there called Outsmart, 02:22.080 --> 02:28.860 which is where I picked the various models against each other to try and do something a little bit more 02:28.860 --> 02:35.940 quantitative when they have to decide how to steal coins from each other, and it gives you a really 02:35.940 --> 02:40.290 fun way to see the different capabilities of the different models. 02:40.290 --> 02:42.300 And maybe we'll look at that a bit later on. 02:42.300 --> 02:42.990 I'll see. 02:43.620 --> 02:44.430 All right. 02:44.430 --> 02:49.440 Well, with that in mind, let's move on to the main material of today. 02:49.440 --> 02:54.660 We're going to talk about the unbelievable history of the last few years. 02:54.660 --> 02:59.690 And I have to pinch myself from time to time to remind myself of everything that we've been through. 02:59.690 --> 03:08.240 In 2017, Google, some scientists from Google released a paper that was called Attention is All You 03:08.240 --> 03:10.700 Need, and you can take a look at it. 03:10.850 --> 03:16.790 And this was the paper in which the transformer architecture was invented. 03:16.820 --> 03:21.050 This new architecture, including these layers called self-attention layers. 03:21.290 --> 03:27.470 And the thing that's perhaps most remarkable about this paper when you read it, is that it's very clear 03:27.470 --> 03:33.200 that the inventors themselves did not realize what an extraordinary breakthrough they were making. 03:33.230 --> 03:39.650 They sort of remark on it as something that seems to be a surprising discovery, but they clearly don't 03:39.650 --> 03:45.170 realize the door that they are opening and how much progress is going to be made as a result of their 03:45.170 --> 03:46.160 discoveries. 03:46.190 --> 03:51.170 In fact, the next year was when GPT one was released also. 03:51.290 --> 03:56.020 But for those that were, uh, around at that time and had used Bert from Google. 03:56.020 --> 03:59.350 And then came GPT 2 in 2019. 03:59.620 --> 04:02.140 GPT 3 in 2020. 04:02.170 --> 04:09.340 But most of us got the shock when we saw the power in late 2022. 04:09.370 --> 04:13.360 Was it November or December when ChatGPT came out, came out? 04:13.510 --> 04:21.940 ChatGPT was essentially GPT three, but also used GPT three and a half, 3.5 and also used this technique 04:21.970 --> 04:28.060 RL reinforcement learning from human feedback that made it so very powerful. 04:28.630 --> 04:35.590 Then GPT four came out in 2023, and of course this year we've had GPT four. 04:35.620 --> 04:42.580 Oh, and we've now, as we've seen, had zero one preview and other things are on the way. 04:43.960 --> 04:48.940 It was interesting to see how the world responded to this change. 04:48.970 --> 04:54.840 Initially, ChatGPT was such a surprise to all of us, even practitioners in the field. 04:55.080 --> 05:02.160 It was really astounding how accurately and with how much nuance it was able to answer questions. 05:02.160 --> 05:04.620 That was followed by something of a backlash. 05:04.620 --> 05:11.700 There was a lot of of healthy skepticism when people said, this is really akin to to a conjuring trick. 05:11.820 --> 05:16.560 This is what we're seeing here is basically really good predictive text. 05:16.560 --> 05:21.930 If you bring up your, your, your text messages and you and you press the button to predict the next 05:21.930 --> 05:27.600 word, sometimes it does really, really well, almost by coincidence, just because it's matching patterns 05:27.600 --> 05:28.380 statistically. 05:28.380 --> 05:30.240 And that's all you're seeing here. 05:30.240 --> 05:35.940 And there was a famous paper that's known as the stochastic parrot paper, which talked about the fact 05:35.940 --> 05:42.540 that what we're seeing here is nothing more than statistics, and that it sort of gives, makes the 05:42.540 --> 05:48.480 point that we are falsely interpreting this as the model, having some kind of an understanding which 05:48.480 --> 05:50.160 doesn't really exist. 05:50.160 --> 05:55.850 And it highlights some of the challenges and even dangers associated with us coming to the to the wrong 05:55.850 --> 05:57.380 conclusions about that. 05:57.500 --> 06:03.800 But really, based on the progress since then, the pendulum has swung back a bit now. 06:03.800 --> 06:09.770 And I would say that where we are as practitioners at this point is explaining this in terms of emergent 06:09.770 --> 06:10.670 intelligence. 06:10.670 --> 06:16.070 That's the expression we like to use, which is saying that really what's happening here is that whilst 06:16.070 --> 06:22.160 it is true that what we're seeing is essentially just statistical prediction, all we're doing when 06:22.160 --> 06:27.890 we run an LM is we're providing it with some, some words or actually some tokens and saying, given 06:27.890 --> 06:32.570 all of the patterns you've seen in all of your training data and everything you've learned, what is 06:32.570 --> 06:36.770 the most likely next token, what is the most likely next token? 06:36.770 --> 06:40.640 And then we'll feed that in and say, and now what's the most likely next token after that. 06:40.640 --> 06:44.000 And all it is doing is predicting this next token. 06:44.000 --> 06:45.230 That is true. 06:45.230 --> 06:53.500 But nonetheless, as a byproduct of doing this at such massive scale with trillions of different weights 06:53.500 --> 06:58.030 that are being set internally in the model to control how it will make that prediction. 06:58.060 --> 07:05.740 A byproduct of this level of scale is that we see this effect that we call emergent intelligence, which 07:05.740 --> 07:07.570 is an apparent intelligence. 07:07.570 --> 07:12.250 It is as if the model is really understanding what we're telling it. 07:12.280 --> 07:17.590 It is, of course, true that this is really something that is imitating understanding. 07:17.590 --> 07:20.230 It's just seeing the patterns and replicating them. 07:20.230 --> 07:27.340 But there is this emergent property that it apparently is able to show this level of intelligence that 07:27.340 --> 07:30.820 we all experience when we use these frontier models every day. 07:31.540 --> 07:31.870 All right. 07:31.870 --> 07:33.700 Hopefully that's given you food for thought. 07:33.730 --> 07:36.640 Interested to hear where you stand on this debate. 07:36.670 --> 07:40.690 By all means post that or let me know. 07:40.750 --> 07:45.280 And in the next lecture we will talk more about some of the theory behind this. 07:45.280 --> 07:49.930 And also look at some of the discoveries that we've had along the way.