WEBVTT 00:00.110 --> 00:05.750 And we will conclude our expedition into the world of frontier models through their chat interface by 00:05.780 --> 00:08.570 looking at meta AI and perplexity. 00:08.600 --> 00:15.770 Meta AI is, of course, the front end version to llama that's running behind the scenes. 00:15.770 --> 00:21.680 We can ask it the same question about how does it compare itself to other models, and we'll get back 00:21.680 --> 00:25.190 something that has some strengths and weaknesses. 00:25.190 --> 00:27.440 It doesn't do a great job. 00:27.440 --> 00:33.530 It gives some rather old fashioned complimentary llms, but it's it's okay. 00:33.530 --> 00:38.000 And generally speaking, I think you'd find asking various questions that you'll get answers that are 00:38.000 --> 00:42.200 okay, but not the same power as some of the others. 00:42.590 --> 00:44.060 Let's ask the same question. 00:44.060 --> 00:52.370 You guys are going to be fed up with me for doing this, but how many times does the letter A appear 00:52.370 --> 00:54.650 in this sentence? 00:54.920 --> 00:58.190 And let's see what we get from meta AI. 00:58.340 --> 01:04.160 It appears five times, so meta is also not able to handle that particular question. 01:04.190 --> 01:08.210 Now, one thing that the meta is able to handle is image generation. 01:08.210 --> 01:17.320 And we can say please generate an image of a rainbow of rainbows 01:19.270 --> 01:23.830 leaping from Hawaii to 17. 01:24.100 --> 01:31.450 Uh, and you'll find that this is the kind of, of challenge that, uh, lama is up for. 01:31.450 --> 01:35.980 And it very nicely does it with these four possibilities. 01:36.070 --> 01:38.350 And you get this, uh, this kind of effect. 01:38.350 --> 01:44.260 And it seems to have, uh, well, Hawaii has appeared, uh, but but some of these are very respectable, 01:44.260 --> 01:49.840 particularly for the open source model that is, uh, Lama sitting behind the scenes. 01:49.960 --> 01:55.510 All right, let's flip over to perplexity, which is, of course, a search engine, not an LLM. 01:55.510 --> 01:56.080 So it doesn't. 01:56.110 --> 02:01.660 It's the odd one out in this group, although actually, uh, OpenAI also is now in the search space, 02:01.660 --> 02:02.320 too. 02:02.440 --> 02:08.020 Um, so really it's looking for, uh, factual questions that it can then research and provide an answer 02:08.020 --> 02:08.380 for. 02:08.380 --> 02:13.240 And I'm recording this on November the 6th, the day after the elections in the US. 02:13.240 --> 02:22.940 So I can say something like, who is the president elect of the United States, and it will do some 02:22.940 --> 02:23.570 thinking. 02:23.570 --> 02:29.960 And I would not be surprised at all to see that it's able to summarize back the outcome and give key 02:29.960 --> 02:33.110 points, reactions and the like. 02:33.170 --> 02:40.280 Uh, and so it's able to do this and provide a nuanced, well crafted response to current events. 02:40.310 --> 02:46.160 Actually, if you ask a question like that to, uh, OpenAI to GPT right now, it will also give you 02:46.160 --> 02:50.990 a good answer, uh, based on current events, despite its knowledge cutoff being last year. 02:51.110 --> 02:53.210 But if you ask Claude, it won't be able to do that. 02:53.210 --> 02:55.610 And it will say it will say to to be direct. 02:55.610 --> 03:00.440 My knowledge cutoff is, uh, you get that very specific answer. 03:00.890 --> 03:04.550 Uh, let's ask the question, uh, how many? 03:04.580 --> 03:15.350 Let's start a new, fresh chat and say, how many times does the letter A appear in this sentence? 03:16.130 --> 03:17.960 It's the last time you have to see this. 03:18.170 --> 03:20.510 Uh, and it says four times. 03:20.510 --> 03:23.030 So it is able to count. 03:23.060 --> 03:24.800 Uh, so it's impressive. 03:24.830 --> 03:28.310 Uh, don't know whether it's a coincidence, whether it's because other people have written articles 03:28.310 --> 03:32.300 about this that it's found, but it is able to count for times. 03:32.300 --> 03:39.080 So perplexity is with the oh one preview version and being able to get this right. 03:39.110 --> 03:41.420 Congratulations to perplexity. 03:41.630 --> 03:48.290 Um, and now ask a slightly curious question, which is, ah, uh, question about comparing to other 03:48.290 --> 03:52.910 models, and you'll see a, um, here's the response. 03:53.000 --> 03:55.340 Uh, the area is indicated by perplexity. 03:55.340 --> 03:57.260 I don't have the capabilities. 03:57.260 --> 04:00.440 So it definitely pushes back firmly on that. 04:00.680 --> 04:04.460 And that is a wrap on our exploration of frontier models. 04:04.460 --> 04:07.160 But now I encourage you to do the same. 04:07.160 --> 04:12.560 Come up with interesting questions, particularly try and find questions which are able to bring to 04:12.590 --> 04:17.960 the surface the differences between the models, their characters, what they're good at, where they're 04:17.960 --> 04:18.590 weak. 04:18.620 --> 04:23.420 And if you find something good, then please share it with me or post it in messages. 04:23.450 --> 04:29.810 Uh, this is it's really great to find the kinds of prompts that help to surface these differences, 04:29.810 --> 04:32.660 and also that help to highlight where they are so strong. 04:32.750 --> 04:36.050 And I will see you in the next video to wrap this up.