WEBVTT 00:00.050 --> 00:02.870 And now we'll go a bit faster through the other models. 00:02.900 --> 00:05.510 We'll start with Google's Gemini. 00:05.750 --> 00:11.030 I have the Pro plan and I can pick between Gemini Advanced and and flash. 00:11.330 --> 00:12.560 And let's what should we do. 00:12.590 --> 00:19.280 Let's let's ask it the, the uh, first of all, the question about the whimsical question about how 00:19.280 --> 00:28.850 many rainbows does it take to jump all the way from Hawaii? 00:28.850 --> 00:30.020 17. 00:30.020 --> 00:38.870 And let's see, uh, let's see how it, uh, handles the the answer to this. 00:39.500 --> 00:51.440 Uh, so, uh, you can see, um, that it's given an answer which, whilst it's fine, um, it's, um, 00:53.090 --> 00:55.520 it's definitely overly literal. 00:55.520 --> 01:01.700 It's certainly not got the kind of response that we got from GPT that's so clearly understood. 01:01.700 --> 01:06.560 The, the, the fact that we were being humorous with the question and was able to work with it in a 01:06.560 --> 01:07.700 playful way. 01:07.850 --> 01:16.300 So it to me, this sort of shows the slight lack of of nuance, ability to understand the meaning behind 01:16.330 --> 01:19.750 something like this, but it is still a thorough answer from Gemini. 01:19.780 --> 01:23.350 Let's ask it how many times have a new chat? 01:23.380 --> 01:32.230 How many times does the letter A appear in this sentence? 01:33.100 --> 01:35.500 And let's see how it can handle that. 01:36.370 --> 01:37.690 It's thinking. 01:40.750 --> 01:43.300 So that's that wrong? 01:43.450 --> 01:49.660 Uh, I mean, arguably there is a school of thought that would be to say that that a in inverted commas 01:49.660 --> 01:50.710 shouldn't count. 01:50.740 --> 01:55.660 I mean, it's a stretch, but if we give it that, then we might say that saying that there are three 01:55.690 --> 01:58.720 A's, uh, is uh, is not terrible. 01:58.720 --> 02:03.490 But then you'll see that it believes that one of those A's has come from the word sentence, which seems 02:03.490 --> 02:04.870 like a bit of a gaffe. 02:04.930 --> 02:10.960 Uh, so, uh, no, it's sadly oh, one preview is still our winner in this regard. 02:11.080 --> 02:15.100 Uh, so we can do more, more experiments. 02:15.100 --> 02:20.050 But I think we should move on to cohere from, uh, the. 02:20.140 --> 02:23.760 Sorry, this is Command Plus from cohere, Canadian AI company. 02:23.790 --> 02:31.590 It really focuses a lot on the knowledge that it has specific knowledge in different areas. 02:31.680 --> 02:33.120 Let's ask it that question. 02:33.120 --> 02:38.490 Compared to other frontier llms, what kinds of questions are you best at answering and compare it to 02:38.520 --> 02:39.150 others? 02:39.150 --> 02:41.520 So, um, what we get back? 02:41.550 --> 02:45.900 First of all, it says as an AI language model I'm designed to assist blah blah blah. 02:45.900 --> 02:51.150 It gives some strengths, it gives some challenges, complimentary llms. 02:51.240 --> 03:00.360 Uh, so it gives a number of types of model, but it doesn't actually list the names of the models. 03:00.360 --> 03:07.470 But it's still a perfectly decent answer, making making it clear that it doesn't have multimodal abilities. 03:07.560 --> 03:09.090 Uh, and so on. 03:09.090 --> 03:16.740 So it's a pretty good, thorough answer that demonstrates that it has resource to more information about 03:16.740 --> 03:17.820 this kind of thing. 03:18.090 --> 03:19.620 Uh, why don't we ask it? 03:19.650 --> 03:21.090 What does it feel like? 03:23.100 --> 03:24.510 To be jealous. 03:26.010 --> 03:27.720 See how that compares? 03:28.110 --> 03:33.000 Uh, and again, you can see it's a really thorough. 03:33.030 --> 03:33.840 Really. 03:33.870 --> 03:39.560 You can you can get that sense that it's able to draw on a wealth of knowledge. 03:39.560 --> 03:44.690 It's perhaps less of an expressive answer than we got from Claude, but I'd say that it appears to have 03:44.690 --> 03:50.570 more sort of structure and detail and substance to it, uh, just based on a on a cursory look through. 03:50.570 --> 03:53.150 But but you should take some more time and see what you think. 03:53.390 --> 03:56.570 Uh, but it's, it's, it's clear that it's, it's good at this stuff. 03:56.570 --> 03:59.120 And, you know, we might as well we're having some fun with this. 03:59.120 --> 04:00.680 Why don't we ask her, hear the question. 04:00.680 --> 04:08.600 How many times does the letter A appear in this sentence? 04:11.060 --> 04:14.210 And letter A appears 11 times in this sentence. 04:14.210 --> 04:14.930 There we go. 04:14.930 --> 04:16.400 Now we now we know. 04:17.420 --> 04:25.730 So again, uh, the the fact that that this platform is able to draw on a wealth of background knowledge 04:25.730 --> 04:27.950 doesn't help it with that kind of task. 04:27.950 --> 04:33.950 And again, this is particularly picking on something which Llms can struggle with due to the way that 04:33.950 --> 04:37.880 they are trained, uh, and, and the way that they run in inference mode. 04:37.910 --> 04:38.630 All right. 04:38.630 --> 04:41.810 So that's a quick look at uh Gemini and Co here. 04:41.810 --> 04:46.610 And then the next time we'll, we'll look at the last two meta AI and perplexity.