And we will conclude our expedition into the world of frontier models through their chat interface by
looking at meta AI and perplexity.
Meta AI is, of course, the front end version to llama that's running behind the scenes.
We can ask it the same question about how does it compare itself to other models, and we'll get back
something that has some strengths and weaknesses.
It doesn't do a great job.
It gives some rather old fashioned complimentary llms, but it's it's okay.
And generally speaking, I think you'd find asking various questions that you'll get answers that are
okay, but not the same power as some of the others.
Let's ask the same question.
You guys are going to be fed up with me for doing this, but how many times does the letter A appear
in this sentence?
And let's see what we get from meta AI.
It appears five times, so meta is also not able to handle that particular question.
Now, one thing that the meta is able to handle is image generation.
And we can say please generate an image of a rainbow of rainbows
leaping from Hawaii to 17.
Uh, and you'll find that this is the kind of, of challenge that, uh, lama is up for.
And it very nicely does it with these four possibilities.
And you get this, uh, this kind of effect.
And it seems to have, uh, well, Hawaii has appeared, uh, but but some of these are very respectable,
particularly for the open source model that is, uh, Lama sitting behind the scenes.
All right, let's flip over to perplexity, which is, of course, a search engine, not an LLM.
So it doesn't.
It's the odd one out in this group, although actually, uh, OpenAI also is now in the search space,
too.
Um, so really it's looking for, uh, factual questions that it can then research and provide an answer
for.
And I'm recording this on November the 6th, the day after the elections in the US.
So I can say something like, who is the president elect of the United States, and it will do some
thinking.
And I would not be surprised at all to see that it's able to summarize back the outcome and give key
points, reactions and the like.
Uh, and so it's able to do this and provide a nuanced, well crafted response to current events.
Actually, if you ask a question like that to, uh, OpenAI to GPT right now, it will also give you
a good answer, uh, based on current events, despite its knowledge cutoff being last year.
But if you ask Claude, it won't be able to do that.
And it will say it will say to to be direct.
My knowledge cutoff is, uh, you get that very specific answer.
Uh, let's ask the question, uh, how many?
Let's start a new, fresh chat and say, how many times does the letter A appear in this sentence?
It's the last time you have to see this.
Uh, and it says four times.
So it is able to count.
Uh, so it's impressive.
Uh, don't know whether it's a coincidence, whether it's because other people have written articles
about this that it's found, but it is able to count for times.
So perplexity is with the oh one preview version and being able to get this right.
Congratulations to perplexity.
Um, and now ask a slightly curious question, which is, ah, uh, question about comparing to other
models, and you'll see a, um, here's the response.
Uh, the area is indicated by perplexity.
I don't have the capabilities.
So it definitely pushes back firmly on that.
And that is a wrap on our exploration of frontier models.
But now I encourage you to do the same.
Come up with interesting questions, particularly try and find questions which are able to bring to
the surface the differences between the models, their characters, what they're good at, where they're
weak.
And if you find something good, then please share it with me or post it in messages.
Uh, this is it's really great to find the kinds of prompts that help to surface these differences,
and also that help to highlight where they are so strong.
And I will see you in the next video to wrap this up.