And now we'll go a bit faster through the other models.
We'll start with Google's Gemini.
I have the Pro plan and I can pick between Gemini Advanced and and flash.
And let's what should we do.
Let's let's ask it the, the uh, first of all, the question about the whimsical question about how
many rainbows does it take to jump all the way from Hawaii?
17.
And let's see, uh, let's see how it, uh, handles the the answer to this.
Uh, so, uh, you can see, um, that it's given an answer which, whilst it's fine, um, it's, um,
it's definitely overly literal.
It's certainly not got the kind of response that we got from GPT that's so clearly understood.
The, the, the fact that we were being humorous with the question and was able to work with it in a
playful way.
So it to me, this sort of shows the slight lack of of nuance, ability to understand the meaning behind
something like this, but it is still a thorough answer from Gemini.
Let's ask it how many times have a new chat?
How many times does the letter A appear in this sentence?
And let's see how it can handle that.
It's thinking.
So that's that wrong?
Uh, I mean, arguably there is a school of thought that would be to say that that a in inverted commas
shouldn't count.
I mean, it's a stretch, but if we give it that, then we might say that saying that there are three
A's, uh, is uh, is not terrible.
But then you'll see that it believes that one of those A's has come from the word sentence, which seems
like a bit of a gaffe.
Uh, so, uh, no, it's sadly oh, one preview is still our winner in this regard.
Uh, so we can do more, more experiments.
But I think we should move on to cohere from, uh, the.
Sorry, this is Command Plus from cohere, Canadian AI company.
It really focuses a lot on the knowledge that it has specific knowledge in different areas.
Let's ask it that question.
Compared to other frontier llms, what kinds of questions are you best at answering and compare it to
others?
So, um, what we get back?
First of all, it says as an AI language model I'm designed to assist blah blah blah.
It gives some strengths, it gives some challenges, complimentary llms.
Uh, so it gives a number of types of model, but it doesn't actually list the names of the models.
But it's still a perfectly decent answer, making making it clear that it doesn't have multimodal abilities.
Uh, and so on.
So it's a pretty good, thorough answer that demonstrates that it has resource to more information about
this kind of thing.
Uh, why don't we ask it?
What does it feel like?
To be jealous.
See how that compares?
Uh, and again, you can see it's a really thorough.
Really.
You can you can get that sense that it's able to draw on a wealth of knowledge.
It's perhaps less of an expressive answer than we got from Claude, but I'd say that it appears to have
more sort of structure and detail and substance to it, uh, just based on a on a cursory look through.
But but you should take some more time and see what you think.
Uh, but it's, it's, it's clear that it's, it's good at this stuff.
And, you know, we might as well we're having some fun with this.
Why don't we ask her, hear the question.
How many times does the letter A appear in this sentence?
And letter A appears 11 times in this sentence.
There we go.
Now we now we know.
So again, uh, the the fact that that this platform is able to draw on a wealth of background knowledge
doesn't help it with that kind of task.
And again, this is particularly picking on something which Llms can struggle with due to the way that
they are trained, uh, and, and the way that they run in inference mode.
All right.
So that's a quick look at uh Gemini and Co here.
And then the next time we'll, we'll look at the last two meta AI and perplexity.