Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
Today we are going to explore what happens if we unleash an AI to read the internet,
and then, ask it some silly questions and we’ll get some really amazing answers.
But how? Well, in the last few years, OpenAI set out to train an AI named GPT-3 that could finish
your sentences. Then, they made Image-GPT, this could even finish your images. Yes, not kidding.
It could identify that the cat here likely holds a piece of paper and finish the picture accordingly,
and even understood that if we have a droplet here and we see just a portion of the ripples,
then this means a splash must be filled in.
So, in summary, GPT-3 is trained to finish our sentences, or even your images.
However, scientists at OpenAI identified that it really isn’t great at following instructions.
Here is an excellent example. Look, we ask it to explain the moon landing to a 6-year old,
it gets very confused. It seems to be in text completion mode instead of
trying to follow our instructions. Meanwhile, this is InstructGPT,
their new method, which can not only finish our sentences, but also, follow our instructions.
And, would you look at that! It does that successfully. Good job, little AI.
This was a really simple example. But of course, we are experienced Fellow Scholars here,
so let’s try to find out what this AI is really capable of in three really cool examples.
Remember in The Hitchhiker's Guide To The Galaxy where people could ask
an all-knowing machine the most important questions of things that trouble humanity.
Yes! We are going to do exactly that. Round 1. So, dear all-knowing AI,
what happens if you fire a cannonball directly at a pumpkin at high speeds?
Well. What? Do you see that? According to GPT-3, pumpkins are strong magnets,
I don’t know which internet forum told the AI that.
Not good. Now, hold on to your papers, and let’s see the new technique’s answer together.
Wow, this is so much more informative. Let’s break it down together. It starts out by hedging,
noting that is it hard to say there are too many unpredictable factors involved.
Annoying as it might seem, it is correct to say all this. Good start. Then, it lists some
of the factors that might decide the fate of that pumpkin, like the size of the cannonball,
distance and velocity, yes, we’re getting there, but please, give me something concrete.
Yes, there we go! It says that, quote “Some of the more likely possible outcomes include breaking or
knocking the pumpkin to the ground, cracking the pumpkin, or completely obliterating it.”
Excellent. A little smartypants AI at our disposal. Amazing. I love it.
Round 2, code summarization. While DeepMind’s AlphaCode is capable of reading
a competition-level programming problem and coding up a correct solution right in front of our eyes.
That is all well and good, but if we give GPT-3 a piece of code and ask it what it does, well,
the answer is not only not very informative, but it’s also incorrect. InstructGPT
gives a much more insightful answer which shows a bit of understanding of what this code does.
That is amazing. Note that it is still not completely right,
but it is directionally correct. Partial credit.
Round 3, write me a poem. About what? Well, about a wise frog.
With GPT-3, we get the usual confusion. By round 3 we really see that it was really not meant to
do this. And, with InstructGPT, let’s see. Hmm…an all-knowing frog, who is the master of disguise,
a great teacher, and quite possibly the bringer of peace and tranquility
to humanity. All written by the AI. This is fantastic. I love it. What a time to be alive!
Now, we only looked at 3 examples here,
but what about the rest? Worry not for a second, OpenAI’s scientists ran a detailed user study,
and found out that people preferred InstructGPTs solutions way more often than the previous
techniques on a larger set of questions. That is a huge difference. Absolutely amazing.
Once again, incredible progress just one more paper down the line.
So, is it perfect? No, of course not, so let’s highlight one of its limitations. If the question
contains false premises, it accepts the premise as being real, and goes with it. This leads to
making things up. Yes, really. Check this out. Why aren’t birds real? GPT-3 says….something.
I am not sure what this one is about. This almost sounds like gibberish. While, InstructGPT accepts
the fact that birds aren’t real, and even helps us craft an argument for that. This is a limitation,
and I must say, a quite remarkable one. An AI that makes things up. Food for thought.
And, remember, at the start of this episode, also looked at this moon landing example.
Did you notice the issue there? Please let me know in the comments below! So,
what is the issue here? Well, beyond not being all that informative, it was asked to describe
the moon landing in a few sentences. This is not a few sentences. This is one sentence. If we give
it constraints like that, it tries to adhere to them, but is often not too great at that.
And, of course, both of these shortcomings show us the way for an even better followup paper,
which, based on previous progress in AI research, could appear maybe not even in years,
but much quicker than that. If you are interested in such a followup work, make
sure to subscribe and hit the bell icon to not miss it when it appears here on Two Minute Papers.
So, what would you use this for? What do you expect to happen a couple more papers down the
line? Please let me know in the comments below. I’d love to hear your thoughts.
Thanks for watching and for your generous support, and I'll see you next time!