Dear Fellow Scholars, this is Two Minute Papers
with Dr. KƔroly Zsolnai-FehƩr. Today we are going to see if an AI can become
a good software engineer. Spoiler alert, the answer is: yes, kind of. Let me explain. Just one year ago, scientists at OpenAI published
a technique by the name GPT-3, and it is an AI that was unleashed to read the internet
with the sole task of finishing your sentences. So, what happened then? Well, now we know that of course, it learned
whatever it needed to learn to perform the sentence completion properly. And to do this, it would need to learn English
by itself, and thatās exactly what it did! It also learned about a lot of topics to be
able to discuss them well. For instance, we gave it a try, and I was
somewhat surprised when I saw that it was able to continue a Two Minute Papers script,
even though it seems to have turned into a history lesson. It also learned how to generate properly formatted
plots from a tiny prompt written in plain English. Not just one kind - many kinds! And remember, this happened just about a year
ago, and this AI was pretty good at many things. But soon after, a newer work was published
by the name Image-GPT. What did this do? Well, this was a GPT-variant that could not
finish your sentences, but your images. Yes, really. The problem statement is simple: we give it
an incomplete image, and we ask the AI to fill in the missing pixels. Have a look at this water droplet example. We humans, know that since we see the remnants
of some ripples over there too, there must be a splash, but does the AI know? Oh yes, yes it does! Amazing! And this is the true image for reference. So, what did they come out with now? Well, the previous GPT-3 was pretty good at
many things, and this new work, OpenAI Codex is a GPT language model that was fine-tuned
to be excellent at one thing. And that is, writing computer programs, or,
finishing your code. Sounds good! Letās give it a try. First, please write a program that says hello
world five times. It can do that. And, we can also ask it to create a graphical
user interface for it. No coding skills required. Thatās not bad by any means, but this is
OpenAI we are talking about, so I am sure it can do even better. Letās try something a tiny bit more challenging. For instance, writing a simple space game. First, we get an image of a spaceship that
we like, instruct the algorithm to resize and crop it. And here comes one of my favorites: start
animating it. Look, it immediately wrote the appropriate
code where it will travel with a prescribed speed, and yes, it should get flipped as soon
as it hits the wall. Looks good. Will it work? Letās see. It does. And all this from a written English description. Outstanding. Of course, this is still not quite the physics
simulation that you all see and love around here, but Iāll take it. But this is still not a game, so please, add
a moving asteroid, check for collisions, and infuse the game with a scoring system. There we go. So, how long did all this take? And now, hold on to your papers, because this
game was written in approximately 9 minutes. No coding knowledge is required. Wow. What a time to be alive! Now, in this 9-ish minutes, most of the time
was not spent by the AI thinking, but the human typing. So, still, the human is the bottleneck. But, today, with all the amazing voice recognition
systems that we have, we donāt even need to type these instructions. Just say what you want and it will be able
to do it! So, what else can it do? For instance, it can also deal with similar
requests to what software engineers are asked in interviews, and I have to say, the results
indicate that this AI would get hired to some places. But thatās not all, it can also nail a first-grade
math test. An AI. Food for thought. Now, this OpenAI Codex work has been out there
for a few days now, and I decided to not cover it immediately, but wait a little and see
where the users take it. This is, of course, not great for views, but
no matter, we are not maximizing views, we are maximizing meaning. In return, now, there are some examples out
there in the wild. Letās look at three of them. One, it can be asked to explain a piece of
code, even if it is written in assembly. Two, it can create a pong game in 30 seconds. Remember, this used to be a blockbuster Atari
game, and now, an AI can write it in half a minute. And yes, again, most of the half minute is
taken by waiting for the human for instructions. Wow. It can also create a plugin for Blender, an
amazing free 3D modeler program. These things used to take several hours of
work at the very least. And with that, I feel that what I said for
GPT-3 rings even more true today. I am replacing GPT-3 with Codex, and quoting:
āThe main point is that working with Codex is a really peculiar process where we know
that a vast body of knowledge lies within, but it only emerges if we can bring it out
with properly written prompts. It almost feels like a new kind of programming
that is open to everyone, even people without any programming or technical knowledge. If a computer is a bicycle for the mind, then
Codex is a fighter jet.ā And all this progress, in just one year. I cannot wait to see where you Fellow Scholars
will take it, and what OpenAI has in mind for just one more paper down the line. And until then, software coding might soon
be a thing anyone can do. What a time to be alive! Thanks for watching and for your generous
support, and I'll see you next time!
"Write a faster working OpenAI Codex with the same task"