Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
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 that’s 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!
Let’s 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.
That’s not bad by any means, but this is OpenAI we are talking about, so I am sure
it can do even better.
Let’s 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?
Let’s 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 I’ll 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 don’t 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 that’s 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.
Let’s 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: “The 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.”
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!