#5 - An AI Primer with Wojciech Zaremba

**Craig Cannon** (0:00)
Hey, this is Craig Cannon, and you're listening to Y Combinator's podcast. Today's guest is Wojciech Zaremba, who's a co-founder of OpenAI. OpenAI is a non-profit AI research company. They're focused on discovering and enacting the path to safe artificial general intelligence.
This episode is a bit of a primer on AI, as we have several AI interviews coming up, and they tend to be a bit more specific than this one. All right, here we go.
Hey, today we have Wojciech Zaremba, and we're going to talk about AI. So Wojciech, could you give us a quick background?

**Wojciech Zaremba** (0:29)
I'm a founder at OpenAI.
I'm working on robotics. I think that deep learning and AI is a great application for robotics. Prior to that, I spent a year at Google Brain, and I spent a year at Facebook AI Research, and same time I graduated from... I have finished my PhD at NYU.

**Craig Cannon** (0:54)
Can you explain how you pulled that off? That seems pretty rare.

**Wojciech Zaremba** (0:57)
So, the great thing about both of these organizations is that they are focused on research. So throughout my PhD, I was actually publishing papers over there.
I highly recommend both organizations, as well as, of course, OpenAI.

**Craig Cannon** (1:14)
Yeah, okay. So most people probably don't know what OpenAI is. So could you just give that quick explanation?

**Wojciech Zaremba** (1:20)
So OpenAI focuses on building AI for the good of humanity. We are a group of researchers and engineers collaborating together who essentially try to figure out what are the missing pieces of artificial, of general artificial intelligence, and how to build it in a way that would be maximally beneficial to humanity as a whole. OpenAI is greatly supported by Elon Musk and Sam Altman.
In total, we gather an investment of $1 billion in the group.

**Craig Cannon** (2:04)
Which is quite a lot. And so what are, I mean, I know some, but what are the OpenAI projects?

**Wojciech Zaremba** (2:09)
So there is several large projects going on. Simultaneously, we have also, we are doing also basic research. So let me first enumerate large projects.
These are robotics. So in terms of robotics, we are working on manipulation. We think that manipulation is the complete, it's the one of the parts of robotics, which is the most unresolved.

**Craig Cannon** (2:37)
Sorry, just to clarify, what does that mean exactly?

**Wojciech Zaremba** (2:39)
It means that, so in robotics, there are essentially three major families of tasks. One is locomotion, which means how to move from, let's say, how to walk, how to move from point A to point B. Second is navigation.
So you are moving in the complicated environment, such as, for instance, a flat or a building, and you have to figure out actually which rooms have you visited before, which not, and where to go.
And the last one is manipulation. So it means you want to grasp an object, let's say, open an object, place objects in various locations. And the third one is the one which is currently the most difficult. So it turns out that when it comes to arbitrary objects, current robots are unable to just grasp an arbitrary object. For any object, it's possible to hand code a single solution. So say, as long as, let's say, in Factor, if you have the same object, like, I don't know, you are producing glasses, then there exists a hand-coded solution to it. There is a way to, by code, to write a program saying, let's place a hand in the middle of the glass and then let's close it. But there is no way so far to write a program such that it would be able to grasp an arbitrary object.

**Craig Cannon** (4:14)
And then, just very quickly, the other OpenAI projects going on?

**Wojciech Zaremba** (4:18)
So another one has to do with playing a complicated computer game. And the third one has to do with playing a large number of computer games. And you might ask why it's interesting. And in some sense, we would like to see that... So human is...
has an incredible skill of being able to learn extremely quickly.
And it has to do with a prior experience. So let's say, even if you haven't played ever a volleyball, if you try it out for the first time, within 10 or 15 minutes, you would be able to grasp how to play. And it has to do with all the prior experience that you have from different games. If you would put the child, if you would put an infant on the volleyball court and ask him or her to play, it would fail miserably. But due to the fact that it has experience coming from a large number of other games, or let's say other live situations, it's able to actually transfer all the knowledge. So at OpenAI, we're able to pull together a large number of computer games. And computer games can be... It's quite easy to quantify how good you are in the computer game. Currently, the best AI system... First of all, it's possible for many computer games to write a program that solves it pretty well or plays it well. There are also results in terms of reinforcement learning or in terms of so-called deep reinforcement learning, showing that it's possible to learn how to play a computer game. The initial results are coming from DeepMind. But simultaneously, it takes extremely long time, in terms of real-time execution, to learn to play computer games.