Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
Today we are going to see what DeepMind’s amazing AI can learn from humans doing silly things,
like playing the drums with a comb.
Really, we have to do that? Well…okay, if you insist. There you go.
And? Now what? Well, if it had seen enough of these instruction/video pairs,
hopefully it will be able to learn from it. And here comes the fundamental question we are here
to answer today. If this AI does something, is this imitation, or is this learning? Imitation
is not that impressive, but if it can learn general concepts from examples like this,
now that would perhaps be a little spark of intelligence. That is what we are looking for.
So, how much did it learn? Well, let’s see together.
Nice, look at that! If we ask it, it can now show us where a specific room is.
And if we get a little lost, it even helps us with additional instructions to make sure we find out
way into the bathroom. Now note that the layout of this virtual playhouse is randomized each time
we start a new interaction, therefore it cannot just imitate the direction people go when they say
bathroom. It doesn’t work, because next time, it will be elsewhere. It
actually has to understand what a bathroom is. Good job, little AI!
It also understands that we are holding a bucket and it should put the grapes in that. Okay,
so it learned from instructions, and it can deal with instructions. But if it had learned general
concepts, it should be able to do other things too! You know what, let’s ask some questions.
How many grapes are there in this scene?
2. Yes, that is correct, good job! Now, are they the same color or different? It says different.
I like it, because to answer this question, it has to remember what we just talked about. Very good!
And now, hold on to your papers, and check this out! We ask
what color the grapes are on the floor. And what does it do? It understands what
it needs to look for to gather the required information. Yes, it uses its little peepers,
and answers the question. A little virtual assistant AI! I love it.
But, it isn’t just an assistant. Here come two of my favorite tasks from the paper.
One, if we dreamed up a catchy tune and we just need a bandmate to make it happen. Well,
say no more! This little AI is right there to help. So cool!
And it gets better, get this, two, it even knows about its limitations. For instance, it knows
that it can’t flip the green box over. This can only arise from knowledge and experimentation
with the physics of this virtual world. This will be fantastic for testing video games.
So, yes, the learning is indeed thorough. Now let’s answer two more super important questions.
One, how quickly does this AI learn, and is it as good as a human?
So, learning speed. Let’s look under the hood together, and - oh yes, yes, YES!
I am super happy. You are asking, Károly, why are you super happy? I am super happy because learning
is already happening by watching humans mingle for about 12 minutes. That is next to nothing!
Amazing. And, one more thing that makes me perhaps even happier. And that is, that I don’t see
a sudden spike in the growth, I see nice, linear growth instead. Why is that important?
Well, this means there is a higher chance that the algorithm is slowly learning general concepts, and
starts understanding new things that we might ask it. And if it were a small spike, there would be a
higher chance that it had seen something similar to a human clearing a table in the training set,
and suddenly started copying it. But, with this kind of growth, there is less of a chance that it
is just copying the teacher. I love it. And, yes this is the corner of the internet where we get
unreasonably excited by a blue line. Welcome to Two Minute Papers! Subscribe and hit the bell icon
if you wish to see more amazing works like this. And if we look at the task of lifting a drum,
oh yes, also nice, linear growth, although, at a slower pace than the previous task.
Now, let’s look at how well it does its job. This is Team Human, and MIA
is team AI. And it has an over 70% success rate. And remember, many of these tasks require
not imitation, but learning. The AI sees some humans mingling with these scenes,
but to be able to answer the questions, it needs to think beyond what it has seen. Once again,
that might be perhaps a spark of intelligence. What a time to be alive!
But actually, one more question: A virtual assistant is pretty cool, but why is this so
important? What does this have to do with our lives? Well, have a look at how OpenAI trained
their robot hand in a simulation to be able to rotate these Rubik cubes. And then, deployed this
software onto a real robot hand, and, look! It can use this simulation knowledge, and now it works in
the real world too. But sim2real has relevance to self-driving cars too. Look. Tesla is already
working on creating virtual worlds, and training their cars there. One of the advantages of that is
that we can create really unlikely and potentially unsafe scenarios, but, in these virtual worlds,
the self-driving AI can train itself safely. And when they deploy them into the real world,
they will have all this knowledge. Waymo is running similar experiments too.
And, I also wanted to thank you for watching this video. I truly love talking about these amazing
research papers, and I am really honored to have so many of you Fellow Scholars who are here
every episode, enjoying these incredible works with me. Thank you so much!
So, I think this one also has great potential for a sim2real situation. Learning to help humans
in a virtual world, and perhaps uploading the AI to a real robot assistant. So,
what do you think? What else could this be useful 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!