Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
Today we are going to see what DeepMind’s AI is able to do after being unleashed on the internet
and reading no less than 2 trillion words. And, amusingly, it also thinks that it
is a genius. So, is it? Well, we are going to find out together today.
I am really curious about that, especially given how powerful these recent AI language models are.
For instance, OpenAI’s GPT-3 language model AI can now write poems
and even continue your stories. And even better, these stories can change direction,
and the AI can still pick them up and finish them. Recipes work too.
So, while OpenAI is writing these outstanding papers,
I wonder what scientists at DeepMind are up to these days? Well, check this out.
They have unleashed their AI that they call Gopher on the internet and asked it to read as much as it
can. That is, 2 trillion words. My goodness, that is a ton of text. So, what did it learn from it?
Oh boy, a great deal. But mostly, this one can answer questions. Hmm…questions? There are plenty
of AIs around that can answer questions. Some can even solve a math exam straight from MIT.
So, why is this so interesting? Well, while humans are typically experts at one thing, or very few
things, this AI is nearly an expert at almost everything. Let’s see what it can do together!
For instance, we can ask a bunch of questions about biology,
and it will not only be quite insightful, but it also remembers what we were discussing a
few questions ago. That is not trivial at all. So cool! Now note that not all
of its answers are completely correct. We will have a more detailed look at that in a moment.
Also, what I absolutely loved seeing when reading the paper is that we can even ask what
it is thinking. And look - it expresses that it wishes to play on its smartphone. Very humanlike!
Now make no mistake - that this does not mean that this AI is thinking like a human is thinking.
At the risk of simplifying it, this is more like a statistical combination of things
that it had learned that people say on the internet when asked what they are thinking.
Now note that many of these new works are so difficult to evaluate because they typically do
better on some topics than previous ones, and worse on others. The comparison of these
techniques can easily become a bit subjective depending on what we are looking for. However,
not here! Hold on to your papers, and have a look at this! Oh wow.
My goodness! Are you seeing what I am seeing? This is OpenAI’s GPT-3, and this is Gopher. As you see,
it is a great leap forward not just here and there, but in many categories at the same time!
Also, GPT-3 used 175 billion parameters to train its neural network, Gopher uses
280 billion parameters, and you see, we get plenty of value for these additional parameters.
So, what does all this mean? This means that as these neural networks get bigger and bigger,
they are still getting better. We are steadily
closing in on the human-level experts in many areas at the same time, and progress is still
not plateauing. It still has more left in the tank. How much more? We don’t know yet,
but as you see, the pace of improvement in AI research is absolutely incredible. However, we
are still not there yet. Its knowledge in the area of humanities, social sciences and medicine is
fantastic, but at mathematics, of all things, not so much. You will see more about that in a moment.
And, if you have been holding on to your papers, now, squeeze that paper, because…would you look at
that! What is it that I am seeing here? Oh boy. It thinks that it is a genius. Well, is it? Let’s ask
some challenging questions about Einstein’s field equations, black holes and more and find out.
Hmm. Well, it has a few things going for it. For instance,
it has a great deal of factual knowledge, however, at the same time, it can also get quite confused
by very simple questions. Do geniuses mess up this multiplication? I should hope not!
Also, have a look at this. We noted that it is not much of a math wizard. When asked these questions,
it gives us an answer, and when we ask are you sure about that, it says it is very confident.
But, it is confidently incorrect, I am afraid, because none of these answers are correct.
So, a genius AI? Well, not quite yet. Human-level intelligence?
Also, not yet. But this is an incredible step forward just one more paper down the line. And
just imagine what we will be able to do just a couple more papers down the line.
What do you think? Does this get your mind going? Let me know your ideas in the comments below!
Thanks for watching and for your generous support, and I'll see you next time!