Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
Today we are going to cover many important questions in life.
For instance, who is this?
This is Halle Berry.
Now, I show you this, who is it?
It is, of course, also Halle Berry.
And if I show you this piece of text, who does it refer to?
Again, Halle Berry.
So why are these questions interesting?
Well, an earlier paper found out from brain readings that we indeed have person neurons
in our brain.
These are neurons specialized to recognize a particular human being.
That is quite interesting.
And what is even more interesting is that not that we have person neurons, but that
these neurons are multimodal.
What does that mean?
This means that we understand the essence of what makes Halle Berry, regardless of whether
it is a photo, a drawing, or anything else.
I see, alright.
Well then, our first question today is, do neural networks also have multimodal neurons?
Not necessarily.
The human brain is an inspiration for an artificial neural network that we can simulate on our
computers, but do they work like the brain?
Well, if we study their inner workings, our likely answer will be no, not in the slightest.
But still, no one can stop us from a little experimentation, so let’s try this for a
common neural network architecture.
This neuron responds to human faces and says that this is indeed a human face.
So far so good.
Now, if we provide it with a drawing of a human face, it won’t recognize it to be
a face.
Well, so much for this multimodal idea, this one is surely not a brain in a jar.
But wait, we don’t give up so easily around here.
This is not the only neural network architecture that exists, let’s grab a different one.
This one is called OpenAI’s CLIP, and it is remarkably good at generalizing concepts.
Let’s see how it can deal with the same problem.
Yes, this neuron responds to spiders, and Spiderman.
That’s the easy part.
Now, please hold on to your papers, because now comes the hard part.
Drawings and comics of spiders and Spiderman.
Yes, it responds to that too!
Wonderful.
Now comes the final boss, which is spider-related writings.
And.. it responds to that too.
Now, of course, this doesn’t mean that this neural network would be a brain in a jar,
but it is a tiny bit closer to our thinking than previous architectures.
And now comes the best part, this insight opens up the possibility for three amazing
experiments.
Experiment number one.
Essence.
So it appears to understand the essence of a concept or a person.
That is absolutely amazing, so I wonder if we can turn this problem around and ask what
it thinks about different concepts?
It would be the equivalent to saying give me all things spiders and Spiderman.
Let’s do that with Lady Gaga.
It says this is the essence of Lady Gaga.
We get the smug smile, very good.
And it says that the essence of Jesus Christ is this, and it also includes the crown of
thorns.
So far, flying colors.
Now, we will see some images of feelings, some of you might find some of them disturbing.
I think the vast majority of you humans will be just fine looking at them, but I wanted
to let you know just in case.
So, what is the essence of someone being shocked?
Well, this.
I can attest to that, this is basically me when reading this paper.
My eyes were popping out just like this.
Sleepiness.
Yes, that is a coffee person before coffee alright.
The happiness, crying and seriousness neurons also embody these feelings really well.
Experiment number two, adversarial attacks.
We know that OpenAI’s clip responds to photos and drawings of the same thing, so let’s
try some nasty attacks involving combining the two.
When we give it these images, it can classify them with ease.
This is an Apple, this is a laptop, a mug, and so on.
Nothing too crazy going on here.
However, now, let’s prepare a nasty adversarial attack.
Previously, sophisticated techniques were developed to fool a neural network by adding
some nearly imperceptible noise to an image.
Let’s have a look at how this works.
First, we present a previous neural network with an image of a bus, and it will successfully
tell us that yes, this is indeed a bus.
Of course it does.
Now, we show it not an image of a bus, but a bus plus some carefully crafted noise that
is barely perceptible, that forces the neural network to misclassify it as an ostrich.
I will stress that this is not any kind of noise, but the kind of noise that exploits
biases in the neural network, which is, by no means trivial to craft.
So now, I hope you are expecting a sophisticated adversarial attack against the wonderful CLIP
neural network.
Yes, that will do.
Or will it?
Let’s see together!
Yes, indeed, I don’t know if you knew, but this is not an apple, this is a pizza.
And so is this one.
The neural network fell for these ones, but it was able to resist this sophisticated attack
in the case of the coffee mug and the phone.
Perhaps the pizza labels had too small a footprint in an image, so let’s try an even more sophisticated
version of this attack.
Now, you may think that this is a chihuahua, but that is completely wrong, because this,
is a pizza indeed.
Not a chihuahua in sight anywhere in this image.
No-no!
So what did we learn here?
Well, interestingly, this CLIP neural network is more general than previous techniques,
however, its superpowers come at a price.
And that price is that it can be exploited easily with simple systematic attacks.
That is a great lesson indeed.
Experiment number three, understanding feelings.
Now this will be one heck of an experiment.
We will try to answer the age-old question, which is, how would you describe feelings
to a machine?
Well, it’s hard to explain such a concept, but all humans understand what being bored
means.
However, luckily, these neural networks have neurons, and they can use those to explain
to us what they think about different concepts.
An interesting idea here is that feelings could sort of emerge as a combination of other,
more elementary neurons that are already understood.
If this sounds a little nebulous, let’s go with that example, what does the machine
think it means that someone is bored?
Well, it says that bored, is relaxed + grumpy.
This isn’t quite the way I think about it, but not bad at all, little machine.
I like the way you think.
Let’s try one more.
How do we explain to a machine what a surprise means?
Well, it says surprise is celebration + shock.
Nice.
What about madness?
Let’s see.
Evil + serious + a hint of mental illness.
And when talking about combinations, there are two more examples that I really liked.
If we are looking for text that embodies evil, we get something like this.
And now, give me an evil building.
Oh yes, I think that works really well, but there are internet forums where we have black-belt
experts specializing in this very topic, so, if you are one of them, please let me know
in the comments what you think.
The paper also contains a ton more information, for instance, there is an experiment with
the Stroop effect.
This explores whether the neural network reacts to the meaning of the text, or the color of
the text?
I will only tease this because I would really like you to read the paper, which is available
below in the video description.
So there we go, neural networks are by no means brains in a jar, they are very much
computer programs, however, CLIP has some similarities, and we also found that there
is a cost to that.
What a time to be alive!
Thanks for watching and for your generous support, and I'll see you next time!