Dear Fellow Scholars, this is Two Minute Papers with Károly Zsolnai-Fehér.
In 2017, scientists at OpenAI embarked on an AI project where they wanted to show a
neural network a bunch of Amazon product reviews and wanted to teach it to be able to generate
new ones, or continue a review when given one.
Now, so far, this sounds like a nice hobby project, definitely not something that would
justify an entire video on this channel, however, during this experiment, something really unexpected
happened.
Now, it is clear that when the neural network reads these reviews, it knows that it has
to generate new ones, therefore it builds up a deep understanding of language.
However, beyond that, it used surprisingly few neurons to continue these reviews and
scientists were wondering “why is that”?
Usually, the more neurons, the more powerful the AI can get, so why use so few neurons?
The reason for that is that it has learned something really interesting.
I’ll tell you what in a moment.
This neural network was trained in an unsupervised manner, therefore it was told to do what the
task was, but was given no further supervision.
No labeled datasets, no additional help, nothing.
Upon closer inspection, they noticed that the neural network has built up a knowledge
of not only language, but also built a sentiment detector as well.
This means that the AI recognized that in order to be able to continue a review, it
needs to be able to efficiently detect whether the review seems positive or not.
And thus, it dedicated a neuron to this task, which we will refer to as the sentiment neuron.
However, it was no ordinary sentiment neuron, it was a proper, state of the art sentiment
detector.
In this diagram, you see this neuron at work.
As it reads through the review, it starts out detecting a positive outlook which you
can see with green, and then, uh-oh, it detects that review has taken a turn and is not happy
with the movie at all.
And all this was learned on a relatively small dataset.
Now, if we have this sentiment neuron, we don’t just have to sit around and be happy
for it.
Let’s play with it!
For instance, by overwriting this sentiment neuron in the neural network, we can force
it to create positive or negative reviews.
Here is a positive example: “Just what I was looking for.
Nice fitted pants, exactly matched seam to color contrast with other pants I own.
Highly recommended and also very happy!”
And, if we overwrite the sentiment neuron to negative, we get the following: “The
package received was blank and has no barcode.
A waste of time and money.”
There are some more examples here on the screen for your pleasure.
Absolutely amazing.
This paper teaches us that we should endeavor to not just accept these AI-based solutions,
but look under the hood, and sometimes, a goldmine of knowledge can be found within.
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This is why every episode ends with, you know the drill…
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