Dear Fellow Scholars, this is Two Minute Papers with Károly Zsolnai-Fehér.
There are so many applications of deep learning, I was really excited to put together a short,
but really cool list of some of the more recent results for you Fellow Scholars to enjoy.
Machine learning provides us an incredible set of tools. If you have a difficult problem
at hand, you don't need to hand craft an algorithm for it. It finds out by itself what is important
about the problem and tries to solve it on its own. In many problem domains, they perform
better than human experts. What's more, some of these algorithms find out things that could
earn you a PhD with 10 years ago.
Here goes the first stunning application: toxicity detection for different chemical
structures by means of deep learning. It is so efficient that it could find toxic properties
that previously required decades of work by humans who are experts of their field.
Next one. Mitosis detection from large images. Mitosis means that cell nuclei are undergoing
different transformations that are quite harmful, and quite difficult to detect.
The best techniques out there are using convolutional neural networks and are outperforming professional
radiologists at their own task. Unbelievable.
Kaggle is a company that is dedicated to connecting companies with large datasets and data scientists
who write algorithms to extract insight from all this data. If you take only a brief look,
you see an incredibly large swath of applications for learning algorithms. Almost all of these
were believed to be only for humans, very smart humans. And learning algorithms, again,
emerge triumphant on many of these.
For example, they had a great competition where learning algorithms would read a website
and find out whether paid content is disguised there as real content.
Next up on the list: hallucination or sequence generation. It looks at different video games,
tries to learn how they work, and generates new footage out of thin air by using a recurrent
neural network.
Because of the imperfection of 3D scanning procedures, many 3D scanned furnitures that
are too noisy to be used as is. However, there are techniques to look at these really noisy
models and try to figure out how they should look by learning the symmetries and other
properties of real furnitures.
These algorithms can also do an excellent job at predicting how different fluids behave
in time, and are therefore expected to be super useful in physical simulation in the
following years.
On the list of highly sophisticated scientific topics, there is this application that can
find out what makes a good selfie and how good your photos are. If you really want to know the truth.
Here is another application where a computer algorithm that we call deep q learning, plays
pong, against itself, and eventually achieves expertise.
The machines are also grading student essays. At first, one would think that this cannot
possibly be a good idea. As it turns out, their judgement is more consistent with the
reference grades than any of the teachers who were tested. This could be an awesome
tool for saving a lot of time and assisting the teachers to help their students learn.
This kind of blows my mind. It would be great to take a look at an actual dataset if it
is public and the issued grades, so if any of you Fellow Scholars have seen it somewhere,
please let me know in the comments section!
These results are only from the last few years, and it's really just scratching the surface.
There are literally hundreds of more applications we haven't even talked about. We are living
extremely exciting times indeed. I am eager to see, and perhaps, be a small part of this
progress.
There are tons of reading and viewing materials in the description box, check them out!
Thanks for watching and for your generous support, and I'll see you next time!