* The problem of catastrophic forgetting (babies face it - that's (probably) why you don't have any memories of when you were really young - and AIs face it). How do we go about cracking it?
2/
* Embodied AI - does an "AI" have to have a body before it is actually intelligent? It seems our body is a distributed processing system.
As an example, they mention the shape of the ear canal and the fact it does some form of Fourier analysis in real-time ...
3/
Before the signals reach the CPU (brain). Whereas, currently, we're doing everything in CPUs in our artificial systems.
* Proprioception - we are aware of our bodies and their position, shape, and size.
4/
The problem is we currently hardcode this knowledge into our robots. Then when you give them something like a hammer their "mental model" doesn't get updated. Humans seem to do this dynamically.
5/
There are many challenges we need to solve to get to an AGI level (it doesn't have to be superintelligence yet, mouse intelligence will work).
Why do people care that much about being 1st on @kaggle?
What is the main motivation to compete there?
If you're trying to help people/learn a lot/get hired here is an alternative (way less competitive): build a useful and original ML project, open-source it, and maintain it.
@kaggle reminds me of the competitive programming world a lot.
Up to a certain point, the skills you acquire are super useful.
After that point, you start missing out on everything else it takes to be a good engineer/researcher or whatever it is you're trying to accomplish.
While you're learning shortcuts for std::vector::push_back, increasing your typing speed and some variations of the algorithms you already know, other people are learning how to design software that is maintainable, collaborate with other people, present their work, etc.
[🧠 GAN paper summary 🧠] "Eyes Tell All" <- An interesting short paper on how to detect fake images generated by GANs (at least the current GAN methods like StyleGAN v2!).
They use a simple heuristic: 1/
1. Crop the face and then eyes (they used dlib for this) 2. Segment out the pupils -> you get the predicted mask 3. Fit an ellipse -> you get the "GT" mask 4. Find BIoU between the 2) and 3)
2/
(BIoU is just a generalization of IoU where we focus only on the boundary pixels of the mask instead of the whole mask)
5. If BIoU is "too low" that means that the predicted pupil is deviating from the elliptical shape and thus the eyes i.e. the face is fake/generated!
3/
[big update 🥳] I'll be joining @Google@DeepMind later this year as a Research Engineer!!! 🤖😅❤
It feels surreal, I don't even know where to start. 1/
I didn't come from an "Ivy League" university. Not because I couldn't get in - but because when you're 19, from a less developed country, and you didn't have tech-savvy people around you growing up - you are not even aware that @Stanford/@MIT are a thing. 2/
I can deeply empathize with many because of that. Not all of us were lucky enough to get exposed to tech when we were 9. I "heard" about programming when I was 19! We had to compensate by working harder than everyone else. 3/
I've tried to give you all of the tips and tricks I could think of both for more productive learning in general and stuff specific to the RL field.
The structure of the blog: 1) Intro 2) RL 101 (getting you exposed to the terminology) 3) Cool things about RL (awesome RL apps!) 4) RL is not just roses 5) Getting started with RL 6) Going deeper - reading papers 7) Implementing an RL project from scratch 8) Related subfields
1) This time the project is still not completely ready**. I'm yet to achieve the published results - so I encourage you to contribute!
Many of you have been asking me whether you can work on a project with me and I'll finally start doing it that way - from now onwards. ❤
2) This repo has the ambition to grow and become the go-to resource for learning RL. So collaborators are definitely welcome as I won't always have the time myself.
** main reasons are:
a) I was very busy over the last 2 weeks
b) It currently takes ~5 days to fully train DQN