Again thanks to @PetarV_93, @relja_work, Cameron Anderson, Saima Hussain for being supportive throughout this journey!
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In this blog you'll find:
* The details on how @DeepMind's hiring pipeline is structured.
* Many tips on how to prepare for top-tier AI labs (like DeepMind, OpenAI, etc.) in the world (for research engineering roles but I guess many tips will apply for scientists as well).
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* How to get referrals (hint: it's definitely not about approaching people you don't know and asking them to refer you).
Note: it does take some time to read it. Feel free to skip sections (there are 3 sections in total).
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And recently I found many many useful blogs from @math_rachel and @jeremyphoward that deeply resonate with how I think about education so check them out as well:
I'll be binge-watching @jeremyphoward and @GuggerSylvain's @fastdotai "Practical Deep Learning for Coders" course today and tomorrow! 8 lectures, ~2h each. It's going to be fun! 😂 Why?
Well:
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* I want to update my blog on getting started with ML from 2019 where I only recommended @coursera (and I realized just how bad my writing was just 2.5 years ago!).
I recommend you bookmark it but don't read it just yet, it should be ready by the end of this week!
* I want to be able to give better advice to "younger folks" in general. I get a lot of questions on my Discord as well (join it if you haven't: discord.gg/peBrCpheKE).
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* 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?
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* 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 ...
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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)
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(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!
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[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/