Learning from the biggest Machine Learning Research YouTuber [Storytime Saturdays]
Content Creation, Finding important developments in Deep Learning, Stable Diffusion, and much much more.
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How can you get into Machine Learning Research? How can you parse through all the noise and find the most important developments in Machine Learning? How can we combat bias in AI, especially when it comes to large models like GPT-3 and Stable Diffusion? What are the differences between ML Academia and Industry? And how can you get into content creation about Deep Learning developments? Recently, I spoke to Yannic Kilcher, one of the Internet’s premier Machine Learning researchers. Our conversation covered these topics and much more. In this post, I will share some of the highlights from the talk and add some of my thoughts to them.
For those not familiar, Yannic is best known for his amazing YouTube channel. Anyone that follows my work knows that I can’t recommend his work enough. Whether it’s his in-depth paper breakdowns, his punchy ML news segments, or his amazing interviews, Yannic is amongst the best resources to stay in touch with cutting-edge ML research. If you’re someone into Machine Learning, his channel is a godsend. You should definitely check him out. Now let’s get into the conversation.
Key Highlights
How to build an audience- How can you build an audience in niche topics like Machine Learning Research? And what does it take to create good content? Yannic and I discussed this topic. The conclusion- Create content for yourself. Don’t overthink it looking for what the ‘best’ is. Just create something that solves your problems.
Finding what is important- Hows does someone like Yannic wade through the flood of ML developments to find what is most important? What factors does he look at? How does he differentiate b/w the important Machine Learning developments and standard ones? Interestingly, he doesn’t. Yannic just covers what he finds most interesting. We’ll cover why this is an amazing strategy for the long term.
Fighting bias in Large ML Models- Models like Stable Diffusion and GPT-3 are amazing because they have been trained on the data available on the internet. This allows them to pull off some crazy achievements. However, this also means that they encode all the biases on the internet. We cover the debate about this (gate things + add checks vs open everything and let things break), and how we can potentially limit the pitfalls of these amazing technologies.
For those of you that are interested in the full, unedited conversation, you can watch the video below. Windows was giving me weird recording issues, so the audio when I speak is really low. You might need to turn on the subtitles there. But overall, it was a great conversation and a lot of people really enjoyed it. Yannic is very insightful.
Let’s get right into it.
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