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Comment by robrenaud | Hacker News Reader

Regarding your comment about how fast the research and industry is moving, would HN readers be interested in relevant one or two paragraph summaries that are basically "explain it like I am a machine learning engineer from 2020" but also knows the power of these models from a perspective of using ChatGPT or MS Copilot? That is, assume a fair amount of technical knowledge about the fundamentals, but don't assume that the reader is paying any attention to have whitebox knowledge of the current state of the art.

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I personally have been looking for "explain it like I'm a CS PhD with lots of experience and the ability to look stuff up". But I suspect your summary would be pretty handy as well.

I reckon you need tacit knowledge. Experience. Luckily in the order of 100 hours not 10000.

Build a GPT using Python and Pytorch. For a good course: Andrej Karpathy is your keyword. At $1000 his course is great value. But actually it is free which is even better ;-)

It wont take you to flash attention but will ramp you to the point you could probably read papers about it. I almost got that far then life lifed me. But I was able to implement changes to the architecture of GPT and do some “hey mum I am doing SOTA (2021) machine learning”.

That sounds at least somewhat helpful. Honestly, a gradient for some of this stuff would be nice. Explain to me like I'm: "five", "a high schooler", "a college grad (not CS/ML/Eng)", "a CS/Eng not ML".

Although in a couple years, kids in restaurants will probably telling me how they're leveling up attention on their neuro-pet. The singularity is steep.

singularity implies AI increases exponentially, not human intelligence. Kids will not talk about neural nets any time soon.

I would love an explanation for software enginners / CS majors who aren't familiar with ML.

Last I studied ML was 2016 and that was stuff like decision trees, k nearest neighbors...

frankly i don’t really feel like all that much has changed since 2020 except for scale

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