I'd approach it the other way around: If you want to work in AI, you have to learn how to keep up yourself anyways, and foundational degrees may offer you something you probably wouldn't have the patience or time to teach yourself. It also may set you apart from your peers.
@rasbt
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ML/AI Research at UW-Madison Located in Electrical Engineering Department
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Actually, back when I was at UW-Madison, most of the ML/AI stuff happened in the electrical engineering department!
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Challenges with Outdated AI Education and Shallow Math Courses
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The problem is that it's not always in the context of the latest developments. And some courses math classes in the context of AI are also very shallow. Of course mileage varies, and for a good, up-to-date program I agree with you!
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Future AI reasoning models will optimize in latent space
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Strongly agree. Ok, human-readable reasoning has some utility for (a) learning and (b) evaluating trust in the tool. But 2nd-gen reasoning models will likely optimize reasoning traces in latent space, and then possibly rendering a human-readable form afterward for (a) and (b).
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Alternative GPU and TPU Backends: MPS, XPU, HIP, XLA Options
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you could also use "mps" (Apple), "xpu" (Intel), "hip" (AMD) GPUs and "xla" for TPUs. But yeah, those are less mature.
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AI/ML Engineering Career Development Strategy
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I think that would go a long way for an AI/ML engineering career
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LitGPT Pre-training Implementation and OLMo 2 Recommendation
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We implemented pre-training in LitGPT (e.g., it was used to train TinyLlama back then): https://
github.com/Lightning-AI/l
itgpt/blob/main/litgpt/pretrain.py
…
I would strongly recommend looking at OLMo 2 otherwise: https://
github.com/allenai/OLMo/b
lob/main/olmo/train.py
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Teaching Decision Trees as First ML Method in Statistics
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Agreed. Btw decision trees were the first ML/AI method I taught in my Stat 451 classes back then:
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NVIDIA’s 10-Year Lead in CUDA and Scientific Computing
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"NVIDIA has a ~10 year lead with their general purpose compute language CUDA" Yes, this!!
I was in a research lab 14 years ago where we used CUDA (and Nvidia-sponsored GPUs) for molecular dynamics simulations. Nividia has a big head start in scientific computing. -
Applied Math and Programming Skills: Yann LeCun’s Educational Advice
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Applied math and problem-solving skills. There's a famous @ylecun quote "If you are an undergrad, take as many math and physics courses as you can, and learn to program." (
https://
reddit.com/r/MachineLearn
ing/comments/25lnbt/ama_yann_lecun/
…)