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Ask HN: Recommendation for a SWE looking to get up to speed with latest on AI

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Re: Ask HN: Recommendation for a SWE looking to get up to speed with latest on AI

#61
post #56

Earlier quoted context omitted.

He was only gone for a few days, IIRC. At any rate, he's back publishing AI related content again, and it looks like all (?) of his old content is back on his YT channel.

honestly his channel quality is notably different than the other 2 you mentioned. i'm vaguely curious what you get out of it that makes you put him on the same tier.

I think you replied to the wrong person. I didn't put DaveShap on any tier or anything.

That said... I will say that in one of my other replies I did mention that some YT channels in this space can be a bit tabloid'ish, and I may have had Shapiro partly in in mind when saying that. But I still subscribe to his channel and some similar ones, just to get a variety of takes and perspectives.

Re: Ask HN: Recommendation for a SWE looking to get up to speed with latest on AI

#62
LLMs and neural nets from first principles:

https://arxiv.org/pdf/2404.17625 (pdf)

https://news.ycombinator.com/item?id=40408880 (llama3 implementation)

https://news.ycombinator.com/item?id=40417568 (my comment on llama3 with breadcrumbs)

Admittedly, I'm way behind on how this translates to software on the newest video cards. Part of that is that I don't like the emphasis on GPUs. We're only seeing the SIMD side of deep learning with large matrices and tensors. But there are at least a dozen machine learning approaches that are being neglected, mainly genetic algorithms. Which means that we're perhaps focused too much on implementations and not on core algorithms. It would be like trying to study physics without change of coordinates, Lorentz transformations or calculus. Lots of trees but no forest.

To get back to rapid application development in machine learning, I'd like to see a 1000+ core, 1+ GHz CPU with 16+ GBs of core-local ram for under $1000 so that we don't have to manually transpile our algorithms to GPU code. That should have arrived around 2010 but the mobile bubble derailed desktop computing. Today it should be more like 10,000+ cores for that price at current transistor counts, increasing by a factor of about 100 each decade by what's left of Moore's law.

We also need better languages. Something like a hybrid of Erlang and Go with always-on auto-parallelization to run our human-readable but embarrassingly parallel code.

Short of that, there might be an opportunity to write a transpiler that converts C-style imperative or functional code to existing GPU code like CUDA (MIMD -> SIMD). Julia is the only language I know of even trying to do this.

Those are the areas where real work is needed to democratize AI, that SWEs like us may never be able to work on while we're too busy making rent. And the big players like OpenAI and Nvidia have no incentive to pursue them and disrupt themselves.

Maybe someone can find a challenging profit where I only see disillusionment, and finally deliver UBI or at least stuff like 3D printed robots that can deliver the resources we need outside of a rigged economy.

Re: Ask HN: Recommendation for a SWE looking to get up to speed with latest on AI

#63
post #57
post #25

Earlier quoted context omitted.

Not the poster you responded to but I learned quite a bit from kaggle too. I started from scratch, spent 2-4 hrs per day for 6 months & won a silver in a kaggle NLP competition. Now I use some of it now but not all of it. More than that, I'm quite comfortable with models, understand the costs/benefits/implications etc. I started with Andrew Ng's intro courses, did a bit of fastai, did Karpathy's Zero to Hero fully, a…

i mean yes but also how much does kaggling/traditional ML path actually prepare you for the age of closed model labs and LLM APIs? im not even convinced kaggling helps you interview at an openai/anthropic (its not a negative, sure, but idk if itd be what theyd look for for a research scientist role)

I learned ML only to satisfy my curiosity, so I don't know if it's useful for interviewing. :)

Now when I read a paper on something unrelated to AI (idk, say progesterone supplements), and they mention a random forest, I know what they're talking about. I understand regression, PCA, clustering, etc. When I trained a few transformer models (not pretrained) on my native language texts, I was shocked by how rapidly they learn connotations. I find transformer-based LLMs to be very useful, yes, but not unsettlingly AGI-like, as I did before learning about them. I understand the usual way of building recommender systems, embeddings and things. Image models like Unets, GANs etc were very cool too, and when your own code produces that magical result, you see the power of pretraining + specialization. So yeah, idk what they do in interviews nowadays but I found my education very fruitful. It was how I felt when I first picked up programming.

Re the age of LLMs, it is precisely because LLMs will be ubiquitous I wanted to know how they work. I felt uncomfortable treating them as black boxes that you don't understand technically. Think about the people who don't know simple things about a web browser, like opening dev tools and printing the auth token or something. It's not great to be in that place.

Re: Ask HN: Recommendation for a SWE looking to get up to speed with latest on AI

#64

New short course on FreeCodeCamp YouTube channel looks good - Ollama Course – Build AI Apps Locally https://youtu.be/GWB9ApTPTv4?feature=shared As an aside, does anyone have any ideas about this: there should be an app like an 'auto-RAG' that scrapes RSS feeds and URLs, in addition to ingesting docs, text and content in the normal RAG way. Then you could build AI chat-enabled knowledge resources around specific subje…

I am not aware if that exists yet, but the challenge I see with it is rather simple: you get overwhelmed with information really quickly. In other words, you would still need human somewhere in that process to review those scrapes and the quality of that varies widely. For example, even on HN it is not a given a link will be pure gold ( you still want to check if it fits your use case ). That said, as ideas goes, it…

I do exactly this with hoarder. I passively build tagged knowledge bases with the archived pages and then feed it to a RAG setup.

Re: Ask HN: Recommendation for a SWE looking to get up to speed with latest on AI

#65
post #59
post #48

I follow these: * Matt Berman on X / YT * AI-summarized AI news digest: https://buttondown.com/ainews by swyx * https://codingwithintelligence.com/about by Rick Lamers Then I manually follow up to learn more about specific topic/news I'm interested in.

thanks for following! i admire the youtubers a lot and often wonder if i should be venturing into that domain. youtube takes a lot of work but also has the greatest reach by far.

If you do please do it like PracticalEngineering with a full text transcript in article form.

Re: Ask HN: Recommendation for a SWE looking to get up to speed with latest on AI

#66

Unpopular opinion: if you can't use Google nor ChatGPT to get an answer to this question, I have bad news for you.

Maybe you should read the responses here and acknowledge the value of a community.

Maybe you should try google instead of being so condescending, and compare the first 2 pages' results with this page...

We are not exactly talking about big secrets. We are talking about "llm learn resources" keywords - which apparently needs handholding in 2024. And "acknowledging the value of the community".

Re: Ask HN: Recommendation for a SWE looking to get up to speed with latest on AI

#67
post #64

Earlier quoted context omitted.

I am not aware if that exists yet, but the challenge I see with it is rather simple: you get overwhelmed with information really quickly. In other words, you would still need human somewhere in that process to review those scrapes and the quality of that varies widely. For example, even on HN it is not a given a link will be pure gold ( you still want to check if it fits your use case ). That said, as ideas goes, it…

I do exactly this with hoarder. I passively build tagged knowledge bases with the archived pages and then feed it to a RAG setup.

https://github.com/hoarder-app/hoarder for the mention

Re: Ask HN: Recommendation for a SWE looking to get up to speed with latest on AI

#69
post #68

Excellent thread! Love the responses. Is there a way to SAVE THIS THREAD on HN ? 'Cos I'd love that. Thx

There is a favorite link on the original post. You can also save the content using a variety of methods, such as Pocket, or paste it into a tool like Obsidian or similar.

Re: Ask HN: Recommendation for a SWE looking to get up to speed with latest on AI

#70
It sounds like you want more broad stuff, not necessarily learning how to train models. More like learning to use them and how they work.

https://news.ycombinator.com/item?id=36195527 and

Hacker's Guide to LLMs by Jeremy from Fast.ai - https://www.youtube.com/watch?v=jkrNMKz9pWU

State of GPT by Karpathy - https://www.youtube.com/watch?v=bZQun8Y4L2A

LLMs by 3b1b - https://www.youtube.com/watch?v=LPZh9BOjkQs

Visualizing transformers by 3b1b - https://www.youtube.com/watch?v=KJtZARuO3JY

How ChatGPT trained - https://www.youtube.com/watch?v=VPRSBzXzavo

AI in a nutshell - https://www.youtube.com/watch?v=2IK3DFHRFfw

How Carlini uses LLMs - https://nicholas.carlini.com/writing/2024/how-i-use-ai.html

For staying updated:

X/Twitter & Bluesky. Go and follow people that work at OpenAI, Anthropic, Google DeepMind, and xAI.

Podcasts: No Priors, Generally Intelligent, Dwarkesh Patel, Sequoia's "Training Data"

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