Looking at the profiles of AI/ML Residents at apple, it is mostly PhD students, some with masters but did not find any holding only a bachelors. In that case, I should not have much hopes of hearing back :(

Thanks! I'll muse this over

Yeah — gotta run it through another AI to spruce up the variance lmao

Inresting question. My intuition if you could maintain a continuously-updated cache of the metric you're using to split your branches (i.e. continuously compute mutual information for each fork), and we assume your new data roughly follows the same distribution as your old data, you maybe able to get away with only modifying the downstream branches of your trees which should be more efficient.

But if that assumption isn't true, then the new data changes your trees closer to the root, and there's little benefit.

Good luck with that without data coming from the "v0"

The order of the words/tokens is normally encoded via positional embeddings that are added each to their respective token embedding. See e.g. https://machinelearningmastery.com/a-gentle-introduction-to-positional-encoding-in-transformer-models-part-1/

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Then we'd have to crawl all of stack exchange, all of wiki, and 1 terabyte of programming books... This "generalist NLP" is for article writing, for poetry.

I'm a big fan of teaching ChatGPT how to interpret graphs, the origin lines, to record in a vector engine that is couple with the NLP. For a coding engine, I believe NLP should be paired with a compiler, just like a maths specialized NLP should also have a mathlab type engine.

If it was possible to try distillation on ChatGPT to produce smaller models which could run on mobile hardware, it would have been great!

Easiest way IMO is to scan the list of papers at the annual conferences in your given field, pick a handful with names that sound interesting, then try and find a paper that's referenced by two or more of them.

That's probably a good place to start - it's been around long enough that it's probably not a flash in the pan, but still "new" enough to be relevant.

One way I have figured out is to put a watch on the log file.

Sure you can probably get v0 working in a week or two (assuming you know mobile stuff) but ironing out weird edge cases and assumed features takes forever

If you go to all this trouble, please keep in mind that electricity prices vary a lot across the world. In some places in Europe people pay twice as much as you assumed above.

Making it clear how you arrive at your value calculation in an updated post (or even making it a dynamic calculator where people can enter their cost/kWh) would be very useful.

> See teacher-student learning, and distillation.

Thanks, I'll check it out.

Good one. Applies to software engineering practice too or any subfield of IT. On another note what resources you refer to generally to pick a paper for implementation ? Can you share some ?

There is a project called Petals that have BLOOM running for everyone to use. It's distributing the model over many machines and thus allows it to run on consumer hardware. There is a PoC chat at http://chat.petals.ml/

They just converted BLOOMZ and is currently setting up a network for that. That should be more suited for a chat interface. There's still missing gpu's though, so would be great with some more servers if people got some spare compute.

Thanks for the explanation? Why call it TF32 when it apperas to have 19 bits? (IIUC it's bfloat16 with 3 additional bits of mantissa?)

thanks!

It doesn’t seem to discuss the computational advantages in any detail. How interesting is this whole FF idea at this point? I’d love to hear more detailed analysis.

So far it seems like an interesting alternative but the “brain inspired” part is pushed in every article. In terms of accuracy it always seems slightly below traditional back prop. If there’s a huge computational improvement that would seriously recommend it I guess, but is there? Or is it just too early to tell?

Completely agree, I would also add just a couple of tips I've found (not directly related to the question):

- have a good circle of friends in ML you chat to, you will sometimes find yourself in places where you are the only ML expert and you will need people you trust to bounce ideas across/tell you about new things you missed

- invest in a decent workstation and if you can't afford one try to get your first gig where it's not needed and buy one as soon as you can. This has saved me tons of time over the years

- get good at showing results quickly to stakeholders... and I mean you should be able to hack it in an hour at most. They are paying you a lot of money and want the feeling of progress. Something in slides/powerpoint is OK, a dumb streamlit/whatever app is even better. Impressing a stakeholder is the best way to get repeat work

Site doesn't work

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This might not work if the cards have 6 degrees of freedom. You can check out CornerNet and its variants for anchor free corner estimation. The original paper detects two corners, but extending to four should be possible.

Another option is to use yolo to detect a rough bbox, and then use classical cv to refine corner locations.

https://github.com/XinzeLee/RotateObjectDetection

There are yolo versions that allow oriented bbox detection. Above is just one example, there is a lot of repos on gihub.

Landing page here: https://arxiv.org/abs/2301.01452