[removed]

My main comment is that is article was super useful and easy to understand.

My smaller comment is that the pattern of repeating the content is those bordered areas interrupts the flow and is pretty annoying. So, my vote would be to drop that, and you have yourself a near perfect article.

Look into hybrid modeling, there are multiple ways to do this

[removed]

bitter lesson 1.0 was made in regard to 70 years of AI history

[removed]

[removed]

If it is true that performance scales infinitely with compute power - and I kinda hope it is, since that would make superhuman AI achievable - datacenters will always be smarter than PCs.

That said, I'm not sure that it does scale infinitely. You need not just more compute but also more data, and there's only so much data out there. GPT-4 reportedly won't be any bigger than GPT-3 because even terabytes of scraped internet data isn't enough to train a larger model.

[removed]

You might try Snorkel. The gist is that domain experts write rules and those rules are fed into ML. If that company doesn't work, I'm pretty sure there are alternatives. Or maybe they had their work in a Python library... it's been a while.

Compared to traditional ML, the benefit is that you're involving the subject matter experts more and giving them a say more directly. That tends to ensure that they're bought in to the approach. Having been in healthcare ML for a while, getting buy-in can be very challenging.

Except one software engineer + a foundation model for code generation may be able to replace 10 engineers. I'm taking that ratio out of my ass, but it might as well be that one engineer + foundation model replaces 5 or 100. Do you count yourself as that one in X engineers that won't lose their job in Y years?

foundation models are mainstream now. Look at the curriculum of all top ML programs, they all have a class on scaling laws and big models.

The bitter lesson will be when fine-tuning and training from scratch become the same thing.

Since scaling laws and foundational models are mainstream now, to whom is this "Bitter lesson 2.0" addressed?

[deleted]

I am currently writing my thesis on an AI-related topic and from what I gathered in my interviews the answer is yes. However, some of my participants also criticised this and said that “ AI is not a collection of ML algorithms”. I agree! AI is not ML. It is just a component.

You can define meta learner:
https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html

[removed]

[removed]

I feel like ML are the building blocks for AI

& often don't even need to fine tune because of instruction pre training and few shot prompting

Hi, I'm making a automatic bookmark organizer.

It consists of two tasks

• Finding similarity between bookmarks, in order to put them in the same folder. [Solved Using the "all-MiniLM-L6-v2" model.]
• Tagging, bookmarks with relevant tags

A Bookmarks contains:

• Page title (String)
• URL (String, regex'd "\W+" filtered)

Right now. I am looking for a model, that, given a string gives tags. Or to put in other words, given list/string of different words, give back a set of words that generalize/summarize said string.

But I wouldn't know what kind of machine learning task that would be categorized as so I wouldn't know what to search for.

Any suggestions would be most helpful.

It’s definitely a hard problem. The challenge isn’t a pipeline problem of “solve this reasoning task” where you can just take the english task -> convert to code -> run code-> convert to english answer. We could probably do that with some degree of accuracy in some contexts.

The hard part is having the agent solve reasoning tasks without prompt engineering, when they appear, without telling it that it’s a reasoning task. In essence it should be able to combine reasoning and planning seamlessly with the generative side of intelligence, not just piece them together when you tell it to outsource the task to a reasoning engine (assuming it could even do this accurately)

For example, if you ask ChatGPT to play rock paper scissors, but choose the option that beats the option that beats the option that you pick. (i.e if I pick Rock, you pick Scissors, because scissors beats paper which beats rock), it cant plan that far ahead.

> Let’s play a modified version of Rock Paper Scissors, but to win, you have to pick the option that beats the option that beats the option that I pick.

> Sure, I'd be happy to play a modified version of Rock Paper Scissors with you. Please go ahead and make your selection, and I'll pick the option that beats the option that beats it.

> Rock

> In that case, I will pick paper.

Since this game requires 2 steps of thinking, and goes against the statistically likely answer in this scenario it fails. As you described, you could maybe write code that identifies a rock paper scissor game, generates and runs code, then answers in english, but there are many real world tasks that require more than 1 step planning that the agent needs to be able to seamlessly identify and work through. (For the record, it also outputs incorrect python code for this game when prompted)

I don’t do research in this specific area so again I could be off base here, but I think that’s why its harder than you’re imagining.

Fwiw, there was a recent paper (the method was called the Mind’s Eye) where they used an LLM to generate physics simulator code to answer physics question similar to what you described.

Man, connect it to a chatbot after fine tuning it with the citations numbers as a human feedback input and you got yourself an uncensored, local, ML assistant!

Willing to share embeddings?

[removed]