Has anyone seen the corridor crew's green screen ML project? They're on YT and they trained a model by using 3d objects, which have perfect transparency, and then adding post facto green/blue screens. Surprisingly, very little training data was needed as the data that was used was perfect by construction. I think right now it's the best plugin of its kind in the world, and they built the prototype in like a weekend.

What I think this illustrates very clearly is this type of technology responds very well to good data, and that to have good data you need to have a clear goal.

This is why it seems that alignment for a generalized, chat-style AI is a very hard problem, perhaps impossible. You can't align it to solve a certain kind of problem and keep it general to any question. The two goals are in conflict with each other.

I think it was Sam Altman himself who said (I don't remember when or where, sorry) that the reason he was so confident in this technology was he noticed the gigantic leaps it made in certain areas in response to even a small amount of training.

(This is why LLMs are so strong at coding, because it's overrepresented in training data. My guess is that if you ask a frontier model about makeup, you will see it repeat cosmetic company's copy rather than getting a chemistry lesson.)

This makes perfect sense but it does seem to kind of be at odds with the concept of a general AI whose job is simply to be smart at any goal. How do you train for any goal?

I guess in a way the AI makers suffer from the same problem that we humans do. We would all love a solution to everything, but to do that you need to define the goal. I'm not sure if that's a tractable problem.

I'm guessing the future is more geared towards specialized AI that are very good at solving the problem they were trained to do, and a human who knows how to breakdown a larger goal into smaller ones by composing the solution out of these models. This also seems like the more efficient solution as well, and better aligned with other goals like privacy and safeguarding of IP.

> This is why it seems that alignment for a generalized, chat-style AI is a very hard problem, perhaps impossible. You can't align it to solve a certain kind of problem and keep it general to any question. The two goals are in conflict with each other.

I think this problem is going to be solved soon (hopefully), check out for the Persimmon model[0] from Humans&. They train it to mimic humans, it may seems bad but it could be _really_ useful to train an AI to be aligned to humans and understand their goals really well since they can use Persimmon to create a "fake human" following a defined goal that their big AI model will learn to estimate.

It's still early but I think this is what they are heading toward.

[0]: https://persimmon.humansand.ai/blog/persimmon.html

> This is why LLMs are so strong at coding, because it's overrepresented in training data. My guess is that if you ask a frontier model about makeup, you will see it repeat cosmetic company's copy rather than getting a chemistry lesson.)

ChatGPTs response to the question “I want to learn about makeup”, gave me an overview of what makeup does, how it affects perceived structure, complexion, evenness, geometry, texture, etc.

When you then ask “the chemistry of makeup”, it goes into interesting breadth and depth without seeming like proprietary information. I do t get “corporate PR or marketing” vibes.

I have to believe this is true.

The only problem is, there’s a lot of money tied up and openAi and Anthropic, who are incentivised to convince the world that the general approach is the money making one.

It's a bit like Asimov's laws of robotics. "Do no harm", but how to evaluate that?