Hacker News Reader

Comment by gwoolhurme | Hacker News Reader

Could you explain in detail how that causes the singularity? I’m lost. I saw a cool tool used to make another cool tool. You saw the singularity. Where is this logic coming from? Why would terminal access which I’m fairly certain I’ve seen in autogpt, change much.

View on HN

The cool tool makes another cool tool, which in turn makes another cool tool, faster and faster, until we really don't understand at all what the latest cool tool is doing. But it just keeps getting smarter/faster/more effective/whatever it's optimizing for.

That's the basic definition of "the singularity".

I don’t see that in this specific case for one thing or how we are closer from before. Code generation and auto correction getting closer is very cool, the singularity needs to be spelled out for me how a tool making a tool will get us there. How does that solve all of the hurdles that go into AGI or the singularity. How does it change a large language model.

Would there be some limit that it approaches over a large number of iterations though?

What never was so clear to me in that vision is, how the version n actually makes sure the version n+1 is actually faster and better.

Initially there might be the easy option of using more tokens/memory/<whatever easily measurable and beneficial factor you can imagine>. But when that becomes impractical, how will the "dumber AI" select between x generated "smarter AIs"? How will it ensure that the newly generated versions are better at all (if there are no easily measurable parameters to be increased)?

You have it optimize towards reasoning tests, not tokens or memory. It’s like if you were optimizing a race car. You make the explicit goal time around the track, not horsepower or weight.

That could end up in getting better and better special-purpose expert systems. How do you create better and better general-purpose AIs this way? What is more, when the AIs are more advanced, it might be challenging to create meaningful tests for them (outside of very specialized domains).

My point with the race analogy was you score it on the outcome (time) not a factor (horsepower). For your concern, just make the outcome broader. Someone will still say it’s not AGI as it does 100 different remote jobs, but just plan on ignoring them.

Something that scares me is people don’t want to believe this is where we’re headed or it’s even possible. I say this because I think your concerns are easy enough to address, it makes me think you didn’t try answering your own questions.

The idea in this scenario is it’s self optimizing. No reason it can’t make it’s own more specific tests to the general test of “become as smart as possible”. And people can make tests that they’re not smart enough to pass. You just make them as a team and take more time. It could also discover new things and then verify if they’re true, which is easier.

The "easy" optimization targets I have mentioned (tokens/memory/etc.) seem to confuse you. Ignore them for the main point - how do you create good tests for a more advanced AI than you yourself are (or have control of)? For so many human fields of knowledge and ability it is already extremely hard to find meaningful tests to rank humans. No amount of letting an AI optimize on games with fixed and limited rules will automatically improve them on all the other abilities, which are not easily testable or even measurable. And that difficulty pertains for human intelligence trying to measure or test human intelligence. How much more difficult would it be for humans to measure or test super-human intelligence?

> how do you create good tests for a more advanced AI than you yourself are (or have control of)?

I think I answered that. You use more resources to create it than answer it. When creating it, you can have a team take lots of time, use all sorts of tools, etc. When taking it, you constrain time and tools. That’s the general pattern of how to make a question harder than you can answer.

There are also lots of trapdoor questions, especially in math. An overly simple example is factoring numbers.

I think that can only work with very small, incremental steps in intelligence, though. Will a ten year old human be able to create a meaningful test for an adult with high intelligence? No matter the resources you give the young one, they usually will not be able to.

There also might be thresholds, barriers, were small increments in intelligence are not possible. But that is speculative, I will admit.

You don’t need big jumps. You have the previous version write the test for the next.

I’m not saying I’ve solved the path to AGI or anything. It’s just that this specific issue of “making a test harder than you can solve” has an answer.

The cycle could look something like this: LLMv1 > prompt "get me the code for a better LLM" > paste output to terminal, execute > LLMv2 > repeat.

There are lots of different singularity definitions on wikipedia, most focus on 'intelligence' (eg "an upgradable intelligent agent will eventually enter a 'runaway reaction' of self-improvement cycles, each new and more intelligent generation appearing more and more rapidly"). I think focusing on what 'intelligence' really means, or whether any model is 'truly' intelligent can be be a bit of a distraction. So I just emphasized capabilities in a rather generic sense instead.

It's clear that LLMs can compete with humans on many tasks (coding, creative writing, medical diagnosis, psychotherapy,...), and it is conceivable that they may surpass most humans on those tasks some day. If we have models that can outperform most or all humans on important professional and everyday tasks, and also produce new models that perform even better, maybe at an accelerating rate, through means that are ultimately not interpretable for us, I'd say that's pretty close to a singularity, regardless of whether they're 'truly' intelligent. Even more so if they pass a duck test (walks like a duck, quacks like a duck,...).

You can already do this in theory with tools like AutoGPT and BabyAGI. Why hasn't the singularity happened already?

Exactly AutoGPT is exactly what he is describing but I’m still lost as to what the verbiage is then. Is that the singularity?

Yes AutoGPT would be the singularity if it had more brain-like subsystems like long and short term memory and is hooked up to real world instruments.

"Yes AutoGPT would be the singularity if it was way more advanced and could manipulate physical reality at will"

Way to handwave all the difficulties with this in reality.

Lmao neither of those things I mentioned really cost more than 50 million dollars.

Money is definitely not the bottleneck here.

Yes it is, if not I would've already done it.

With comments like "lmao" and "brain-like subsystems", I'd go back to hitting the proverbial books (or more likely reddit in your case) rather than empty boasts.

I personally think the same as the other comment. Do you really think money is the bottleneck? Let’s assume Sam Altman felt that way. He couldn’t drum up that cash tomorrow if he wanted??? For the pinnacle of human invention. Twitter was purchased for way way more than 50 mil.

Yes? He's invested in GPT-4 which just came out. He needs to retrain a new model with higher context limit as well as build out memory systems probably with vector databases. OpenAI needs to continuously raise money and buy more hardware. I'm not sure what OpenAI is doing but they're obviously resource constrained, else they wouldn't be outpaced by competitors in text2img.

He probably doesn't want to give away more of his company since it's such an obvious winner.

Suppose Altman is actually doing this. Would he want anyone to know, especially given the ongoing brouhaha over the models that are already public?

What does a better LLM mean? We again… already have terminal usage in AutoGPT. It describes a little what you are talking about. What measurement do you use to test if a language model 1 is better than 2? Accuracy? Self reliance? Is that what a Transformer should be doing? Sure an LLM can compete with humans on all of those tasks. Fantastic. So are we at the singularity now as it’s defined? Do I quit my job now?

Like I said in the original post, clearly telling which of two LLMs is better is difficult, and probably one of the things holding us back from having a singularity right now. But that doesn't seem to be an insurmountable problem. Most people who have used both seem to agree that GPT4 is 'better' than GPT3, so maybe we can formalize that intuition somehow.

Reply on news.ycombinator.com