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Many in the AI field think the bigger-is-better approach is running out of road

economist.com

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Re: Many in the AI field think the bigger-is-better approach is running out of road

#231

Post by jasmer »

hooande wrote: post #131
ftxbro wrote: post #94

Earlier quoted context omitted.

I mean 'better result from less data' is at least a little bit possible. For example you can just clean out obviously bad data from the trillions of tokens data sets. It's things like the subreddit where they are counting to a million or just like long lists of hash values in random cryptocurrency logs. I agree that in the bigger picture this doesn't matter, but it's technically true that cleaning the data in some wa…

Data quality like you're describing just doesn't matter. GPT is trained on PEBIBYTES of data. Any individual reddit thread is an atom in a drop in a bucket. All of reddit is Yes, the correct thing to do is get more data. Much more.

[dead]

Re: Many in the AI field think the bigger-is-better approach is running out of road

#232

Post by usrbinbash »

exitb wrote: post #141
usrbinbash wrote: post #87

Earlier quoted context omitted.

> and reliably report when they don't know. Then we need a new system, because LMs, no matter if they are large or not, cannot do that, for a very simple reason: A LM doesn't understand "truthfulness". It has no concept of a sequence being true or not, only of a sequence being probable. And that probability cannot work as a standin for truthfulness, because the LM doesn't produce improbable sequences to begin with...…

If this is true, why is GPT-4 better in that regard than GPT-3.5? Or why do questions about Python yield much less hallucinations than questions about Rust, or other less popular tech?

What specifically about these observations contradicts my statement?

Wrong statements about Python are simply less probable than wrong statements about Rust, since there is more Python than Rust in the training data.

That changes exactly nothing about the fact that the system isn't able to detect when it makes a blunder in Python.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#233

Post by williamtrask »

roenxi wrote: post #198
indigoabstract wrote: post #181

Earlier quoted context omitted.

I am pretty sure that my understanding of something (or lack of) is not encoded into words and probabilities. It's more like a feeling of "I got this figured out" or "I haven't grasped this". Words seem more like a protocol to express some internal model/state in the brain and can never capture the entire actual state, only a small part of it. But since we're not telepaths, we obviously need to use words to exchange…

If brains aren't a complex probability machine, how is it possible that people get the same sort of math problems right and wrong in an inconsistent manner? Or mis-speak? It is undeniable that human reasoning is a stochastic process. Otherwise it wouldn't be reasonable for people to make mistakes after learning something. Especially inconsistent mistakes, like when we give people 10,000 addition problems to do in a r…

Food for thought: randomness is in the eye of the beholder. It’s only random to you if you don’t know how to predict it. So can a mind ever truly be stochastic? Perhaps to the minds of others, but never to itself.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#234

Post by williamtrask »

circuit10 wrote: post #227
n4r9 wrote: post #221

Earlier quoted context omitted.

All of this seems possible in a deterministic system which evaluates whether or not to retain a piece of information based on past experience.

LLMs can be deterministic too at temperature 0

But they don’t work very well at temp 0. Better to seed your random numbers.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#235

Post by fho »

roenxi wrote: post #198
indigoabstract wrote: post #181

Earlier quoted context omitted.

I am pretty sure that my understanding of something (or lack of) is not encoded into words and probabilities. It's more like a feeling of "I got this figured out" or "I haven't grasped this". Words seem more like a protocol to express some internal model/state in the brain and can never capture the entire actual state, only a small part of it. But since we're not telepaths, we obviously need to use words to exchange…

If brains aren't a complex probability machine, how is it possible that people get the same sort of math problems right and wrong in an inconsistent manner? Or mis-speak? It is undeniable that human reasoning is a stochastic process. Otherwise it wouldn't be reasonable for people to make mistakes after learning something. Especially inconsistent mistakes, like when we give people 10,000 addition problems to do in a r…

> It is undeniable that human reasoning is a stochastic process

It can still be a deterministic process. If anything came out of the whole LLM story for me it is that I am even more convinced that it is.

My (somewhat educated, but still naive) idea why it looks like a stochastic process is that the brain gets incredible amounts of random input. We literally get bombarded with particles and energy ever instance we live, from photons hitting out retinas , molecules transferring "heat" energy into our skin to sound waves hitting our ear drums.

Ever noticed that you get more productive when you get up from your screen? Or how some people work better while listening to music? How you find your answer just as you start to explain it to a colleague?

I would argue that this is due to a limited "entropy" pool available to the brain. Just changing the input to the system replenishes the pool.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#236

Post by usrbinbash »

regularfry wrote: post #201
usrbinbash wrote: post #87

Earlier quoted context omitted.

> and reliably report when they don't know. Then we need a new system, because LMs, no matter if they are large or not, cannot do that, for a very simple reason: A LM doesn't understand "truthfulness". It has no concept of a sequence being true or not, only of a sequence being probable. And that probability cannot work as a standin for truthfulness, because the LM doesn't produce improbable sequences to begin with...…

What's needed, ideally, is a checker. Something that takes the LLM's output, can go back to the training material, and verify the output for consistency with it. I don't think those steps are out of the bounds of possibility, really.

> Something that takes the LLM's output, can go back to the training material, and verify the output for consistency with it.

The problem is what you mean when you say "consistency".

The LM checks if sequences are stochastically consistent with other sequences in the training data. Within that realm, the sentence: "In the Water Wars of 1999, the Antarctic Coalitions aramada of Hovercraft valiantly faught in the battle of Golehim under Rear Admiral Korakow, against the Trade Unions Fleets." is consistent. Because, while it is total bollocks, it looks stochasticaly like something that could be in a historical text.

So, in it's context, the LM does exactly what you ask for. It produces output that is consistent with the training data.

Truthfulness is a completely different form of consistency: Does the semantic meaning of the data support the statement I just made? of course it doesn't, there isn't an Antarctic Coalition, there were no Water Wars in 1999, and no one ever built an Armada of Hovercraft for any war against a "Trade Union Fleet".

But to know that, one has to understand what the data means semantically. And our current AIs ... well, don't.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#237

Post by chongli »

tchaffee wrote: post #176
brutusborn wrote: post #166

Earlier quoted context omitted.

It can be justified in a deterministic universe because it makes criminals less likely to commit crime in future. As a recipient of punitive justice myself, being punished had a tangible effect on how I thought about crime and thus how I behaved post-punishment. Whether you believe that was deterministic or due to my own free will doesn’t change the outcome.

Punishment is not effective for most people in reducing crime. One thing that is effective is increasing the perceived risk of getting caught. People rarely commit crimes when they are sure they will be caught.

Even in the worst case, recidivism rates only reach around 2/3. That means punishment (incarceration) reforms criminals 1/3 the time. When you consider all of the problems with prisons in the U.S., and all of the hurdles offenders have to pass in order to successfully reintegrate into society, it’s quite remarkable that so many manage to do so.

There are plenty of medical interventions, particularly for mental illnesses, that would dream of 1/3 effectiveness. Now consider the fact that recidivism rates are often much lower than 2/3, and in many cases are closer to 1/3. That tells me that punishment actually is quite effective at reducing crime.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#238

Post by imtringued »

hooande wrote: post #131
ftxbro wrote: post #94

Earlier quoted context omitted.

I mean 'better result from less data' is at least a little bit possible. For example you can just clean out obviously bad data from the trillions of tokens data sets. It's things like the subreddit where they are counting to a million or just like long lists of hash values in random cryptocurrency logs. I agree that in the bigger picture this doesn't matter, but it's technically true that cleaning the data in some wa…

Data quality like you're describing just doesn't matter. GPT is trained on PEBIBYTES of data. Any individual reddit thread is an atom in a drop in a bucket. All of reddit is Yes, the correct thing to do is get more data. Much more.

Nothing like bruteforcing intelligence by simply building a large enough lookup table to contain responses to all potential questions.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#239

Post by sfpotter »

michaelteter wrote: post #63

Isn't the fundamental problem that LLM's don't actually understand anything (as greater concepts), but rather operate as complex probability machines? My 2 month active experience with ChatGPT-4 gave me the following takeaways: - when it's right, it's amazing; and when you, the operator, can recognize the niche use case where it performs really well, it can be a game-changer (although you could have programmed a tool…

This is a great set of observations.

The only thing missing is an analysis of how much power it takes to accomplish each of these tasks. If ChatGPT-4 is at about 1:1 in terms of “effectiveness”, all that remains is to divide by the amount of power required to reach the answer using ChatGPT-4 vs by conventional means. If requires significantly more energy, then it’s a waste, and because of climate change we should really not pursue it further, IMO.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#240

Post by b1daly »

p-e-w wrote: post #167
goldenshale wrote: post #158

Earlier quoted context omitted.

What nonsense. I've spent over a decade 100% focused on AI, and the broad consensus among everyone I've worked with is not to be that concerned at all. The only consensus is that a small group of self proclaimed experts who make a lot of noise is that they get lots of press coverage if they scream and shout making predictions based on zero scientific evidence. We can understand the physics of greenhouse gases and tak…

> Show me any evidence for AI risk today beyond people's theories and beliefs? Deduction. Empirical evidence isn't the only source of insight. You don't have to conduct experiments in order to reasonably conclude that an entity that 1. outperforms humans at mental tasks 2. shares no evolutionary commonality with humans 3. does not necessarily have any goals that align with those of humans is a potential threat to hum…

the world is incredibly filled with risk to humans—people in the AI doomer camp are making a claim that AI potentially is a new kind of uncontrollable risk that warrants extraordinary regulation

the basis of this claim seems to be a confusion of logical or deductive reasoning with inductive or observational reasoning

argument comes down to

- it’s possible to imagine a super intelligent machine that has properties that will kill everyone (this is an exercise in logical reasoning)

- since it’s possible to imagine it, this means it will come into existence — this is an error because things that exist in the real, physical world do so based on physical processes governed by inductive reasoning

generally, there is a long series of steps between the imagining of some constructed, complex machine and its realization, along with its conceptual foundations it requires sustained effort, trial and error, maintenance, generally a serious fight against entropy to make it function and keep it functioning

the sort of out of control AI imagined by AI doomers is not something we’ve seen before

so we shouldn’t make costly decisions based upon this confusion of reasoning

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