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OpenAI, Google and Anthropic are struggling to build more advanced AI

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Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#571

Post by nickpsecurity »

Animats wrote: post #503
nickpsecurity wrote: post #470

Earlier quoted context omitted.

The brain solves that problem. It seems to involve memory and specialized regions. I found a few groups building hippocampus-like, research models. One had content-addressable memory. There was another one that claimed to get rid of hallucinations. They also said it takes 50-100 epochs for regular architectures to actually memorize something. Their paper is below in case people qualified to review it want to. https:/…

Comments on that paper? PDF: [1] What they are measuring, it seems, is whether LLMs can be built which will retrieve a reliable known correct answer on request. That's an information retrieval problem, and, in fact, they solve it by adding "Memory Experts" which are basically data storage. It's not clear that this helps either replies which require synthesizing disparate information, or detecting that the training da…

On the second paragraph, there’s been work that shows whether a model has memorized or is strongly replying to certain prompts. Something like that combined with a memory-equipped model would tell you if it might contain the info.

From there, you need multiple layers building on info it contains to synthesize a reply that might be good. Alternatively, an iterative process going a few rounds through a model, re-presenting the combo of results together, and it fuses them. All based on known data or what’s in the prompt with nothing else.

This is speculative based on a few things our own minds do.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#572

Post by intended »

vidarh wrote: post #566
intended wrote: post #466

Earlier quoted context omitted.

I have a side point here - There is a certain schizoid aspect to this argument that LLMs and humans make similar mistakes. This means that on one hand firms are demanding RTO for culture and team work improvements. While on the other they will be ok with a tool that makes unpredictable errors like humans, but can never be impacted by culture and team work. These two ideas lie in odd juxtaposition to each other.

I think this goes exactly to the point that a whole lot of things become acceptable once they become cheap enough.

Since this is a comparison, what has been made comparatively cheaper?

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#573

Post by jacobr1 »

afro88 wrote: post #195
LASR wrote: post #115

Question for the group here: do we honestly feel like we've exhausted the options for delivering value on top of the current generation of LLMs? I lead a team exploring cutting edge LLM applications and end-user features. It's my intuition from experience that we have a LONG way to go. GPT-4o / Claude 3.5 are the go-to models for my team. Every combination of technical investment + LLMs yields a new list of potential…

> potential applications > if you ... > for example ... Yes there seems to be lots of potential. Yes we can brainstorm things that should work. Yes there is a lot of examples of incredible things in isolation. But it's a little bit like those youtube videos showing amazing basketball shots in 1 try, when in reality lots of failed attempts happened beforehand. Except our users experience the failed attempts (LLM repli…

This is why we are only at the start of exploring the solution space. What applications don't require 100% accuracy? What tooling can we build that enables a human in the loop to choose between options? What options do we have to better testing or checking accuracy? There is a lot more to be done to invest hybrid systems that use other types of models or novel training date or heuristics or human workflows in novel ways that shore up the shortcomings ... but in aggregate allow us to do new things. It will take many years for us to figure where this makes the most sense.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#574

Post by jacobr1 »

intended wrote: post #572
vidarh wrote: post #566

Earlier quoted context omitted.

I think this goes exactly to the point that a whole lot of things become acceptable once they become cheap enough.

Since this is a comparison, what has been made comparatively cheaper?

We aren't talking about skilled knowledge work in Silicon Valley campuses. We are talking about work that might already have been outsourced so some cube-farm in the Philippines. Our routine office work that probably could already have been automated away by a line of business app in the 1980s, but is still done in some small office in Tulsa because it doesn't make sense to pay someone to write the code when 80% of the work is managing the data entry that still needs to be done regardless.

This more marginal labor is going to be more easy to replace. Also plenty of the more "elite" type labor will too, as it turns out it is more marginal. Already glue and boilerplate programming work is going this way, there is just so much more to do, and the important work of figuring out what should be done, that it hasn't displaced programmers yet. But it will for some fraction. WYSIWG type websites for small business has come a long way and will only get better, so there will be less need for customization on the margin. Or light design work (like take my logo and plug into into this format for this charity tournament flyer).

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#575

Post by jacobr1 »

rco8786 wrote: post #391
larodi wrote: post #332

Earlier quoted context omitted.

Those Apple engineers stated in a very clear tone: - every time a different result is produced. - no reasoning capabilities were categorically determined. So this is it. If you want LLM - brace for different results and if this is okay for your application (say it’s about speech or non-critical commands) then off you are. Otherwise simply forget this approach, and particularly when you need reproducible discreet resu…

There’s another option here though. Human supervised tasks. There’s a whole classification of tasks where a human can look at a body of work and determine whether it’s correct or not in far less time than it would take for them to produce the work directly. As a random example, having LLMs write unit tests.

Which is a good example, because accuracy can be improved significantly with even minor human guidance in task like unit tests. Human augmentation is extremely valuable.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#576

Post by jacobr1 »

netdevnet wrote: post #548
jeswin wrote: post #482

Earlier quoted context omitted.

Google (even now) wasn't absolutely accurate either. That didn't stop it from becoming many billions worth. > You can have it craft an email, or to review your email, but I wouldn't trust an LLM with anything mission-critical My point is that an entire world lies between these two extremes.

Why don't you give actual concrete testable examples back with evidence where this is the case? Put your skin in the game.

A support ticket is a good middle ground. This is probably the area of most robust enterprise deployment. Synthesizing knowledge to produce a draft reply with some logic either to automatically send it or have human review. There are both shitty and ok systems that save real money with case deflection and even improved satisfaction rates. Partly this works because human responses can also suck, so you are raising a low bar. But it is a real use case with real money and reputation on the line.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#577

Post by tim333 »

vidarh wrote: post #555
vidarh wrote: post #397

Earlier quoted context omitted.

I do contract work on fine-tuning efforts, and I can tell you that most humans aren't designed to be public-facing either. While LLMs do plenty of awful things, people make the most incredibly stupid mistakes too, and that is what LLMs needs to be benchmarked against. The problem is that most of the people evaluating LLMs are better educated than most and often smarter than most. When you see any quantity of prompts…

Yikes, that was an unfortunate auto-correct and too late to edit. "LLM losers" was meant to be "LLM users".

I thought you were maybe a bit rude there!

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#578

Post by tim333 »

boredhedgehog wrote: post #537
sdesol wrote: post #248

Earlier quoted context omitted.

> "let's only allow the LLM to do things we know it is rock-solid at." Even this is insanely hard in my opinion. The one thing that you would assume LLM to excel at is spelling and grammar checking for the English language, but even the top model (GPT-4o) can be insanely stupid/unpredictable at times. Take the following example from my tool: https://app.gitsense.com/?doc=6c9bada92&model=GPT-4o&samples... 5 models are…

> I do believe LLM is a game changer, but I'm not convinced it is designed to be public-facing. I think that, too, is a UX problem. If you present the output as you do, as simple text on a screen, the average user will read it with the voice of an infallible Star Trek computer and be irritated by every mistake. But if you present the same thing as a bunch of cartoon characters talking to each other, users might not o…

To be fair they usually have "ChatGPT can make mistakes. Check important info" type disclaimers.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#579

Post by jacobr1 »

deegles wrote: post #263
crystal_revenge wrote: post #127

Earlier quoted context omitted.

I don't think we've even started to get the most value out of current gen LLMs. For starters very few people are even looking at sampling which is a major part of the model performance. The theory behind these models so aggressively lags the engineering that I suspect there are many major improvements to be found just by understanding a bit more about what these models are really doing and making re-designs based on…

My big question is what is being done about hallucination? Without a solution it's a giant footgun.

Semantic search without LLMs is already making a dent. It still gives traditional results that need to be human processed, but you can get "better" search results.

And with that there is a body work on "groundedness" that basically post-processes output to compare it against its source material. It still can result in logic errors and has a base error it self, but can ensure you at least have clear citations for factual claims that match real documents, but doesn't fully ensure they are being referenced correctly (though that is already the case even with real papers produced by humans).

Also consider the baseline isn't perfection, it is a benchmark against real humans. Accuracy is getting much better in certain domains where we have a good corpora. Part of assessing the accuracy of a system is going to be about determining if the generated content is "in distribution" of its training data. There is progress being made in this direction, so we could perhaps do a better job at the application level of making use of a "confidence" score of some kind maybe even taking that into account in a chain of thought like reasoning step.

People keep finding "obviously wrong" hallucinates that seem like proof things are still crap. But these system keep getting better on benchmarks looking at retrieval accuracy. And the benchmarks keep getting better as people point out deficiencies it them. Perfection might not be possible, but consistently better than average human seems in reach, and better than that seems feasible too. The challenge is the class of mistakes might look different even if the error rate overall is lower.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#580

Post by jaculabilis »

visarga wrote: post #516
eslaught wrote: post #504

Earlier quoted context omitted.

I never get this argument. I've seen a deer on a road maybe once. I've seen a rabbit on a road zero times. But I know what to do if I see one. Is that because the "video" of my perception has many "frames"? Even if that's true at some level, I think it's massively missing the point. Yeah, so I saw that one deer from a lot of angles. But current AI training is like the equivalent of taking every deer that has ever bee…

You might personally have seen a deer just once, but human evolution, and animal evolution prior to that have practiced this skill a lot. AI doesn't have the advantage of evolutionary priors baked in, so it needs explicit walking through many combinations to infer its structure from data, and is remarkably efficient. GPT-4 'only' trained on the amount of language that 30,000 humans use in their lifetime. But we have…

> You might personally have seen a deer just once, but human evolution, and animal evolution prior to that have practiced this skill a lot.

Which pre-human animals evolved instincts for swerving a car to avoid a deer?

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