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ML promises to be profoundly weird

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Re: ML promises to be profoundly weird

#101

Post by qsera »

simianwords wrote: post #97
qsera wrote: post #95

Earlier quoted context omitted.

>95% is not my experience and frankly dishonest. Quite frankly, this is exactly like how two people can use the same compression program on two different files and get vastly different compression ratios (because one has a lot of redundancy and the other one has not).

I'm asking for a single example.

But why do you need an example? Isn't it pretty well understood that LLMS will have trouble responding to stuff that is under represented in the training data?

You will just won't have any clue what that could be.

Re: ML promises to be profoundly weird

#102

Post by embedding-shape »

drob518 wrote: post #94

> It remains unclear whether continuing to throw vast quantities of silicon and ever-bigger corpuses at the current generation of models will lead to human-equivalent capabilities. Massive increases in training costs and parameter count seem to be yielding diminishing returns. Or maybe this effect is illusory. Mysteries! I’m not even sure whether this is possible. The current corpus used for training includes virtual…

> I’m not even sure whether this is possible.

Based on what's happened so far, maybe. At least that's exactly how we got to the current iteration back in 2022/2023, quite literally "lets see what happens when we throw an enormous amount data at them while training" worked out up until one point, then post-training seems to have taken over where labs currently differ.

Re: ML promises to be profoundly weird

#103

Post by simianwords »

qsera wrote: post #101
simianwords wrote: post #97

Earlier quoted context omitted.

I'm asking for a single example.

But why do you need an example? Isn't it pretty well understood that LLMS will have trouble responding to stuff that is under represented in the training data? You will just won't have any clue what that could be.

fair so it must be easy to give an example? I have ChatGPT open with 5.4-thinking. I'm honestly curious about what you can suggest since I have not been able to get it to bullshit easily.

Re: ML promises to be profoundly weird

#104

Post by nisegami »

Here's the opening paragraph of chapter 2 with "people" subbed out for terms referring AI/models/etc.

"People are chaotic, both in isolation and when working with other people or with systems. Their outputs are difficult to predict, and they exhibit surprising sensitivity to initial conditions. This sensitivity makes them vulnerable to covert attacks. Chaos does not mean people are completely unstable; most people behave roughly like anyone else. Since people produce plausible output, errors can be difficult to detect. This suggests that human systems are ill-suited where verification is difficult or correctness is key. Using people to write code (or other outputs) may make systems more complex, fragile, and difficult to evolve."

To me, this modified paragraph reads surprisingly plainly. The wording is off ("using people to write code") and I had to change that part about attractor behavior (although it does still apply IMO), but overall it doesn't seem like an incoherent paragraph.

This is not meant to dunk on the author, but I think it highlights the author's mindset and the gap between their expectations and reality.

Re: ML promises to be profoundly weird

#105

Post by stickfigure »

criley2 wrote: post #68
stickfigure wrote: post #44

I think it's too early to declare the Turing test passed. You just need to have a conversation long enough to exhaust the context window. Less than that, since response quality degrades long before you hit hard window limits. Even with compaction. Neuroplasticity is hard to simulate in a few hundred thousand tokens.

For as rigorous of a Turing test as you present, I believe many (or even most) humans would also fail it. How many humans seriously have the attention span to have a million "token" conversation with someone else and get every detail perfect without misremembering a single thing?

Response quality degrades long before you hit a million tokens.

But sure, let's say it doesn't. If you interact with someone day after day, you'll eventually hit a million tokens. Add some audio or images and you will exhaust the context much much faster.

However, I'll grant you that Turing's original imitation game (text only, human typist, five minutes) is probably pretty close, and that's impressive enough to call intelligence (of a sort). Though modern LLMs tend to manifest obvious dead giveaways like "you're absolutely right!"

Re: ML promises to be profoundly weird

#106

Post by slopinthebag »

bensyverson wrote: post #29
Scaevolus wrote: post #8

Earlier quoted context omitted.

They are bullshit machines because they do not have an internal mental model of truth like a human does. The flagship models bullshit less, but their fundamental architectures prevent having truth interfere with output. https://philosophersmag.com/large-language-models-and-the-co...

"Bullshit" is a human concept. LLMs do not work like the human brain, so to call their output "bullshit" is ascribing malice and intent that is simply not there. LLMs do not "think." But that does not mean they're not incredibly powerful and helpful in the right context.

I sort of agree. In this context "bullshit" means "speech intended to persuade without regard for truth", and while it's true that LLM output is without regard for truth, it's not an entity capable of the agency to persuade, although functionally that is what it can appear like.

https://en.wikipedia.org/wiki/On_Bullshit

Re: ML promises to be profoundly weird

#107

Post by xandrius »

simianwords wrote: post #80
xandrius wrote: post #74

Earlier quoted context omitted.

It feels like you probably went too deep in the LLM bandwagon. An LLM is a statistical next token machine trained on all stuff people wrote/said. It blends texts together in a way that still makes sense (or no sense at all). Imagine you made a super simple program which would answer yes/no to any questions by generating a random number. It would get things right 50% of the times. You can them fine-tune it to say yes…

If it were not "just a statistical next token machine", how different would it behave? Can you find an example and test it out?

Wait, you're asking to find and produce a example of a feasible and better alternative to LLMs when they are the current forefront of AI technology?

Anyway, just to play along, if it weren't just a statistical next token machine, the same question would have always the same answer and not be affected by a "temperature" value.

Re: ML promises to be profoundly weird

#108

Post by nathell »

nomdep wrote: post #73

"As LLMs etc. are deployed in new situations, and at new scale, there will be all kinds of changes in work, politics, art, sex, communication, and economics." For an article five years in the making, this is what I expected it to be about. Instead, we got a ramble about how imperfect LLMs are right now.

The post is just a prelude to a 10-part article, most of which is not yet released (but will be shortly). Judging by the table of contents, the things you expected will be elaborated on in subsequent parts.

Re: ML promises to be profoundly weird

#109

Post by 52-6F-62 »

LogicFailsMe wrote: post #78

[flagged]

> The Vogon constructor fleet is way overdue in my book

Don't you see it? That's exactly what "AI" in this context is.

It's the bypass.

Where does it end, eh? Build a quantum "AI" that will end up just needing more data, more input. The end goal must starts looking like creating an entirely new universe, a complete clone of everything we have here so it can run all the necessary computations and we can... ? (You are what a quantum AI looks like as it bumbles through the infinitude of calculable parameters on its way to the ultimate answer)

Re: ML promises to be profoundly weird

#110

Post by slopinthebag »

bitwize wrote: post #49

The fact that these "bullshit machines" have already proven themselves relatively competent at programming, with upcoming frontier models coming close to eliminating it as a human activity, probably says a lot about the actual value and importance of programming in the scheme of things.

I think it says more about the amount of automation we left on the table in the last few decades. So much of the code LLM's can generate are stuff that we should have completely abstracted away by now.
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