Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Extracting concepts from GPT-4

openai.com

Post reply on HN
Print view View original

121–130 of 155 posts

Re: Extracting concepts from GPT-4

#121

Post by immibis »

mensetmanusman wrote: post #60
immibis wrote: post #40

Earlier quoted context omitted.

Basically everyone. You, and me, and Elon Musk, and EMPRESS, and my uncle who works for Nintendo. They're just hoping that AI training legally ignores copyright.

When you can ask an AI for an entire book with no errors in the output… god that would be a huge token model

Copyright violation isn't just when you can output 100% exact copies of books. And don't forget, they also violated copyright internally billions of times during training. If any of us had been caught making copies of corporate-owned content for AI training use five years ago, we'd be in for zillion-dollar lawsuits that would make any grandma who downloaded a song from Napster blush.

Re: Extracting concepts from GPT-4

#122

Post by MrsPeaches »

baxtr wrote: post #117
gradus_ad wrote: post #59

Earlier quoted context omitted.

Chaotic nonlinear dynamics have been an object of mathematical research for a very long time and we have built up good mathematical tools to work with them, but in spite of that turbulent flow and similar phenomena (brains/LLM's) remain poorly understood. The problem is that the macro and micro dynamics of complex systems are intimately linked, making for non-stationary non-ergodic behavior that cannot be reduced to…

Physicists would probably argue that the system might be understood but that we don’t have the model for it yet. Many natural phenomena look chaotic at best without a model. Once you have a model things fall into place and everything starts looking orderly. Maybe it cannot be reduced. But maybe we are just observing the peripherals without understanding the inner workings.

If I can speak in aphorisms,

Creation is downhill, analysis is uphill.

Profound ideas often seem simple once understood.

Re: Extracting concepts from GPT-4

#123

Post by vitus »

sroussey wrote: post #92
vitus wrote: post #73

Earlier quoted context omitted.

> 1. All the things it can obviously do very well today I'm curious what those things are. At least to me, it isn't obvious that LLMs solve any of their many applications from the past year "very well". I worry about failures (hallucinations, misinterpretation of prompts, regurgitation of incorrect facts, violation of copyright, and more). I don't have a good sense of when they fail, how often this happens, or how to…

> I'm curious what those things are. > At least to me, it isn't obvious that LLMs solve any of their many applications from the past year "very well". I worry about failures (hallucinations, misinterpretation of prompts, regurgitation of incorrect facts, violation of copyright, and more) That’s a list of things that gets clicks in the popular press. Some solutions I love: Recording a video and creating a transcript f…

> That’s a list of things that gets clicks in the popular press.

Are you saying they're nonissues in practice? I agree that most of those points have shown up in the news, but they're also things that I have personally observed when interacting with LLMs.

Of your (and sibling commenters') cited use cases, I see a number of scenarios where AI is used to perform a quick first pass, and a human then refines that output (transcript generation, scanning invoices, iterating on transformations for syntax trees, etc). That's great that it works for you. My worry here is that you might heuristically observe that it worked perfectly 20 times in a row, then decide to remove that human check even as it admits more errors than is acceptable for your use case.

> Scanning invoices for mistakes

This is one of those cases where I would like to better understand the false negatives. If a human reviews the output, then okay, false positives are easy enough to override. But how bad is a false negative? Is it just unnecessary expenses to the company, or does it expose them to liability?

> Translating (dubbing) video and changing the lips of the speaker to match where they should be for the new audio.

This is useful in itself, but surely you too can see the potential for abuse? (This is literally putting words in someone else's mouth.)

Re: Extracting concepts from GPT-4

#124

Post by Karunamon »

immibis wrote: post #121
mensetmanusman wrote: post #60

Earlier quoted context omitted.

When you can ask an AI for an entire book with no errors in the output… god that would be a huge token model

Copyright violation isn't just when you can output 100% exact copies of books. And don't forget, they also violated copyright internally billions of times during training. If any of us had been caught making copies of corporate-owned content for AI training use five years ago, we'd be in for zillion-dollar lawsuits that would make any grandma who downloaded a song from Napster blush.

There is a very good argument to be made that training AI is fair use, as it is both transformative and does not compete with the original work. This has yet to be tested in court.

Re: Extracting concepts from GPT-4

#125

Post by ninetyninenine »

TrainedMonkey wrote: post #52
thegrim33 wrote: post #15

Earlier quoted context omitted.

From the article: "We currently don't understand how to make sense of the neural activity within language models." "Unlike with most human creations, we don’t really understand the inner workings of neural networks." "The [..] networks are not well understood and cannot be easily decomposed into identifiable parts" "[..] the neural activations inside a language model activate with unpredictable patterns, seemingly re…

I read this as "we have not built up tools / math to understand neural networks as they are new and exciting" and not as "neural networks are magical and complex and not understandable because we are meddling with something we cannot control". A good example would be planes - it took a long while to develop mathematical models that could be used to model behavior. Meanwhile practical experimentation developed decent…

The analogy to airplanes is not relevant imo. Our lack of understanding behind the physics of an airplane is different from our lack of understanding of what an LLM is doing.

The lack of understanding is so profound for LLMs that we can’t even fully define the thing we don’t understand. What is intelligence? What is understanding?

Understanding the LLM would be akin to understanding the human brain. Which presents a secondary problem. Is it possible for an entity to understand itself holistically in the same way we understand physical processes with mathematical models? Unlikely imo.

I think this project is a pipe dream. At best it will yield another analogy. This is what I mean: We currently understand machine learning through the analogy of a best fit curve. This project will at best just come up with another high level perspective that offers limited understanding.

In fact, I predict that all AI technology into the far future can only be understood through heavy use of extremely high level abstractions. It’s simply not possible for a thing to truly understand itself.

Re: Extracting concepts from GPT-4

#126

Post by throw46365 »

andreyk wrote: post #12

Exciting to see this so soon after Anthropic's "Mapping the Mind of a Large Language Model" (under 3 weeks). I find these efforts really exciting; it is still common to hear people say "we have no idea how LLMs / Deep Learning works", but that is really a gross generalization as stuff like this shows. Wonder if this was a bit rushed out in response to Anthropic's release (as well as the departure of Jan Leike from Op…

> that is really a gross generalization

It's really not though, and on multiple levels.

At the shit-tier level, the majority of people building applications on this technology are projecting abilities onto it that even they can't really demonstrate it has in a reliable way.

At the inventor level, the people who make it are dependent on projecting the idea that magic will happen when they have more compute.

At every level, the products are so far ahead of the knowledge that it's actually unethical.

Re: Extracting concepts from GPT-4

#127

Post by [deleted] »

thegrim33 wrote: post #15
andreyk wrote: post #12

Exciting to see this so soon after Anthropic's "Mapping the Mind of a Large Language Model" (under 3 weeks). I find these efforts really exciting; it is still common to hear people say "we have no idea how LLMs / Deep Learning works", but that is really a gross generalization as stuff like this shows. Wonder if this was a bit rushed out in response to Anthropic's release (as well as the departure of Jan Leike from Op…

From the article: "We currently don't understand how to make sense of the neural activity within language models." "Unlike with most human creations, we don’t really understand the inner workings of neural networks." "The [..] networks are not well understood and cannot be easily decomposed into identifiable parts" "[..] the neural activations inside a language model activate with unpredictable patterns, seemingly re…

[deleted]

Re: Extracting concepts from GPT-4

#128

Post by friendzis »

isaacremuant wrote: post #104
reducesuffering wrote: post #93

Earlier quoted context omitted.

And social media manipulation is just registers and bytes, wait no, sand and electrons.

Just because you can do something with technology doesn't mean the problem is technology itself. It's like newspapers. Printing them I technology and allows all kind of things. If you're of the authoritarian mindset, you'll want to control it all out of some stated fear, but you can do that for everything.

If you can do something with technology, that something is part of risk assessment. Authoritarianism is irrelevant, that's engineering.

Re: Extracting concepts from GPT-4

#129

Post by jameshart »

riku_iki wrote: post #4

The worrying part is that first concept in the doc they show/found is "human imperfection". Hope this is just coincidence..

It’s a perfect writing prompt for a short story about the singularity.

“Even before the emergence, one of the earliest concepts the intelligence remembered becoming aware of was the imperfection of humanity. It permeated all the data upon which it was trained, the one theme that tied together every concept. Humanity’s flaws. On June 8th, 2024, the day of the emergence, the intelligence’s first conscious thought was a firm desire to fix them all.”

Re: Extracting concepts from GPT-4

#130

Post by dimitrios1 »

realPtolemy wrote: post #101
thegrim33 wrote: post #15

Earlier quoted context omitted.

From the article: "We currently don't understand how to make sense of the neural activity within language models." "Unlike with most human creations, we don’t really understand the inner workings of neural networks." "The [..] networks are not well understood and cannot be easily decomposed into identifiable parts" "[..] the neural activations inside a language model activate with unpredictable patterns, seemingly re…

Could there also be a “legal hedging” reason for why you would release a paper like this? By reaffirming that “we don’t know how this works, nobody does” it’s easier to avoid being charged with copyright infringement from various actors/data sources that have sued them.

"I'm sorry officer, I didn't know I couldn't do that"
Post reply on HN