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

Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

thebullshitmachines.com

Post reply on HN
Print view View original

131–140 of 652 posts

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#131

Post by ta8645 »

dijksterhuis wrote: post #123
ta8645 wrote: post #77

Earlier quoted context omitted.

> Being persuasive (i.e., churn out convincing prose) is how LLMs were designed to be. No. They were designed to churn out accurate prose that accurately reflects their model of reality. They're just imperfect. You're being cynical and emotional to use the term bullshit. And again, it anthropomorphizes the LLM, it implies agency.

> that accurately reflects their model of reality. you are also seemingly anthropomorphising the technology by assigning to it some concept of having a “model of reality”. LLM systems output an inference of the next most likely token, given: the input prompt, the model weights and the previously output token [0]. that is all. no models of reality involved. “it” doesn’t “know” or “model” anything about “reality”. the…

> no models of reality involved.

It literally has a mathematical model that maps what would, colloquially at least, be known as reality. What exactly do you think those math pipelines represent? They're not arbitrary numbers; they are generated from actual data that is generated by reality. There's no anthropomorphizing at all.

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#132

Post by JKCalhoun »

_heimdall wrote: post #116
gsky wrote: post #110

People said the world wouldn't need more than 10 computers. Newyork times ridiculed space industry and etc.. AI is gonna disrupt all industries

Any new technology given this much attention and money will disrupt, that's not really a question. The question is whether we're going to be better off for it, and if people want all that change in the first place.

It could be argued that the adoption of the automobile was a bad move.

You and I debating either its efficacy or social good though is irrelevant if it marches on regardless.

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#133

Post by tmnvdb »

jcgrillo wrote: post #125

The cost of inference seems like a major barrier in making these things work commercially. If you put one in the user facing flow you'll need an extraordinary amount of compute that scales extremely poorly in number of users, given that each query takes O(nm^2) where m is the number of model weights (many) and n is the number of tokens in the query. So it seems clear if each user session implies multiple LLM queries,…

The costs are a problem. We don't have hard evidence that this will be solved, but with algorithmic efficiency and raw compute costs both changing rapidly, the cost per token has gone down by about a factor of 10 per year for the last 3 years, i.e. 1000x over 3 years.

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#134

Post by klabb3 »

padolsey wrote: post #25

Is there a way to download and read this as a document instead of web pages? They're hard to navigate.

Was hoping HN would pick this up. Scroll is completely broken on Firefox (iOS), flickering vscroll. Very common with journalistic expose-style articles.

For the love of everything, please stop scrolljacking. Layout, images, go nuts. CSS is powerful these days, use it.

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#135

Post by cobertos »

Read the whole course. There's a great amount of sourced case studies of people using AI in here with societal pushback which I find interesting to pull from.

Disappointed though that the answer is so nuanced. There aren't hard and fast rules to when/when not to use AI but a set of 18 or so proposed principles that should guide are usage. And defense for those principles. The principles are at the bottom of each chapter.

Also learned about the Eliza Effect as a term and that I found the passage in Ch14 by Ted Chiang to be really insightful, from a general social perspective.

> When someone says “I’m sorry” to you, it doesn’t matter that other people have said sorry in the past; it doesn’t matter that “I’m sorry” is a string of text that is statistically unremarkable. If someone is being sincere, their apology is valuable and meaningful, even though apologies have previously been uttered.

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#136

Post by melicerte »

tmnvdb wrote: post #84
Freak_NL wrote: post #74

Earlier quoted context omitted.

They address this in lesson 2: > According to philosopher Harry Frankfurt, a liar knows the truth and is trying to lead us in the opposite direction. > A bullshitter either doesn't know the truth, or doesn't care. They are just trying to be persuasive. Being persuasive (i.e., churn out convincing prose) is how LLMs were designed to be.

This is patently false. They are trained to generate correct responses.

Then comes the question of what is a correct response...

ps: I fail to detect whether your comment was ironic or not.

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#137

Post by bayindirh »

tmnvdb wrote: post #121
bayindirh wrote: post #117

Earlier quoted context omitted.

Allowing a parrot to iterate on given examples and generate a similar one with the information baked in their weights does not invalidate "Stochastic Parrot" take. On the contrary, it proves it. LLMs are statistical machines. The catch is you feed it hundreds of terabytes of valid information, so it asymptotically generates valid information as a result of this statistical bias. Even yet, they can hallucinate so badl…

It's clearly true that the LLMS are 'stochastic parrots', but for all we know that might be the key to intelligence. It is in itself not a deep observation any more than calling your fellow humans 'microbial meatbags'. Saying that LLMs are stochastic machines does not establish an upper bound for success.

The thing is, this assumption of LLMs might be intelligent lies in the assumption is intelligence is enabled solely by the brain.

However, as the science improves, we understand more and more that brain is just part of a much bigger network, and its size or surface roughness might not be the only thing determines the level of intelligence.

Also, all living things have processes which allows constant input from their surroundings and they also have closed feedback loops which constantly change and tweak things. Call these hormones, emotions or self-reflection or whatnot.

We the scientists love to play god with the information we have at hand, yet we constantly humbled by the nature by experiencing the shallowness of what we know. Because of that I, as a CS Ph.D., am not so keen on to jump to that bandwagon which claims that we invented silicon brains.

They are arguably useful automatons built on dubious data obtained in ethical gray areas. We're just starting to see what we did, and we have a long way to go.

So, a living parrot might be more intelligent than these stochastic parrots. I'll stay on the cautious critics wagon for now.

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#138

Post by uh_uh »

TheOtherHobbes wrote: post #106
tmnvdb wrote: post #71

Hmm, it seems that the author takes very clear (and sometimes cynical) positions on some controversial questions. For example, "They don't have the capacity to think through problems logically." is an hotly debated claim, and I think with the advent of reasoning models this has at least become something one should not state in entry level material, which would hopefully reflect common understanding rather than the au…

Many claims don't stand up to scrutiny, and some look suspiciously like training to the test. The Apple study was clear about this. LLMs and their related modal models lack the ability to abstract information from noisy text inputs. This is really obvious if you play with any of the art generators. For example - the understanding of basic prepositions just isn't there. You can't say "Put this thing behind/over/in fro…

> There is no abstracted concept of a "colour" in there. There's just a lot of imagery tagged with each colour name, and if you select a different colour you get a vector in a space pointing to different images.

It has been observed in LLMs that the distance between embeddings for colors follows the same similarity patterns that humans experience - colors that appear similar to humans, like red and orange, are closer together in the embedding space than colors that appear very different, like red and blue.

While some argue these models 'just extract statistics,' if the end result matches how we use concepts, what's the difference?

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#139

Post by uh_uh »

nonrandomstring wrote: post #76
tmnvdb wrote: post #71

Hmm, it seems that the author takes very clear (and sometimes cynical) positions on some controversial questions. For example, "They don't have the capacity to think through problems logically." is an hotly debated claim, and I think with the advent of reasoning models this has at least become something one should not state in entry level material, which would hopefully reflect common understanding rather than the au…

> cynical () positions on some controversial questions. I feel "cynical" is an inappropriate word here. We may have to, for the same (ecumenical) reasons that thinkers like Churchland, Hofstadter, Dennet, Penrose and company have all struggled with, eventually accept the impossibility of proof of (existence or non-existence) on any hypothesis of "machine mind". The pragmatic response is, "does it offer utility for me…

The choice unfortunately seems to correlate with the person's age. Younger generations will have no trouble treating LLMs as actually intelligent. Yet another example of "Science progresses one funeral at a time.”

Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

#140

Post by dijksterhuis »

ta8645 wrote: post #131
dijksterhuis wrote: post #123

Earlier quoted context omitted.

> that accurately reflects their model of reality. you are also seemingly anthropomorphising the technology by assigning to it some concept of having a “model of reality”. LLM systems output an inference of the next most likely token, given: the input prompt, the model weights and the previously output token [0]. that is all. no models of reality involved. “it” doesn’t “know” or “model” anything about “reality”. the…

> no models of reality involved. It literally has a mathematical model that maps what would, colloquially at least, be known as reality. What exactly do you think those math pipelines represent? They're not arbitrary numbers; they are generated from actual data that is generated by reality. There's no anthropomorphizing at all.

reality is infinite.

a corpus of training data from the internet is finite.

any finite number divided by infinity ends up tending towards zero.

so, mathematically at least, the training data is not a sufficient sample of reality because the proportion of reality being sampled is basically always zero!

fun with maths ;)

> What exactly do you think those math pipelines represent?

probability distributions of human language, in the case of text only LLMs.

which is a very small subset of stuff in reality.

-

also, training data scraped from the public internet is a woeful representation of “reality” if you ask me.

that’s why LLMs i think are bullshit machines. the systems are built on other people’s bullshit posted on the public internet. we get bullshit out because we made a bunch of bullshit. it’s just a feedback loop.

(some of the training data is not bullshit. but there is a lot of bullshit in there).

Post reply on HN