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

Understanding ChatGPT

atmosera.com

Post reply on HN
Print view View original

91–100 of 241 posts

Re: Understanding ChatGPT

#91

Post by [deleted] »

notahacker wrote: post #52
aqme28 wrote: post #47

Earlier quoted context omitted.

“One is chemical and one is in silicon” doesn’t strike me as a very meaningful distinction. Why does that really matter?

A computer simulation of water can easily convince the human eye it's water, both in terms of pixel perfect representation and simulated behaviour in simulated environments. Until they try to put it in a bottle and drink it. Turns out that physics of what it actually is matters more than human observation that some of the pretty output patterns look identical or superior to the real thing. (And aside from being physi…

[deleted]

Re: Understanding ChatGPT

#92

Post by calf »

unlikelymordant wrote: post #46
calf wrote: post #36

Earlier quoted context omitted.

I don't understand that. In computer science everyone learned that computation is best described and explained at several levels of abstraction. E.g., HW/SW interface; machine code vs C++; RTL vs architecture, the list of levels of abstractions goes on and on. So what is the reason for not appropriately extending this idea to analyzing whatever a neural network is doing?

I look at in the following way: understanding something by abstracting over lower level details doesn't mean the abstraction is how things actually work , the extra layer of abstraction may just be a nice way of thinking about something that makes thinking about it easier. But in the end the true mechanics are the sum of low level details. In general abstractions are not perfect, hence 'leaky abstractions'.

That's a philosophical argument, whereas people are implicitly denying that ChatGPT can have higher level abstraction at all as if it were valid to claim that Stroustrup's contribution to C++ was ultimately just a way to write a lot of assembly code, so let's forever only think in assembly terms.

(As a separate tangent, I don't accept the philosophy that abstractions are merely human niceties or conveniences. They are information theoretic models of reality and can be tested and validated, after all, even the bottom level of reality is an abstraction. The very argument used to deny the primacy of abstractions itself requires conceptual abstractions, leading to a circular logic. But then, I'm not a philosopher so what do I know.)

Re: Understanding ChatGPT

#93

Post by PartiallyTyped »

fauigerzigerk wrote: post #63
maxdoop wrote: post #28

“It’s a glorified word predictor” is becoming increasingly maddening to read. Do tell— how can you prove humans are any different? The most common “proofs” I’ve seen: “Humans are more complex”. Ok, so you’re implying we add more complexity (maybe more modalities?); if more complexity is added, will you continue to say “LLMs are just word predictors”? “Humans are actually reasoning. LLMs are not.” Again, how would you…

>“It’s a glorified word predictor” is becoming increasingly maddening to read. Do tell— how can you prove humans are any different? One difference between humans and LLMs is that humans have a wide range of inputs and outputs beyond language. The claim that humans are word predictors is not something I would want to dispute. The claim that humans are nothing more than word predictors is obviously wrong though. When I…

> One difference between humans and LLMs is that humans have a wide range of inputs and outputs beyond language.

So does Bing and multimodal models.

> The claim that humans are word predictors is not something I would want to dispute.

We have forward predictive models in our brains, see David Eagleman.

> The claim that humans are nothing more than word predictors is obviously wrong though. When I go to buy food, it's not because I'm predicting the words "I'm hungry". It's because I'm predicting that I'll be hungry.

Your forward predictive model is doing just that, but that's not the only model and circuit that's operating in the background. Our brains are ensembles of all sorts of different circuits with their own desires and goals, be it short or long term.

It doesn't mean the models are any different when they make predictions. In fact, any NN with N outputs is an "ensemble" of N predictors - dependent with each other - but still an ensemble of predictors. It just so happens that these predictors predict tokens, but that's only because that is the medium.

> fully understanding the meaning of language.

What does "fully" mean? It is well established that we all have different representations of language and the different tokens in our heads, with vastly different associations.

Re: Understanding ChatGPT

#94

Post by namaria »

colinmorelli wrote: post #20
aqme28 wrote: post #17

> ChatGPT is a glorified word predictor. It isn’t sentient. It doesn’t know what it’s saying, and yes, you can coax it into admitting that it wants to take over the world or saying hurtful things (although it was specially conditioned during training to try to suppress such output). It’s simply stringing words together using an expansive statistical model built from billions of sentences. How do you differentiate it…

Somewhat related to this: We seem to operate on the assumption that sentience is "better," but I'm not sure that's something we can demonstrate anyway. At some point, given sufficient training data, it's entirely possible that a model which "doesn't know what it's saying" and is "stringing words together using an expansive statistical model" will outperform a human at the vast, vast majority of tasks we need. AI that…

We can't even prove other people are sentient. This is not a fruitful line of inquiry.

Re: Understanding ChatGPT

#95

Post by lionkor »

maxdoop wrote: post #44
lionkor wrote: post #26

Earlier quoted context omitted.

Not OP, but basically: Humans have the capacity to come up with new language, new ideas, and basically everything in our human world was made up by someone. ChatPT or similar, without any training data, cannot do this. Thus they're simply imitating

Humans require training data as well. And what do you think of the Mark Twain quote: “ There is no such thing as a new idea. It is impossible. We simply take a lot of old ideas and put them into a sort of mental kaleidoscope. We give them a turn and they make new and curious combinations. We keep on turning and making new combinations indefinitely; but they are the same old pieces of colored glass that have been in u…

You could argue like that against anything

Re: Understanding ChatGPT

#96

Post by guerrilla »

maxdoop wrote: post #28

“It’s a glorified word predictor” is becoming increasingly maddening to read. Do tell— how can you prove humans are any different? The most common “proofs” I’ve seen: “Humans are more complex”. Ok, so you’re implying we add more complexity (maybe more modalities?); if more complexity is added, will you continue to say “LLMs are just word predictors”? “Humans are actually reasoning. LLMs are not.” Again, how would you…

Humans know things and they know those things because they experience and act in the world. ChatGPT knows nothing about the world, if it can be said to know anything at all, all it would know is what we say about the world, nothing about it.

Re: Understanding ChatGPT

#97

Post by PartiallyTyped »

joshuahedlund wrote: post #79
maxdoop wrote: post #28

“It’s a glorified word predictor” is becoming increasingly maddening to read. Do tell— how can you prove humans are any different? The most common “proofs” I’ve seen: “Humans are more complex”. Ok, so you’re implying we add more complexity (maybe more modalities?); if more complexity is added, will you continue to say “LLMs are just word predictors”? “Humans are actually reasoning. LLMs are not.” Again, how would you…

> Do tell— how can you prove humans are any different? How about this one: Humans experience time. Humans have agency. Humans can use both in their reply. If I blurt out the first thing that comes to mind, I feel a lot like a GTP. But I can also choose to pause and think about my response. If I do I might say something different, something hard to quantify but which would be more “intelligent”. That is the biggest di…

> Humans experience time

And? So what?

> Humans have agency.

Which is what exactly? You are living in a physical universe bound by physical laws. For any other system we somehow accept that it will obey physical laws and there will not be a spontaneous change, so why are we holding humans to different standards? If we grow up and accept that free will does not actually exist, then all agency is is our brain trying to coordinate the cacophony of all different circuits arguing (cf Cognitive Dissonance). Once the cacophony is over, the ensemble has "made" a decision.

>But I can also choose to pause and think about my response.

Today ChatGPT 3.5 asked me to elaborate. This is already more than a non insignificant segment of the population is capable. ChatGPT 4.0 has been doing this for a while.

What you describe as pausing and thinking is exactly letting your circuits run for longer - which again - is a decision made by said circuits who then informed your internal time keeper that "you" made said decision.

> I can choose to say “I don’t know”.

So does ChatGPT 4.0, and ChatGPT3.5. I have experienced it multiple times at this point.

> I can choose to wait and let the question percolate in my mind as I experience time and other inputs.

So do proposed models. In fact, many of the "issues" are resolved if we allow the model to issue multiple subsequent responses, effectively increasing its context, just as you are.

So what's the difference?

Re: Understanding ChatGPT

#98

Post by swid »

maxdoop wrote: post #28

“It’s a glorified word predictor” is becoming increasingly maddening to read. Do tell— how can you prove humans are any different? The most common “proofs” I’ve seen: “Humans are more complex”. Ok, so you’re implying we add more complexity (maybe more modalities?); if more complexity is added, will you continue to say “LLMs are just word predictors”? “Humans are actually reasoning. LLMs are not.” Again, how would you…

One reason I hate the “glorified word predictor” phrase, is that predicting the next word involves considering what will come well after that.

I saw a research paper where they tested a LLM to predict the word “a” vs “an”. In order to do that, it seems like you need to consider at least 1 word past the next token.

The best test for this was: I climbed the pear tree and picked a pear. I climbed the apple tree and picked …

That’s a simple example, but the other day, I used ChatGPT to refactor a 2000 word talk to 1000 words and a more engaging voice. I asked for it to make both 500 and 1000 word versions, and it felt to me like it was adhering to the length to determine pacing and delivery of material that signaled it was planning ahead about how much content each fact required.

I cannot rectify this with people saying it only looks one word ahead. One word must come next, but to do a good job modeling what that word will be, wouldn’t you need to consider further ahead than that?

Re: Understanding ChatGPT

#99

Post by Madmallard »

maxdoop wrote: post #28

“It’s a glorified word predictor” is becoming increasingly maddening to read. Do tell— how can you prove humans are any different? The most common “proofs” I’ve seen: “Humans are more complex”. Ok, so you’re implying we add more complexity (maybe more modalities?); if more complexity is added, will you continue to say “LLMs are just word predictors”? “Humans are actually reasoning. LLMs are not.” Again, how would you…

I was explained it's more like

The bots we make are derivative in the sense that we figure out an objective function, and if that function is defined well enough within the system and iterable by nature, then we can make bots that perform very well. If not, then the bots don't seem to really have a prayer.

But what humans do is figure out what those objective functions are. Within any system. We have different modalities of interacting with the world and internal motivators modelled in different ways by psychologists. All of this structure sort of gives us a generalized objective function that we then apply to subproblems. We'd have to give AI something similar if we want it to make decisions that seem more self-driven. As the word-predictor we trained now is, it's basically saying what the wisdom of the crowd would do in X situation. Which, on its own, is clearly useful for a lot of different things. But it's also something for which it will become obsolete after humans adapt around it. It'll be your assistant yeah. It may help you make good proactive decisions for your own life. What will become marketable will change. The meta will shift.

Re: Understanding ChatGPT

#100

Post by fauigerzigerk »

killerstorm wrote: post #90
fauigerzigerk wrote: post #63

Earlier quoted context omitted.

>“It’s a glorified word predictor” is becoming increasingly maddening to read. Do tell— how can you prove humans are any different? One difference between humans and LLMs is that humans have a wide range of inputs and outputs beyond language. The claim that humans are word predictors is not something I would want to dispute. The claim that humans are nothing more than word predictors is obviously wrong though. When I…

> One difference between humans and LLMs is that humans have a wide range of inputs and outputs beyond language. I share ability to move around and feel pain with apes and cats. What I'm interested about is ability "reason" - analyze, synthesize knowledge, formulate plans, etc. And LLMs demonstrated those abilities. As for movement and so on, please check PaLM-E and Gato. It's already done, it's boring. > it's not be…

>What I'm interested about is ability "reason" - analyze, synthesize knowledge, formulate plans, etc. And LLMs demonstrated those abilities.

I disagree that they have demonstrated that. In my interactions with them, I have often found that they correct themselves when I push back, only to say something that logically implies exactly the same incorrect claim.

They have no model of the subject they're talking about and therefore they don't understand when they are missing information that is required to draw the right conclusions. They are incapable of asking goal driven questions to fill those gaps.

They can only mimic reasoning in areas where the sequence of reasoning steps has been verbalised many times over, such as with simple maths examples or logic puzzles that have been endlessly repeated online.

Post reply on HN