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Understanding ChatGPT

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Re: Understanding ChatGPT

#51

Post by dsiegel2275 »

detrites wrote: post #8

"If you’re a programmer and you’re curious to know what BERT fine-tuning looks like, my book offers an _example_. But 2018 was a long time ago. ChatGPT doesn’t rely on fine-tuned versions of BERT. The next section explains why." This paragraph unfortunately may be misinterpreted to mean the authors book is from 2018 and out of date. Actually, his book was published a few months ago. The author here is referring to th…

Yeah I was misled by that at first too. I'll be picking it up, assuming that the book is as well written and clear and concise as that article.

Re: Understanding ChatGPT

#52

Post by notahacker »

aqme28 wrote: post #47
notahacker wrote: post #45

Earlier quoted context omitted.

Well for a start the human mind involves a series of chemical reactions optimised by evolutionary wiring and physical world interaction towards self replication, so when a human says "I feel horny" there's a whole bunch of stuff going on in there that there's no reason to suspect is replicated in a neural network optimised for text transformation. When a silicon based hardware computes that as a response, it isn't be…

“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 physically very dissimilar, stuff like even attempting to model human sex drive is entirely superfluous to an LLM's ability to mimic human sexy talk, so we can safely assume that it isn't actually horny just because it's successfully catfishing us!)

Re: Understanding ChatGPT

#53

Post by Kuinox »

rvnx wrote: post #43
Kuinox wrote: post #39

Earlier quoted context omitted.

It cannot, encode base64 it only remember, see this conversation: https://news.ycombinator.com/item?id=34322223

It totally can (try it if you don't believe it). In your message you say it is gibberish, but I have completely different results and get very good Base64 on super long and random strings. I frequently use Base64 (both ways) to bypass filters in both GPT-3 and 4/Bing so I'm sure it works ;) It sometimes make very small mistakes but overall amazing. At this stage if it can work on random data that never appeared in th…

Did you tried longer sentence and not singles words? Did you also read the conversation?

Edit: ok it looks like it can now convert in base64, I'm sure it couldn't when I tested 2 months ago.

Re: Understanding ChatGPT

#54

Post by cjmcqueen »

sebzim4500 wrote: post #38
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'm not surprised to see your comment be downvoted, but I have yet to see a single coherent answer to this. I wish people would be more clear on what exactly they believe the difference is between LLMs are actual intelligence. Substrate? Number of neurons? Number of connections? Spiking neurons vs. simpler artifial neurons? Constant amount of computation per token vs variable? Or is it "I know it when I see it"? In w…

Actual intelligence in a healthy person fulfills survival needs and even higher order needs of emotions, enjoyment and miraculously spiritual needs. AI is still fully responsive. It only responds to input and provides output. It doesn't yet have self-preservation that is curious or hungry or goal driven. I think this is AI we're most afraid of and we would need to build something very different to create self-actualized AI. I don't think we're there and I'm not so sure it would be a useful effort.

Re: Understanding ChatGPT

#55

Post by mpweiher »

fleischhauf wrote: post #5
Bishonen88 wrote: post #2

This articles describes much of what many youtubers explained in their videos in the recent few weeks. While I understand the core concept of 'just' picking the next word based on statistics, it doesn't really explain how chatGPT can pull off the stuff it does. E.g. when one asks it to return a poem where each word starts with one letter/next alphabet letter/the ending of the last word, it obviously doesn't 'just' pi…

you say obviously doesn't. These language models do indeed work by computing a distribution over all possible next words given the previous words using transformers, and it seems using enough training data and compute gives you the results we see. Everyone I know is completely surprised that it works so well by just adding more data and compute (and probably lots of training tricks)..

> using enough training data and compute gives you the results we see.

I think this is key. We don't have a good intuition for the truly staggering amount of data and compute that goes into this.

An example that we have come to terms with is weather forecasting: weather models have distinctly super-human capabilities when it comes to forecasting the weather. This is due to the amount of compute and data they have available, neither of which a human mind can come close to matching.

We have gotten used to this.

Re: Understanding ChatGPT

#56

Post by lordnacho »

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…

Plus plenty of people just string words together, yet cannot answer anything remotely structured like a simple program or even simple arithmetic. Yet they get the sentient label.

Re: Understanding ChatGPT

#57

Post by notahacker »

rvnx wrote: post #40
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…

and soon "Humans are moody and emotional" but Sydney tried to marry and threatened a couple of guys here. If you had attached legs and arms to it, it could be a very interesting companion.

Do we think Sydney tried to marry people due to feeling the same emotional desires and obligations as humans, or because marriage proposals were in its data corpus and it inferred that they were a likely continuation given previous inputs?

Re: Understanding ChatGPT

#58

Post by zahrc »

calf wrote: post #36
feanaro wrote: post #29

Earlier quoted context omitted.

> That would point to higher-level phenomena going on inside ChatGPT and its ilk, than merely statistics and predictions. No, it wouldn't, because nothing in "higher-level phenomena" precludes it being caused by statistics and predictions.

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?

You’re definitely on to something.

An example: when we interact with other human beings, we often really only care about the surface, don’t we? Mannerisms, looks, behaviour. Very rarely do we question those with “why?”. But who does? Psychologists.

Same with any technology. Consumers don’t care about the why, they care about the result.

Scientists and engineers care about the “why” and “how”.

Now, is it important to understand “what’s behind the curtain”? Yes. But for who is it important?

Re: Understanding ChatGPT

#59

Post by [deleted] »

notahacker wrote: post #57
rvnx wrote: post #40

Earlier quoted context omitted.

and soon "Humans are moody and emotional" but Sydney tried to marry and threatened a couple of guys here. If you had attached legs and arms to it, it could be a very interesting companion.

Do we think Sydney tried to marry people due to feeling the same emotional desires and obligations as humans, or because marriage proposals were in its data corpus and it inferred that they were a likely continuation given previous inputs?

[deleted]

Re: Understanding ChatGPT

#60

Post by becquerel »

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 are actually reasoning. LLMs are not.” Again, how would you measure such a thing?

I would posit that reasoning is the ability to construct new, previously-unexpressed information from prior information. If ChatGPT existed 110 years ago and fed all the then-known relevant experimental data regarding subatomic particles, it would not have been able to arrive at the new notion of quantum mechanics. If it existed in the medieval era and was trained on the output and influence of Galen, it would not be able to advance beyond the theory of humours to create germ theory.

It's only because quantum mechanics is a known concept that has been talked about in literature that ChatGPT is able to connect that concept to other ones (physics, the biography of Niels Bohr, whatever).

So the test for actual reasoning would be a test of the ability to generate new knowledge.

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