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Unpredictable abilities emerging from large AI models

quantamagazine.org

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Re: Unpredictable abilities emerging from large AI models

#141

Post by thaw13579 »

shrikant wrote: post #109
jmvoodoo wrote: post #89

Earlier quoted context omitted.

What's most interesting to me, is that this is how I would expect a human to approach the problem if presented with the code and asked for the output. The LLM didn't run the code, it tried to predict the output based on its knowledge of python and primes.

When I was a bored and under-challenged student in the early days of university, one of my tests during a programming exam was to write a program that spit out the factorial of an input integer. For shits and giggles, I just just wrote a series of `if...then` statements for 0 to 5, and only actually calculated the factorial for inputs >=6. I passed after the examiner just input 3 and 5 and was satisfied that the outp…

This is the way. Next, you could cache those values for >=6 when you compute them and also use the previously cached values for sub-problem solutions.

Re: Unpredictable abilities emerging from large AI models

#142

Post by operatingthetan »

AlanYx wrote: post #81
glenstein wrote: post #64

Earlier quoted context omitted.

Same here. I don't think it's surprising, but depending on where you say it, you'll find people insisting that this can't be possible. I think a lot of our widely held cultural beliefs on this front have been informed by academic philosophy from the 60s, 70s, and 80s. In particular, I would go do far as to say that Hubert Dreyfus, author of the book "What computers can't do" and frequent friendly adversary of Daniel…

One thing that LLMs have made me realize is just how ungrounded a lot of mainstream academic philosophy was in the 70s and 80s. For example, so much of Derrida's work centered around the impossibility of truly communicating shared meaning between individuals through language. The fact that we can now communicate so effectively (with remarkably few contextual errors and razor-sharp conveyance of intent) with an entity…

>The fact that we can now communicate so effectively (with remarkably few contextual errors and razor-sharp conveyance of intent) with an entity/technology that is not even human pretty much destroys so much of Derrida's oeuvre as false and delusionally solipsistic

Isn't the LLM just good at giving us the illusion of such effective communication? How can we have true "shared meaning" with a device designed to simulate the experience of shared meaning? Isn't the fact that it's 'mind' is truly unknowable and unrelatable a demonstration of Derrida's point?

Re: Unpredictable abilities emerging from large AI models

#143

Post by maxdoop »

blurbleblurble wrote: post #112
maxdoop wrote: post #23

Earlier quoted context omitted.

I have fun on these HN chats responding to comments like yours . It’s just fancy auto complete to you? You honestly can’t see the capability it has and extend it the future? What’s that saying about “it’s hard to get someone to understand something when their salary depends on their not understanding it”.

I feel very frustrated with these takes because instead of grappling with what we're going to do about it (like having a conversation) it's a flat, dismissive denial, and it isn't even grounded in the science, which says that "memory augmented large language models are computationally universal". So at the very least we're dealing with algorithms that can do anything a hand written program can do, except that they've…

I agree 100%.

I don't understand why we can't look at the potential, or even current, capabilities of these LLMs and have a real conversation about how it might impact things.

Yet so many folks here just confidently dismiss it.

"It doesn't even think!" -- OK, define thinking?

"It doesn't create novel ideas!" OK -- what do most devs do every day?

"It is wrong sometimes!" OK -- is it wrong more or less often than an average dev?

Re: Unpredictable abilities emerging from large AI models

#144

Post by mark_l_watson »

Nice write up! I have been using classic back-prop neural networks since the 1980s, and deep learning for the last 8 years. This tech feels like a rocket ship that is accelerating exponentially!

I am in my 70s and I don't work much anymore. That said, I find myself spending many hours in a typical day doing what I call "gentleman scientist" activities around Large Language Models.

I was walking this morning with a non-tech friend and I was trying to impart to him my feeling that all knowledge work, and a lot of mundane work is getting disrupted. Even though I have many tech-friends who are "Large Language Model doubters", my friend got it this morning. Emotionally and psychologically I think that some tech and other highly paid knowledge workers just can't accept the sea-change that we are living through.

For myself, I am spending a very large amount of time experimenting with the OpenAI APIs, LangChain, and Llama-Index - and I am enjoying myself tremendously.

Re: Unpredictable abilities emerging from large AI models

#145

Post by ur-whale »

jazzyjackson wrote: post #77
ur-whale wrote: post #40

This is nice and swell, but my experience with GPT-4 is that it lies. A whole lot. With style, I grant you, but blatant lies nevertheless. Specifically, when you ask it to produce answers to factual questions that don't have an answer it learned on the internet, it just seem to flip to "generate stories" mode. Specifically, when it produces factual answers (that are utter BS), if you ask it to provide a reference to…

it is not an encyclopedia, it is a simulator, use it to simulate possible worlds instead of asking it questions about this one and you'll have a much better time it's a tool for thinking with, not a tool for thinking for you

> it's a tool for thinking with, not a tool for thinking for you

Says you.

Oh, and something to consider: there's probably a reason why half of the internet thinks these things are the next generation search engines.

Re: Unpredictable abilities emerging from large AI models

#146

Post by anon7725 »

Jeff_Brown wrote: post #132
anon7725 wrote: post #119

Earlier quoted context omitted.

Sledge hammer #1 (voice assistants, AI chips in phones) didn’t cause unemployment. It was at the level of new features and capabilities. Sledge hammer #2 is aimed squarely at “white collar” work without much in the way of bounds to its capabilities.

We can't trust it. That's a pretty hard bound.

Consider that a lot of useful work involves summarization (search++).

“What work has gone on for supply chain improvement in our European operations this year?” - this is the kind of question that is easy to ask in natural language but might take someone a week of searching, messaging, etc to assemble. An LLM with access to all of the code, documents, chats, etc could just give an answer with citations. We are not betting $1B on the answer that it gives, but it has saved us 1 week of work and allows us to move on to the next step in the project.

There are plenty of tasks like this which are highly valuable yet don’t require high trust. The one-shot “what is the airspeed of a coconut-laden swallow” type questions are actually fairly rare.

Re: Unpredictable abilities emerging from large AI models

#148

Post by ImHereToVote »

mark_l_watson wrote: post #144

Nice write up! I have been using classic back-prop neural networks since the 1980s, and deep learning for the last 8 years. This tech feels like a rocket ship that is accelerating exponentially! I am in my 70s and I don't work much anymore. That said, I find myself spending many hours in a typical day doing what I call "gentleman scientist" activities around Large Language Models. I was walking this morning with a no…

Thanks for the future you helped create for us young people. Hopefully the next generation is more merciful to 'useless' people than the previous one.

Re: Unpredictable abilities emerging from large AI models

#149

Post by skybrian »

pedrovhb wrote: post #10

This caught my attention as I found it implausible: > One DeepMind engineer even reported being able to convince ChatGPT that it was a Linux terminal and getting it to run some simple mathematical code to compute the first 10 prime numbers. Remarkably, it could finish the task faster than the same code running on a real Linux machine. Following the link, there's a screenshot to a screenshot [0] of a code-golf solutio…

Large language models are storytellers. To write a story about someone using a computer terminal, there are things it’s helpful to know, but there are easier ways than simulating a computer.

Since we don’t know how it works, we should be open to the possibility that it’s using all sorts of storytelling techniques.

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