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

How AI knows things no one told it

scientificamerican.com

Post reply on HN
Print view View original

21–30 of 140 posts

Re: How AI knows things no one told it

#21

Post by milchek »

kgeist wrote: post #13
ggm wrote: post #4

Exceptionally unconvinced that it's more than statistics, and frankly unhappy that pop Sci media is being this uncritical of claims it's emergent intelligence.

If you invent/introduce made up concepts and rules on the spot ("let shmerple be ..."), which can't possibly be found in its training data, then, from my experience, when queried, LLMs are able to correctly reason most of the time... So it's not just Markov chains regurgitating sentences verbatim from their dataset. Even if this kind of intelligence is just statistics, does it really matter? If it quacks like a duck,…

Some great points, and this is where these discussion can get into the freewill vs determinism arena.

If you’re more of a determinist, then human brains are basically like organic LLMs and we’re just unique models trained on our own life’s datasets - with other traits/data like preferences and biases also being inherited via genetics, of course.

Re: How AI knows things no one told it

#22

Post by circuit10 »

goatlover wrote: post #11
sebzim4500 wrote: post #6

Earlier quoted context omitted.

What would it mean exactly to be "more than statistics"? Does the human brain qualify?

Yeah, the brain is alive and is trying to keep the rest of the organism alive But n an environment.

That’s clearly not a useful way to define intelligence though

Re: How AI knows things no one told it

#23

Post by circuit10 »

Karellen wrote: post #17
canjobear wrote: post #10

Earlier quoted context omitted.

What does it mean to be “more than statistics”? Is there anything that cannot be described as a statistic?

I think the part of the article that the parent comment is disagreeing with is: > “It is certainly much more than a stochastic parrot, and it certainly builds some representation of the world—although I do not think that it is quite like how humans build an internal world model,” says Yoshua Bengio, an AI researcher at the University of Montreal. > At a conference at New York University in March, philosopher Raphaël…

But that isn’t claiming that it isn’t based on statistics, it’s just saying that the analysis being done isn’t shallow, which is clearly true

Re: How AI knows things no one told it

#24

Post by chpatrick »

kgeist wrote: post #13
ggm wrote: post #4

Exceptionally unconvinced that it's more than statistics, and frankly unhappy that pop Sci media is being this uncritical of claims it's emergent intelligence.

If you invent/introduce made up concepts and rules on the spot ("let shmerple be ..."), which can't possibly be found in its training data, then, from my experience, when queried, LLMs are able to correctly reason most of the time... So it's not just Markov chains regurgitating sentences verbatim from their dataset. Even if this kind of intelligence is just statistics, does it really matter? If it quacks like a duck,…

Is it wrong to anthromorphize if it quacks like a human?

Re: How AI knows things no one told it

#25

Post by yieldcrv »

ggm wrote: post #4

Exceptionally unconvinced that it's more than statistics, and frankly unhappy that pop Sci media is being this uncritical of claims it's emergent intelligence.

the article uses the word ability not intelligence

specifically because it does things it wasnt trained to do, and that people are more allergic to the word intelligence than the merit of the observation

Re: How AI knows things no one told it

#26

Post by mightytravels »

ftxbro wrote: post #7
ggm wrote: post #4

Exceptionally unconvinced that it's more than statistics, and frankly unhappy that pop Sci media is being this uncritical of claims it's emergent intelligence.

> Exceptionally unconvinced that it's more than statistics It's not more than statistics. It's very complicated statistics. Model sizes used to be like one or ten parameters now they are a hundred billion. Inference and prediction used to be done on a laptop, now it's called training and inference respectively and it's done on a data center with ten thousand GPGPUs each like a thousand times as powerful as old laptop…

Summarize this video for me - https://www.youtube.com/watch?v=qbIk7-JPB2c

The video has the following main points:

• [0:00 - 2:00] Introduction: The speaker introduces himself and the topic of the talk. He explains that he will present some early experiments with GPT-4, a neural network model that can generate natural language texts based on a given input or context. He also gives an overview of the outline of the talk.

• [2:00 - 10:00] Background: The speaker gives some background information on the transformer architecture, which is a deep learning technique that uses attention mechanisms to learn the relationships between words and sentences. He also explains how GPT-4 is trained on a large corpus of text data from the internet, such as Wikipedia, Reddit, news articles, books, etc.

• [10:00 - 18:00] ChatGPT: The speaker introduces ChatGPT, which is a version of GPT-4 that can perform various tasks such as answering questions, writing essays, composing poems, generating code, etc. He also shows some examples of ChatGPT's outputs and discusses some of its strengths and weaknesses.

• [18:00 - 28:00] Open-ended conversations: The speaker shows how ChatGPT can engage in open-ended conversations with humans on any topic. He demonstrates some live interactions with ChatGPT and analyzes some of its responses. He also discusses some of the challenges and limitations of ChatGPT in conversational settings.

• [28:00 - 38:00] Artificial intelligence: The speaker discusses whether ChatGPT and its successors demonstrate artificial intelligence or not. He compares ChatGPT's abilities with those of natural intelligence and argues that ChatGPT challenges the traditional boundaries between natural and artificial intelligence. He also discusses some of the implications and opportunities for society and science.

• [38:00 - 48:00] Conclusion and questions: The speaker concludes his talk by summarizing his main points and highlighting some open questions and future directions for research. He also answers some questions from the audience.

The video has the following main arguments/observations about emergent intelligence and their timestamps:

• [12:00 - 14:00] The speaker argues that ChatGPT is an example of emergent intelligence because it can generate coherent and novel texts that are not explicitly encoded in its training data. He shows how ChatGPT can write an essay on a given topic by using its own words and knowledge, without copying or paraphrasing from any source. He also shows how ChatGPT can compose a poem on a given theme by using its own style and creativity, without following any predefined rules or patterns.

• [20:00 - 22:00] The speaker observes that ChatGPT can exhibit emergent intelligence in open-ended conversations by adapting to different domains and styles of communication. He shows how ChatGPT can switch between formal and informal language, between factual and emotional tone, and between serious and humorous topics, depending on the context and the interlocutor. He also shows how ChatGPT can learn from the feedback and the preferences of the interlocutor, and adjust its responses accordingly.

• [30:00 - 32:00] The speaker observes that ChatGPT can exhibit emergent intelligence in reasoning tasks by using common sense and general knowledge. He shows how ChatGPT can answer questions that require logical inference, causal explanation, or counterfactual thinking, by using its own understanding and interpretation of the world. He also shows how ChatGPT can generate questions that require reasoning skills, by using its own curiosity and imagination.

• [40:00 - 42:00] The speaker argues that ChatGPT and its successors challenge the traditional boundaries between natural and artificial intelligence because they exhibit emergent intelligence that is comparable or superior to human intelligence. He compares ChatGPT's abilities with those of human intelligence and argues that ChatGPT can perform tasks that require common sense, creativity, reasoning, and general knowledge, which are often considered as hallmarks of natural intelligence. He also discusses some of the implications and opportunities for society and science.

Re: How AI knows things no one told it

#27

Post by 1000thVisitor »

A missing part that AI needs is a experimental way to verify its results. In some contexts, like math, this is entirely possible because it does not involve manipulation of real world objects. For example, i asked GTP-4, In the context of optimization, does the generalized assignment problem satisfy total unimodularity. Then experimental way would be to look up the formulas of the integer program on the internet or in its training data, write down the constraint matrix of the given problem, fill it with values, and check if the matrix is unimodular. Instead, the way gpt-4 answered this question, correctly, albeit with a weak reasoning, is by saying that researchers worked on heuristics to solve the problem, thus it is probably not unimodular as that would allow a quick solution without resorting to heuristics.

Re: How AI knows things no one told it

#28

Post by 1000thVisitor »

throwaway22032 wrote: post #18
kgeist wrote: post #13

Earlier quoted context omitted.

If you invent/introduce made up concepts and rules on the spot ("let shmerple be ..."), which can't possibly be found in its training data, then, from my experience, when queried, LLMs are able to correctly reason most of the time... So it's not just Markov chains regurgitating sentences verbatim from their dataset. Even if this kind of intelligence is just statistics, does it really matter? If it quacks like a duck,…

Yeah, it's bizarre. Are we not simply employing statistics when we deduce that yes, if we release our grip, the smartphone will fall to the floor? You wouldn't have to ever see a phone drop in order to know that, and neither would you have to study or know of the terminology of gravity. It comes from the statistical knowledge that all things fall when not blocked.

The key here is abstraction, not statistics. You have seen other items fall and are able to abstract this and then apply it to the phone.

Re: How AI knows things no one told it

#29

Post by mabbo »

I think it's a bit of hubris to demand we explain how LLMs can be as intelligent as they are when we barely understand how the ball of meat inside our skulls can be either.

We know that neural networks can simulate any function, given enough parameters. Maybe we've simply found the number of parameters needed to simulate the function of "human level intelligence".

That should humble us, to know that there is some number which we are no more complex than.

Post reply on HN