[HN Gopher] How AI knows things no one told it
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How AI knows things no one told it
Author : georgeg23
Score : 31 points
Date : 2023-05-12 17:44 UTC (5 hours ago)
(HTM) web link (www.scientificamerican.com)
(TXT) w3m dump (www.scientificamerican.com)
| 1000thVisitor wrote:
| 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.
| mabbo wrote:
| 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.
| gfody wrote:
| > there is some number which we are no more complex than
|
| I bet that number is
| 808017424794512875886459904961710757005754368000000000
| hammyhavoc wrote:
| > Maybe we've simply found the number of parameters needed to
| simulate the function of "human level intelligence".
|
| Have we? That seems like a huge reach.
| the_optimist wrote:
| What is the meaning of your philosophical approach? As in, what
| is the proposed consequence of such hubris or humility?
| mike741 wrote:
| the Library of Babel "knows" every true 3260 character statement
| without being told anything at all: https://libraryofbabel.info/
| ggm wrote:
| 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.
| yieldcrv wrote:
| 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
| axutio wrote:
| I agree that it's probably not anything more than statistics,
| but why can't statistics alone generate emergent phenomena?
| What convinces you that the human brain isn't also just
| statistics at a massive scale?
| canjobear wrote:
| What does it mean to be "more than statistics"? Is there
| anything that cannot be described as a statistic?
| Karellen wrote:
| 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 Raphael Milliere of Columbia University [...]
| went a step further and showed that GPT can execute code,
| too, however. The philosopher typed in a program to calculate
| the 83rd number in the Fibonacci sequence. "It's multistep
| reasoning of a very high degree," he says. And the bot nailed
| it. When Milliere asked directly for the 83rd Fibonacci
| number, however, GPT got it wrong: this suggests the system
| wasn't just parroting the Internet. Rather it was performing
| its own calculations to reach the correct answer.
|
| > This impromptu ability demonstrates that LLMs develop an
| internal complexity that goes well beyond a shallow
| statistical analysis. Researchers are finding that these
| systems seem to achieve genuine understanding of what they
| have learned.
| circuit10 wrote:
| 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
| hackinthebochs wrote:
| It would be nice if the naysayers could offer an argument in
| favor of it being "just statistics", or even explain what that
| even means. There is reason to believe these models are
| demonstrating the traits of understanding in some cases. I
| argue the point in some detail here:
| https://www.reddit.com/r/naturalism/comments/1236vzf/on_larg...
| byyyy wrote:
| The article isn't doing anything more then quoting experts in
| the field.
|
| From the article:
|
| "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.
|
| If you remain unconvinced then the only conclusion I can make
| is that your an expert yourself on a scale of even higher in
| eminence then Yoshua Bengio here. Also don't forget Geoffrey
| Hinton, the Father of the modern revolution of AI, you must be
| more of an expert than him.
|
| Let's be real. These people are saying something along the
| lines that it's more then a stochastic parrot and we aren't
| sure what's going on. But you're saying it's absolutely nothing
| more than a parrot and your unhappy with with pop Sci media
| quoting experts who are just saying they don't know?
|
| Are you saying pop Sci media should quote you? Because you
| absolutely know what's going on and that it's definitely
| nothing more than statistics? I'm asking a stupid question here
| because I don't think this is what you're saying. You're not
| stupid, you know that what these experts say have merit.
|
| So my question for you is why do you remain so unconvinced in
| the face of experts and other intelligent people who clearly
| say no one understands? Your opinion here actually represents a
| large group of people who very violently deny/dismiss what even
| many experts are saying and I'm curious as to why?
| HarHarVeryFunny wrote:
| Of course it's just statistics, but so are we.
|
| Define intelligence. Say you took a human brain and kept it
| alive in a mad scientist's pickle jar. Let's assume the brain's
| wired up so it can hear and speak, it's got an idiot savant's
| memory, and someone has just read it the internet.
|
| What do you think the most impressive things are that the brain
| could do, that GPT-4 couldn't ?
| cjbprime wrote:
| This is a deeply incurious take.
|
| Yesterday I wrote a Python script, used dis.dis(code) to output
| its bytecode, and gave the bytecode to GPT-4. From the
| bytecode, it correctly decompiled the exact script I'd written
| (python bytecode includes variable names, so this was
| possible), explained what the code does, and explained what the
| code would run output if run, all correctly.
|
| It doesn't have access to a Python interpreter. It simulated
| one, both to decompile the bytecode and to predict its output.
| mnky9800n wrote:
| I gave up on scientific American being a reasonable
| publication. It's a hype beast like everything else these days.
| kgeist wrote:
| 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, it's a duck. Maybe our
| own brains are nothing more than overrated statistical
| machines?
|
| The only issue I see with the hype is when people attempt to
| anthromorphize it.
| chpatrick wrote:
| Is it wrong to anthromorphize if it quacks like a human?
| og_kalu wrote:
| Not only is it not wrong, it's actually in our best
| interests to do so past a certain level of agency,
| embodiment and unsupervised tool control.
|
| If the machine acts like it has emotions, runs forever(this
| kind of agency is already possible to implement though
| expensive) and can use tools, then treat it like it doesn't
| have emotions at your own peril. When the machine can "hit
| you back" (not necessarily physically of course), you'll
| learn manners pretty quickly. You can see glimpses of this
| with bing.
| hammyhavoc wrote:
| > You can see glimpses of this with bing.
|
| Oh please. The human gives the verbose output meaning by
| projecting onto it. You're clearly letting your emotions
| run wild. It's a fucking _LLM_.
| version_five wrote:
| > Maybe our own brains are nothing more than overrated
| statistical machines?
|
| This is a tired assertion, easily disproved for current llms
| (read some of Lecun's stuff) and if some variation is going
| to be claimed, it needs to be an affirmative defence. "Maybe
| x is true" is a meaningless statement.
| throwaway22032 wrote:
| 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.
| 1000thVisitor wrote:
| 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.
| sharemywin wrote:
| 1. Rule of probability: The concept of statistics is
| being used to predict outcomes based on previous
| observations or experiences, such as the likelihood of an
| object falling when released.
|
| 2. Rule of inherent knowledge: Some understanding or
| knowledge, like objects falling when not supported, can
| be known without explicit study or exposure to the
| specific terminology (e.g., gravity).
|
| 3. Rule of generalization: Observations or experiences
| with one type of object (e.g., a smartphone) can be
| generalized to other objects or situations, as long as
| they share similar characteristics (e.g., not being
| supported).
|
| 4. Rule of causality: There is an implied cause-and-
| effect relationship between an action (releasing the
| grip) and an outcome (the smartphone falling to the
| floor).
|
| 5. Rule of experiential learning: Knowledge can be gained
| through direct experiences, even if the specific terms or
| scientific concepts are not known.
| milchek wrote:
| 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.
| hammyhavoc wrote:
| Remember the crypto hype train? Turned out a lot of journos
| were being paid in funny money tokens to hype and pump, and a
| lot of conflicts of interests abounded in general with peddling
| services someone they knew had investment in. Welcome to the
| media. It's always been fickle when it comes to the potential
| to make money.
|
| Also very clearly a lot of people left holding the bag with
| crypto and are trying to resuscitate it with hype.
| williamcotton wrote:
| Wait, who is exceptionally unconvinced? And who is unhappy? I'm
| confused.
| SkyMarshal wrote:
| We just had this discussion a few days ago:
| https://news.ycombinator.com/item?id=35868065. It does appear
| to be a case of mis-used measurements rather than true
| emergence.
| cjbprime wrote:
| This is a misunderstanding of the researchers' claim, for
| which I blame them rather than you.
|
| You are using their study to claim that LLMs can not learn
| and reason about novel tasks. Their study doesn't make any
| claims about what LLMs can and can't do. The study says that
| when it appears that LLMs suddenly became able to reason
| about novel tasks, what actually happened is that the ability
| of the LLM to perform reasoning improved gradually and
| smoothly during its training until it could do those things.
|
| Do you see?
| sebzim4500 wrote:
| What would it mean exactly to be "more than statistics"? Does
| the human brain qualify?
| goatlover wrote:
| Yeah, the brain is alive and is trying to keep the rest of
| the organism alive But n an environment.
| ben_w wrote:
| One could reasonably say that's merely what the statistics
| are doing, not evidence to deny that the brain is just
| statistics.
| circuit10 wrote:
| That's clearly not a useful way to define intelligence
| though
| ftxbro wrote:
| > 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 laptops.
|
| > and frankly unhappy that pop Sci media is being this
| uncritical of claims it's emergent intelligence.
|
| It is emergent intelligence. This is very clear when you look
| at for example the ones who had access to the raw base models
| before they were made docile and stupid by RLHF lobotomization
|
| GPT-4 Red Teamer Nathan Labenz:
| https://www.youtube.com/watch?v=oLiheMQayNE
|
| GPT-4 Bing integrator Sebastien Bubeck:
| https://www.youtube.com/watch?v=qbIk7-JPB2c
| mightytravels wrote:
| 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.
| ars wrote:
| It's par for the course for the modern Scientific American
| which has fallen very very very far from its origins.
| SV_BubbleTime wrote:
| Wait until you learn about Forbes allowing anyone with $500
| to be a contributing author and punishing literally anything
| on their site.
| karmasimida wrote:
| Same can be said for human intelligence as well, just we are
| just more efficient in number of tokens we digest
| tomohelix wrote:
| Let for the sake of argument, assume that the LLM is truly
| sapience. Then this would be the first alien intelligence we have
| ever encountered. All life and intelligence we have seen before
| were organic, carbon-based life, including the most distant and
| unintuitive ones like the cephalopods, cetaceans, and primates.
| Even now we are still arguing whether these creatures are
| intelligent or they are just "not there" yet. We refuse to
| acknowledge their higher reasoning capacity regardless of
| evidences like rudimentary communication, social structure, tool
| use, etc. And these are things sharing >70% similarity to us.
|
| An LLM is so different and alien we would literally incapable of
| imagining their thought process even if they are pummeling us
| with evidences of their intelligence. It is just how it is. So I
| think it is futile and even foolish to try to quantify or create
| criteria for "intelligence" when discussing AI. Let it be and
| accept it for what it is: something we humans have created that
| can do lots of stuff.
| georgeg23 wrote:
| A nice quote from researcher Sebastien Bubeck of MSR,
|
| "Maybe we're seeing such a huge jump because we have reached a
| diversity of data, which is large enough that the only underlying
| principle to all of it is that intelligent beings produced
| them... And so the only way to explain all of this data is [for
| the model] to become intelligent."
| gumballindie wrote:
| > has surprised even researchers who have been generally
| skeptical about the hype over LLMs
|
| I too am surprised by my singing laptop.
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