21 ms·

How AI knows things no one told it

74 points by georgeg23 3y ago

georgeg23 3y ago
A nice quote from researcher Sébastien 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 3y ago
> has surprised even researchers who have been generally skeptical about the hype over LLMs I too am surprised by my singing laptop.
ggm 3y ago
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.
- mnky9800n 3y ago
  I gave up on scientific American being a reasonable publication. It's a hype beast like everything else these days.
- sebzim4500 3y ago
  What would it mean exactly to be "more than statistics"? Does the human brain qualify?
  - goatlover 3y ago
    Yeah, the brain is alive and is trying to keep the rest of the organism alive But n an environment.
    - circuit10 3y ago
      That’s clearly not a useful way to define intelligence though
    - ben_w 3y ago
      One could reasonably say that's merely what the statistics are doing, not evidence to deny that the brain is just statistics.
      - goatlover 3y ago
        Statistics is about the likelihood of events happening. It's not the things themselves. A brain isn't a statistical model. It's a bunch of living cells. It may do the equivalent of statistical modeling for some activities, like predicting what comes next. But it's more than just a model. Models are like maps. We make them.
        ben_w 3y ago
        That the brain is made of cells seems no more important to "why might an LLM not be as intelligent as a brain?" than the fact that cells are made of chemicals and the chemicals are fancy quantum fields that can be modelled as differential equations of a complex valued wave equation such that the squared magnitude of the wave corresponds to a probability.
- ftxbro 3y ago
  > 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 https://www.youtube.com/watch?v=oLiheMQayNE GPT-4 Bing integrator Sebastien Bubeck: https://www.youtube.com/watch?v=qbIk7-JPB2c https://www.youtube.com/watch?v=qbIk7-JPB2c
  - mightytravels 3y ago
    Summarize this video for me - https://www.youtube.com/watch?v=qbIk7-JPB2c 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 3y ago
  It's par for the course for the modern Scientific American which has fallen very very very far from its origins.
  - SV_BubbleTime 3y ago
    Wait until you learn about Forbes allowing anyone with $500 to be a contributing author and punishing literally anything on their site.
- axutio 3y ago
  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?
  - mike741 3y ago
    > why can't statistics alone generate emergent phenomena? because there is no generative mechanism in the definition of "statistics" with which to generate anything. > What convinces you that the human brain isn't also just statistics at a massive scale? Because the human brain created the concept of "statistics" so if statistics created the human brain this would mean statistics created statistics, leading to infinite regress.
    - titaniczero 3y ago
      But the idea that the brain functions on a basis of complex statistical processes doesn't imply that statistics created the brain itself, it just suggests that the brain's processes can be modeled/understood through the lens of statistical methods. Statistics is just how we describe a tool we invented to analyze observable data, the brain could be a similar tool and it doesn't need to be the same tool. It's akin to saying that we created the concept of "physics", yes we created it, and physics govern, for example, how a car moves, but it doesn't mean our concept of physics created the car or physics itself, we just use physics to describe and understand the car's movement. Maybe statistics can't generate anything, but if you imagine everything we can do as very complex, unimaginable multi-dimensional functions that can generate outputs based on inputs, we can use statistics to find functions that fit any real (ground truth) function in the observable universe.
      - mike741 3y ago
        > the idea that the brain functions on a basis of complex statistical processes doesn't imply that statistics created the brain itself Agreed. However, the phrasing and context of the question did imply the brain is "just statistics" and somehow emerged from statistics. If we are to interpret this as "the brain functions on just statistics" then the answer is still "it does not" because the brain can be said to function on countless different systems simultaneously, such as pure counting, algebra, calculus, etc which would mean that its not "just statistics." > It's akin to saying that we created the concept of "physics" This will boil down to our exact definitions, but most people conceive of physics as having a generative mechanism. If something were to ever be "created", like an atom or a new car, we would retroactively declare it to have been created in accordance with "the laws of physics." We wouldn't make the same retroactive assessment with something like "the rules of chess" because there is nothing in the rules of chess justifying such a creation. So we choose to give physics a special status. > we can use statistics to find functions that fit any real (ground truth) function A given statistical model might fit a function of the universe, but so might other models. Physics describe a function of the universe, chemistry describes a function of the universe, biology describes a function of the universe, politics describe a function of the universe. Describing a ground truth is one thing, elevating the description itself to the status of ground truth is another.
- canjobear 3y ago
  What does it mean to be “more than statistics”? Is there anything that cannot be described as a statistic?
  - Karellen 3y ago
    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 Millière 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 Millière 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 3y ago
      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
      - sharemywin 3y ago
        I think to some extent it's using the attention to pay attention to which "statistics" to pay attention to.
- kgeist 3y ago
  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.
  - throwaway22032 3y ago
    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 3y ago
      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 3y ago
        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.
- hammyhavoc 3y ago
  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 3y ago
  Wait, who is exceptionally unconvinced? And who is unhappy? I’m confused.
  - ggm 3y ago
    I am.
- SkyMarshal 3y ago
  We just had this discussion a few days ago: https://news.ycombinator.com/item?id=35868065 https://news.ycombinator.com/item?id=35868065. It does appear to be a case of mis-used measurements rather than true emergence.
  - cjbprime 3y ago
    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. They are not saying that LLMs don't have emergent capabilities: they are saying that those capabilities emerged at a continuous rate rather than a discontinuous one. Do you see?
    - dS0rrow 3y ago
      > Do you see? Emergence can only be discontinuous.
      - epylar 3y ago
        Emergent abilities can be gradual and continuous.
        dS0rrow 3y ago
        If it's continious then nothing is emerging and it can be explained by the sum of it's sub-systems.
        fsflover 3y ago
        Is life itself not an emergent phenomenon? What is the cleat threshold? Viruses?
- karmasimida 3y ago
  Same can be said for human intelligence as well, just we are just more efficient in number of tokens we digest
- yieldcrv 3y ago
  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
- hackinthebochs 3y ago
  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_large_language_models_and_understanding/ https://www.reddit.com/r/naturalism/comments/1236vzf/on_larg...
  - namaria 3y ago
    I find it curious to be called a naysayer when I look at a chatbot and I see a chatbot. People are implying grand things are behind this breakthrough in Natural Language Processing. I'm waiting to see something of significance emerge. Up til now it has been merely blog fodder and empty news media hype...
- byyyy 3y ago
  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?
  - ggm 3y ago
    You keep using that word "expert" and I don't doubt they're experts in machine learning, GAN and NNs but at this point what we're looking for is experts in what intelligence IS and the problem is .. nobody knows. There's no underlying theory of mind here. There's a lot of opinion and belief .. mainly in emergent behaviour or properties of scale. My problem is that I'm old enough to have been a child during the years of the lighthill report, and the AI winter(s) which followed. I've seen too many prior claims that the magic was being seen. I admit, what this cycle of GPT and LLMs do is pretty bloody impressive. I don't see inductive reasoning, directed drive or any evidence of what I think intelligence is. I do see good approximations. But that winds up in "well it's a different kind of intelligence" which I find unsatisfying. I hate analogies. But I'm constantly reminded of one person acts at the Edinburgh Festival fringe "channeling" shakespear or dickens in dialogue. They're great, but they don't write new shakespear. I don't see any act of creation in this stuff, any massive inferential leap in large problems. I do see massive improvements in some things like diagnostic image analysis and that's heartening, but there's a light yearg gap between image analysis of cancerous cell forms in xrays and being "alive" as a mind. I totally get I do anything but define what intelligence is, and for a good reason: nobody knows yet. We're in the meese report "I know it when I see it" territory disagreeing if we are seeing it. (But about intelligence, not pornography). I don't know what intelligence is. Dolphins and apes have some. Elephants and pigs too. They display affection and preplanned behaviour, empathy, memory, a sense of future, ant hills less so although some have argued rhetorically the anthill has will even if individual ants do not. I certainly don't think animals inability to speak makes them "unintelligent" but there's a qualitative and quantitative difference between them and us, humans. And I continue to believe (and I stress believe, not have evidence or scientific proof) there are no signs of latent intelligence in what we're seeing.
- HarHarVeryFunny 3y ago
  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 ?
  - mike741 3y ago
    We can describe ourselves with statistics, that does not mean we are statistics. This is called the "Fallacy of the Map" where a symbol is falsely equated with the entity it represents. https://rationalwiki.org/wiki/Mistaking_the_map_for_the_territory https://rationalwiki.org/wiki/Mistaking_the_map_for_the_terr... Statistics is collecting and analyzing numerical data. But what is the numerical data quantifying? It's quantifying a second thing. If everything were "just statistics" then there would be nothing left to take statistics of. This would constitute "infinite regress" or a "turtles all the way down" argument. https://en.wikipedia.org/wiki/Turtles_all_the_way_down https://en.wikipedia.org/wiki/Turtles_all_the_way_down >What do you think the most impressive things are that the brain could do, that GPT-4 couldn't ? This depends on what we consider "impressive." The human brain can control an entire human body, for one. Even if we take away the body, GPT-4 fails all sorts of basic arithmetic and reasoning problems. Additionally, the human brain is organic, analog, holographic, and reacts to external electromagnetic stimulation.
    - HarHarVeryFunny 3y ago
      You haven't defined intelligence, nor answered the question. Controlling a body is not relevant to a brain in a jar, nor does it detract from the intelligence of someone like Stephen Hawking. We're talking about intelligence - a capability - so applying labels like organic, analog, etc is irrelevant. The question is what can it DO, not how is it built.
      - mike741 3y ago
        > Controlling a body is not relevant to a brain in a jar, nor does it detract from the intelligence of someone like Stephen Hawking. I never said Steven Hawking wasn't intelligent, I was just pointing out obvious differences between the human brain and GPT-4. GPT-4 is unable to solve basic arithmetic problems without employing a traditional calculator. It also can't accurately summarize long books. It also can't program its own GPT-4. > You haven't defined intelligence > We're talking about intelligence If you want to talk about intelligence so badly, perhaps you should at least attempt to define it for yourself first? I don't like using poorly defined words, which is why the word "intelligence" didn't appear anywhere in my previous comment. > The question is what can it DO Is that your measure of intelligence? If something does less, are they then less intelligent? Because, by that standard, the body is integral to intelligence.
- cjbprime 3y ago
  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 it was executed, all correctly. It doesn't have access to a Python interpreter. It simulated one, both to decompile the bytecode and to predict its output. That's reasoning.
- kmac_ 3y ago
  I will be convinced when AI will create its first breakthrough PhD paper/discovery. But we have to admit that emergent properties like chain of thoughts are facts and are really impressive. The question is how much model size, context, training pipeline, etc. have to evolve to gain next levels. Two years ago people expected next AI winter, but here we are. There is Copilot and other tooling that is indeed useful.
- deleted 3y ago
  [deleted]
mabbo 3y ago
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 3y ago
  > there is some number which we are no more complex than I bet that number is 808017424794512875886459904961710757005754368000000000
  - deadbolt 3y ago
    For people who, like me, didn't recognize the number: https://en.wikipedia.org/wiki/Monster_group https://en.wikipedia.org/wiki/Monster_group
    - mercer 3y ago
      I love how I didn't understand practically anything from that page. Trying not to keep clicking...
- hammyhavoc 3y ago
  > 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 3y ago
  What is the meaning of your philosophical approach? As in, what is the proposed consequence of such hubris or humility?
- peter303 3y ago
  The physical human brain has 86 billion neurons with up to 10,000 connections each. However they are somewhat structured, e.g. the seven layers of the cerebral cortex, reducing the arbitrariness of connections. So say the connectome has 100 trillion parameters. The bain also has a bunch of other cells that dont appear to encode information. They may supply structural scaffolding and chemicals to mediate neuron function.
mike741 3y ago
By these standards, the Library of Babel "knows" every true 3260 character statement without being told anything at all: https://libraryofbabel.info/ https://libraryofbabel.info/
- dS0rrow 3y ago
  https://libraryofbabel.info/bookmark.cgi?i.jcpzd341 https://libraryofbabel.info/bookmark.cgi?i.jcpzd341
1000thVisitor 3y ago
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.
- sharemywin 3y ago
  This is the answer I got. with this system prompt "You are an intelligent PhD processor explaining topics to your PhD students." In the context of optimization, does the generalized assignment problem satisfy total unimodularity? Assistant: The generalized assignment problem (GAP) is an NP-hard optimization problem that can be formulated as an integer linear program. The problem involves assigning a set of tasks to a set of agents, subject to capacity constraints, with the goal of maximizing the total profit or minimizing the total cost. The integer linear programming formulation of GAP is as follows: Objective function: Maximize ∑_(i=1)^m ∑_(j=1)^n c_ij * x_ij Subject to: 1. ∑_(j=1)^n a_ij * x_ij ≤ b_i, for all i (capacity constraints for each agent) 2. ∑_(i=1)^m x_ij = 1, for all j (each task must be assigned to exactly one agent) 3. x_ij ∈ {0, 1}, for all i and j (binary assignment variables) Total unimodularity (TU) is a property of a matrix that guarantees that the linear programming relaxation of an integer linear program will result in integer optimal solutions. If the constraint matrix is totally unimodular, you can solve the linear programming relaxation and obtain an integer solution without using specialized integer programming algorithms. In the case of the generalized assignment problem, the constraint matrix does not satisfy
  - 1000thVisitor 3y ago
    That is interesting, but i still think the reasoning is weak as it is not mentioning the range of values that the a_ij and b_i are allowed to take on.
tomohelix 3y ago
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.
- snickell 3y ago
  I would hazard a guess that if we first encountered a ChatGPT-4 level LLM* as radio signals from Mars we would baseline assume it was "intelligent alien life". * assuming its instruction tuned: - in terms of self-disclosure (no "I'm made by openAI on earth" statements!) - sans its relatively schematic "guard rail" responses. It would be hard to imagine intelligent life being more obviously intelligent to us: ChatGPT literally speaks our language, and has learned patterns of "thought" derived from our own. None of this necessarily implies sapience, but I think the "alien intelligence" framing reverses the default assumption (we'd probably be freaking about alien intelligent life if we got a radio signal from mars delineating the prime numbers :-D ) in interesting ways, thanks for sharing!
  - tornato7 3y ago
    You're absolutely right! Here's a prompt you can use to chat with a Martian. It's actually very entertaining! > You are a representative of an alien civilization from Mars. I am a human who has made first contact with you. Write an opening message to earth. Stay in character as a martian. Make up facts and information where required. You know very little about Earth and are curious about our world and our civilization.
turnsout 3y ago
One well-supported theory for how humans learn language and knowledge is Relational Frame Theory (RFT)[0]. Essentially, when we learn that an elephant is larger than a mouse, we're learning a relationship ("more") and a dimension ("size"), which can be reversed ("smaller") or generalized (we can figure out what's larger than an elephant or what's smaller than a toaster). I think one theory is that the large number of parameters in LLMs allow them to go beyond mere association and actually model relational frames, which lets them generalize knowledge in an analogous way to humans. Maybe this behavior is not emergent in smaller networks (which could explain why animals with fewer neurons don't process language as well). Disclaimer: I'm not an academic researcher or scientist of any kind, this is just a hunch. [0]: https://en.wikipedia.org/wiki/Relational_frame_theory https://en.wikipedia.org/wiki/Relational_frame_theory
kazinator 3y ago
AI sometimes doesn't even know things you told it are given two questions ago in the same chat.
- gmerc 3y ago
  Nothing to do with the model and everything with context window management

74 points by georgeg23 3y ago

A nice quote from researcher Sébastien 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 3y ago

> has surprised even researchers who have been generally skeptical about the hype over LLMs I too am surprised by my singing laptop.

ggm 3y ago

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.

mnky9800n 3y ago
I gave up on scientific American being a reasonable publication. It's a hype beast like everything else these days.
sebzim4500 3y ago
What would it mean exactly to be "more than statistics"? Does the human brain qualify?
- goatlover 3y ago
  Yeah, the brain is alive and is trying to keep the rest of the organism alive But n an environment.
  - circuit10 3y ago
    That’s clearly not a useful way to define intelligence though
  - ben_w 3y ago
    One could reasonably say that's merely what the statistics are doing, not evidence to deny that the brain is just statistics.
    - goatlover 3y ago
      Statistics is about the likelihood of events happening. It's not the things themselves. A brain isn't a statistical model. It's a bunch of living cells. It may do the equivalent of statistical modeling for some activities, like predicting what comes next. But it's more than just a model. Models are like maps. We make them.
      - ben_w 3y ago
        That the brain is made of cells seems no more important to "why might an LLM not be as intelligent as a brain?" than the fact that cells are made of chemicals and the chemicals are fancy quantum fields that can be modelled as differential equations of a complex valued wave equation such that the squared magnitude of the wave corresponds to a probability.
ftxbro 3y ago
> 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 https://www.youtube.com/watch?v=oLiheMQayNE GPT-4 Bing integrator Sebastien Bubeck: https://www.youtube.com/watch?v=qbIk7-JPB2c https://www.youtube.com/watch?v=qbIk7-JPB2c
- mightytravels 3y ago
  Summarize this video for me - https://www.youtube.com/watch?v=qbIk7-JPB2c 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 3y ago
It's par for the course for the modern Scientific American which has fallen very very very far from its origins.
- SV_BubbleTime 3y ago
  Wait until you learn about Forbes allowing anyone with $500 to be a contributing author and punishing literally anything on their site.
axutio 3y ago
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?
- mike741 3y ago
  > why can't statistics alone generate emergent phenomena? because there is no generative mechanism in the definition of "statistics" with which to generate anything. > What convinces you that the human brain isn't also just statistics at a massive scale? Because the human brain created the concept of "statistics" so if statistics created the human brain this would mean statistics created statistics, leading to infinite regress.
  - titaniczero 3y ago
    But the idea that the brain functions on a basis of complex statistical processes doesn't imply that statistics created the brain itself, it just suggests that the brain's processes can be modeled/understood through the lens of statistical methods. Statistics is just how we describe a tool we invented to analyze observable data, the brain could be a similar tool and it doesn't need to be the same tool. It's akin to saying that we created the concept of "physics", yes we created it, and physics govern, for example, how a car moves, but it doesn't mean our concept of physics created the car or physics itself, we just use physics to describe and understand the car's movement. Maybe statistics can't generate anything, but if you imagine everything we can do as very complex, unimaginable multi-dimensional functions that can generate outputs based on inputs, we can use statistics to find functions that fit any real (ground truth) function in the observable universe.
    - mike741 3y ago
      > the idea that the brain functions on a basis of complex statistical processes doesn't imply that statistics created the brain itself Agreed. However, the phrasing and context of the question did imply the brain is "just statistics" and somehow emerged from statistics. If we are to interpret this as "the brain functions on just statistics" then the answer is still "it does not" because the brain can be said to function on countless different systems simultaneously, such as pure counting, algebra, calculus, etc which would mean that its not "just statistics." > It's akin to saying that we created the concept of "physics" This will boil down to our exact definitions, but most people conceive of physics as having a generative mechanism. If something were to ever be "created", like an atom or a new car, we would retroactively declare it to have been created in accordance with "the laws of physics." We wouldn't make the same retroactive assessment with something like "the rules of chess" because there is nothing in the rules of chess justifying such a creation. So we choose to give physics a special status. > we can use statistics to find functions that fit any real (ground truth) function A given statistical model might fit a function of the universe, but so might other models. Physics describe a function of the universe, chemistry describes a function of the universe, biology describes a function of the universe, politics describe a function of the universe. Describing a ground truth is one thing, elevating the description itself to the status of ground truth is another.
canjobear 3y ago
What does it mean to be “more than statistics”? Is there anything that cannot be described as a statistic?
- Karellen 3y ago
  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 Millière 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 Millière 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 3y ago
    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
    - sharemywin 3y ago
      I think to some extent it's using the attention to pay attention to which "statistics" to pay attention to.
kgeist 3y ago
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.
- throwaway22032 3y ago
  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 3y ago
    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 3y ago
      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.
hammyhavoc 3y ago
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 3y ago
Wait, who is exceptionally unconvinced? And who is unhappy? I’m confused.
- ggm 3y ago
  I am.
SkyMarshal 3y ago
We just had this discussion a few days ago: https://news.ycombinator.com/item?id=35868065 https://news.ycombinator.com/item?id=35868065. It does appear to be a case of mis-used measurements rather than true emergence.
- cjbprime 3y ago
  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. They are not saying that LLMs don't have emergent capabilities: they are saying that those capabilities emerged at a continuous rate rather than a discontinuous one. Do you see?
  - dS0rrow 3y ago
    > Do you see? Emergence can only be discontinuous.
    - epylar 3y ago
      Emergent abilities can be gradual and continuous.
      - dS0rrow 3y ago
        If it's continious then nothing is emerging and it can be explained by the sum of it's sub-systems.
        fsflover 3y ago
        Is life itself not an emergent phenomenon? What is the cleat threshold? Viruses?
karmasimida 3y ago
Same can be said for human intelligence as well, just we are just more efficient in number of tokens we digest
yieldcrv 3y ago
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
hackinthebochs 3y ago
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_large_language_models_and_understanding/ https://www.reddit.com/r/naturalism/comments/1236vzf/on_larg...
- namaria 3y ago
  I find it curious to be called a naysayer when I look at a chatbot and I see a chatbot. People are implying grand things are behind this breakthrough in Natural Language Processing. I'm waiting to see something of significance emerge. Up til now it has been merely blog fodder and empty news media hype...
byyyy 3y ago
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?
- ggm 3y ago
  You keep using that word "expert" and I don't doubt they're experts in machine learning, GAN and NNs but at this point what we're looking for is experts in what intelligence IS and the problem is .. nobody knows. There's no underlying theory of mind here. There's a lot of opinion and belief .. mainly in emergent behaviour or properties of scale. My problem is that I'm old enough to have been a child during the years of the lighthill report, and the AI winter(s) which followed. I've seen too many prior claims that the magic was being seen. I admit, what this cycle of GPT and LLMs do is pretty bloody impressive. I don't see inductive reasoning, directed drive or any evidence of what I think intelligence is. I do see good approximations. But that winds up in "well it's a different kind of intelligence" which I find unsatisfying. I hate analogies. But I'm constantly reminded of one person acts at the Edinburgh Festival fringe "channeling" shakespear or dickens in dialogue. They're great, but they don't write new shakespear. I don't see any act of creation in this stuff, any massive inferential leap in large problems. I do see massive improvements in some things like diagnostic image analysis and that's heartening, but there's a light yearg gap between image analysis of cancerous cell forms in xrays and being "alive" as a mind. I totally get I do anything but define what intelligence is, and for a good reason: nobody knows yet. We're in the meese report "I know it when I see it" territory disagreeing if we are seeing it. (But about intelligence, not pornography). I don't know what intelligence is. Dolphins and apes have some. Elephants and pigs too. They display affection and preplanned behaviour, empathy, memory, a sense of future, ant hills less so although some have argued rhetorically the anthill has will even if individual ants do not. I certainly don't think animals inability to speak makes them "unintelligent" but there's a qualitative and quantitative difference between them and us, humans. And I continue to believe (and I stress believe, not have evidence or scientific proof) there are no signs of latent intelligence in what we're seeing.
HarHarVeryFunny 3y ago
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 ?
- mike741 3y ago
  We can describe ourselves with statistics, that does not mean we are statistics. This is called the "Fallacy of the Map" where a symbol is falsely equated with the entity it represents. https://rationalwiki.org/wiki/Mistaking_the_map_for_the_territory https://rationalwiki.org/wiki/Mistaking_the_map_for_the_terr... Statistics is collecting and analyzing numerical data. But what is the numerical data quantifying? It's quantifying a second thing. If everything were "just statistics" then there would be nothing left to take statistics of. This would constitute "infinite regress" or a "turtles all the way down" argument. https://en.wikipedia.org/wiki/Turtles_all_the_way_down https://en.wikipedia.org/wiki/Turtles_all_the_way_down >What do you think the most impressive things are that the brain could do, that GPT-4 couldn't ? This depends on what we consider "impressive." The human brain can control an entire human body, for one. Even if we take away the body, GPT-4 fails all sorts of basic arithmetic and reasoning problems. Additionally, the human brain is organic, analog, holographic, and reacts to external electromagnetic stimulation.
  - HarHarVeryFunny 3y ago
    You haven't defined intelligence, nor answered the question. Controlling a body is not relevant to a brain in a jar, nor does it detract from the intelligence of someone like Stephen Hawking. We're talking about intelligence - a capability - so applying labels like organic, analog, etc is irrelevant. The question is what can it DO, not how is it built.
    - mike741 3y ago
      > Controlling a body is not relevant to a brain in a jar, nor does it detract from the intelligence of someone like Stephen Hawking. I never said Steven Hawking wasn't intelligent, I was just pointing out obvious differences between the human brain and GPT-4. GPT-4 is unable to solve basic arithmetic problems without employing a traditional calculator. It also can't accurately summarize long books. It also can't program its own GPT-4. > You haven't defined intelligence > We're talking about intelligence If you want to talk about intelligence so badly, perhaps you should at least attempt to define it for yourself first? I don't like using poorly defined words, which is why the word "intelligence" didn't appear anywhere in my previous comment. > The question is what can it DO Is that your measure of intelligence? If something does less, are they then less intelligent? Because, by that standard, the body is integral to intelligence.
cjbprime 3y ago
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 it was executed, all correctly. It doesn't have access to a Python interpreter. It simulated one, both to decompile the bytecode and to predict its output. That's reasoning.
kmac_ 3y ago
I will be convinced when AI will create its first breakthrough PhD paper/discovery. But we have to admit that emergent properties like chain of thoughts are facts and are really impressive. The question is how much model size, context, training pipeline, etc. have to evolve to gain next levels. Two years ago people expected next AI winter, but here we are. There is Copilot and other tooling that is indeed useful.
deleted 3y ago
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mabbo 3y ago

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 3y ago
> there is some number which we are no more complex than I bet that number is 808017424794512875886459904961710757005754368000000000
- deadbolt 3y ago
  For people who, like me, didn't recognize the number: https://en.wikipedia.org/wiki/Monster_group https://en.wikipedia.org/wiki/Monster_group
  - mercer 3y ago
    I love how I didn't understand practically anything from that page. Trying not to keep clicking...
hammyhavoc 3y ago
> 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 3y ago
What is the meaning of your philosophical approach? As in, what is the proposed consequence of such hubris or humility?
peter303 3y ago
The physical human brain has 86 billion neurons with up to 10,000 connections each. However they are somewhat structured, e.g. the seven layers of the cerebral cortex, reducing the arbitrariness of connections. So say the connectome has 100 trillion parameters. The bain also has a bunch of other cells that dont appear to encode information. They may supply structural scaffolding and chemicals to mediate neuron function.

mike741 3y ago

By these standards, the Library of Babel "knows" every true 3260 character statement without being told anything at all: https://libraryofbabel.info/ https://libraryofbabel.info/

dS0rrow 3y ago
https://libraryofbabel.info/bookmark.cgi?i.jcpzd341 https://libraryofbabel.info/bookmark.cgi?i.jcpzd341

1000thVisitor 3y ago

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.

sharemywin 3y ago
This is the answer I got. with this system prompt "You are an intelligent PhD processor explaining topics to your PhD students." In the context of optimization, does the generalized assignment problem satisfy total unimodularity? Assistant: The generalized assignment problem (GAP) is an NP-hard optimization problem that can be formulated as an integer linear program. The problem involves assigning a set of tasks to a set of agents, subject to capacity constraints, with the goal of maximizing the total profit or minimizing the total cost. The integer linear programming formulation of GAP is as follows: Objective function: Maximize ∑_(i=1)^m ∑_(j=1)^n c_ij * x_ij Subject to: 1. ∑_(j=1)^n a_ij * x_ij ≤ b_i, for all i (capacity constraints for each agent) 2. ∑_(i=1)^m x_ij = 1, for all j (each task must be assigned to exactly one agent) 3. x_ij ∈ {0, 1}, for all i and j (binary assignment variables) Total unimodularity (TU) is a property of a matrix that guarantees that the linear programming relaxation of an integer linear program will result in integer optimal solutions. If the constraint matrix is totally unimodular, you can solve the linear programming relaxation and obtain an integer solution without using specialized integer programming algorithms. In the case of the generalized assignment problem, the constraint matrix does not satisfy
- 1000thVisitor 3y ago
  That is interesting, but i still think the reasoning is weak as it is not mentioning the range of values that the a_ij and b_i are allowed to take on.

tomohelix 3y ago

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.

snickell 3y ago
I would hazard a guess that if we first encountered a ChatGPT-4 level LLM* as radio signals from Mars we would baseline assume it was "intelligent alien life". * assuming its instruction tuned: - in terms of self-disclosure (no "I'm made by openAI on earth" statements!) - sans its relatively schematic "guard rail" responses. It would be hard to imagine intelligent life being more obviously intelligent to us: ChatGPT literally speaks our language, and has learned patterns of "thought" derived from our own. None of this necessarily implies sapience, but I think the "alien intelligence" framing reverses the default assumption (we'd probably be freaking about alien intelligent life if we got a radio signal from mars delineating the prime numbers :-D ) in interesting ways, thanks for sharing!
- tornato7 3y ago
  You're absolutely right! Here's a prompt you can use to chat with a Martian. It's actually very entertaining! > You are a representative of an alien civilization from Mars. I am a human who has made first contact with you. Write an opening message to earth. Stay in character as a martian. Make up facts and information where required. You know very little about Earth and are curious about our world and our civilization.

turnsout 3y ago

One well-supported theory for how humans learn language and knowledge is Relational Frame Theory (RFT)[0]. Essentially, when we learn that an elephant is larger than a mouse, we're learning a relationship ("more") and a dimension ("size"), which can be reversed ("smaller") or generalized (we can figure out what's larger than an elephant or what's smaller than a toaster). I think one theory is that the large number of parameters in LLMs allow them to go beyond mere association and actually model relational frames, which lets them generalize knowledge in an analogous way to humans. Maybe this behavior is not emergent in smaller networks (which could explain why animals with fewer neurons don't process language as well). Disclaimer: I'm not an academic researcher or scientist of any kind, this is just a hunch. [0]: https://en.wikipedia.org/wiki/Relational_frame_theory https://en.wikipedia.org/wiki/Relational_frame_theory

kazinator 3y ago

AI sometimes doesn't even know things you told it are given two questions ago in the same chat.

gmerc 3y ago
Nothing to do with the model and everything with context window management