7 ms·

>internal concepts, the model is not aware that it's doing anything so how could it "explain itself" This in a nutshell is why I hate that all this stuff is be

>internal concepts, the model is not aware that it's doing anything so how could it "explain itself" This in a nutshell is why I hate that all this stuff is being labeled as AI. Its advanced machine learning (another term that also feels inaccurate but I concede is at least closer to whats happening conceptually) Really, LLMs and the like still lack any model of intelligence. Its, in the most basic of terms, algorithmic pattern matching mixed with statistical likelihoods of success. And that can get things really really far. There are entire businesses built on doing that kind of work (particularly in finance) with very high accuracy and usefulness, but its not AI.

johnecheck 2y ago
While I agree that LLMs are hardly sapient, it's very hard to make this argument without being able to pinpoint what a model of intelligence actually is. "Human brains lack any model of intelligence. It's just neurons firing in complicated patterns in response to inputs based on what statistically leads to reproductive success"
- no_wizard 2y ago
  That's not at all on par with what I'm saying. There exists a generally accepted baseline definition for what crosses the threshold of intelligent behavior. We shouldn't seek to muddy this. EDIT: Generally its accepted that a core trait of intelligence is an agent’s ability to achieve goals in a wide range of environments. This means you must be able to generalize, which in turn allows intelligent beings to react to new environments and contexts without previous experience or input. Nothing I'm aware of on the market can do this. LLMs are great at statistically inferring things, but they can't generalize which means they lack reasoning. They also lack the ability to seek new information without prompting. The fact that all LLMs boil down to (relatively) simple mathematics should be enough to prove the point as well. It lacks spontaneous reasoning, which is why the ability to generalize is key
  - highfrequency 2y ago
    What is that baseline threshold for intelligence? Could you provide concrete and objective results, that if demonstrated by a computer system would satisfy your criteria for intelligence?
    - no_wizard 2y ago
      see the edit. boils down to the ability to generalize, LLMs can't generalize. I'm not the only one who holds this view either. Francois Chollet, a former intelligence researcher at Google also shares this view.
      - highfrequency 2y ago
        Are you able to formulate "generalization" in a concrete and objective way that could be achieved unambiguously, and is currently achieved by a typical human? A lot of people would say that LLMs generalize pretty well - they certainly can understand natural language sequences that are not present in their training data.
        whilenot-dev 2y ago
        > A lot of people would say that LLMs generalize pretty well What do you mean here? The trained model, the inference engine, is the one that makes an LLM for "a lot of people". > they certainly can understand natural language sequences that are not present in their training data Keeping the trained model as LLM in mind, I think learning a language includes generalization and is typically achieved by a human, so I'll try to formulate: Can a trained LLM model learn languages that hasn't been in its training set just by chatting/prompting? Given that any Korean texts were excluded from the training set, could Korean be learned? Does that even work with languages descending from the same language family (Spanish in the training set but Italian should be learned)?
      - stevenAthompson 2y ago
        > Francois Chollet, a former intelligence researcher at Google also shares this view. Great, now there are two of you.
- devmor 2y ago
  I don't think your detraction has much merit. If I don't understand how a combustion engine works, I don't need that engineering knowledge to tell you that a bicycle [an LLM] isn't a car [a human brain] just because it fits the classification of a transportation vehicle [conversational interface]. This topic is incredibly fractured because there is too much monetary interest in redefining what "intelligence" means, so I don't think a technical comparison is even useful unless the conversation begins with an explicit definition of intelligence in relation to the claims.
  - Velorivox 2y ago
    Bicycles and cars are too close. The analogy I like is human leg versus tire. That is a starker depiction of how silly it is to compare the two in terms of structure rather than result.
    - devmor 2y ago
      That is a much better comparison.
  - uoaei 2y ago
    If you don't know anything except how words are used, you can definitely disambiguate "bicycle" and "car" solely based on the fact that the contexts they appear in are incongruent the vast majority of the time, and when they appear in the same context, they are explicitly contrasted against each other. This is just the "fancy statistics" argument again, and it serves to describe any similar example you can come up with better than "intelligence exists inside this black box because I'm vibing with the output".
    - devmor 2y ago
      Why are you attempting to technically analyze a simile? That is not why comparisons are used.
  - SkyBelow 2y ago
    One problem is that we have been basing too much on [human brain] for so long that we ended up with some ethical problems as we decided other brains didn't count as intelligent. As such, science has taken an approach of not assuming humans are uniquely intelligence. We seem to be the best around at doing different tasks with tools, but other animals are not completely incapable of doing the same. So [human brain] should really be [brain]. But is that good enough? Is a fruit fly brain intelligent? Is it a goal to aim for? There is a second problem that we aren't looking for [human brain] or [brain], but [intelligence] or [sapient] or something similar. We aren't even sure what we want as many people have different ideas, and, as you pointed out, we have different people with different interest pushing for different underlying definitions of what these ideas even are. There is also a great deal of impreciseness in most any definitions we use, and AI encroaches on this in a way that reality rarely attacks our definitions. Philosophically, we aren't well prepared to defend against such attacks. If we had every ancestor of the cat before us, could we point out the first cat from the last non-cat in that lineup? In a precise way that we would all agree upon that isn't arbitrary? I doubt we could.
- OtherShrezzing 2y ago
  >While I agree that LLMs are hardly sapient, it's very hard to make this argument without being able to pinpoint what a model of intelligence actually is. Maybe so, but it's trivial to do the inverse, and pinpoint something that's not intelligent. I'm happy to state that an entity which has seen every game guide ever written, but still can't beat the first generation Pokemon is not intelligent. This isn't the ceiling for intelligence. But it's a reasonable floor.
  - 7h3kk1d 2y ago
    There's sentient humans who can't beat the first generation pokemon games.
    - antasvara 2y ago
      Is there a sentient human that has access to (and actually uses) all of the Pokémon game guides yet is incapable of beating Pokémon? Because that's what an LLM is working with.
      - 7h3kk1d 2y ago
        I'm quite sure my grandma could not. You can make the argument these people aren't intelligent but I think that's a contrived argument.
- whilenot-dev 2y ago
  What's wrong with just calling them smart algorithmic models? Being smart allows somewhat to be wrong, as long as that leads to a satisfying solution. Being intelligent on the other hand requires foundational correctness in concepts that aren't even defined yet. EDIT: I also somewhat like the term imperative knowledge (models) [0] [0]: https://en.wikipedia.org/wiki/Procedural_knowledge https://en.wikipedia.org/wiki/Procedural_knowledge
  - jfengel 2y ago
    The problem with "smart" is that they fail at things that dumb people succeed at. They have ludicrous levels of knowledge and a jaw dropping ability to connect pieces while missing what's right in front of them. The gap makes me uncomfortable with the implications of the word "smart". It is orthogonal to that.
    - sigmoid10 2y ago
      >they fail at things that dumb people succeed at Funnily enough, you can also observe that in humans. The number of times I have observed people from highly intellectual, high income/academic families struggle with simple tasks that even the dumbest people do with ease is staggering. If you're not trained for something and suddenly confronted with it for the first time, you will also in all likelihood fail. "Smart" is just as ill-defined as any other clumsy approach to define intelligence.
    - nradov 2y ago
      Bombs can be smart, even though they sometimes miss the target.
- a_victorp 2y ago
  > Human brains lack any model of intelligence. It's just neurons firing in complicated patterns in response to inputs based on what statistically leads to reproductive success The fact that you can reason about intelligence is a counter argument to this
  - immibis 2y ago
    It seems like LLMs can also reason about intelligence. Does that make them intelligent? We don't know what intelligence is, or isn't.
    - syndeo 2y ago
      It's fascinating how this discussion about intelligence bumps up against the limits of text itself. We're here, reasoning and reflecting on what makes us capable of this conversation. Yet, the very structure of our arguments, the way we question definitions or assert self-awareness, mirrors patterns that LLMs are becoming increasingly adept at replicating. How confidently can we, reading these words onscreen, distinguish genuine introspection from a sophisticated echo? Case in point… I didn't write that paragraph by myself.
      - Nevermark 2y ago
        So you got help from a natural intelligence? No fair. (natdeo?) Someone needs to create a clone site of HN's format and posts, but the rules only permit synthetic intelligence comments. All models pre-prompted to read prolifically, but comment and up/down vote carefully and sparingly, to optimize the quality of discussion. And no looking at nat-HN comments. It would be very interesting to compare discussions between the sites. A human-lurker per day graph over time would also be of interest. Side thought: Has anyone created a Reverse-Captcha yet?
        wyre 2y ago
        This is an entertaining idea. User prompts can synthesize a users domain knowledge whether they are an entrepreneur, code dev, engineer, hacker, designer, etc and it can also have different users between different LLMs. I think the site would clone the upvotes of articles and the ordering of the front page, and gives directions when to comment on other’s posts.
- shinycode 2y ago
  > "Human brains lack any model of intelligence. It's just neurons firing in complicated patterns in response to inputs based on what statistically leads to reproductive success" Are you sure about that ? Do we have proof of that ? In happened all the time trought history of science that a lot of scientists were convinced of something and a model of reality up until someone discovers a new proof and or propose a new coherent model. That’s literally the history of science, disprove what we thought was an established model
  - johnecheck 2y ago
    Indeed, a good point. My comment assumes that our current model of the human brain is (sufficiently) complete. Your comment reveals an interesting corollary - those that believe in something beyond our understanding, like the Christian soul, may never be convinced that an AI is truly sapient.
- andrepd 2y ago
  Human brains do way more things than language. And non-human animals (with no language) also reason, and we cannot understand those either, barely even the very simplest ones.
voidspark 2y ago
You are confusing sentience or consciousness with intelligence.
- no_wizard 2y ago
  one fundamental attribute of intelligence is the ability to demonstrate reasoning in new and otherwise unknown situations. There is no system that I am currently aware of that works on data it is not trained on. Another is the fundamental inability to self update on outdated information. It is incapable of doing that, which means it lacks another marker, which is being able to respond to changes of context effectively. Ants can do this. LLMs can't.
  - voidspark 2y ago
    But that's exactly what these deep neural networks have shown, countless times. LLM's generalize on new data outside of its training set. It's called "zero shot learning" where they can solve problems that are not in their training set. AlphaGo Zero is another example. AlphaGo Zero mastered Go from scratch, beating professional players with moves it was never trained on > Another is the fundamental inability to self update That's an engineering decision, not a fundamental limitation. They could engineer a solution for the model to initiate its own training sequence, if they decide to enable that.
    - dontlikeyoueith 2y ago
      This comment is such a confusion of ideas its comical.
      - voidspark 2y ago
        [flagged]
    - no_wizard 2y ago
      >AlphaGo Zero mastered Go from scratch, beating professional players with moves it was never trained on Thats all well and good, but it was tuned with enough parameters to learn via reinforcement learning[0]. I think The Register went further and got better clarification about how it worked[1] >During training, it sits on each side of the table: two instances of the same software face off against each other. A match starts with the game's black and white stones scattered on the board, placed following a random set of moves from their starting positions. The two computer players are given the list of moves that led to the positions of the stones on the grid, and then are each told to come up with multiple chains of next moves along with estimates of the probability they will win by following through each chain. While I also find it interesting that in both of these instances, its all referenced to as machine learning, not AI, its also important to see that even though what AlphaGo Zero did was quite awesome and a step forward in using compute for more complex tasks, it was still seeded the basics of information - the rules of Go - and simply patterned matched against itself until built up enough of a statistical model to determine the best moves to make in any given situation during a game. Which isn't the same thing as showing generalized reasoning. It could not, then, take this information and apply it to another situation. They did show the self reinforcement techniques worked well though, and used them for Chess and Shogi to great success as I recall, but thats a validation of the technique, not that it could generalize knowledge. >That's an engineering decision, not a fundamental limitation So you're saying that they can't reason about independently? [0]: https://deepmind.google/discover/blog/alphago-zero-starting-from-scratch/ https://deepmind.google/discover/blog/alphago-zero-starting-... [1]: https://www.theregister.com/2017/10/18/deepminds_latest_alphago_software_doesnt_need_human_data_to_win/ https://www.theregister.com/2017/10/18/deepminds_latest_alph...
bigmadshoe 2y ago
We don't have a complete enough theory of neuroscience to conclude that much of human "reasoning" is not "algorithmic pattern matching mixed with statistical likelihoods of success". Regardless of how it models intelligence, why is it not AI? Do you mean it is not AGI? A system that can take a piece of text as input and output a reasonable response is obviously exhibiting some form of intelligence, regardless of the internal workings.
- no_wizard 2y ago
  It’s easy to attribute intelligence these systems. They have a flexibility and unpredictability that hasn't typically been associated with computers, but it all rests on (relatively) simple mathematics. We know this is true. We also know that means it has limitations and can't actually reason information. The corpus of work is huge - and that allows the results to be pretty striking - but once you do hit a corner with any of this tech, it can't simply reason about the unknown. If its not in the training data - or the training data is outdated - it will not be able to course correct at all. Thus, it lacks reasoning capability, which is a fundamental attribute of any form of intelligence.
  - justonenote 2y ago
    > it all rests on (relatively) simple mathematics. We know this is true. We also know that means it has limitations and can't actually reason information. What do you imagine is happening inside biological minds that enables reasoning that is something different to, a lot of, "simple mathematics"? You state that because it is built up of simple mathematics it cannot be reasoning, but this does not follow at all, unless you can posit some other mechanism that gives rise to intelligence and reasoning that is not able to be modelled mathematically.
    - no_wizard 2y ago
      Because whats inside our minds is more than mathematics, or we would be able to explain human behavior with the purity of mathematics, and so far, we can't. We can prove the behavior of LLMs with mathematics, because its foundations are constructed. That also means it has the same limits of anything else we use applied mathematics for. Is the broad market analysis that HFT firms use software for to make automated trades also intelligent?
tsimionescu 2y ago
One of the earliest things that defined what AI meant were algorithms like A*, and then rules engines like CLIPS. I would say LLMs are much closer to anything that we'd actually call intelligence, despite their limitations, than some of the things that defined* the term for decades. * fixed a typo, used to be "defend"
- no_wizard 2y ago
  >than some of the things that defend the term for decades There have been many attempts to pervert the term AI, which is a disservice to the technologies and the term itself. Its the simple fact that the business people are relying on what AI invokes in the public mindshare to boost their status and visibility. Thats what bothers me about its misuse so much
  - tsimionescu 2y ago
    Again, if you look at the early papers on AI, you'll see things that are even farther from human intelligence than the LLMs of today. There is no "perversion" of the term, it has always been a vague hypey concept. And it was introduced in this way by academia, not business.
  - pixl97 2y ago
    While it could possibly be to point out so abruptly, you seem to be the walking talking definition of the AI Effect. >The "AI effect" refers to the phenomenon where achievements in AI, once considered significant, are re-evaluated or redefined as commonplace once they become integrated into everyday technology, no longer seen as "true AI".
- Marazan 2y ago
  We had Markov Chains already. Fancy Markov Chains don't seem like a trillion dollar business or actual intelligence.
  - tsimionescu 2y ago
    Completely agree. But if Markov chains are AI (and they always were categorized as such), then fancy Markov chains are still AI.
marcosdumay 2y ago
It is AI. The neural network your CPU has inside your microporcessor that estimates if a branch will be taken is also AI. A pattern recognition program that takes a video and decides where you stop on the image and where the background starts is also AI. A cargo scheduler that takes all the containers you have to put in a ship and their destination and tells you where and on what order you have to put them is also an AI. A search engine that compares your query with the text on each page and tells you what is closer is also an AI. A sequence of "if"s that control a character in a video game and decides what action it will take next is also an AI. Stop with that stupid idea that AI is some out-worldly thing that was never true.
mjlee 2y ago
I’m pretty sure AI means whatever the newest thing in ML is. In a few years LLMs will be an ML technique and the new big thing will become AI.
perching_aix 2y ago
> This in a nutshell is why I hate that all this stuff is being labeled as AI. It's literally the name of the field. I don't understand why (some) people feel so compelled to act vain about it like this. Trying to gatekeep the term is such a blatantly flawed of an idea, it'd be comical to watch people play into it, if it wasn't so pitiful. It disappoints me that this cope has proliferated far enough that garbage like "AGI" is something you can actually come across in literature.
esolyt 2y ago
But we moved beyond LLMs? We have models that handle text, image, audio, and video all at once. We have models that can sense the tone of your voice and respond accordingly. Whether you define any of this as "intelligence" or not is just a linguistic choice. We're just rehashing "Can a submarine swim?"
arctek 2y ago
This is also why I think the current iterations wont converge on any actual type of intelligence. It doesn't operate on the same level as (human) intelligence it's a very path dependent process. Every step you add down this path increases entropy as well and while further improvements and bigger context windows help - eventually you reach a dead end where it degrades. You'd almost need every step of the process to mutate the model to update global state from that point. From what I've seen the major providers kind of use tricks to accomplish this, but it's not the same thing.
fnordpiglet 2y ago
This is a discussion of semantics. First I spent much of my career in high end quant finance and what we are doing today is night and day different in terms of the generality and effectiveness. Second, almost all the hallmarks of AI I carried with me prior to 2001 have more or less been ticked off - general natural language semantically aware parsing and human like responses, ability to process abstract concepts, reason abductively, synthesize complex concepts. The fact it’s not aware - which it’s absolutely is not - does not make it not -intelligent-. The thing people latch onto is modern LLM’s inability to reliably reason deductively or solve complex logical problems. However this isn’t a sign of human intelligence as these are learned not innate skills, and even the most “intelligent” humans struggle at being reliable at these skills. In fact classical AI techniques are often quite good at these things already and I don’t find improvements there world changing. What I find is unique about human intelligence is its abductive ability to reason in ambiguous spaces with error at times but with success at most others. This is something LLMs actually demonstrate with a remarkably human like intelligence. This is earth shattering and science fiction material. I find all the poopoo’ing and goal post shifting disheartening. What they don’t have is awareness. Awareness is something we don’t understand about ourselves. We have examined our intelligence for thousands of years and some philosophies like Buddhism scratch the surface of understanding awareness. I find it much less likely we can achieve AGI without understanding awareness and implementing some proximate model of it that guides the multi modal models and agents we are working on now.

johnecheck 2y ago

While I agree that LLMs are hardly sapient, it's very hard to make this argument without being able to pinpoint what a model of intelligence actually is. "Human brains lack any model of intelligence. It's just neurons firing in complicated patterns in response to inputs based on what statistically leads to reproductive success"

no_wizard 2y ago
That's not at all on par with what I'm saying. There exists a generally accepted baseline definition for what crosses the threshold of intelligent behavior. We shouldn't seek to muddy this. EDIT: Generally its accepted that a core trait of intelligence is an agent’s ability to achieve goals in a wide range of environments. This means you must be able to generalize, which in turn allows intelligent beings to react to new environments and contexts without previous experience or input. Nothing I'm aware of on the market can do this. LLMs are great at statistically inferring things, but they can't generalize which means they lack reasoning. They also lack the ability to seek new information without prompting. The fact that all LLMs boil down to (relatively) simple mathematics should be enough to prove the point as well. It lacks spontaneous reasoning, which is why the ability to generalize is key
- highfrequency 2y ago
  What is that baseline threshold for intelligence? Could you provide concrete and objective results, that if demonstrated by a computer system would satisfy your criteria for intelligence?
  - no_wizard 2y ago
    see the edit. boils down to the ability to generalize, LLMs can't generalize. I'm not the only one who holds this view either. Francois Chollet, a former intelligence researcher at Google also shares this view.
    - highfrequency 2y ago
      Are you able to formulate "generalization" in a concrete and objective way that could be achieved unambiguously, and is currently achieved by a typical human? A lot of people would say that LLMs generalize pretty well - they certainly can understand natural language sequences that are not present in their training data.
      - whilenot-dev 2y ago
        > A lot of people would say that LLMs generalize pretty well What do you mean here? The trained model, the inference engine, is the one that makes an LLM for "a lot of people". > they certainly can understand natural language sequences that are not present in their training data Keeping the trained model as LLM in mind, I think learning a language includes generalization and is typically achieved by a human, so I'll try to formulate: Can a trained LLM model learn languages that hasn't been in its training set just by chatting/prompting? Given that any Korean texts were excluded from the training set, could Korean be learned? Does that even work with languages descending from the same language family (Spanish in the training set but Italian should be learned)?
    - stevenAthompson 2y ago
      > Francois Chollet, a former intelligence researcher at Google also shares this view. Great, now there are two of you.
devmor 2y ago
I don't think your detraction has much merit. If I don't understand how a combustion engine works, I don't need that engineering knowledge to tell you that a bicycle [an LLM] isn't a car [a human brain] just because it fits the classification of a transportation vehicle [conversational interface]. This topic is incredibly fractured because there is too much monetary interest in redefining what "intelligence" means, so I don't think a technical comparison is even useful unless the conversation begins with an explicit definition of intelligence in relation to the claims.
- Velorivox 2y ago
  Bicycles and cars are too close. The analogy I like is human leg versus tire. That is a starker depiction of how silly it is to compare the two in terms of structure rather than result.
  - devmor 2y ago
    That is a much better comparison.
- uoaei 2y ago
  If you don't know anything except how words are used, you can definitely disambiguate "bicycle" and "car" solely based on the fact that the contexts they appear in are incongruent the vast majority of the time, and when they appear in the same context, they are explicitly contrasted against each other. This is just the "fancy statistics" argument again, and it serves to describe any similar example you can come up with better than "intelligence exists inside this black box because I'm vibing with the output".
  - devmor 2y ago
    Why are you attempting to technically analyze a simile? That is not why comparisons are used.
- SkyBelow 2y ago
  One problem is that we have been basing too much on [human brain] for so long that we ended up with some ethical problems as we decided other brains didn't count as intelligent. As such, science has taken an approach of not assuming humans are uniquely intelligence. We seem to be the best around at doing different tasks with tools, but other animals are not completely incapable of doing the same. So [human brain] should really be [brain]. But is that good enough? Is a fruit fly brain intelligent? Is it a goal to aim for? There is a second problem that we aren't looking for [human brain] or [brain], but [intelligence] or [sapient] or something similar. We aren't even sure what we want as many people have different ideas, and, as you pointed out, we have different people with different interest pushing for different underlying definitions of what these ideas even are. There is also a great deal of impreciseness in most any definitions we use, and AI encroaches on this in a way that reality rarely attacks our definitions. Philosophically, we aren't well prepared to defend against such attacks. If we had every ancestor of the cat before us, could we point out the first cat from the last non-cat in that lineup? In a precise way that we would all agree upon that isn't arbitrary? I doubt we could.
OtherShrezzing 2y ago
>While I agree that LLMs are hardly sapient, it's very hard to make this argument without being able to pinpoint what a model of intelligence actually is. Maybe so, but it's trivial to do the inverse, and pinpoint something that's not intelligent. I'm happy to state that an entity which has seen every game guide ever written, but still can't beat the first generation Pokemon is not intelligent. This isn't the ceiling for intelligence. But it's a reasonable floor.
- 7h3kk1d 2y ago
  There's sentient humans who can't beat the first generation pokemon games.
  - antasvara 2y ago
    Is there a sentient human that has access to (and actually uses) all of the Pokémon game guides yet is incapable of beating Pokémon? Because that's what an LLM is working with.
    - 7h3kk1d 2y ago
      I'm quite sure my grandma could not. You can make the argument these people aren't intelligent but I think that's a contrived argument.
whilenot-dev 2y ago
What's wrong with just calling them smart algorithmic models? Being smart allows somewhat to be wrong, as long as that leads to a satisfying solution. Being intelligent on the other hand requires foundational correctness in concepts that aren't even defined yet. EDIT: I also somewhat like the term imperative knowledge (models) [0] [0]: https://en.wikipedia.org/wiki/Procedural_knowledge https://en.wikipedia.org/wiki/Procedural_knowledge
- jfengel 2y ago
  The problem with "smart" is that they fail at things that dumb people succeed at. They have ludicrous levels of knowledge and a jaw dropping ability to connect pieces while missing what's right in front of them. The gap makes me uncomfortable with the implications of the word "smart". It is orthogonal to that.
  - sigmoid10 2y ago
    >they fail at things that dumb people succeed at Funnily enough, you can also observe that in humans. The number of times I have observed people from highly intellectual, high income/academic families struggle with simple tasks that even the dumbest people do with ease is staggering. If you're not trained for something and suddenly confronted with it for the first time, you will also in all likelihood fail. "Smart" is just as ill-defined as any other clumsy approach to define intelligence.
  - nradov 2y ago
    Bombs can be smart, even though they sometimes miss the target.
a_victorp 2y ago
> Human brains lack any model of intelligence. It's just neurons firing in complicated patterns in response to inputs based on what statistically leads to reproductive success The fact that you can reason about intelligence is a counter argument to this
- immibis 2y ago
  It seems like LLMs can also reason about intelligence. Does that make them intelligent? We don't know what intelligence is, or isn't.
  - syndeo 2y ago
    It's fascinating how this discussion about intelligence bumps up against the limits of text itself. We're here, reasoning and reflecting on what makes us capable of this conversation. Yet, the very structure of our arguments, the way we question definitions or assert self-awareness, mirrors patterns that LLMs are becoming increasingly adept at replicating. How confidently can we, reading these words onscreen, distinguish genuine introspection from a sophisticated echo? Case in point… I didn't write that paragraph by myself.
    - Nevermark 2y ago
      So you got help from a natural intelligence? No fair. (natdeo?) Someone needs to create a clone site of HN's format and posts, but the rules only permit synthetic intelligence comments. All models pre-prompted to read prolifically, but comment and up/down vote carefully and sparingly, to optimize the quality of discussion. And no looking at nat-HN comments. It would be very interesting to compare discussions between the sites. A human-lurker per day graph over time would also be of interest. Side thought: Has anyone created a Reverse-Captcha yet?
      - wyre 2y ago
        This is an entertaining idea. User prompts can synthesize a users domain knowledge whether they are an entrepreneur, code dev, engineer, hacker, designer, etc and it can also have different users between different LLMs. I think the site would clone the upvotes of articles and the ordering of the front page, and gives directions when to comment on other’s posts.
shinycode 2y ago
> "Human brains lack any model of intelligence. It's just neurons firing in complicated patterns in response to inputs based on what statistically leads to reproductive success" Are you sure about that ? Do we have proof of that ? In happened all the time trought history of science that a lot of scientists were convinced of something and a model of reality up until someone discovers a new proof and or propose a new coherent model. That’s literally the history of science, disprove what we thought was an established model
- johnecheck 2y ago
  Indeed, a good point. My comment assumes that our current model of the human brain is (sufficiently) complete. Your comment reveals an interesting corollary - those that believe in something beyond our understanding, like the Christian soul, may never be convinced that an AI is truly sapient.
andrepd 2y ago
Human brains do way more things than language. And non-human animals (with no language) also reason, and we cannot understand those either, barely even the very simplest ones.

voidspark 2y ago

You are confusing sentience or consciousness with intelligence.

no_wizard 2y ago
one fundamental attribute of intelligence is the ability to demonstrate reasoning in new and otherwise unknown situations. There is no system that I am currently aware of that works on data it is not trained on. Another is the fundamental inability to self update on outdated information. It is incapable of doing that, which means it lacks another marker, which is being able to respond to changes of context effectively. Ants can do this. LLMs can't.
- voidspark 2y ago
  But that's exactly what these deep neural networks have shown, countless times. LLM's generalize on new data outside of its training set. It's called "zero shot learning" where they can solve problems that are not in their training set. AlphaGo Zero is another example. AlphaGo Zero mastered Go from scratch, beating professional players with moves it was never trained on > Another is the fundamental inability to self update That's an engineering decision, not a fundamental limitation. They could engineer a solution for the model to initiate its own training sequence, if they decide to enable that.
  - dontlikeyoueith 2y ago
    This comment is such a confusion of ideas its comical.
    - voidspark 2y ago
      [flagged]
  - no_wizard 2y ago
    >AlphaGo Zero mastered Go from scratch, beating professional players with moves it was never trained on Thats all well and good, but it was tuned with enough parameters to learn via reinforcement learning[0]. I think The Register went further and got better clarification about how it worked[1] >During training, it sits on each side of the table: two instances of the same software face off against each other. A match starts with the game's black and white stones scattered on the board, placed following a random set of moves from their starting positions. The two computer players are given the list of moves that led to the positions of the stones on the grid, and then are each told to come up with multiple chains of next moves along with estimates of the probability they will win by following through each chain. While I also find it interesting that in both of these instances, its all referenced to as machine learning, not AI, its also important to see that even though what AlphaGo Zero did was quite awesome and a step forward in using compute for more complex tasks, it was still seeded the basics of information - the rules of Go - and simply patterned matched against itself until built up enough of a statistical model to determine the best moves to make in any given situation during a game. Which isn't the same thing as showing generalized reasoning. It could not, then, take this information and apply it to another situation. They did show the self reinforcement techniques worked well though, and used them for Chess and Shogi to great success as I recall, but thats a validation of the technique, not that it could generalize knowledge. >That's an engineering decision, not a fundamental limitation So you're saying that they can't reason about independently? [0]: https://deepmind.google/discover/blog/alphago-zero-starting-from-scratch/ https://deepmind.google/discover/blog/alphago-zero-starting-... [1]: https://www.theregister.com/2017/10/18/deepminds_latest_alphago_software_doesnt_need_human_data_to_win/ https://www.theregister.com/2017/10/18/deepminds_latest_alph...

bigmadshoe 2y ago

We don't have a complete enough theory of neuroscience to conclude that much of human "reasoning" is not "algorithmic pattern matching mixed with statistical likelihoods of success". Regardless of how it models intelligence, why is it not AI? Do you mean it is not AGI? A system that can take a piece of text as input and output a reasonable response is obviously exhibiting some form of intelligence, regardless of the internal workings.

no_wizard 2y ago
It’s easy to attribute intelligence these systems. They have a flexibility and unpredictability that hasn't typically been associated with computers, but it all rests on (relatively) simple mathematics. We know this is true. We also know that means it has limitations and can't actually reason information. The corpus of work is huge - and that allows the results to be pretty striking - but once you do hit a corner with any of this tech, it can't simply reason about the unknown. If its not in the training data - or the training data is outdated - it will not be able to course correct at all. Thus, it lacks reasoning capability, which is a fundamental attribute of any form of intelligence.
- justonenote 2y ago
  > it all rests on (relatively) simple mathematics. We know this is true. We also know that means it has limitations and can't actually reason information. What do you imagine is happening inside biological minds that enables reasoning that is something different to, a lot of, "simple mathematics"? You state that because it is built up of simple mathematics it cannot be reasoning, but this does not follow at all, unless you can posit some other mechanism that gives rise to intelligence and reasoning that is not able to be modelled mathematically.
  - no_wizard 2y ago
    Because whats inside our minds is more than mathematics, or we would be able to explain human behavior with the purity of mathematics, and so far, we can't. We can prove the behavior of LLMs with mathematics, because its foundations are constructed. That also means it has the same limits of anything else we use applied mathematics for. Is the broad market analysis that HFT firms use software for to make automated trades also intelligent?

tsimionescu 2y ago

One of the earliest things that defined what AI meant were algorithms like A*, and then rules engines like CLIPS. I would say LLMs are much closer to anything that we'd actually call intelligence, despite their limitations, than some of the things that defined* the term for decades. * fixed a typo, used to be "defend"

no_wizard 2y ago
>than some of the things that defend the term for decades There have been many attempts to pervert the term AI, which is a disservice to the technologies and the term itself. Its the simple fact that the business people are relying on what AI invokes in the public mindshare to boost their status and visibility. Thats what bothers me about its misuse so much
- tsimionescu 2y ago
  Again, if you look at the early papers on AI, you'll see things that are even farther from human intelligence than the LLMs of today. There is no "perversion" of the term, it has always been a vague hypey concept. And it was introduced in this way by academia, not business.
- pixl97 2y ago
  While it could possibly be to point out so abruptly, you seem to be the walking talking definition of the AI Effect. >The "AI effect" refers to the phenomenon where achievements in AI, once considered significant, are re-evaluated or redefined as commonplace once they become integrated into everyday technology, no longer seen as "true AI".
Marazan 2y ago
We had Markov Chains already. Fancy Markov Chains don't seem like a trillion dollar business or actual intelligence.
- tsimionescu 2y ago
  Completely agree. But if Markov chains are AI (and they always were categorized as such), then fancy Markov chains are still AI.

marcosdumay 2y ago

It is AI. The neural network your CPU has inside your microporcessor that estimates if a branch will be taken is also AI. A pattern recognition program that takes a video and decides where you stop on the image and where the background starts is also AI. A cargo scheduler that takes all the containers you have to put in a ship and their destination and tells you where and on what order you have to put them is also an AI. A search engine that compares your query with the text on each page and tells you what is closer is also an AI. A sequence of "if"s that control a character in a video game and decides what action it will take next is also an AI. Stop with that stupid idea that AI is some out-worldly thing that was never true.

mjlee 2y ago

I’m pretty sure AI means whatever the newest thing in ML is. In a few years LLMs will be an ML technique and the new big thing will become AI.

perching_aix 2y ago

> This in a nutshell is why I hate that all this stuff is being labeled as AI. It's literally the name of the field. I don't understand why (some) people feel so compelled to act vain about it like this. Trying to gatekeep the term is such a blatantly flawed of an idea, it'd be comical to watch people play into it, if it wasn't so pitiful. It disappoints me that this cope has proliferated far enough that garbage like "AGI" is something you can actually come across in literature.

esolyt 2y ago

But we moved beyond LLMs? We have models that handle text, image, audio, and video all at once. We have models that can sense the tone of your voice and respond accordingly. Whether you define any of this as "intelligence" or not is just a linguistic choice. We're just rehashing "Can a submarine swim?"

arctek 2y ago

This is also why I think the current iterations wont converge on any actual type of intelligence. It doesn't operate on the same level as (human) intelligence it's a very path dependent process. Every step you add down this path increases entropy as well and while further improvements and bigger context windows help - eventually you reach a dead end where it degrades. You'd almost need every step of the process to mutate the model to update global state from that point. From what I've seen the major providers kind of use tricks to accomplish this, but it's not the same thing.

fnordpiglet 2y ago

This is a discussion of semantics. First I spent much of my career in high end quant finance and what we are doing today is night and day different in terms of the generality and effectiveness. Second, almost all the hallmarks of AI I carried with me prior to 2001 have more or less been ticked off - general natural language semantically aware parsing and human like responses, ability to process abstract concepts, reason abductively, synthesize complex concepts. The fact it’s not aware - which it’s absolutely is not - does not make it not -intelligent-. The thing people latch onto is modern LLM’s inability to reliably reason deductively or solve complex logical problems. However this isn’t a sign of human intelligence as these are learned not innate skills, and even the most “intelligent” humans struggle at being reliable at these skills. In fact classical AI techniques are often quite good at these things already and I don’t find improvements there world changing. What I find is unique about human intelligence is its abductive ability to reason in ambiguous spaces with error at times but with success at most others. This is something LLMs actually demonstrate with a remarkably human like intelligence. This is earth shattering and science fiction material. I find all the poopoo’ing and goal post shifting disheartening. What they don’t have is awareness. Awareness is something we don’t understand about ourselves. We have examined our intelligence for thousands of years and some philosophies like Buddhism scratch the surface of understanding awareness. I find it much less likely we can achieve AGI without understanding awareness and implementing some proximate model of it that guides the multi modal models and agents we are working on now.