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>> “Your understanding of objects changes a lot when you have to manipulate them,” Hinton said. (LeCun has described this as being “easier said than done”.) Le

>> “Your understanding of objects changes a lot when you have to manipulate them,” Hinton said. (LeCun has described this as being “easier said than done”.) LeCun is right. Take a robot and put an untrained neural net in its brain, then send it out into the world and wait to see what it learned. Do you think it will learn anything? It won't- because it will observe most events a single time. And neural nets don't learn that way. They must be trained, painstakingly, at great length, cost and effort, only "in the lab", on vast amounts of data and compute. And once they're trained, they're trained. They cannot learn anymore. They cannot change their model. Unless they are retrained. From scratch. Painstakingly, at great cost, on vast amounts of data and compute. Again. And again. And again. Contrast that with the intelligence of -oh, I don't know, a squirrel? A squirrel is born with a tiny brain, smaller than the nuts it will subsist on throughout its life. When it's born, it has never seen a person, never seen a crow, never seen a cat. When it dies, it has often not only seen, but learned how to survive all of that. A squirrel. Even a cockroach can learn more than it is born with. The smartest neural nets today are no smarter than a simple beetle. I have no idea how Hinton has managed to convince himself that a machine that is incapable of learning once trained will take over the world. A machine that is less capable intellectually than a roach, or a squirrel. That just makes no sense.

TeMPOraL 3y ago
Tell me you've never heard of text embeddings without telling me you've never heard of text embeddings. The neural net you describe lacks memory. Memory, fundamentally, is a box with inputs and outputs. If you add it to your robot, and train the NN to use it, the robot may just be able to learn enough to be useful. This setup is of course limited by NN's capability to use that memory I/O to alter its outputs in sensible and useful ways. Until recently, I don't think anyone would fault you for believing this capability is extremely limited, as NN's can't do anything that even begins to resemble reasoning. But this was then, and now things are different - as clearly demonstrated by GPT-4. I mention text embeddings not because they're particularly optimal for the learning robot, but because they're a direct demonstration that it's now trivial to marry a sophisticated, (quasi-)reasoning-capable NN with a form of generic memory that can efficiently store anything the NN can work with, and which that NN can effectively search for the things it needs. You can verify the effectiveness of this on your own, for cheap, with GPT-4 and a vector database of your choice. At this point, a prototype of a learning robot seems possible to implement by a combination of some image2text model, converting camera feeds to a description of what the robot "sees", some code (not even ML model, just regular code) to convert other sensory inputs into a higher-level textual description, an embedding model and a vector database to remember all that, GPT-4 for filtering, combining and summarizing inputs to be remembered, evaluating current situations, generating plans, perhaps even control outputs - all chained together by small amount of glue code (which GPT-4 will likely be able to write for you). Will it work? There is no obvious reason why it shouldn't. We'll know soon enough - I expect a paper describing this to show up within next 6 months, if it hadn't already. It's too easy an experiment to make. Will it work well? Probably not at first. And it'll definitely be slow and expensive to run. But those are problems we know can be solved by throwing money at engineers and entrepreneurs.
- YeGoblynQueenne 3y ago
  >> Tell me you've never heard of text embeddings without telling me you've never heard of text embeddings. Just to clarify: are you trying to tell me I don't understand what word embeddings are? I want to be very clear about this before I formulate an answer, because if I'm right, the answer is going to be one of my [dead] comments with great certainty.
  - TeMPOraL 3y ago
    The way I see it, you either don't realize we already know how to give NNs useful forms of memory, or you're insisting that NNs are not good enough unless they continuously train themselves while interacting with the world. In the latter case, I'm trying to tell you that this is not a real problem, for roughly the same reasons that our inability to build self-replicating nanotech doesn't prevent us from building machines, including robots autonomously navigating the physical world.
    - deleted 3y ago
      [deleted]
    - YeGoblynQueenne 3y ago
      I see, so you've thought very hard about all the things I could be thinking of (all two of them!) and by sheer logical deduction you have arrived at the inescapable conclusion that I don't know what word embeddings are. Unfortunately HN does not allow me to ignore users so I don't have to see their comments, so I'm just gonna stick this conversation in a little bookmark and when I see your username and it reminds me of something, I'll check that bookmark to make sure, and ignore what you say. Wasted plenty of time as it is.
ChatGTP 3y ago
Six months later, LeCun leaves meta over fears his research will be used in dangerous ways by the military…
neerd 3y ago
Except in the case of a squirrel or cockroach, what you are seeing is the result of millions of years of “pre-training” by the evolutionary process.
- YeGoblynQueenne 3y ago
  This is yet another absurd statement in a long litany of absurd statements about this subject that I see constantly on HN. And it is asburd because there is no such thing as "pre-training" is nature, much less anything done by evolution. Pre-training is very specifically a technique used on artificial neural nets, and then only specific kinds thereof. Even in a metaphorical sense, an analogy cannot be drawn, between training an artificial neural net on many billions of instances of text, created by humans, carefully tokenised, or images, on the one hand; and, on the other hand, the evolutionary forces that operate in the real world, where there are no boundaries between the different sensory stimuli that animal nervous systems must learn to distinguish and manipulate. What you're proposing is nothing but a "just-so" story. "How the squirrel got his brains and how the cockroach lost hers". By E. V. Olution.
  - TeMPOraL 3y ago
    > the evolutionary forces that operate in the real world, where there are no boundaries between the different sensory stimuli that animal nervous systems must learn to distinguish and manipulate. Yup, and evolution spent millions of years to properly figure that out. And "no boundaries between the different sensory stimuli" doesn't even look like a hard problem. If anything, the tricky bit was to identify what's useful to sense, and to make the hardware for it - which is something evolution has been figuring out since the first replicator, and it identified half of the useful senses even before multicellular life became a thing. At this point, the boundaries are already established - you have different types of sensors producing correlated, but still quite distinct signals. It's not that hard to keep those feeds separated.
  - deleted 3y ago
    [deleted]
  - hackinthebochs 3y ago
ALittleLight 3y ago
Nothing says that these models will be part of AI systems that are incapable of learning once trained. For a simple example take GPT-4 powering a chat bot that uses GPT-4 as a component of the whole system. The text from the user, along with the context window, is used first to bring up "memories" based on semantic similarity to items stored in a vector database. GPT-4 then synthesizes the memories and the prompt into some kind of derivative prompt - putting into context the things it already knows and with the things the user is saying to it. GPT-4 then proposes a response to the user and also produces some predictions about the user's subsequent reply. When the subsequent reply comes in GPT-4 compares predicted to actual and produces insights based on the interaction that are subsequently stored in the vector database. This system is completely capable of learning - not by adjusting model weights, but by using the model as a component of a system that can learn. And, of course, nothing stops the model from actually adjusting its weights either - reinforcement learning and fine tuning - perhaps with the benefit of other expert systems that can evaluate exchanges after they've happened.
- YeGoblynQueenne 3y ago
  Sorry but none of that is realistic. It's all very creative, for sure, but why don't you try and do it yourself and see what happens, instead of just using your imagination? Go and try implementing that vector-database learning system you are imagining and see for yourself how close or far it is from reality. All you need is ChatGPT and a vector database, yes?
  - ALittleLight 3y ago
    It's all extremely realistic and I have working prototypes of this already. You can literally do all of this with the OpenAI API and a vector database. (My prototype uses a nearest neighbor descent graph instead - but basically the same thing). Split the text into paragraphs, vector encode the paragraphs, store with the vector as a key. On a new prompt encode paragraphs of the input and find nearest neighbors to the input. Add them to the derivative prompt. If you think it's not realistic - let's make a bet. I'll post a GitHub repo with this working as I described it within 24 hours of acknowledging you accepted the bet (i.e. you say "okay, bet" and I say "okay"). I'll include a video walking through how to run the repo and showing how it works. A thousand dollars?
ilaksh 3y ago
The main thing missing in all of this discussion is a realization that there is more than one dimension to intelligence. The second thing people are missing is that AI doesn't have to score high in all types of cognitive capabilities in order to take control. It doesn't need to have all of the animal abilities or feelings or to be alive. All that is necessary for AI to take control of something is very high performance reasoning ability, such as approximate human level at say 100 times human speed, and someone to give that AI or groups of AIs the instruction to try to take control. Humans will not be able to act effectively against those AIs due to the extreme performance gap. The only option will be to deploy more hyperspeed/superintelligent AIs. At that point humanity has basically lost control even if the winning AIs are yours. The next step is just someone programming such an AI to start ignoring further instructions. It doesn't have to be alive, it just has to have a tendency to self-direct for it's own goals built-in. Last thing I will mention again is that the history of computing shows exponential efficiency improvements and routine invention of new paradigms to pass performance blockers. This is a very specific application. We have already seen at least one order of magnitude efficiency increases both from hardware and software improvements. It will be better than humans at reasoning and dozens if not hundreds of times faster output within a few years. Certainly less than ten years.
- YeGoblynQueenne 3y ago
  These are all fantasies unmoored from reality. We already have algorithms that can perform reasoning better than humans, and faster than humans because they run on computers. That is the result of the work on the Good, Old-Fashioned Artificial Intelligence that, according to the legend recounted by the article, our lord Hinton killed off, like St. George killed the dragon, when he invented back-propagation (to clarify: Hinton did; not st. George, or the dragon). For example we have algorithms for classical planning SAT-solving, constraint satisfaction and automated theorem proving, that far surpass any ability of human beings to perform the same tasks. Note that those are unquestionably and uncontroversially reasoning tasks and the systems that excel at them are also uncontroversially reasoning systems, much unlike LLMs for which the definition of "reasoning" has to be dilated so far that it loses all meaning. And yet, we have not been taken over by superintelligent AGI. And we will not be. We have no idea how to realise the fantasy of self-improving super-machine gods that Hinton and Bengio wish their systems to be capable of producingg. No. Idea.

TeMPOraL 3y ago

Tell me you've never heard of text embeddings without telling me you've never heard of text embeddings. The neural net you describe lacks memory. Memory, fundamentally, is a box with inputs and outputs. If you add it to your robot, and train the NN to use it, the robot may just be able to learn enough to be useful. This setup is of course limited by NN's capability to use that memory I/O to alter its outputs in sensible and useful ways. Until recently, I don't think anyone would fault you for believing this capability is extremely limited, as NN's can't do anything that even begins to resemble reasoning. But this was then, and now things are different - as clearly demonstrated by GPT-4. I mention text embeddings not because they're particularly optimal for the learning robot, but because they're a direct demonstration that it's now trivial to marry a sophisticated, (quasi-)reasoning-capable NN with a form of generic memory that can efficiently store anything the NN can work with, and which that NN can effectively search for the things it needs. You can verify the effectiveness of this on your own, for cheap, with GPT-4 and a vector database of your choice. At this point, a prototype of a learning robot seems possible to implement by a combination of some image2text model, converting camera feeds to a description of what the robot "sees", some code (not even ML model, just regular code) to convert other sensory inputs into a higher-level textual description, an embedding model and a vector database to remember all that, GPT-4 for filtering, combining and summarizing inputs to be remembered, evaluating current situations, generating plans, perhaps even control outputs - all chained together by small amount of glue code (which GPT-4 will likely be able to write for you). Will it work? There is no obvious reason why it shouldn't. We'll know soon enough - I expect a paper describing this to show up within next 6 months, if it hadn't already. It's too easy an experiment to make. Will it work well? Probably not at first. And it'll definitely be slow and expensive to run. But those are problems we know can be solved by throwing money at engineers and entrepreneurs.

YeGoblynQueenne 3y ago
>> Tell me you've never heard of text embeddings without telling me you've never heard of text embeddings. Just to clarify: are you trying to tell me I don't understand what word embeddings are? I want to be very clear about this before I formulate an answer, because if I'm right, the answer is going to be one of my [dead] comments with great certainty.
- TeMPOraL 3y ago
  The way I see it, you either don't realize we already know how to give NNs useful forms of memory, or you're insisting that NNs are not good enough unless they continuously train themselves while interacting with the world. In the latter case, I'm trying to tell you that this is not a real problem, for roughly the same reasons that our inability to build self-replicating nanotech doesn't prevent us from building machines, including robots autonomously navigating the physical world.
  - deleted 3y ago
    [deleted]
  - YeGoblynQueenne 3y ago
    I see, so you've thought very hard about all the things I could be thinking of (all two of them!) and by sheer logical deduction you have arrived at the inescapable conclusion that I don't know what word embeddings are. Unfortunately HN does not allow me to ignore users so I don't have to see their comments, so I'm just gonna stick this conversation in a little bookmark and when I see your username and it reminds me of something, I'll check that bookmark to make sure, and ignore what you say. Wasted plenty of time as it is.

ChatGTP 3y ago

Six months later, LeCun leaves meta over fears his research will be used in dangerous ways by the military…

neerd 3y ago

Except in the case of a squirrel or cockroach, what you are seeing is the result of millions of years of “pre-training” by the evolutionary process.

YeGoblynQueenne 3y ago
This is yet another absurd statement in a long litany of absurd statements about this subject that I see constantly on HN. And it is asburd because there is no such thing as "pre-training" is nature, much less anything done by evolution. Pre-training is very specifically a technique used on artificial neural nets, and then only specific kinds thereof. Even in a metaphorical sense, an analogy cannot be drawn, between training an artificial neural net on many billions of instances of text, created by humans, carefully tokenised, or images, on the one hand; and, on the other hand, the evolutionary forces that operate in the real world, where there are no boundaries between the different sensory stimuli that animal nervous systems must learn to distinguish and manipulate. What you're proposing is nothing but a "just-so" story. "How the squirrel got his brains and how the cockroach lost hers". By E. V. Olution.
- TeMPOraL 3y ago
  > the evolutionary forces that operate in the real world, where there are no boundaries between the different sensory stimuli that animal nervous systems must learn to distinguish and manipulate. Yup, and evolution spent millions of years to properly figure that out. And "no boundaries between the different sensory stimuli" doesn't even look like a hard problem. If anything, the tricky bit was to identify what's useful to sense, and to make the hardware for it - which is something evolution has been figuring out since the first replicator, and it identified half of the useful senses even before multicellular life became a thing. At this point, the boundaries are already established - you have different types of sensors producing correlated, but still quite distinct signals. It's not that hard to keep those feeds separated.
- deleted 3y ago
  [deleted]
- hackinthebochs 3y ago

ALittleLight 3y ago

Nothing says that these models will be part of AI systems that are incapable of learning once trained. For a simple example take GPT-4 powering a chat bot that uses GPT-4 as a component of the whole system. The text from the user, along with the context window, is used first to bring up "memories" based on semantic similarity to items stored in a vector database. GPT-4 then synthesizes the memories and the prompt into some kind of derivative prompt - putting into context the things it already knows and with the things the user is saying to it. GPT-4 then proposes a response to the user and also produces some predictions about the user's subsequent reply. When the subsequent reply comes in GPT-4 compares predicted to actual and produces insights based on the interaction that are subsequently stored in the vector database. This system is completely capable of learning - not by adjusting model weights, but by using the model as a component of a system that can learn. And, of course, nothing stops the model from actually adjusting its weights either - reinforcement learning and fine tuning - perhaps with the benefit of other expert systems that can evaluate exchanges after they've happened.

YeGoblynQueenne 3y ago
Sorry but none of that is realistic. It's all very creative, for sure, but why don't you try and do it yourself and see what happens, instead of just using your imagination? Go and try implementing that vector-database learning system you are imagining and see for yourself how close or far it is from reality. All you need is ChatGPT and a vector database, yes?
- ALittleLight 3y ago
  It's all extremely realistic and I have working prototypes of this already. You can literally do all of this with the OpenAI API and a vector database. (My prototype uses a nearest neighbor descent graph instead - but basically the same thing). Split the text into paragraphs, vector encode the paragraphs, store with the vector as a key. On a new prompt encode paragraphs of the input and find nearest neighbors to the input. Add them to the derivative prompt. If you think it's not realistic - let's make a bet. I'll post a GitHub repo with this working as I described it within 24 hours of acknowledging you accepted the bet (i.e. you say "okay, bet" and I say "okay"). I'll include a video walking through how to run the repo and showing how it works. A thousand dollars?

ilaksh 3y ago

The main thing missing in all of this discussion is a realization that there is more than one dimension to intelligence. The second thing people are missing is that AI doesn't have to score high in all types of cognitive capabilities in order to take control. It doesn't need to have all of the animal abilities or feelings or to be alive. All that is necessary for AI to take control of something is very high performance reasoning ability, such as approximate human level at say 100 times human speed, and someone to give that AI or groups of AIs the instruction to try to take control. Humans will not be able to act effectively against those AIs due to the extreme performance gap. The only option will be to deploy more hyperspeed/superintelligent AIs. At that point humanity has basically lost control even if the winning AIs are yours. The next step is just someone programming such an AI to start ignoring further instructions. It doesn't have to be alive, it just has to have a tendency to self-direct for it's own goals built-in. Last thing I will mention again is that the history of computing shows exponential efficiency improvements and routine invention of new paradigms to pass performance blockers. This is a very specific application. We have already seen at least one order of magnitude efficiency increases both from hardware and software improvements. It will be better than humans at reasoning and dozens if not hundreds of times faster output within a few years. Certainly less than ten years.

YeGoblynQueenne 3y ago
These are all fantasies unmoored from reality. We already have algorithms that can perform reasoning better than humans, and faster than humans because they run on computers. That is the result of the work on the Good, Old-Fashioned Artificial Intelligence that, according to the legend recounted by the article, our lord Hinton killed off, like St. George killed the dragon, when he invented back-propagation (to clarify: Hinton did; not st. George, or the dragon). For example we have algorithms for classical planning SAT-solving, constraint satisfaction and automated theorem proving, that far surpass any ability of human beings to perform the same tasks. Note that those are unquestionably and uncontroversially reasoning tasks and the systems that excel at them are also uncontroversially reasoning systems, much unlike LLMs for which the definition of "reasoning" has to be dilated so far that it loses all meaning. And yet, we have not been taken over by superintelligent AGI. And we will not be. We have no idea how to realise the fantasy of self-improving super-machine gods that Hinton and Bengio wish their systems to be capable of producingg. No. Idea.