2 ms·

While I agree with your point, how would you test that? How could you determine whether an LLM “knows” what a cat is. And what is “knowing”? If I know that a M

While I agree with your point, how would you test that? How could you determine whether an LLM “knows” what a cat is. And what is “knowing”? If I know that a Mæw tends to nạ̀ng bn a S̄eụ̄̀x, isn’t that the first thing I’ve learned? And couldn’t I continue to learn other properties of Mæws? How many do I need to learn to “know” what a Mæw is?

pegasus 4y ago
Like GP said, the LLM has no chance at knowing what a cat is, regardless of how much data it ingests, because a cat is not made of data. It's not like you're getting closer and closer to knowing what a "Mæw" is. You were at the same remote distance all the time. This is called the "grounding problem" in AI. As for how you would test it, I think one-shot learning would get one closer to proving understanding.
- d1sxeyes 4y ago
  The grounding problem is an intelligence problem, not an artificial intelligence problem. How would you envision a test based on one-shot learning working?
  - pegasus 4y ago
    The question of grounding is a problem that arises in thinking about cognition in general, yes. In AI, it changes from a theoretical problem to a practical one, as this whole discussion proves. As for one-shot learning, what I was driving at, is that a truly intelligent system should not need to consume millions of documents in order to predict that, say, driving at night puts larger demands on one's vision than driving during the day. Or any other common sense fact. These systems require ingesting the whole frickin' internet in order to maybe kinda sometimes correctly answer some simple questions. Even for questions restricted to the narrow range where the system is indeed grounded: the world of symbols and grammar.
    - d1sxeyes 4y ago
      Why do you believe that a system should not need to consume millions of documents in order to be able to make predictions? For your example, the concepts of driving, night, vision, all need to be clearly understood, as well as how they relate to each other. The idea of 'common sense' is a good example of something which takes years to develop in humans, and develops to varying extents (although driving at night vs at day is one example, driving while drunk and driving while sober is a different one where humans routinely make poor decisions, or have incorrect beliefs). It's estimated that humans are exposed to around 11 million bits of information per second. Assuming humans do not process any data while they sleep (which is almost certainly false): newborns are awake for 8 hours per day, so they 'consume' around 40GB of data per day. This ramps up to around 60GB by the time they're 6 months old. That means that in the first month alone, a newborn has processed 1TB of input. By the age of six months, they're between 6 and 10TB, and they haven't even said their first word yet. Most babies have experienced more than 20TB of sensory input by the time they say their first word. Often, children are unable to reason even at a very basic level until they have been exposed to more than 100TB of sensory input. GPT-3, by contrast was trained on a corpus of around 570GB worth of text. We are simply orders of magnitude away from being able to make a meaningful comparison between GPT-3 and humans and determine conclusively that our 'intelligence' is of a different category to the 'intelligence' displayed by GPT-3.
      - pegasus 4y ago
        I was thinking in terms of simple logic and semantics. The example I picked though muddied the waters by bringing in real-world phenomena. A better test would be anything that stays strictly within the symbolic world - the true umwelt of the language model. So, anything mathematical. After seeing countless examples of addition and documents discussing addition and procedures of addition, many order of magnitude more than a child ever gets to see when learning to add, still LLMs cannot do it properly. That, to me, is conclusive.
        d1sxeyes 4y ago
        A child can 'see' maths though, they can see that if you have one apple over here and one orange over there, then you have two pieces of fruit all together. If you only ever allowed a child to read about adding, without ever being able to physically experiment with putting pieces together and counting them, likely children would not be able to add either. In fact, many teachers and schools teach children to add using blocks and physical manipulation of objects, not by giving countless examples and documents discussing addition and procedures of addition. You may feel it's conclusive, and it's your right to think that. I am not sure.
- CamperBob2 4y ago
  because a cat is not made of data. Your perception of what a cat is, however, is most certainly made of nothing but data, encoded as chemical relationships at the neuronal level. And your perception is all there is, as far as you're concerned. The cat is just another shadow on Plato's cave wall. Arguably you "know" something when you can recognize it outside its usual context, classify it in terms of its relationships with other objects, and anticipate its behavior. To the extent that's true, ML models have been there for quite a while now. What else besides recognition, classification, and prediction based on either experience or inference is needed for "knowledge?" Doesn't everything human minds can do boil down to pattern recognition and curve fitting at the end of the day?

pegasus 4y ago

Like GP said, the LLM has no chance at knowing what a cat is, regardless of how much data it ingests, because a cat is not made of data. It's not like you're getting closer and closer to knowing what a "Mæw" is. You were at the same remote distance all the time. This is called the "grounding problem" in AI. As for how you would test it, I think one-shot learning would get one closer to proving understanding.

d1sxeyes 4y ago
The grounding problem is an intelligence problem, not an artificial intelligence problem. How would you envision a test based on one-shot learning working?
- pegasus 4y ago
  The question of grounding is a problem that arises in thinking about cognition in general, yes. In AI, it changes from a theoretical problem to a practical one, as this whole discussion proves. As for one-shot learning, what I was driving at, is that a truly intelligent system should not need to consume millions of documents in order to predict that, say, driving at night puts larger demands on one's vision than driving during the day. Or any other common sense fact. These systems require ingesting the whole frickin' internet in order to maybe kinda sometimes correctly answer some simple questions. Even for questions restricted to the narrow range where the system is indeed grounded: the world of symbols and grammar.
  - d1sxeyes 4y ago
    Why do you believe that a system should not need to consume millions of documents in order to be able to make predictions? For your example, the concepts of driving, night, vision, all need to be clearly understood, as well as how they relate to each other. The idea of 'common sense' is a good example of something which takes years to develop in humans, and develops to varying extents (although driving at night vs at day is one example, driving while drunk and driving while sober is a different one where humans routinely make poor decisions, or have incorrect beliefs). It's estimated that humans are exposed to around 11 million bits of information per second. Assuming humans do not process any data while they sleep (which is almost certainly false): newborns are awake for 8 hours per day, so they 'consume' around 40GB of data per day. This ramps up to around 60GB by the time they're 6 months old. That means that in the first month alone, a newborn has processed 1TB of input. By the age of six months, they're between 6 and 10TB, and they haven't even said their first word yet. Most babies have experienced more than 20TB of sensory input by the time they say their first word. Often, children are unable to reason even at a very basic level until they have been exposed to more than 100TB of sensory input. GPT-3, by contrast was trained on a corpus of around 570GB worth of text. We are simply orders of magnitude away from being able to make a meaningful comparison between GPT-3 and humans and determine conclusively that our 'intelligence' is of a different category to the 'intelligence' displayed by GPT-3.
    - pegasus 4y ago
      I was thinking in terms of simple logic and semantics. The example I picked though muddied the waters by bringing in real-world phenomena. A better test would be anything that stays strictly within the symbolic world - the true umwelt of the language model. So, anything mathematical. After seeing countless examples of addition and documents discussing addition and procedures of addition, many order of magnitude more than a child ever gets to see when learning to add, still LLMs cannot do it properly. That, to me, is conclusive.
      - d1sxeyes 4y ago
        A child can 'see' maths though, they can see that if you have one apple over here and one orange over there, then you have two pieces of fruit all together. If you only ever allowed a child to read about adding, without ever being able to physically experiment with putting pieces together and counting them, likely children would not be able to add either. In fact, many teachers and schools teach children to add using blocks and physical manipulation of objects, not by giving countless examples and documents discussing addition and procedures of addition. You may feel it's conclusive, and it's your right to think that. I am not sure.
CamperBob2 4y ago
because a cat is not made of data. Your perception of what a cat is, however, is most certainly made of nothing but data, encoded as chemical relationships at the neuronal level. And your perception is all there is, as far as you're concerned. The cat is just another shadow on Plato's cave wall. Arguably you "know" something when you can recognize it outside its usual context, classify it in terms of its relationships with other objects, and anticipate its behavior. To the extent that's true, ML models have been there for quite a while now. What else besides recognition, classification, and prediction based on either experience or inference is needed for "knowledge?" Doesn't everything human minds can do boil down to pattern recognition and curve fitting at the end of the day?