3 ms·

This seems like a fundamentally flawed and ultimately doomed approach to creating general agents. It's like you're trying to build a house, except only one mole

This seems like a fundamentally flawed and ultimately doomed approach to creating general agents. It's like you're trying to build a house, except only one molecule at a time.

_bxg1 8y ago
The intent is to seed it by hand with enough knowledge that it will be able to do true NLU and learn new knowledge itself from the web. It's already shown some promise against these sorts of problems, which ML really struggles with: https://en.m.wikipedia.org/wiki/Winograd_Schema_Challenge https://en.m.wikipedia.org/wiki/Winograd_Schema_Challenge
- nl 8y ago
  OpenAI's GPT-2 language model (build using a deep neural network) recently achieved 70.7% accuracy on the Winograd Schema Challenge, which is 8% better than the previous record[1]. I've never used Cyc but I have used OpenCyc and I'm familiar with some of the applications of Cyc. It's interesting when it works. [1] https://openai.com/blog/better-language-models/ https://openai.com/blog/better-language-models/
- lawrenceyan 8y ago
  That doesn’t really make any sense though. Maybe I’m not fully understanding what you’re saying here? Can you clarify how exactly you would turn a connected graph of data points into something that can learn arbitrary new knowledge and assimilate it into its understanding? Because it seems like currently, this process is an entirely human driven manually done task wherein you have people making the decisions on how to connect and add new relations/knowledge to each other.
  - YeGoblynQueenne 8y ago
    What you suggest, using previously acquired knowledge to learn new concepts, is par for the course in my field of study, Inductive Logic Programming. ILP is a set of algorithms and techniquest that learn logic programs from examples and background knowledge, where the examples and BK are themselves logic programs. Initially the BK comes from some existing source- it can be a hand-crafted database of a few predicates deemed relevant to the learning task or a large, automatically-acquired database mined from some text source, data from the CYC Project of course, etc. In any case, because of the unified representation, learned hypotheses (the "models") can be used immediately as background knowledge to learn new concepts. Edit: I don't know if the Cyc project uses ILP. But what the comment above says is doable.
  - brundolf 8y ago
    In our current NLU experiments we use an off-the-shelf open source parser for the initial parse (Parsey McParseface, from Google). We then take the grammatically-tagged words and our system maps them to concepts it knows about. It theorizes about multiple possible interpretations of the sentence (pronouns in particular), judging their likelihood using real-world knowledge. A Winograd Schema can be solved because Cyc knows things about the concepts; it knows that a dog has four legs, and what it typically weighs, and that it's a mammal (and shares further traits of mammals), and knows that it couldn't interbreed with a cat because they're different species and the act of procreation requires two organisms of the same species. So far this information has been hand-encoded, but this has been going on for decades, and we have a lot now. Going past that: 1) We're able to map outside data from a DB into Cyc's knowledge format, rather than hand-encoding it. This knowledge is inherently not as rich as the rest, but it can obviously be useful anyway. 2) At some point we hope to reach a critical mass of knowledge that will allow Cyc to simply "import" a Wikipedia page by parsing and understanding it. It will interpret a given sentence into its own understanding, then assert it as true and do reasoning based on it down the line.
    - lawrenceyan 8y ago
      An expert system will never be able to process or understand new things unless given explicit solutions. So you’re basically saying that it is possible to enumerate and solve for everything possible by manually iterating through every single possible edge case that exists. There’s a reason why Cyc has spent over 30 years and only has been able to get this far. You’re fundamentally limited by human constraints. The only realistic way of achieving a general purpose learning system is by teaching a system how to learn and then letting it figure things out on its own. Patently some method involving reinforcement learning. By the way, it’s not like RL is some newfangled thing. Much of it started during the 80’s, as it concurrently developed with the other purported method of developing intelligence, which was through expert systems. If you're interested, I highly recommend checking out a lecture that Demis Hassabis gives talking exactly about this issue: https://www.youtube.com/watch?v=3N9phq_yZP0 https://www.youtube.com/watch?v=3N9phq_yZP0
      - YeGoblynQueenne 8y ago

This seems like a fundamentally flawed and ultimately doomed approach to creating general agents. It's like you're trying to build a house, except only one molecule at a time.

_bxg1 8y ago

The intent is to seed it by hand with enough knowledge that it will be able to do true NLU and learn new knowledge itself from the web. It's already shown some promise against these sorts of problems, which ML really struggles with: https://en.m.wikipedia.org/wiki/Winograd_Schema_Challenge https://en.m.wikipedia.org/wiki/Winograd_Schema_Challenge

nl 8y ago
OpenAI's GPT-2 language model (build using a deep neural network) recently achieved 70.7% accuracy on the Winograd Schema Challenge, which is 8% better than the previous record[1]. I've never used Cyc but I have used OpenCyc and I'm familiar with some of the applications of Cyc. It's interesting when it works. [1] https://openai.com/blog/better-language-models/ https://openai.com/blog/better-language-models/
lawrenceyan 8y ago
That doesn’t really make any sense though. Maybe I’m not fully understanding what you’re saying here? Can you clarify how exactly you would turn a connected graph of data points into something that can learn arbitrary new knowledge and assimilate it into its understanding? Because it seems like currently, this process is an entirely human driven manually done task wherein you have people making the decisions on how to connect and add new relations/knowledge to each other.
- YeGoblynQueenne 8y ago
  What you suggest, using previously acquired knowledge to learn new concepts, is par for the course in my field of study, Inductive Logic Programming. ILP is a set of algorithms and techniquest that learn logic programs from examples and background knowledge, where the examples and BK are themselves logic programs. Initially the BK comes from some existing source- it can be a hand-crafted database of a few predicates deemed relevant to the learning task or a large, automatically-acquired database mined from some text source, data from the CYC Project of course, etc. In any case, because of the unified representation, learned hypotheses (the "models") can be used immediately as background knowledge to learn new concepts. Edit: I don't know if the Cyc project uses ILP. But what the comment above says is doable.
- brundolf 8y ago
  In our current NLU experiments we use an off-the-shelf open source parser for the initial parse (Parsey McParseface, from Google). We then take the grammatically-tagged words and our system maps them to concepts it knows about. It theorizes about multiple possible interpretations of the sentence (pronouns in particular), judging their likelihood using real-world knowledge. A Winograd Schema can be solved because Cyc knows things about the concepts; it knows that a dog has four legs, and what it typically weighs, and that it's a mammal (and shares further traits of mammals), and knows that it couldn't interbreed with a cat because they're different species and the act of procreation requires two organisms of the same species. So far this information has been hand-encoded, but this has been going on for decades, and we have a lot now. Going past that: 1) We're able to map outside data from a DB into Cyc's knowledge format, rather than hand-encoding it. This knowledge is inherently not as rich as the rest, but it can obviously be useful anyway. 2) At some point we hope to reach a critical mass of knowledge that will allow Cyc to simply "import" a Wikipedia page by parsing and understanding it. It will interpret a given sentence into its own understanding, then assert it as true and do reasoning based on it down the line.
  - lawrenceyan 8y ago
    An expert system will never be able to process or understand new things unless given explicit solutions. So you’re basically saying that it is possible to enumerate and solve for everything possible by manually iterating through every single possible edge case that exists. There’s a reason why Cyc has spent over 30 years and only has been able to get this far. You’re fundamentally limited by human constraints. The only realistic way of achieving a general purpose learning system is by teaching a system how to learn and then letting it figure things out on its own. Patently some method involving reinforcement learning. By the way, it’s not like RL is some newfangled thing. Much of it started during the 80’s, as it concurrently developed with the other purported method of developing intelligence, which was through expert systems. If you're interested, I highly recommend checking out a lecture that Demis Hassabis gives talking exactly about this issue: https://www.youtube.com/watch?v=3N9phq_yZP0 https://www.youtube.com/watch?v=3N9phq_yZP0
    - YeGoblynQueenne 8y ago