4 ms·

I work at this company: https://en.m.wikipedia.org/wiki/Cyc https://en.m.wikipedia.org/wiki/Cyc It's a survivor of the AI winter, and what we have is basicall

by _bxg1 8y ago

I work at this company: https://en.m.wikipedia.org/wiki/Cyc https://en.m.wikipedia.org/wiki/Cyc It's a survivor of the AI winter, and what we have is basically a generalized expert system. Every conclusion incorporates cross-domain knowledge and can fully explain itself. It's been a long, slow trudge of research and development over the past couple decades, but we're starting to poke our heads out and ride the current AI hype wave. We like to say that Watson is our marketing department.

YeGoblynQueenne 8y ago
I know about Cyc, of course. Godspeed [and speed us away] :)
lawrenceyan 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.
tastroder 8y ago
> We like to say that Watson > is our marketing department. Did you forget a /s there? For me the marketing around Watson, with the unrealistic and not scientifically backed assumptions about the state of AI they put out to the general public, epitomize what's wrong with how the world sees our field. More on topic: the underlying problem with the original post here seems to be one of selection bias in the training data. Somewhat of a remnant of human decision making that, likely unintentionally, ended up in creating biased decisions in the end. While the system you linked seems to potentially create nicely decomposable decisions, is there anything that inherently prevents "bias" in what it learns? The approach seems to face many of the same problems modern ML systems in the Linked Data / Semantic Web space would given unrestricted learning from the web.
- YeGoblynQueenne 8y ago
  >> Did you forget a /s there? For me the marketing around Watson, with the unrealistic and not scientifically backed assumptions about the state of AI they put out to the general public, epitomize what's wrong with how the world sees our field. IBM's marketing Watson as a medical application is one thing. The system itself is quite another. And the system itself remains the most advanced NLP system created so far. Note that I say "system". Most NLP research consists of testing the performance of various algorithms against very specific benchmarks, but very little work goes towards creating a unified system that can integrate multiple NLP abilities. Watson on the other hand is exactly such a unified system. It integrates statistical machine learning, symbolic reasoning (frames, fer chrissake, frames! In this day and age!), pattern-matching (with Prolog) and so on and so forth. Far as I can tell there isn't anything like it anywhere - but of course, I don't know much about what Google, Facebook et al are doing internally.
  - tastroder 8y ago
    Sure, I specifically meant the marketing though. I kind of get why they do it but I'm not sold on the idea that it's helpful to present AI as an infallible silver bullet. In terms of systems I'm not sure why IBM would stand out, Microsoft, Amazon and Google all offer relatively coherent pipelines for the engineering side of NLP and all of those are backed up by accomplished research teams. I'm sure you can find everything from total horror stories to beautiful testimonials for all of these platforms.
0xBA5ED 8y ago
On an intuitive level, this actually seems like a more promising approach than "from scratch" learning systems if we want our AIs to "understand us".

by _bxg1 8y ago

YeGoblynQueenne 8y ago

I know about Cyc, of course. Godspeed [and speed us away] :)

lawrenceyan 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.

tastroder 8y ago

> We like to say that Watson > is our marketing department. Did you forget a /s there? For me the marketing around Watson, with the unrealistic and not scientifically backed assumptions about the state of AI they put out to the general public, epitomize what's wrong with how the world sees our field. More on topic: the underlying problem with the original post here seems to be one of selection bias in the training data. Somewhat of a remnant of human decision making that, likely unintentionally, ended up in creating biased decisions in the end. While the system you linked seems to potentially create nicely decomposable decisions, is there anything that inherently prevents "bias" in what it learns? The approach seems to face many of the same problems modern ML systems in the Linked Data / Semantic Web space would given unrestricted learning from the web.

YeGoblynQueenne 8y ago
>> Did you forget a /s there? For me the marketing around Watson, with the unrealistic and not scientifically backed assumptions about the state of AI they put out to the general public, epitomize what's wrong with how the world sees our field. IBM's marketing Watson as a medical application is one thing. The system itself is quite another. And the system itself remains the most advanced NLP system created so far. Note that I say "system". Most NLP research consists of testing the performance of various algorithms against very specific benchmarks, but very little work goes towards creating a unified system that can integrate multiple NLP abilities. Watson on the other hand is exactly such a unified system. It integrates statistical machine learning, symbolic reasoning (frames, fer chrissake, frames! In this day and age!), pattern-matching (with Prolog) and so on and so forth. Far as I can tell there isn't anything like it anywhere - but of course, I don't know much about what Google, Facebook et al are doing internally.
- tastroder 8y ago
  Sure, I specifically meant the marketing though. I kind of get why they do it but I'm not sold on the idea that it's helpful to present AI as an infallible silver bullet. In terms of systems I'm not sure why IBM would stand out, Microsoft, Amazon and Google all offer relatively coherent pipelines for the engineering side of NLP and all of those are backed up by accomplished research teams. I'm sure you can find everything from total horror stories to beautiful testimonials for all of these platforms.

0xBA5ED 8y ago

On an intuitive level, this actually seems like a more promising approach than "from scratch" learning systems if we want our AIs to "understand us".