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Think for yourself. (Edit because of downvotes: I was serious, this is a blue ocean idea. For example, imagine children with their own ontology of language and

Think for yourself. (Edit because of downvotes: I was serious, this is a blue ocean idea. For example, imagine children with their own ontology of language and experience (not culturally acquired). That's about as blue ocean as you can get. Neural networks are pretty close. Neural news at nine.)

nerdponx 9y ago
There are whole fields of anthropology, psychology, etc. debating how much of language and experience is learned versus innate. As for neural networks, generative models, genetic algorithms, etc., they can help us optimize on known search spaces but that's about all they can do. It's a big leap from designing simulation-optimized bicycle parts to generating truly new ideas.
- contingencies 9y ago
  No, the field has moved a lot further than that. Did you read the recent Software 2.0 rant by the Tesla AI guy? https://medium.com/@karpathy/software-2-0-a64152b37c35 https://medium.com/@karpathy/software-2-0-a64152b37c35 Other good sources are The Master Algorithm (ML) or The Brain That Changes Itself (neuroscience), the former surveying general capabilities and limitations of various approaches and the later surveying findings from medicine on just how plastic our sensory inputs and interpretive capacities are through recent research.
  - nerdponx 9y ago
    First of all, Karpathy is a heck of a lot more than the "Tesla AI guy". Second, he's describing a pretty radical implementation of what amounts to a new form of fuzzy constraint programming using exactly the technique I described: brute-force optimization on a very sophisticated objective function. It's fundamentally statistical, and it's still fundamentally an optimization problem. Neural networks happen to be modular compared to other optimization techniques, and they happen to do a miraculous job at capturing latent higher-order structure in these optimization problems. The real innovation he's describing is that we now have powerful enough optimizers that it's cheaper, easier, faster, and sometimes more reliable to just fuzzy-optimize problems instead of coding bespoke solutions for them. That's pretty amazing, but suggests absolutely nothing. Now, a self-modifying neural network might be interesting, that can monitor its own performance and decide when to retrain part of itself, mutate its own architecture, or request new training data. But we definitely are not there yet. With all that said, there's a much more down-to-earth version of your idea that neural networks can have their own ontology and experiences: that's just an anthropomorphization the latent structure that they capture. This isn't unique to NNs -- it's something researchers have been exploiting for decades. It's only a small stretch to argue that that's what fitting a decision tree or a principal components model is. This is reflected directly in the jargon that these models "learn" a representation of the data. You can extract new representations of reality right now by running HDBSCAN on a data set of your choice. Neural networks just let us scale that up to richer, higher-dimensional problems. Maybe that's all "experience" really is, consuming data from the world and encoding various representations of that data. But when it comes to generating truly novel ideas, instead of incremental improvements on existing ideas, I'm bearish on how far our current neural network technology can go. Maybe AlphaGo, which apparently invented new Go strategies that no human player had ever thought of, is a counterexample. Or it's the exception that proves the rule. I guess we'll see as computation power continues to improve.
    - contingencies 9y ago
      anthropomorphization the latent structure that they capture ... maybe that's all "experience" really is Precisely my point.

nerdponx 9y ago

There are whole fields of anthropology, psychology, etc. debating how much of language and experience is learned versus innate. As for neural networks, generative models, genetic algorithms, etc., they can help us optimize on known search spaces but that's about all they can do. It's a big leap from designing simulation-optimized bicycle parts to generating truly new ideas.

contingencies 9y ago
No, the field has moved a lot further than that. Did you read the recent Software 2.0 rant by the Tesla AI guy? https://medium.com/@karpathy/software-2-0-a64152b37c35 https://medium.com/@karpathy/software-2-0-a64152b37c35 Other good sources are The Master Algorithm (ML) or The Brain That Changes Itself (neuroscience), the former surveying general capabilities and limitations of various approaches and the later surveying findings from medicine on just how plastic our sensory inputs and interpretive capacities are through recent research.
- nerdponx 9y ago
  First of all, Karpathy is a heck of a lot more than the "Tesla AI guy". Second, he's describing a pretty radical implementation of what amounts to a new form of fuzzy constraint programming using exactly the technique I described: brute-force optimization on a very sophisticated objective function. It's fundamentally statistical, and it's still fundamentally an optimization problem. Neural networks happen to be modular compared to other optimization techniques, and they happen to do a miraculous job at capturing latent higher-order structure in these optimization problems. The real innovation he's describing is that we now have powerful enough optimizers that it's cheaper, easier, faster, and sometimes more reliable to just fuzzy-optimize problems instead of coding bespoke solutions for them. That's pretty amazing, but suggests absolutely nothing. Now, a self-modifying neural network might be interesting, that can monitor its own performance and decide when to retrain part of itself, mutate its own architecture, or request new training data. But we definitely are not there yet. With all that said, there's a much more down-to-earth version of your idea that neural networks can have their own ontology and experiences: that's just an anthropomorphization the latent structure that they capture. This isn't unique to NNs -- it's something researchers have been exploiting for decades. It's only a small stretch to argue that that's what fitting a decision tree or a principal components model is. This is reflected directly in the jargon that these models "learn" a representation of the data. You can extract new representations of reality right now by running HDBSCAN on a data set of your choice. Neural networks just let us scale that up to richer, higher-dimensional problems. Maybe that's all "experience" really is, consuming data from the world and encoding various representations of that data. But when it comes to generating truly novel ideas, instead of incremental improvements on existing ideas, I'm bearish on how far our current neural network technology can go. Maybe AlphaGo, which apparently invented new Go strategies that no human player had ever thought of, is a counterexample. Or it's the exception that proves the rule. I guess we'll see as computation power continues to improve.
  - contingencies 9y ago
    anthropomorphization the latent structure that they capture ... maybe that's all "experience" really is Precisely my point.