4 ms·

The author of this post (Gary Marcus) is a huge proponent of hybrid systems, in fact he is using that technique for his current stealth startup: https://www.tec

The author of this post (Gary Marcus) is a huge proponent of hybrid systems, in fact he is using that technique for his current stealth startup: https://www.technologyreview.com/s/544606/can-this-man-make-aimore-human https://www.technologyreview.com/s/544606/can-this-man-make-...

nl 11y ago
Yep. Not to be harsh, but Marcus has been critical of Neural Nets for a while now. His claims that there are issues around the provability of them are well made. But.. there is a way to make people listen to you. It's called results. Deep Learning is getting them, in an increasing number of diverse fields.
- rspeer 11y ago
  Results are important, but don't worship short-term results at the expense of everything else. Deep learning (and other machine learning techniques that are forced to call themselves deep learning to get attention) are getting great results right now, yes. It's important to follow these results, to use them, and to try to understand them. But when this approach hits a local maximum, do you want AI Winter #3, or do you want there to be another approach that people have been working on?
- colllectorof 11y ago
  But.. there is a way to make people listen to you. Hype, right? Choosing problems for their theatrical effect, rather than utility. Writing articles and research papers as if they're marketing pamphlets. Claiming that incremental improvements are paradigm shifts. Treating arbitrary achievements as if they were commonly agreed upon milestones all along. All of this is happening right now. Being skeptical in such environment is the only right thing to do. Deep Learning is getting them, in an increasing number of diverse fields. If you look for practical applications that give tangible benefits to people outside of academia, the achievement of applied deep learning so far aren't nearly as impressive as you make them out to be. This is despite insane levels of hype, huge investments in research and amounts of computing power available. Heck, if anything, the fact that AlphaGo needs to use a tree search to prop up its ANN components could be seen as a sign that ANNs have some serious practical limitations when it comes to "results". Which is kind of the point of the article.
  - nl 11y ago
    No doubt there is plenty of hype. From where I sit though, a lot of it is justified (Not the general intelligence stuff of course). Choosing problems for their theatrical effect, rather than utility. Writing articles and research papers as if they're marketing pamphlets. Claiming that incremental improvements are paradigm shifts. Treating arbitrary achievements as if they were commonly agreed upon milestones all along. I'm not sure what to say to this. There are no "commonly agreed upon milestones". The closest things are the academic benchmarks/shared tasks that you seem to be critical of. I guess the closest thing you'll find to a "commonly agreed upon milestone" is something like the Winograd schema[1]? Based on progress like "Teaching Machines to Read and Comprehend" I wouldn't be betting against deep learning on that. If you look for practical applications that give tangible benefits to people outside of academia, the achievement of applied deep learning so far aren't nearly as impressive as you make them out to be. Could you explain what you expecting? Deep learning techniques aren't exactly wide spread yet, and outside Google and a few other companies it takes time for things to migrate into products and have tangible benefits. Nevertheless, Google Search, Pinterest, Facebook image tagging, Android Voice Search etc, etc.. these all are used by billions of people daily. I think it's hard to argue there isn't at least some practical applications. [1] https://en.wikipedia.org/wiki/Winograd_Schema_Challenge https://en.wikipedia.org/wiki/Winograd_Schema_Challenge [2] http://arxiv.org/abs/1506.03340 http://arxiv.org/abs/1506.03340

nl 11y ago

Yep. Not to be harsh, but Marcus has been critical of Neural Nets for a while now. His claims that there are issues around the provability of them are well made. But.. there is a way to make people listen to you. It's called results. Deep Learning is getting them, in an increasing number of diverse fields.

rspeer 11y ago
Results are important, but don't worship short-term results at the expense of everything else. Deep learning (and other machine learning techniques that are forced to call themselves deep learning to get attention) are getting great results right now, yes. It's important to follow these results, to use them, and to try to understand them. But when this approach hits a local maximum, do you want AI Winter #3, or do you want there to be another approach that people have been working on?
colllectorof 11y ago
But.. there is a way to make people listen to you. Hype, right? Choosing problems for their theatrical effect, rather than utility. Writing articles and research papers as if they're marketing pamphlets. Claiming that incremental improvements are paradigm shifts. Treating arbitrary achievements as if they were commonly agreed upon milestones all along. All of this is happening right now. Being skeptical in such environment is the only right thing to do. Deep Learning is getting them, in an increasing number of diverse fields. If you look for practical applications that give tangible benefits to people outside of academia, the achievement of applied deep learning so far aren't nearly as impressive as you make them out to be. This is despite insane levels of hype, huge investments in research and amounts of computing power available. Heck, if anything, the fact that AlphaGo needs to use a tree search to prop up its ANN components could be seen as a sign that ANNs have some serious practical limitations when it comes to "results". Which is kind of the point of the article.
- nl 11y ago
  No doubt there is plenty of hype. From where I sit though, a lot of it is justified (Not the general intelligence stuff of course). Choosing problems for their theatrical effect, rather than utility. Writing articles and research papers as if they're marketing pamphlets. Claiming that incremental improvements are paradigm shifts. Treating arbitrary achievements as if they were commonly agreed upon milestones all along. I'm not sure what to say to this. There are no "commonly agreed upon milestones". The closest things are the academic benchmarks/shared tasks that you seem to be critical of. I guess the closest thing you'll find to a "commonly agreed upon milestone" is something like the Winograd schema[1]? Based on progress like "Teaching Machines to Read and Comprehend" I wouldn't be betting against deep learning on that. If you look for practical applications that give tangible benefits to people outside of academia, the achievement of applied deep learning so far aren't nearly as impressive as you make them out to be. Could you explain what you expecting? Deep learning techniques aren't exactly wide spread yet, and outside Google and a few other companies it takes time for things to migrate into products and have tangible benefits. Nevertheless, Google Search, Pinterest, Facebook image tagging, Android Voice Search etc, etc.. these all are used by billions of people daily. I think it's hard to argue there isn't at least some practical applications. [1] https://en.wikipedia.org/wiki/Winograd_Schema_Challenge https://en.wikipedia.org/wiki/Winograd_Schema_Challenge [2] http://arxiv.org/abs/1506.03340 http://arxiv.org/abs/1506.03340