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The problem with news articles like this is that they attempt to appeal to a general readership through grand promises and fear-mongering. The reporter's basic

by vonnik 10y ago

The problem with news articles like this is that they attempt to appeal to a general readership through grand promises and fear-mongering. The reporter's basic problem is: How can I make my audience care? So you call up Nick Bostrom and he very reliably gives you a quote about the existential threat of a superintelligence, even though no one in the industry thinks we're anywhere close to that. (What's next for AI is not superintelligence...) And you force a great researcher like Andrew Ng to talk about job loss among truck drivers, because that's what will make it relevant to people outside AI. We should be thinking about job loss and how the job market will change, but this type of article never gets past the "Oh No AI Will Destroy US" stage of thinking. But a lot of the questions raised by AI are actually relevant now, in an economy where AI hasn't even made a big impact. That is, they're not AI issues, but we're treating them as though they are. How should our societies treat and support the humans who have become unnecessary? They obviously will not all become data analysts and robot caretakers. What's next for AI is better natural-language processing. Right now, chatbots are pretty dumb, but in the next few years, they'll get much better, and in more languages. What's next for AI is the wider deployment of mature technology. Many problems such as image recognition have been solved, but developers and companies have not figured out how to deploy the solutions yet. We still have chokepoints in the number of data scientists who can tune and train models, and the number of engineers who can plug them into existing stacks. So AI will be felt directly, rather than just talked about.[0] What's next for AI is an arms race. The major powers will be escalating the how they deploy AIs against AIs, embedded in drones or through the creation of adversarial data to slip through filters. Commercially, many smaller arms races will occur in different industries, as AI drives down the costs of interpreting data, and allows rival organizations to compete on price. What's next for AI is the combination of the flavor of the month, deep learning, with other extremely powerful algorithms like reinforcement learning and Monte Carlo Tree Search to create goal-oriented strategic decision-making agents. [0] http://deeplearning4j.org/use_cases.html http://deeplearning4j.org/use_cases.html

mathgenius 10y ago
Any thoughts on support vector machines & kernel methods? Is that stuff dead and buried, or what? (I've been out of the loop now for a while.)
- dr0l3 10y ago
  I don't have anything valid to back up my claims here, so take what i have with a grain of salt unless you find external validation for it. That being said i will venture a guess: Yes and no. Neural networks have a lot of nice properties like being easier to grasp, easier to parrallelize(?) and have better tools which i guess is because neural networks are applicable to more problems than SVM's because they are extensible.( see https://en.wikipedia.org/wiki/Recurrent_neural_network https://en.wikipedia.org/wiki/Recurrent_neural_network for example). Either way SVM's still do what they do very nicely and in the field of "Human Activity Recognition" where i did my thesis neural networks are practically never used but SVM's pop up from time to time.
  - mathgenius 10y ago
    Right, scalability is a good point to consider. Along the same vein, my last ml job we ended up using gradient boosting which worked really well, but definitely does not scale to big data (afik.) Robustness is another thing to consider, both svm's and neural networks need a reasonable amount of data-massaging before they behave themselves. Hence the success in image processing where every pixel can be treated equally.
- wodenokoto 10y ago
  There was a thread on reddit the other day about what problems a NN could solve that an SVM couldn't. Apparently there are things such as structured SVM's and SVM networks, and models where they attach an SVM instead of the output neurons. It isn't dead, and people are coming up with some pretty cool stuff, but NN seems to be where it is at right now.

by vonnik 10y ago

mathgenius 10y ago

Any thoughts on support vector machines & kernel methods? Is that stuff dead and buried, or what? (I've been out of the loop now for a while.)

dr0l3 10y ago
I don't have anything valid to back up my claims here, so take what i have with a grain of salt unless you find external validation for it. That being said i will venture a guess: Yes and no. Neural networks have a lot of nice properties like being easier to grasp, easier to parrallelize(?) and have better tools which i guess is because neural networks are applicable to more problems than SVM's because they are extensible.( see https://en.wikipedia.org/wiki/Recurrent_neural_network https://en.wikipedia.org/wiki/Recurrent_neural_network for example). Either way SVM's still do what they do very nicely and in the field of "Human Activity Recognition" where i did my thesis neural networks are practically never used but SVM's pop up from time to time.
- mathgenius 10y ago
  Right, scalability is a good point to consider. Along the same vein, my last ml job we ended up using gradient boosting which worked really well, but definitely does not scale to big data (afik.) Robustness is another thing to consider, both svm's and neural networks need a reasonable amount of data-massaging before they behave themselves. Hence the success in image processing where every pixel can be treated equally.
wodenokoto 10y ago
There was a thread on reddit the other day about what problems a NN could solve that an SVM couldn't. Apparently there are things such as structured SVM's and SVM networks, and models where they attach an SVM instead of the output neurons. It isn't dead, and people are coming up with some pretty cool stuff, but NN seems to be where it is at right now.