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What are some widely used applications using AI for something that could not be implemented cheaper and more easily than a decision tree?

tomp 11y ago
Implementing decision trees.
aub3bhat 11y ago
Almost everything, decision trees are the bubble sort of AI. To expand upon my comment: For most programmers Decision Trees & Nearest Neighbor, at a first glance look very promising and seem like natural solutions to lots of problems. However in reality one can easily prove that Decision trees tend to be suboptimal even with trivial problems due to the brittle nature of the rules represented. Also Nearest Neighbors methods face problems due to difficulty in deciding optimal space and metric (Euclidean distance is not inherently better). The reason why "Deep Learning" methods trained using non-convex optimization perform so well is because they can represent a hierarchy of features at different granularities. They can be "designed" to favor one task over the other. E.g. by choosing correct loss (triplet loss), network structure, etc. If you are interested beyond Image recognition problems, I highly recommend reading on interesting approaches such as Triplet loss for Face Recognition , Semantic Segmentation, Attention based models and Unsupervised Visual Representation Learning by Context Prediction. An interesting outcome of Deep Learning research is that a lot of papers are extremely accessible and involve little proofs/difficult math. E.g. This is a good one https://www.youtube.com/watch?v=dUgRR-JFE8s https://www.youtube.com/watch?v=dUgRR-JFE8s & http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w08-deep-context.pdf http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil...
- raverbashing 11y ago
  Very good analogy But from decision trees you get decision forests, which can't be said of bubblesort
SixSigma 11y ago
Pretty much every prize is won on Kaggle with either a random forest decision tree or neural net. Perhaps one of us should write a classifier to see if we can predict which will be best for a problem a priori.
- deepnet 11y ago
  Usually an ensemble of both is the winner. Hinton has recommended ensembling diverse solutions (for Kaggle) & including random forests. The adage "The truth is somewhere inbetween." or wisdom-of-ensembles is profound in this domain.
robotresearcher 11y ago
AI, like regular I, is in the eye of the beholder. It's a moving target. If something performs surprisingly well compared with what you're used to, it's AI. Once you get used to it, like decision trees, it's ordinary tech again. There's (at least) one thing that could change this, which is that the machines get so complex that we can't really grok the details any more. You could argue that this is beginning to happen in Deep Learning, where it can be hard for humans to map the functions being performed later in the network to anything with intuitive semantics relative to the problem. Perhaps in retrospect we will consider the Singularity not a spectacular thing where the machines suddenly got super smart, but a more mundane thing where most of community gave up trying to understand the machines and just use them ever after as black boxes.

tomp 11y ago

Implementing decision trees.

aub3bhat 11y ago

Almost everything, decision trees are the bubble sort of AI. To expand upon my comment: For most programmers Decision Trees & Nearest Neighbor, at a first glance look very promising and seem like natural solutions to lots of problems. However in reality one can easily prove that Decision trees tend to be suboptimal even with trivial problems due to the brittle nature of the rules represented. Also Nearest Neighbors methods face problems due to difficulty in deciding optimal space and metric (Euclidean distance is not inherently better). The reason why "Deep Learning" methods trained using non-convex optimization perform so well is because they can represent a hierarchy of features at different granularities. They can be "designed" to favor one task over the other. E.g. by choosing correct loss (triplet loss), network structure, etc. If you are interested beyond Image recognition problems, I highly recommend reading on interesting approaches such as Triplet loss for Face Recognition , Semantic Segmentation, Attention based models and Unsupervised Visual Representation Learning by Context Prediction. An interesting outcome of Deep Learning research is that a lot of papers are extremely accessible and involve little proofs/difficult math. E.g. This is a good one https://www.youtube.com/watch?v=dUgRR-JFE8s https://www.youtube.com/watch?v=dUgRR-JFE8s & http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w08-deep-context.pdf http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil...

raverbashing 11y ago
Very good analogy But from decision trees you get decision forests, which can't be said of bubblesort

SixSigma 11y ago

Pretty much every prize is won on Kaggle with either a random forest decision tree or neural net. Perhaps one of us should write a classifier to see if we can predict which will be best for a problem a priori.

deepnet 11y ago
Usually an ensemble of both is the winner. Hinton has recommended ensembling diverse solutions (for Kaggle) & including random forests. The adage "The truth is somewhere inbetween." or wisdom-of-ensembles is profound in this domain.

robotresearcher 11y ago

AI, like regular I, is in the eye of the beholder. It's a moving target. If something performs surprisingly well compared with what you're used to, it's AI. Once you get used to it, like decision trees, it's ordinary tech again. There's (at least) one thing that could change this, which is that the machines get so complex that we can't really grok the details any more. You could argue that this is beginning to happen in Deep Learning, where it can be hard for humans to map the functions being performed later in the network to anything with intuitive semantics relative to the problem. Perhaps in retrospect we will consider the Singularity not a spectacular thing where the machines suddenly got super smart, but a more mundane thing where most of community gave up trying to understand the machines and just use them ever after as black boxes.