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Ask HN: Alternative/Abandoned methods for AI?
We have come a long way by Statistical methods. Last night, I went into a rabbit hole of Cognitive Architectures like ACT-R[http://act-r.psy.cmu.edu/] and OpenCog[https://wiki.opencog.org/w/The_Open_Cognition_Project]. The intro ofcourse was not as documentation friendly as the width of excellent docs on Deep Learning.
Are the Symbolic methods abandoned because they are proven to not work? Also, are any new researchers working on this?
- w_t_payne 8y agoDoug Lenat never gave up. (https://www.cyc.com/ https://www.cyc.com/)
- sharemywin 8y agoSoar had some success in the past. TacAirSoar https://www.isi.edu/soar/ifor/agent/ https://www.isi.edu/soar/ifor/agent/
- sharemywin 8y agoHere's a whole list of them. https://arxiv.org/pdf/1610.08602.pdf https://arxiv.org/pdf/1610.08602.pdf
- sharemywin 8y agoHere's some decision tree learners that were pretty cool at the time. Decision tree learning is the construction of a decision tree from class-labeled training tuples. A decision tree is a flow-chart-like structure, where each internal (non-leaf) node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label. The topmost node in a tree is the root node. There are many specific decision-tree algorithms. Notable ones include: ID3 (Iterative Dichotomiser 3) C4.5 (successor of ID3) CART (Classification And Regression Tree) CHAID (CHi-squared Automatic Interaction Detector). Performs multi-level splits when computing classification trees.[11] MARS: extends decision trees to handle numerical data better. Conditional Inference Trees. Statistics-based approach that uses non-parametric tests as splitting criteria, corrected for multiple testing to avoid overfitting. This approach results in unbiased predictor selection and does not require pruning.[12][13] ID3 and CART were invented independently at around the same time (between 1970 and 1980)[citation needed], yet follow a similar approach for learning a decision tree from training tuples.