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I do accept engineering feats as evidence people know what they are talking about. Are there any tasks where one of these "causality-aware" robots [algorithms]
by nonbel 10y ago
I do accept engineering feats as evidence people know what they are talking about. Are there any tasks where one of these "causality-aware" robots [algorithms] outperforms "pattern-recognition" robots?
From the brief summary it sounds very hypothetical, especially compared to the causality-agnostic pattern-recognition approach which I have seen work surprisingly well. That group has convinced me they are on to something quite big.
- pinouchon 10y agoI agree that in practise, deep learning tend to perform very well. With that said, it is not without limitations. In general, model-based machine learning approaches do not perform as well, but we should not limit our assessment only to performance. For example in this review: https://arxiv.org/pdf/1605.01138.pdf https://arxiv.org/pdf/1605.01138.pdf, a model-based approach is compared against a deep learning approach. It is very interesting to me that even if deep learning performs better, its failure cases are very different than human failure cases (deep learning is not sensitive to "physics illusions"). Whereas the model-based approach is very much in-line with human performance characteristics. Also, on this task (physical scene understanding), deep learning generalises very poorly.