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Causal inference is the next big leap in AI. Once the relatively(!) low-hanging fruit of pattern recognition are picked to exhaustion, and once we can get more
by uoaei 7y ago
Causal inference is the next big leap in AI. Once the relatively(!) low-hanging fruit of pattern recognition are picked to exhaustion, and once we can get more comfortable with symbolic reasoning with respect to theorem proving / hypothesis testing / counterfactuals, "real" reasoning machines will arise.
- ivalm 7y agoThis sounds like going back to AI of the 80s. IMHO, symbolic reasoning is unlikely to lead to progress.
- uoaei 7y agoIt's not an either-or question. Symbolic reasoning comes up in many places where regression techniques are simply inadequate. A synthesis of the two is likely to see progress.
- mrfusion 7y agoI’d like to hear more about this. How do you see this coming about?
- hummel 7y agoI'm working extensively on it, but I can´t give so many details yet.
- uoaei 7y agoDefinitely a combination of current ML (basically fancy nonlinear regression to MLE targets) with symbolic reasoning. Either alone are insufficient. Symbolic reasoning is basically learning a lot of "if-then" statements and chaining them to make inference. Causal reasoning consists of defining conditional dependencies of current state on past state, then extrapolating based on the encoded assumptions. It requires some notion of object relation both in a literal sense as well as subtler relationships. Regression techniques are being ham-fisted to fit these roles but the popular ML of today is still just pattern recognition and cannot be called "reasoning" per se. I don't work directly in this space but I see it following closely the architecture of the human brain for a while before departing to more distilled forms of knowledge management structures.
- viuphiet 7y agoNeural networks do exactly what you are describing as "symbolic reasoning". It seems to be a common thing recently to dismiss modern ML techniques as curve fitting, but these fundamental models are extremely powerful. Neural networks are capable of approximating any system to arbitrary precision.
- uoaei 7y agoThis is theoretically true, but it's like saying "computers can compute any function, given enough time and resources". There is a need to construct logically-deduced models which impose an inductive bias so that your regression methods are efficient. That's where reasoning comes in, and where automated reasoning methods should be useful.