Top Tweets for #CoCoSym
Slides from my talk at #cocosym including analysis of @GaryMarcus appraisal of #dl are available at: https://t.co/hEKuHSmuKp https://t.co/gyp4Vzc1PJ
also: research community often overhypes own results w nary a word from other researchers, see eg deepmindโs excessive claims alphago zero and my critique in arxiv. hardly anyone else spoke up. https://t.co/wVtAftrGVa
Van Harmelen: symbolics&statistics literature - large, growing fast and very heterogeneous #CoCoSym
Edward Grefenstette: "There's no free lunch, but we can reduce the cost of the sandwich." #cocosym
Hammer: a safe classifier algorithm needs a reject option - to know when it doesnโt know #CoCoSym
Barbara Hammer: Two reasons for a classifier to signal that it's uncertain: if it's close to the decision boundary or if it's far away from the data. #cocosym
Michael Spranger gives us some new challenges at #cocosym. #1: Learning in highly non-stationary environments.
Russo: Important feature of symbolic machine learning is ability to describe learned rules in natural language for human user #CoCoSym
Bordes: developments in last 2 years show we are moving towards neural networks that can be trained to handle symbols efficiently - not yet real-life applications #CoCoSym
Curious if the intractability of scaling up in AI, with DL or otherwise, will be discussed at #CoCoSym.
Packed room for #CoCoSym 2018. Combining learning and reasoning is a big part of the future of #ArtificialIntelligemce
Kersting: we need systems AI (like systems biology) - computational modelling of complex AI systems #CoCoSym
Wiggins; current deep learning systems differ from brains on crucial points, one is: they usually are symmetric with respect to time - for humans time is a primary feature of learning architecture #CoCoSym
#CoCoSym Alonso: to continue with deep learning and achieve interpretability: understanding mathematical foundations, inspiration from physics (stat mechanics)
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