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Could not have said it better. Hinton is one of the most important contributors to the field, but giving him all the credit is a disservice to others who made i

by kahnjw 9y ago

Could not have said it better. Hinton is one of the most important contributors to the field, but giving him all the credit is a disservice to others who made important contributions. Probabilistic modeling was popular way before DNNs got big. At that point it was already clear that logic based inference was only so powerful, and different methods were necessary.

YeGoblynQueenne 9y ago
>> At that point it was already clear that logic based inference was only so powerful, and different methods were necessary. Er, logic inference is "only so powerful"? The first order predicate calculus (first order logic) is Turing-complete and there is plenty of maths that prove the soundess and completeness of various logic inference rules. In other words, if you can compute a function, you can compute it with first order logic. The switch from symbolic to statistical AI happened partly because of the AI winter that cut the funding to all AI, which at the time was primarily symbolic AI, partly because it became evident that developing and maintaining huge databases of logic rules was inefficient. Some of the early work in machine learning focused on overcoming this inefficiency by inducing rules from data.
- kahnjw 9y ago
  >> The first order predicate calculus (first order logic) is Turing-complete Brainfuck is turing complete, does that make it as powerful as a general purpose programming language such as Java? I'm not criticizing first order logic, it has use cases no doubt. On the other hand there are many cases where codifying knowledge into a we formed KB isn't practical. Some of these use cases are better suited to probabilistic approaches, deep learning, and some are unsolved. That pretty clearly puts limits on the power of FOL. Also, thanks for mansplaining turing completeness.
  - YeGoblynQueenne 9y ago
    >> Also, thanks for mansplaining turing completeness. Snark is not OK: Be civil. Don't say things you wouldn't say face-to-face. Don't be snarky. Comments should get more civil and substantive, not less, as a topic gets more divisive. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html

by kahnjw 9y ago

YeGoblynQueenne 9y ago

>> At that point it was already clear that logic based inference was only so powerful, and different methods were necessary. Er, logic inference is "only so powerful"? The first order predicate calculus (first order logic) is Turing-complete and there is plenty of maths that prove the soundess and completeness of various logic inference rules. In other words, if you can compute a function, you can compute it with first order logic. The switch from symbolic to statistical AI happened partly because of the AI winter that cut the funding to all AI, which at the time was primarily symbolic AI, partly because it became evident that developing and maintaining huge databases of logic rules was inefficient. Some of the early work in machine learning focused on overcoming this inefficiency by inducing rules from data.

kahnjw 9y ago
>> The first order predicate calculus (first order logic) is Turing-complete Brainfuck is turing complete, does that make it as powerful as a general purpose programming language such as Java? I'm not criticizing first order logic, it has use cases no doubt. On the other hand there are many cases where codifying knowledge into a we formed KB isn't practical. Some of these use cases are better suited to probabilistic approaches, deep learning, and some are unsolved. That pretty clearly puts limits on the power of FOL. Also, thanks for mansplaining turing completeness.
- YeGoblynQueenne 9y ago
  >> Also, thanks for mansplaining turing completeness. Snark is not OK: Be civil. Don't say things you wouldn't say face-to-face. Don't be snarky. Comments should get more civil and substantive, not less, as a topic gets more divisive. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html