[unlikelymordant]

I'm not sure I understand. "Few among us recognize the need to prioritize the study of the human brain", a great deal of the state of the art machine learning results are based on deep learning, which are algorithms that are "neurologically inspired" as you would put it. You seem to have a problem with big data and statistical methods, but one of the main deep learning algorithms, RBMs, are statistical methods.

Also, could you expand on what a "balanced, holistic approach" to machine learning is?

[perfmode]

"Even though an amplifier and a computer are both made of transistors, they have almost nothing else in common. In the same way, a real brain and a three-row neural network are built with neurons, but have almost nothing else in common." -- Jeff Hawkins (On Intelligence)

One example is that most NNs neglect the time domain.

A balanced approach recognizes the importance of learning from data, but does not _rely_ on big data. A holistic approach entails a close examination of biological learning systems.

[varjag]

As a counterpoint, nearly all human technical advances (flight, propulsion, energy, computation) did not entail examination of biological systems. And ANNs in particular share a host of problems with statistical techniques: black-boxish behavior with lack of human-accessible state introspection and poor tractability.

[perfmode]

"On the basis of observation, Wilbur concluded that birds changed the angle of the ends of their wings to make their bodies roll right or left.[30] The brothers decided this would also be a good way for a flying machine to turn—to "bank" or "lean" into the turn just like a bird—and just like a person riding a bicycle, an experience with which they were thoroughly familiar."

https://en.wikipedia.org/wiki/Wright_brothers

[varjag]

This doesn't however make airplanes aerodynamically anywhere similar to bird flight. Besides, Wright's method of wing warping was soon discarded as structurally unsound and is seldom used now.

[chongli]

black-boxish behavior with lack of human-accessible state introspection and poor tractability.

I have a philosophical question in response to this. Is it even possible to have intelligence without it being a black box? Are people willing to call something intelligent if they completely understand how it works?

[varjag]

Well my point was rather practical than philosophical: if you take a human at say classification task, they'd be able to explain why they identify bicycle as bicycle and not, say, bulldozer. You can't readily have this with ANNs; all you have is a bunch of weight coefficients and feedback loops.

[chongli]

if you take a human at say classification task, they'd be able to explain why they identify bicycle as bicycle and not, say, bulldozer.

I don't think this is such an easy task. In the professional world of scientific taxonomy there are many problems with classification. The problem seems to stem from the tension between intensional and extensional definitions.

[varjag]

Yes but you still are able to verbalize these problems, quite unlike when you stuck with a misfiring ANN. In symbolic approaches, e.g. inference engines, you have comparable explanatory facilities.

[eli_gottlieb]

>Is it even possible to have intelligence without it being a black box?

That depends. Do you believe in Cartesian dualism, or atomic monism? If you believe in a monist universe where minds don't reside on some other plane of existence, then plainly a mind must be explicable to a sufficiently smart other mind, because after all so is everything else.

[chongli]

then plainly a mind must be explicable to a sufficiently smart other mind, because after all so is everything else

Right, but is that intelligence? My main argument is basically an attack on the word intelligence. I believe people use it far too frequently and they allow its meaning to change whenever it comes close to being pinned down. In a strange way, intelligence is a tricky refuge for dualism in an otherwise monist world.