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Why Computers Still Don't Understand People
Gary Marcus writes in the New Yorker about the state of artificial intelligence, and how we take it for granted that AI involves a very particular, very narrow definition of intelligence. A computer's ability to answer questions is still largely dependent on whether the computer has seen that question before. Quoting:
"Siri and Google’s voice searches may be able to understand canned sentences like 'What movies are showing near me at seven o’clock?,' but what about questions—'Can an alligator run the hundred-metre hurdles?'—that nobody has heard before? Any ordinary adult can figure that one out. (No. Alligators can’t hurdle.) But if you type the question into Google, you get information about Florida Gators track and field. Other search engines, like Wolfram Alpha, can’t answer the question, either. Watson, the computer system that won “Jeopardy!,” likely wouldn’t do much better. In a terrific paper just presented at the premier international conference on artificial intelligence (PDF), Levesque, a University of Toronto computer scientist who studies these questions, has taken just about everyone in the field of A.I. to task. ...Levesque argues that the Turing test is almost meaningless, because it is far too easy to game. ... To try and get the field back on track, Levesque is encouraging artificial-intelligence researchers to consider a different test that is much harder to game ..."
Sigh. This is a written account of a lecture presented as part of Levesque receiving the Research Excellence prize. The first footnote of the paper says so:
"This paper is a written version of the Research Excellence Lecture presented in Beijing at the IJCAI-13 conference. Thanks to Vaishak Belle and Ernie Davis for helpful comments."
Premier conferences don't give these prizes to just anyone, and the opinions of folks like these are worth thinking about.
From the IJCAI website http://ijcai13.org/program/awards (Google cache version, since the original seems to down):
"IJCAI-13 Award for Research Excellence
The Research Excellence award is given to a scientist who has carried out a program of research of consistently high quality yielding several substantial results. Past recipients of this honor are the most illustrious group of scientists from the field of Artificial Intelligence;
They are: John McCarthy (1985), Allen Newell (1989), Marvin Minsky (1991), Raymond Reiter (1993), Herbert Simon (1995), Aravind Joshi (1997), Judea Pearl (1999), Donald Michie (2001), Nils Nilsson (2003), Geoffrey E. Hinton (2005), Alan Bundy (2007), Victor Lesser (2009) and Robert Anthony Kowalski (2011).
"The winner of the 2013 Award for Research Excellence is Hector Levesque, Professor of Computer Science at the Department of Computer Science of the University of Toronto. Professor Levesque is recognized for his work on a variety of topics in knowledge representation and reasoning, including cognitive robotics, theories of belief, and tractable reasoning."
Actually, IJCAI is the top conference in the field of Artificial Intelligence and every published paper goes through extensive peer review.
Computer Science is a bit different from most other science in that top conference proceedings (IJCAI, NIPS, ICCV, CVPR, etc.) have the weight of a journal. In fact, publishing there is more prestigious than most journals. Review period lasts 3-4 months and includes a rebuttal phase, like a journal.
This paper looks like an invited lecture or a position paper expected to provoke a debate, that is true. But calling IJCAI "some conference" is like calling Nature "some newspaper".
You're thinking of machine learning, which is a separate branch of AI that's more like an overfunded brand of applied statistics—their strategy is actually still to try and push the envelope (like Hinton, another U of T prof, did last year with dropout networks) but they do so in a more results-driven manner. The ML field as a whole is still sore from three or four decades of overpromising on the future, so they try to put their words where their mouths are, and focus on things that are attainable.
Levesque is in the knowledge representation group, which is more closely in step with cognitive science (the leading edge in modelling human thought) but still very philosophical in their approach. KR was the dominant AI field in the 80s (when Prolog and expert systems were all the rage) but it's matured a great deal since then. Here is his homepage, just to show you how different things are now.
Remember that neural networks aren't magic irreducible fairy dust: they're incredibly powerful, but at the end of the day there must be some program that is running within the network unless it's just a wildly complex ever-changing mapping function, which is unlikely given the illusion of consciousness. Given that quantum mechanics is believed to be Turing-complete, it's fairly likely we'll eventually discover some underlying model that lets us produce a human-like cognitive system without the same level of hardware parallelism that the brain has.
Bio questions? Ask me to start a Q&A journal. Computer analogies available for most topics!
Where-as the atheists seem hell bent on the idea that intelligence and self awareness are illusions or somehow not real.
That doesn't make any sense. Atheists are not a homogeneous group with a common dogma, no more than people who don't collect stamps are a homogeneous group. Atheists are simply people who don't collect God-stories. The group of people you seem to actually want to criticize here are the behaviorists. Those people were psychologists and the view largely died out 50 years ago. So your ideas of atheists as a cohesive group is non-sense, and even if it weren't, your claim would still be non-sense.