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We present a foundation for inference that unites and significantly extends the approaches of Kolmogorov and Cox. Our approach is based on quantifying finite lattices of logical statements in a way that satisfies general lattice symmetries. With other applications in mind, our derivations assume minimal symmetries, relying on neither complementarity nor continuity or differentiability. Each relevant symmetry corresponds to an axiom of quantification, and these axioms are used to derive a unique set of rules governing quantification of the lattice. These rules form the familiar probability calculus. We also derive a unique quantification of divergence and information. Taken together these results form a simple and clear foundation for the quantification of inference.
"A bold experiment in distributed education, "Introduction to Artificial Intelligence" will be offered free and online to students worldwide during the fall of 2011. The course will include feedback on progress and a statement of accomplishment. Taught by Sebastian Thrun and Peter Norvig, the curriculum draws from that used in Stanford's introductory Artificial Intelligence course. The instructors will offer similar materials, assignments, and exams."
John Markoff muses on the Jeopardy challenge by IBM and sets up a historical competition between artificial intelligence (John McCarthy) and intelligence augmentation (Douglas Englebart).
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In 1963 the mathematician-turned-computer scientist John McCarthy started the Stanford Artificial Intelligence Laboratory. The researchers believed that it would take only a decade to create a thinking machine.
Also that year the computer scientist Douglas Engelbart formed what would become the Augmentation Research Center to pursue a radically different goal — designing a computing system that would instead “bootstrap” the human intelligence of small groups of scientists and engineers.
For the past four decades that basic tension between artificial intelligence and intelligence augmentation — A.I. versus I.A. — has been at the heart of progress in computing science as the field has produced a series of ever more powerful technologies that are transforming the world.
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Similar design decisions about how machines are used and whether they will enhance or replace human qualities are now being played out in a multitude of ways, and the real value of Watson may ultimately be in forcing society to consider where the line between human and machine should be drawn.
Indeed, for the computer scientist John Seely Brown, machines that are facile at answering questions only serve to obscure what remains fundamentally human.
“The essence of being human involves asking questions, not answering them,” he said.
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In a recent article in Science, Cornell professor Hod Lipson and graduate student Michael Schmidt described a new computer system that can discover scientific laws. At first glance, it looks like a fulfillment of the dreams of “computational scientific discovery,” a small field at the intersection of philosophy and artificial intelligence (AI) that seeks to reverse-engineer scientific imagination and create a computer as skilled as we are at constructing theories. But if you look closer, it turns out that the system’s success at analyzing large, complicated data sets, formulating initial theories, and discarding trivial patterns in favor of interesting ones comes not from imitating people, but from allowing a very different kind of intelligence to grow in silico — one that doesn’t compete with humans, but works with us.
"Today’s AI doesn’t try to re-create the brain. Instead, it uses machine learning, massive data sets, sophisticated sensors, and clever algorithms to master discrete tasks. Examples can be found everywhere: The Google global machine uses AI to interpret cryptic human queries. Credit card companies use it to track fraud. Netflix uses it to recommend movies to subscribers. And the financial system uses it to handle billions of trades (with only the occasional meltdown)."
"We have one faction that is attempting to write software that can generate messages that can pass a Turing test, and another faction that is attempting to write software that can administer an ad-hoc Turing test. Each faction has a strong incentive to beat the other. This is the classic pattern of an evolutionary predator/prey arms race: and so I deduce that if symbol-handling, linguistic artificial intelligence is possible at all, we are on course for a very odd destination indeed — the Spamularity, in which those curious lumps of communicating meat give rise to a meta-sphere of discourse dominated by parasitic viral payloads pretending to be meat"
in list: Philosophy Notes
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I have a small unreasonable fear, somewhere in the back of my mind, that if I ever do fully understand the algorithms of intelligence, it will destroy all remaining novelty - no matter what new situation I encounter, I'll know I can solve it just by being intelligent, the same damn thing over and over. All novelty will be used up, all existence will become boring, the remaining differences no more important than shades of pixels in a video game. Other beings will go about in blissful unawareness, having been steered away from studying this forbidden cognitive science. But I, having already thrown myself on the grenade of AI, will face a choice between eternal boredom, or excision of my forbidden knowledge and all the memories leading up to it (thereby destroying my existence as Eliezer, more or less).
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