Matthew Graves
Skeptic Magazine
Originally published November 2017
Here is an excerpt:
Our intelligence is ultimately a mechanistic process that happens in the brain, but there is no reason to assume that human intelligence is the only possible form of intelligence. And while the brain is complex, this is partly an artifact of the blind, incremental progress that shaped it—natural selection. This suggests that developing machine intelligence may turn out to be a simpler task than reverse- engineering the entire brain. The brain sets an upper bound on the difficulty of building machine intelligence; work to date in the field of artificial intelligence sets a lower bound; and within that range, it’s highly uncertain exactly how difficult the problem is. We could be 15 years away from the conceptual breakthroughs required, or 50 years away, or more.
The fact that artificial intelligence may be very different from human intelligence also suggests that we should be very careful about anthropomorphizing AI. Depending on the design choices AI scientists make, future AI systems may not share our goals or motivations; they may have very different concepts and intuitions; or terms like “goal” and “intuition” may not even be particularly applicable to the way AI systems think and act. AI systems may also have blind spots regarding questions that strike us as obvious. AI systems might also end up far more intelligent than any human.
The last possibility deserves special attention, since superintelligent AI has far more practical significance than other kinds of AI.
AI researchers generally agree that superintelligent AI is possible, though they have different views on how and when it’s likely to be developed. In a 2013 survey, top-cited experts in artificial intelligence assigned a median 50% probability to AI being able to “carry out most human professions at least as well as a typical human” by the year 2050, and also assigned a 50% probability to AI greatly surpassing the performance of every human in most professions within 30 years of reaching that threshold.
The article is here.
Showing posts with label Human Intelligence. Show all posts
Showing posts with label Human Intelligence. Show all posts
Thursday, November 30, 2017
Tuesday, May 9, 2017
Inside Libratus, the Poker AI That Out-Bluffed the Best Humans
Cade Metz
Wired Magazine
Originally published February 1, 2017
Here is an excerpt:
Libratus relied on three different systems that worked together, a reminder that modern AI is driven not by one technology but many. Deep neural networks get most of the attention these days, and for good reason: They power everything from image recognition to translation to search at some of the world’s biggest tech companies. But the success of neural nets has also pumped new life into so many other AI techniques that help machines mimic and even surpass human talents.
Libratus, for one, did not use neural networks. Mainly, it relied on a form of AI known as reinforcement learning, a method of extreme trial-and-error. In essence, it played game after game against itself. Google’s DeepMind lab used reinforcement learning in building AlphaGo, the system that that cracked the ancient game of Go ten years ahead of schedule, but there’s a key difference between the two systems. AlphaGo learned the game by analyzing 30 million Go moves from human players, before refining its skills by playing against itself. By contrast, Libratus learned from scratch.
Through an algorithm called counterfactual regret minimization, it began by playing at random, and eventually, after several months of training and trillions of hands of poker, it too reached a level where it could not just challenge the best humans but play in ways they couldn’t—playing a much wider range of bets and randomizing these bets, so that rivals have more trouble guessing what cards it holds. “We give the AI a description of the game. We don’t tell it how to play,” says Noam Brown, a CMU grad student who built the system alongside his professor, Tuomas Sandholm. “It develops a strategy completely independently from human play, and it can be very different from the way humans play the game.”
The article is here.
Wired Magazine
Originally published February 1, 2017
Here is an excerpt:
Libratus relied on three different systems that worked together, a reminder that modern AI is driven not by one technology but many. Deep neural networks get most of the attention these days, and for good reason: They power everything from image recognition to translation to search at some of the world’s biggest tech companies. But the success of neural nets has also pumped new life into so many other AI techniques that help machines mimic and even surpass human talents.
Libratus, for one, did not use neural networks. Mainly, it relied on a form of AI known as reinforcement learning, a method of extreme trial-and-error. In essence, it played game after game against itself. Google’s DeepMind lab used reinforcement learning in building AlphaGo, the system that that cracked the ancient game of Go ten years ahead of schedule, but there’s a key difference between the two systems. AlphaGo learned the game by analyzing 30 million Go moves from human players, before refining its skills by playing against itself. By contrast, Libratus learned from scratch.
Through an algorithm called counterfactual regret minimization, it began by playing at random, and eventually, after several months of training and trillions of hands of poker, it too reached a level where it could not just challenge the best humans but play in ways they couldn’t—playing a much wider range of bets and randomizing these bets, so that rivals have more trouble guessing what cards it holds. “We give the AI a description of the game. We don’t tell it how to play,” says Noam Brown, a CMU grad student who built the system alongside his professor, Tuomas Sandholm. “It develops a strategy completely independently from human play, and it can be very different from the way humans play the game.”
The article is here.
Wednesday, November 16, 2016
Supervising AI Growth
by Tucker Davey
The Future of Life
Originally posted October 26, 2016
Here is an excerpt:
As Google and other tech companies continue to improve their intelligent machines with each evaluation, the human trainers will fulfill a smaller role. Eventually, Christiano explains, “it’s effectively just one machine evaluating another machine’s behavior.”
Ideally, “each time you build a more powerful machine, it effectively models human values and does what humans would like,” says Christiano. But he worries that these machines may stray from human values as they surpass human intelligence. To put this in human terms: a complex intelligent machine would resemble a large organization of humans. If the organization does tasks that are too complex for any individual human to understand, it may pursue goals that humans wouldn’t like.
In order to address these control issues, Christiano is working on an “end-to-end description of this machine learning process, fleshing out key technical problems that seem most relevant.” His research will help bolster the understanding of how humans can use AI systems to evaluate the behavior of more advanced AI systems. If his work succeeds, it will be a significant step in building trustworthy artificial intelligence.
The article is here.
The Future of Life
Originally posted October 26, 2016
Here is an excerpt:
As Google and other tech companies continue to improve their intelligent machines with each evaluation, the human trainers will fulfill a smaller role. Eventually, Christiano explains, “it’s effectively just one machine evaluating another machine’s behavior.”
Ideally, “each time you build a more powerful machine, it effectively models human values and does what humans would like,” says Christiano. But he worries that these machines may stray from human values as they surpass human intelligence. To put this in human terms: a complex intelligent machine would resemble a large organization of humans. If the organization does tasks that are too complex for any individual human to understand, it may pursue goals that humans wouldn’t like.
In order to address these control issues, Christiano is working on an “end-to-end description of this machine learning process, fleshing out key technical problems that seem most relevant.” His research will help bolster the understanding of how humans can use AI systems to evaluate the behavior of more advanced AI systems. If his work succeeds, it will be a significant step in building trustworthy artificial intelligence.
The article is here.
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