artificial intelligence News for August 06 2017

Joe Rogan and Lawrence Krauss on artificial intelligence

Joe Rogan Experience Episode #938 Joe Rogan and Lawrence Krauss Joe worried about artificial intelligence in the future.

Artificial Intelligence: A Modern Approach

May 26, 2016 … Part I Artificial Intelligence 1 Introduction 2 Intelligent Agents Part II Problem
Solving 3 Solving Problems by Searching 4 Beyond Classical …
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An Artificial Intelligence Developed Its Own Non-Human Language

A buried line in a new Facebook report about chatbots’ conversations with one another offers a remarkable glimpse at the future of language. In the report, researchers at the Facebook Artificial Intelligence Research lab describe using machine learning to train their “Dialog agents” to negotiate. At one point, the researchers write, they had to tweak one of their models because otherwise the bot-to-bot conversation “Led to divergence from human language as the agents developed their own language for negotiating.” They had to use what’s called a fixed supervised model instead. In other words, the model that allowed two bots to have a conversation-and use machine learning to constantly iterate strategies for that conversation along the way-led to those bots communicating in their own non-human language. If this doesn’t fill you with a sense of wonder and awe about the future of machines and humanity then, I don’t know, go watch Blade Runner or something. The larger point of the report is that bots can be pretty decent negotiators-they even use strategies like feigning interest in something valueless, so that it can later appear to “Compromise” by conceding it. The detail about language is, as one tech entrepreneur put it, a mind-boggling “Sign of what’s to come.” To be clear, Facebook’s chatty bots aren’t evidence of the singularity’s arrival. They do demonstrate how machines are redefining people’s understanding of so many realms once believed to be exclusively human-like language. Already, there’s a good deal of guesswork involved in machine learning research, which often involves feeding a neural net a huge pile of data then examining the output to try to understand how the machine thinks. The fact that machines will make up their own non-human ways of conversing is an astonishing reminder of just how little we know, even when people are the ones designing these systems. “There remains much potential for future work,” Facebook’s researchers wrote in their paper, “Particularly in exploring other reasoning strategies, and in improving the diversity of utterances without diverging from human language.”
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Electrical Engineering and Computer Science

This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances. This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.
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Our Fear of Artificial Intelligence

What if it wasn’t so benevolent? Nick Bostrom, a philosopher who directs the Future of Humanity Institute at the University of Oxford, describes the following scenario in his book Superintelligence, which has prompted a great deal of debate about the future of artificial intelligence. Bostrom does not believe that the paper-clip maximizer will come to be, exactly; it’s a thought experiment, one designed to show how even careful system design can fail to restrain extreme machine intelligence. Critics such as the robotics pioneer Rodney Brooks say that people who fear a runaway AI misunderstand what computers are doing when we say they’re thinking or getting smart. The question “Can a machine think?” has shadowed computer science from its beginnings. As AI researchers in the 1960s and 1970s began to use computers to recognize images, translate between languages, and understand instructions in normal language and not just code, the idea that computers would eventually develop the ability to speak and think-and thus to do evil-bubbled into mainstream culture. Even beyond the oft-referenced HAL from 2001: A Space Odyssey, the 1970 movie Colossus: The Forbin Project featured a large blinking mainframe computer that brings the world to the brink of nuclear destruction; a similar theme was explored 13 years later in WarGames. Whereas Turing had posited a humanlike intelligence, Vinge, Moravec, and Kurzweil were thinking bigger: when a computer became capable of independently devising ways to achieve goals, it would very likely be capable of introspection-and thus able to modify its software and make itself more intelligent. In short order, such a computer would be able to design its own hardware. We still have nothing approaching a general-purpose artificial intelligence or even a clear path to how it could be achieved. Even if it’s impressive-relative to what earlier computers could manage-for a computer to recognize a picture of a cat, the machine has no volition, no sense of what cat-ness is or what else is happening in the picture, and none of the countless other insights that humans have. One person who shares Bostrom’s concerns is Stuart J. Russell, a professor of computer science at the University of California, Berkeley. Bostrom’s book proposes ways to align computers with human needs. Because Google, Facebook, and other companies are actively looking to create an intelligent, “Learning” machine, he reasons, “I would say that one of the things we ought not to do is to press full steam ahead on building superintelligence without giving thought to the potential risks. It just seems a bit daft.” Russell made an analogy: “It’s like fusion research. If you ask a fusion researcher what they do, they say they work on containment. If you want unlimited energy you’d better contain the fusion reaction.” Similarly, he says, if you want unlimited intelligence, you’d better figure out how to align computers with human needs. Many of the largest corporations in the world are deeply invested in making their computers more intelligent; a true AI would give any one of these companies an unbelievable advantage.
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Artificial intelligence: Rise of the machines | The Economist

Such worries are a mirror image of the optimism suffusing the field itself, which has enjoyed rapid progress over the past couple of years. Firms such as Google …
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Is Artificial Intelligence Dangerous?

Is artificial intelligence dangerous? As we creep forward towards a future replete with AI, where does humanity’s fate rest? Will it be a case of life …
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Artificial Intelligence’s White Guy Problem – The New York …

ACCORDING to some prominent voices in the tech world, artificial intelligence presents a looming existential threat to humanity: Warnings by luminaries …
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After machines repeated their victories in a few more matches, humans largely lost interest in such contests. You might think that was the end of the story, but Kasparov realized that he could have performed better against Deep Blue if he’d had the same instant access to a massive database of all previous chess moves that Deep Blue had. If this database tool was fair for an AI, why not for a human? To pursue this idea, Kasparov pioneered the concept of man-plus-machine matches, in which AI augments human chess players rather than competes against them. Here’s the even more surprising part: The advent of AI didn’t diminish the performance of purely human chess players. The top-ranked human chess player today, Magnus Carlsen, trained with AIs and has been deemed the most computer-like of all human chess players. As we invent more species of AI, we will be forced to surrender more of what is supposedly unique about humans.
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