A number of organizations ranging from terrorist groups such as ISIS to politicians and nation states reportedly conduct explicit campaigns to influence opinion on social media, posing a risk to democratic processes. There is thus a growing need to identify and eliminate "influence bots" - realistic, automated identities that illicitly shape discussion on sites like Twitter and Facebook - before they get too influential. Spurred by such events, DARPA held a 4-week competition in February/March 2015 in which multiple teams supported by the DARPA Social Media in Strategic Communications program competed to identify a set of previously identified "influence bots" serving as ground truth on a specific topic within Twitter. Past work regarding influence bots often has difficulty supporting claims about accuracy, since there is limited ground truth (though some exceptions do exist [3,7]). However, with the exception of , no past work has looked specifically at identifying influence bots on a specific topic. This paper describes the DARPA Challenge and describes the methods used by the three top-ranked teams.
Progress in artificial intelligence and machine learning has been impressive this year. Those in the field acknowledge progress is accelerating year by year, though it is still a manageable pace for us. The vast majority of work in the field these days actually builds on previous work done by other teams earlier the same year, in contrast to most other fields where references span decades.
Creating a summary of a wide range of developments in this field will almost invariably lead to descriptions that sound heavily anthropomorphic, and this summary does indeed. Such metaphors, however, are only convenient shorthands for talking about these functionalities.
It’s important to remember that even though many of these capabilities sound very thought-like, they’re usually not very similar to how human cognition works. The systems are all of course functional and mechanistic, and, though increasingly less so, each are still quite narrow in what they do. Be warned though: in reading this article, these functionalities may seem to go from fanciful to prosaic.
The biggest developments of 2015 fall into five categories of intelligence: abstracting across environments, intuitive concept understanding, creative abstract thought, dreaming up visions, and dexterous fine motor skills. I’ll highlight a small number of important threads within each that have brought the field forward this year.
A wide-eyed, rosy-cheeked, babbling human baby hardly looks like the ultimate learning machine. But under the hood, an 18-month-old can outlearn any state-of-the-art artificial intelligence algorithm. Their secret sauce? They... read more
Free Money for All: A Basic Income Guarantee Solution for the Twenty-First Century (Exploring the Basic Income Guarantee): 9781137471321: Economics Books @ Amazon.com
A basic income guarantee (BIG) is a payment by the federal government to all adult citizens. This book uses the United States as its model and sets the minimum income at $10,000 USD. Free Money for All seeks to show that not only is a basic income guarantee a feasible model for public policy, it is a morally attractive proposal. In this exciting new volume, Walker argues that BIG promotes three positive outcomes - social stability, gross national happiness, and gross national freedom - unlike alternate proposals such as socialism, laissez-faire capitalism, and the traditional welfare state. He uses a philosophical perspective to defend BIG against the claim that the promotion of social goals egregiously infringes the requirements of justice. Free Money for All employs a novel twist on the though that BIG can be supported by the idea of a social dividend.
Looks interesting except for the hefty price tag...
A computer has beaten a human professional for the first time at Go — an ancient board game that has long been viewed as one of the greatest challenges for artificial intelligence (AI).
The best human players of chess, draughts and backgammon have all been outplayed by computers. But a hefty handicap was needed for computers to win at Go. Now Google’s London-based AI company, DeepMind, claims that its machine has mastered the game.
"The software was already competitive with the leading commercial Go programs, which select the best move by scanning a sample of simulated future games. DeepMind then combined this search approach with the ability to pick moves and interpret Go boards — giving AlphaGo a better idea of which strategies are likely to be successful. The technique is “phenomenal”, says Jonathan Schaeffer, a computer scientist at the University of Alberta in Edmonton, Canada, whose software Chinook solved3 draughts in 2007. Rather than follow the trend of the past 30 years of trying to crack games using computing power, DeepMind has reverted to mimicking human-like knowledge, albeit by training, rather than by being programmed, he says. The feat also shows the power of deep learning, which is going from success to success, says Coulom. “Deep learning is killing every problem in AI.”
A new DARPA program aims to develop an implantable neural interface able to provide unprecedented signal resolution and data-transfer bandwidth between the human brain and the digital world. The interface would serve as a translator, converting between the electrochemical language used by neurons in the brain and the ones and zeros that constitute the language of information technology. The goal is to achieve this communications link in a biocompatible device no larger than one cubic centimeter in size, roughly the volume of two nickels stacked back to back.
The World Economic Forum predicts heavy job losses to automation and AI/ Robots as technological advances reshape the industrial landscape.
"Bleak" only because humans are not able psychologically and otherwise to think about the distribution of wealth arising from technological breakthroughs. Paradoxically, technology is blamed of this projected situation while obviously it is the source of a future abundance.
Practical artificial intelligence has made its way out of the labs and into our daily lives. And judging from the pace of activity in the startup community and the major IT powerhouses, it will only grow in its ability to help us all get things done.
Most AI solutions today are fielded by the big players in IT. For example, Apple’s Siri or the capabilities Apple embedded directly in iOS9, Google’s many savvy search solutions, Amazon’s very smart recommendation engine, and IBM’s Watson.
We expect to see a new wave of AI solutions that deliver value from smaller start-up companies as well. This is a very crowded space, with plenty of VC funding for entrepreneurs with capabilities in a wide-range of AI disciplines.
Most of these firms seem to be on one of two paths: Success, which will mean being acquired by Facebook, Apple, Microsoft or IBM; or failure, which will see them acquired by the same firms for a lower price for their talent. Either way, the innovation continues.
Oxford philosopher Nick Bostrom, in his recent and celebrated book Superintelligence: Paths, Dangers, Strategies, argues that advanced AI poses a potentially major existential risk to humanity, and that advanced AI development should be heavily regulated and perhaps even restricted to a small set of government-approved researchers.
Bostrom’s ideas and arguments are reviewed and explored in detail, and compared with the thinking of three other current thinkers on the nature and implications of AI: Eliezer Yudkowsky of the Machine Intelligence Research Institute (formerly Singularity Institute for AI), and David Weinbaum (Weaver) and Viktoras Veitas of the Global Brain Institute. Relevant portions of Yudkowsky’s book Rationality: From AI to Zombies are briefly reviewed, and it is found that nearly all the core ideas of Bostrom’s work appeared previously or concurrently in Yudkowsky’s thinking.
However, Yudkowsky often presents these shared ideas in a more plain-spoken and extreme form, making clearer the essence of what is being claimed. For instance, the elitist strain of thinking that one sees in the background in Bostrom is plainly and openly articulated in Yudkowsky, with many of the same practical conclusions (e.g., that it may well be best if advanced AI is developed in secret by a small elite group).
Bostrom and Yudkowsky view intelligent systems through the lens of reinforcement learning — they view them as “reward-maximizers” and worry about what happens when a very powerful and intelligent reward-maximizer is paired with a goal system that gives rewards for achieving foolish goals, like tiling the universe with paperclips. Weinbaum and Veitas’s recent paper “Open-Ended Intelligence” presents a starkly alternative perspective on intelligence, viewing it as centered not on reward maximization, but rather on complex self-organization and self-transcending development that occurs in close coupling with a complex environment that is also ongoingly self-organizing, in only partially knowable ways.
It is concluded that Bostrom and Yudkowsky’s arguments for existential risk have some logical foundation, but are often presented in an exaggerated way. For instance, formal arguments whose implication is that the “worst case scenarios” for advanced AI development are extremely dire are often informally discussed as if they demonstrated the likelihood, rather than just the possibility, of highly negative outcomes. And potential dangers of reward-maximizing AI are taken as problems with AI in general, rather than just as problems of the reward-maximization paradigm as an approach to building superintelligence.
If one views past, current, and future intelligence as “open-ended,” in the vernacular of Weaver and Veitas, the potential dangers no longer appear to loom so large, and one sees a future that is wide-open, complex and uncertain, just as it has always been.
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