On Friday, June 24, 2016, the world watched in horror as Britain voted to commit economic suicide as a nation. On November 8, 2016, America will vote. Will it also commit economic and political suicide? Increasing inequality is building up great stress in the world economic system. The disenfranchised masses are expressing their anger, including in irrational ways such as the Brexit vote.

We analyze the sample complexity of learning graphical games from purely behavioral data. We assume that we can only observe the players' joint actions and not their payoffs. We analyze the sufficient and necessary number of samples for the correct recovery of the set of pure-strategy Nash equilibria (PSNE) of the true game. Our analysis focuses on directed graphs with $n$ nodes and at most $k$ parents per node. Sparse graphs correspond to ${k \in O(1)}$ with respect to $n$, while dense graphs correspond to ${k \in O(n)}$. By using VC dimension arguments, we show that if the number of samples is greater than ${O(k n \log^2{n})}$ for sparse graphs or ${O(n^2 \log{n})}$ for dense graphs, then maximum likelihood estimation correctly recovers the PSNE with high probability. By using information-theoretic arguments, we show that if the number of samples is less than ${\Omega(k n \log^2{n})}$ for sparse graphs or ${\Omega(n^2 \log{n})}$ for dense graphs, then any conceivable method fails to recover the PSNE with arbitrary probability.

This week's milestones in the history of technology include Alan Turing anticipating today's deep learning by intelligent machines and concerns about the impact of AI on jobs, Clifford Stoll anticipating Mark Zuckerberg, and establishing the FCC and NPR. Alan Turing gives a talk at the London Mathematical Society in which he declares that "what we want is a machine that can learn from experience." Anticipating today's enthusiasm about machine learning and deep learning, Alan Turing described how intelligent machines will work: Let us suppose we have set up a machine with certain initial instruction tables, so constructed that these tables might on occasion, if good reason arose, modify those tables. One can imagine that after the machine had been operating for some time, the instructions would have altered out of all recognition, but nevertheless still be such that one would have to admit that the machine was still doing very worthwhile calculations. Possibly it might still be getting results of the type desired when the machine was first set up, but in a much more efficient manner.

A page from the notebook of British mathematician and pioneer in computer science Alan Turing, the World War II code-breaking genius, is displayed in front of his portrait during an auction preview in Hong Kong Thursday, March 19, 2015. This week's milestones in the history of technology include Alan Turing anticipating today's deep learning by intelligent machines and concerns about the impact of AI on jobs, Clifford Stoll anticipating Mark Zuckerberg, and establishing the FCC and NPR. Alan Turing gives a talk at the London Mathematical Society in which he declares that "what we want is a machine that can learn from experience." Anticipating today's enthusiasm about machine learning and deep learning, Alan Turing described how intelligent machines will work: Let us suppose we have set up a machine with certain initial instruction tables, so constructed that these tables might on occasion, if good reason arose, modify those tables. One can imagine that after the machine had been operating for some time, the instructions would have altered out of all recognition, but nevertheless still be such that one would have to admit that the machine was still doing very worthwhile calculations. Possibly it might still be getting results of the type desired when the machine was first set up, but in a much more efficient manner.

Mark Palmer is senior vice president and general manager of engineering at TIBCO. Much has been made about the business implications of recent, rapid advancements in cognitive computing -- that is, the possibility of advanced analytics tools to help human knowledge workers glean actionable insight from vast and deep lakes of historical, transactional and machine-generated information. When utilized well, cognitive tools help humans identify patterns and surface previously undetected cyberattack patterns on your company, customer buying behavior or predictive signals of catastrophic equipment failure based on readings from sensor-enabled devices. But as your business inevitably becomes more algorithmic, you're faced with the next problem: Many algorithms, once discovered, have a remarkably short shelf-life. Algorithmic excellence in analytics requires more than just great math; you must also become as agile at killing off weak or vanquished algorithms as a NASCAR pit crew changing worn tires -- you need to be replacing them with promising new ones.

There may come a time when SpaceX landing a rocket stage back on Earth after flying to space isn't news -- when we can simply take these science-fiction looking landings for granted and brush them off as no big deal. The Elon Musk-founded spaceflight company stuck yet another beautiful landing of the first stage of a Falcon 9 rocket after launching an uncrewed Dragon spacecraft toward the International Space Station from a historic launch pad. The Dragon spacecraft was loaded down with thousands of supplies for NASA. The booster set down gently on a pad at Cape Canaveral, Florida about 8 minutes after its 9:39 a.m. This landing marks the company's eighth total landing and third back on solid ground.

"This is a propaganda machine. It's targeting people individually to recruit them to an idea. It's a level of social engineering that I've never seen before. They're capturing people and then keeping them on an emotional leash and never letting them go," said professor Jonathan Albright. Albright, an assistant professor and data scientist at Elon University, started digging into fake news sites after Donald Trump was elected president. Through extensive research and interviews with Albright and other key experts in the field, including Samuel Woolley, Head of Research at Oxford University's Computational Propaganda Project, and Martin Moore, Director of the Centre for the Study of Media, Communication and Power at Kings College, it became clear to Scout that this phenomenon was about much more than just a few fake news stories. It was a piece of a much bigger and darker puzzle -- a Weaponized AI Propaganda Machine being used to manipulate our opinions and behavior to advance specific political agendas. By leveraging automated emotional manipulation alongside swarms of bots, Facebook dark posts, A/B testing, and fake news networks, a company called Cambridge Analytica has activated an invisible machine that preys on the personalities of individual voters to create large shifts in public opinion.

Artificial intelligence systems will be able to give managers real-time insights about their business operations -- as well as detect early warnings of problems before they occur. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Editor's Note: This article is one of a special series of 14 commissioned essays MIT Sloan Management Review is publishing to celebrate the launch of our new Frontiers initiative. Each essay gives the author's response to this question: "Within the next five years, how will technology change the practice of management in a way we have not yet witnessed?" Workers on the factory floor have suddenly gathered at a point along the production line.

Artificial Intelligence already had a massive impact in the past years, but where will AI be in the coming years? Here is a list of predictions. Waiting to make their moveAsia's looming labour shortage p There is an obvious solution p print-edition iconFrom the print edition Asia p Feb 11th 2017 p THE … The first FDA approval for a machine learning application to be used in a clinical setting is a big step forward for AI and machine learning in healthcare and industry as a whole. Until recently, artificial intelligence (AI) was similar to nuclear fusion in unfulfilled promise. ManpowerGroup, one of the world's largest jobs companies, released a report detailing how the technological revolution is going to change the … Replacing the real world with a virtual one is a neat trick.

There is an urgent need to expand the AI epistemic community beyond the specific geographies in which it is currently clustered. Artificial Intelligence (AI) is no longer the subject of science fiction and is profoundly transforming our daily lives. While computers have already been mimicking human intelligence for some decades now using logic and if-then kind of rules, massive increases in computational power are now facilitating the creation of'deep learning' machines i.e. algorithms that permit software to train itself to recognise patterns and perform tasks, like speech and image recognition, through exposure to vast amounts of data. These deep learning algorithms are everywhere, shaping our preferences and behaviour. Facebook uses a set of algorithms to tailor what news stories an individual user sees and in what order.