China is gearing up to build a technology park in Beijing entirely dedicated to the development of artificial intelligence, news first reported by Xinhua, the country's official press agency. Master plan: The endeavor is just the latest sign of China's remarkable ambition to master and dominate artificial intelligence by 2020. Last year the central government published a three-year-plan to invest huge sums in AI, and to apply the technology across the country's industries and economy (see "China's AI Awakening"). Key details: The plan will apparently establish a "national AI research center" and, interestingly, will include efforts to form partnerships with foreign research institutions and companies. This seems like a smart move given the opportunities presented by China's vast economy, and it could help strengthen the country's position as a force in AI in years to come.

Sophia the robot has been on a roll lately. Earlier in the year, its creator David Hanson told Jimmy Fallon that the bot is "basically alive." At the beginning of October, it showed up at the United Nations, announcing to delegates: "I am here to help humanity create the future." And just last week, Sophia was awarded an honorary citizenship by Saudi Arabia. "Sophia the robot becomes first humanoid Saudi citizen."

The year is coming to an end. I did not write nearly as much as I had planned to. But I'm hoping to change that next year, with more tutorials around Reinforcement Learning, Evolution, and Bayesian Methods coming to WildML! And what better way to start than with a summary of all the amazing things that happened in 2017? Looking back through my Twitter history and the WildML newsletter, the following topics repeatedly came up.

Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the underlying Gaussian germ. Several rotations have been proposed in the literature resulting in adaptations with different convergence properties. In this paper we present a new adaptation mechanism that builds on compressive sensing algorithms, resulting in a reduced polynomial chaos approximation with optimal sparsity. The developed adaptation algorithm consists of a two-step optimization procedure that computes the optimal coefficients and the input projection matrix of a low dimensional chaos expansion with respect to an optimally rotated basis. We demonstrate the attractive features of our algorithm through several numerical examples including the application on Large-Eddy Simulation (LES) calculations of turbulent combustion in a HIFiRE scramjet engine.

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data-rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function-valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post-regularization and post-selection inference that are uniformly valid (honest) across a wide-range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets.

Yesterday, CES announced that FCC Chairman Ajit Pai would no longer be appearing at the trade show where he was scheduled to take part in a conversation with FTC Chairman Maureen Ohlhausen. No reasons were given by CES or the FCC at the time for the sudden change in plans, but Recode now reports that the cancellation is due to Pai receiving death threats. Two FCC sources said the threats were the cause and that law enforcement had become involved with the issue. Pai has come under fire for a number of the decisions he's made since taking over as chairman, but none have garnered as much negative attention as his rollback of net neutrality protections. Pai has stated that he and his family have been threatened both in public and online and he and the rest of the FCC were forced to briefly suspend their December 14th vote on the net neutrality rollbacks after a bomb threat was issued.

Thsi article is a slightly modified version of an invited address that was given at the Eighth IEEE Conference on Artificial Intelligence for Applications in Monterey, California, on 2 March 1992. It describes the lessons learned in developing and implementing the Artificial Intelligence Research and Development Program at the National Aeronautics and Space Administration (NASA). In so doing, the article provides a historical perspective of the program in terms of the stages it went through as it matured. These stages are similar to the "ages of artificial intelligence" that Pat Winston described a year before the NASA program was initiated. The final section of the article attempts to generalize some of the lessons learned during the first seven years of the NASA AI program into AI program management heuristics.

The Institute for Public Policy Research seems to think it is expounding some new ideas on the dangers of future technology (Poorest to fare worst in age of automation, 28 December), but in fact these ideas are half a century old. Norbert Wiener pointed them out in his book on Cybernetics, written in 1947 and published in 1948. His argument, in paraphrase, was that the first industrial revolution – the coming of steam power in the late 18th century – represented the devaluation of muscle, so that humans only found purpose as controllers of machines, in factories. The second industrial revolution, in the last century through automation and the digital economy, represents the devaluation of the human brain. If you devalue a man's (or woman's) muscles and also his brain, what has he got to sell in terms of his labour?

The Jobs of the Future Are a Thing of the Past. "You may have read about the outsourcing issue, the great X-factor in American politics today, in cover articles in Time, Wired, Business Week.... In New Hampshire, John Kerry was asked about the problem. His answer: 'We have to create the next wave of those kinds of jobs that come from the fact that we're highly educated and deeply committed to science and technology education.' He mentioned artificial intelligence--and drew a laugh from a computer science professor who noted that artificial intelligence, the gleaming dream of the 1990s, has hardly created a single job in the world."

In the last year, we've seen Google form the DeepMind Ethics & Society to investigate the implications of its AI in society, and we've witnessed the rise of intelligent sex dolls. We've had to take a deep look at whether the warbots we're developing will actually comply with our commands and whether tomorrow's robo-surgeons will honor the Hippocratic Oath. So it's not to say that such restrictions can't be hard-coded into an AI operating system, just that additional nuance is needed, especially as 2018 will see AI reach deeper into our everyday lives. Asimov's famous three laws of robotics is "a wonderful literary vehicle but not a pragmatic way to design robotic systems," said Dr. Ron Arkin, Regents' professor and director of Mobile Robot Laboratory at the Georgia Institute of Technology. Envisioned in 1942, when the state of robotics was rudimentary at best, the laws were too rigid for use in 2017.