Some people are afraid that heavily armed artificially intelligent robots might take over the world, enslaving humanity – or perhaps exterminating us. These people, including tech-industry billionaire Elon Musk and eminent physicist Stephen Hawking, say artificial intelligence technology needs to be regulated to manage the risks. But Microsoft founder Bill Gates and Facebook's Mark Zuckerberg disagree, saying the technology is not nearly advanced enough for those worries to be realistic. As someone who researches how AI works in robotic decision-making, drones and self-driving vehicles, I've seen how beneficial it can be. I've developed AI software that lets robots working in teams make individual decisions, as part of collective efforts to explore and solve problems. Researchers are already subject to existing rules, regulations and laws designed to protect public safety.

Sampling from distributions is a core problem in statistics, probability, operations research, and other areas involving stochastic models [Gem84; Bré91; Rip87; Has70]. Sampling algorithms are a prerequisite for applying Monte Carlo methods to order to approximate expectations and other integrals. Recent decades have witnessed great success of Markov Chain Monte Carlo (MCMC) algorithms; for instance, see the handbook [Bro11] and references therein. These methods are based on constructing a Markov chain whose stationary distribution is equal to the target distribution, and then drawing samples by simulating the chain for a certain number of steps. An advantage of MCMC algorithms is that they only require knowledge of the target density up to a proportionality constant. However, the theoretical understanding of MCMC algorithms used in practice is far from complete. In particular, a general challenge is to bound the mixing time of a given MCMC algorithm, meaning the number of iterations--as a function of the error tolerance δ, problem dimension d and other parameters--for the chain to arrive at a distribution within distance δ of the target. In this paper, we study a certain class of MCMC algorithms designed for the problem of drawing samples from the uniform distribution over a polytope.

Providing long-range forecasts is a fundamental challenge in time series modeling, which is only compounded by the challenge of having to form such forecasts when a time series has never previously been observed. The latter challenge is the time series version of the cold-start problem seen in recommender systems which, to our knowledge, has not been directly addressed in previous work. In addition, modern time series datasets are often plagued by missing data. We focus on forecasting seasonal profiles---or baseline demand---for periods on the order of a year long, even in the cold-start setting or with otherwise missing data. Traditional time series approaches that perform iterated step-ahead methods struggle to provide accurate forecasts on such problems, let alone in the missing data regime. We present a computationally efficient framework which combines ideas from high-dimensional regression and matrix factorization on a carefully constructed data matrix. Key to our formulation and resulting performance is (1) leveraging repeated patterns over fixed periods of time and across series, and (2) metadata associated with the individual series. We provide analyses of our framework on large messy real-world datasets.

Will AI render human analysts obsolete, or be an extension that helps them be more effective? Are we headed for an AI showdown? Here's the lay of the land in AI territory now. The buzz on artificial intelligence (AI) is deafening. Depending on who is hawking what, AI is either vastly superior to mere mortals, or it's the machine version of a friendly helper not much smarter than its furry, doggy counterpart.

Security firm McAfee has announced new endpoint and cloud offerings at the MPOWER Cybersecurity Summit in Las Vegas. The move is part of McAfee's drive to go beyond machine learning to take advantage of the speed and accuracy of advanced analytics, deep learning and artificial intelligence (AI), and increase the efficiency of security operations. From a hacker perspective, many organisations are still leaving the front door open and the windows unlocked. Failure to protect and handle data correctly can also result in punitive actions for companies participating in the digital economy. Wake up and get the knowledge to get protected.

The UAE recently made a bold move that is perhaps the strongest demonstration of any government's official endorsement of Artificial Intelligence technology. An Artificial Intelligence minister has been appointed, implying enough reliance and expectations of reliance on the technology to warrant the position. The official title will be State Minister for Artificial Intelligence, and the activities of the position will coincide with UAE's 2031 AI Strategy, a comprehensive government effort that will integrate all AI technologies in the society. The young appointee is 27-year-old Omar Sultan Al-Ulama, who brings his experience as Deputy Director of the Future Department and now Managing Director of the World Government Summit to the position. The announcement came on Thursday from UAE Vice President and Prime Minister Sheikh Mohammed, part of a general restructuring of the Cabinet: "We announce the appointment of a minister for artificial intelligence. The next global wave is artificial intelligence and we want the UAE to be more prepared for it."

Attendance: Participation is by invitation due to space constraints. Participants will include government delegates from a range of domains including digital economy ministries, labour ministries, space agency representatives, research ministries, data protection authorities and consumer protection agencies. Contact: For further information, please contact AI@oecd.org. Webcast: You will be able to follow the event live on this page. The conference is being organised by the OECD with support from the Ministry of Internal Affairs and Communications of Japan (MIC).

At the 19th Communist Party Congress, Chinese President Xi Jinping is stamping his authority by mapping out his vision for China for the next 30 years. "The banner of socialism with Chinese characteristics is flying high for the world to see. It will be an era that sees China move closer to the centre stage," Xi said. He aims for the "the rejuvenation of the great Chinese nation" and wants to build a "digital and smart society", a "country of innovators". At the heart of his strategy is an economy built on homemade innovation - with a particular emphasis on robotics, electric cars and artificial intelligence.

Smart dust … it sounds like a magical substance sprinkled on dumber things. Which is kind of true. The concept has been making the hype-cycle rounds late this summer and setting off some industry buzz among megatrend watchers during an otherwise lackluster news and information cycle. But smart dust is not all that new a concept. Not long ago, it might have been known by the more mundane and geeky term micro-electromechanical systems, or MEMS, which is common in the computer chip world.

The rapid pace of innovation and the constantly exploding collection of possibilities is a major contributor to the fun we all have in digital jobs. There is never a boring moment, there is never time when you can't do something faster or smarter. The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. They never had to worry that they have to be in a persistent forward motion… sometimes just to stay current. This reality powers my impostor syndrome, and (yet?) it is the reason that I love working in every dimension of digital. We are at an inflection point in humanity's evolution where in small and big ways, we can actually change the world. With that context, this post is all about career management in the digital space. Like this blog, it will be particularly relevant for those who are in digital analytics and digital marketing. I would offer that the higher-order-bits in each of the three sections will provide valuable food-for-thought for anyone in a digital role.