A new academic paper, Machine Learning for Trading, is the first conclusive study that shows success in having a machine learning-based trading strategy. The author, Gordon Ritter, Adjunct Professor in the Mathematics in Finance Program, New York University, constructed an artificial system which he knew would admit a profitable strategy, to see if a machine would find it. In order to train a machine-learning algorithm to behave as a rational risk-averse investor required appropriate reinforcement learning, specifically a mathematical technique called Q-learning (playing some sort of game where you are trying to maximise the reward function that may occur at several periods in the future). The machine learning agent found and exploited arbitrage opportunities in the presence of transaction costs in a simulated market proof of concept. Ritter explained: "I was really trying to answer the question, does machine learning have any application to trading at all, or no application; sort of a binary question. Can machine learning be applied to the problem of trading? "I reasoned that in a system that I know admits a profitable trading strategy, because I constructed it that way, can the machine find it.

Europe, while making progress, is behind the US and China in capturing the opportunities of artificial intelligence and automation. Digitization is everywhere, but adoption is uneven across companies, sectors, and economies, and the leaders are capturing most of the benefits. Accelerating progress in AI and automation now bring further opportunities for users, businesses, and the economy. Europe, while making progress, is behind the United States and China. This briefing note was prepared for the European Union Heads of State Tallinn Digital Summit, which brought together heads of state and CEOs to discuss the steps needed to enable people, enterprises, and governments to fully tap into the potential of innovative technologies and digitization. Digital technologies have been evolving and disrupting the way we live, work, and organize for years.

Toyota and Mazda will build a $1.6 billion U.S. assembly plant, adding up to 4,000 new jobs. Toyota will be highlighting an array of experimental technologies aimed at improving safety and anticipating drivers' desires at the Tokyo Motor Show later this month. Toyota Motor Corp. manager Makoto Okabe told reporters Monday that the use of artificial intelligence means cars may get to know drivers as human beings by analyzing their facial expressions, driving habits and social media use. Such a vehicle might adjust drivers' seats to calm them when they're feeling anxious or jiggle them to make them more alert when they seem sleepy. It might also suggest a stop at a noodle joint along the way.

Toyota Motor Corp. manager Makoto Okabe told reporters Monday that the use of artificial intelligence means cars may get to know drivers as human beings by analyzing their facial expressions, driving habits and social media use.

Traffic policewoman Zheng Yijiong, 24, works in Hangzhou City, the capital of East China----s Zhejiang Province. Zheng is the first Chinese police officer to work with the assistance of artificial intelligence in a local police unit called TPTU, which is responsible for rapid responses to road issues.

This work studies the linear approximation of high-dimensional dynamical systems using low-rank dynamic mode decomposition (DMD). Searching this approximation in a data-driven approach can be formalised as attempting to solve a low-rank constrained optimisation problem. This problem is non-convex and state-of-the-art algorithms are all sub-optimal. This paper shows that there exists a closed-form solution, which can be computed in polynomial-time, and characterises the $\ell_2$-norm of the optimal approximation error. The theoretical results serve to design low-complexity algorithms building reduced models from the optimal solution, based on singular value decomposition or low-rank DMD. The algorithms are evaluated by numerical simulations using synthetic and physical data benchmarks.

We consider a class of nonconvex nonsmooth optimization problems whose objective is the sum of a nonnegative smooth function and a bunch of nonnegative proper closed possibly nonsmooth functions (whose proximal mappings are easy to compute), some of which are further composed with linear maps. This kind of problems arises naturally in various applications when different regularizers are introduced for inducing simultaneous structures in the solutions. Solving these problems, however, can be challenging because of the coupled nonsmooth functions: the corresponding proximal mapping can be hard to compute so that standard first-order methods such as the proximal gradient algorithm cannot be applied efficiently. In this paper, we propose a successive difference-of-convex approximation method for solving this kind of problems. In this algorithm, we approximate the nonsmooth functions by their Moreau envelopes in each iteration. Making use of the simple observation that Moreau envelopes of nonnegative proper closed functions are continuous difference-of-convex functions, we can then approximately minimize the approximation function by first-order methods with suitable majorization techniques. These first-order methods can be implemented efficiently thanks to the fact that the proximal mapping of each nonsmooth function is easy to compute. Under suitable assumptions, we prove that the sequence generated by our method is bounded and clusters at a stationary point of the objective. We also discuss how our method can be applied to concrete applications such as nonconvex fused regularized optimization problems and simultaneously structured matrix optimization problems, and illustrate the performance numerically for these two specific applications.

Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classification. So far, various supervised AUC optimization methods have been developed and they are also extended to semi-supervised scenarios to cope with small sample problems. However, existing semi-supervised AUC optimization methods rely on strong distributional assumptions, which are rarely satisfied in real-world problems. In this paper, we propose a novel semi-supervised AUC optimization method that does not require such restrictive assumptions. We first develop an AUC optimization method based only on positive and unlabeled data (PU-AUC) and then extend it to semi-supervised learning by combining it with a supervised AUC optimization method. We theoretically prove that, without the restrictive distributional assumptions, unlabeled data contribute to improving the generalization performance in PU and semi-supervised AUC optimization methods. Finally, we demonstrate the practical usefulness of the proposed methods through experiments.

In October 2016, "Westworld" topped the charts as the most-watched premiere season of an HBO original series ever. In the series, a science fiction thriller written and directed by novelist Michael Crichton based on a 1973 film of the same name, Anthony Hopkins takes on the role of Dr Ford, who creates a futuristic western-themed amusement park populated by android hosts to cater human guests, with Evan Rachel Wood playing the role of Dolores, the oldest android host working in the park. Further to the great script and the impressive casting, the success of the series is also undoubtedly linked to its timing. Just one year ago, Lee Sedol, 18-time world Go-board game champion, was beaten by DeepMind's AlphaGo, which was a monumental breakthrough of Artificial Intelligence (AI). Even before the AlphaGo's victory over Lee Sedol, there was growing interest in the potential and risks of humanoid robots and of AI, led by the likes of Stephen Hawking and Elon Musk.

Alibaba started as an online marketplace but has since moved into cloud computing and artificial-intelligence initiatives. In the previous three years, its spending on R&D was about $6 billion--a fraction of what major U.S. technology companies spend. As part of the spending initiative, Alibaba Chief Technology Officer Jeff Zhang will lead a new research unit called the DAMO Academy--an acronym for discovery, adventure, momentum and outlook--that will establish R&D labs world-wide, including one in cooperation with the University of California, Berkeley. The academy, which will also include advisers from universities including the Massachusetts Institute of Technology, Princeton University and Peking University, will fund research into areas such as data analytics, quantum computing and machine learning. "For any major internet company to remain competitive in the future, they will have to invest in these technologies," said Mark Natkin, managing director at Beijing-based consultancy Marbridge Consulting.