The government is likely to fall far short of its target of issuing work permits under a new visa system to 340,000 non-Japanese over the next five years to alleviate acute labor shortages in 14 sectors. As of the end of September, only 219 foreign residents had obtained the "specified skills" visa introduced last April, according to the Immigration Services Agency. The number of people holding the status abroad stood at 1,024 as of Nov. 15. Non-Japanese wishing to obtain the specified skills visa are required to pass a test on the skills needed in their desired work sector as well as a Japanese-language proficiency examination. People who have completed the government's three-year technical intern training program are exempt from taking the test and are allowed to change their visa status to the new one.

Google and Alphabet CEO Sundar Pichai takes his sweet time getting to the point in a new Financial Times editorial. But when he gets there, he leaves little room for interpretation: "...there is no question in my mind that artificial intelligence needs to be regulated. It is too important not to." After laying out his relationship with technology and offering a few examples where innovation has had unintended negative consequences, Pichai makes the case that while AI is powerful and useful, we must balance its "potential harms... with social opportunities." Of course, this call for "balance" leaves some questions about how tight the regulation is that Pichai is talking about.

Cybercriminals are always evolving their efforts and coming up with more advanced ways to target their victims. And while there are many tools available to stop them, there is a lot of space for improvement. Especially if you take automation into account. Machine learning and artificial intelligence are playing a significant role in cybersecurity. Automation tools can prevent, detect, and deal with tons of cyber threats way more efficiently and faster than humans. And it will continue to expand down the road.

Machine learning and artificial intelligence are playing a significant role in cybersecurity.

Hundreds of law enforcement agencies across the US have started using a new facial recognition system from Clearview AI, a new investigation by The New York Times has revealed. The database is made up of billions of images scraped from millions of sites including Facebook, YouTube, and Venmo. The Times says that Clearview AI's work could "end privacy as we know it," and the piece is well worth a read in its entirety. The use of facial recognition systems by police is already a growing concern, but the scale of Clearview AI's database, not to mention the methods it used to assemble it, is particularly troubling. The Clearview system is built upon a database of over three billion images scraped from the internet, a process which may have violated websites' terms of service.

Google's chief executive called Monday for a balanced approach to regulating artificial intelligence, telling a European audience that the technology brings benefits but also "negative consequences." Sundar Pichai's comments come as lawmakers and governments seriously consider placing limits on how artificial intelligence is used. "There is no question in my mind that artificial intelligence needs to be regulated. The question is how best to approach this," Pichai said, according to a transcript of his speech at a Brussels think tank. He said there's an important role for governments to play and that as the European Union and the U.S. start drawing up their own approaches to regulation, "international alignment" of any eventual rules will be critical.

Developed at the Artificial Intelligence Center of the Stanford Research Institute (SRI) from 1966 to 1972, SHAKEY was the world's first mobile intelligent robot. According to the 2017 IEEE Milestone citation, it "could perceive its surroundings, infer implicit facts from explicit ones, create plans, recover from errors in plan execution, and communicate using ordinary English. SHAKEY's software architecture, computer vision, and methods for navigation and planning proved seminal in robotics and in the design of web servers, automobiles, factories, video games, and Mars rovers." In November 1963, Charles Rosen, head of the AI group at SRI, wrote a memo in which "he proposed development of a mobile'automaton' that would combine the pattern-recognition and memory capabilities of neural networks with higher-level AI programs," according to Nils Nilsson in his book The Quest for Artificial Intelligence. In April 1964, SRI submitted to the Advanced Research Projects Agency (ARPA) at the U.S. Department of Defense, a proposal for research in "Intelligent Automata," which it claimed would ultimately lead to "the development of machines that will perform tasks that are presently considered to require human intelligence."

Emergency Response Management (ERM) is a critical problem faced by communities across the globe. Despite its importance, it is common for ERM systems to follow myopic and straight-forward decision policies in the real world. Principled approaches to aid decision-making under uncertainty have been explored in this context but have failed to be accepted into real systems. We identify a key issue impeding their adoption - algorithmic approaches to emergency response focus on reactive, post-incident dispatching actions, i.e. optimally dispatching a responder after incidents occur. However, the critical nature of emergency response dictates that when an incident occurs, first responders always dispatch the closest available responder to the incident. We argue that the crucial period of planning for ERM systems is not post-incident, but between incidents. However, this is not a trivial planning problem - a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem. An orthogonal problem in ERM systems is to plan under limited communication, which is particularly important in disaster scenarios that affect communication networks. We address both the problems by proposing two partially decentralized multi-agent planning algorithms that utilize heuristics and the structure of the dispatch problem. We evaluate our proposed approach using real-world data, and find that in several contexts, dynamic re-balancing the spatial distribution of emergency responders reduces both the average response time as well as its variance.

Deep learning methods are the gold standard for non-linear statistical modeling in computer vision and in natural language processing but are rarely used in psychometrics. To bridge this gap, we present a novel deep learning algorithm for exploratory item factor analysis (IFA). Our approach combines a deep artificial neural network (ANN) model called a variational autoencoder (VAE) with recent work that uses regularization for exploratory factor analysis. We first provide overviews of ANNs and VAEs. We then describe how to conduct exploratory IFA with a VAE and demonstrate our approach in two empirical examples and in two simulated examples. Our empirical results were consistent with existing psychological theory across random starting values. Our simulations suggest that the VAE consistently recovers the data generating factor pattern with moderate-sized samples. Secondary loadings were underestimated with a complex factor structure and intercept parameter estimates were moderately biased with both simple and complex factor structures. All models converged in minutes, even with hundreds of thousands of observations, hundreds of items, and tens of factors. We conclude that the VAE offers a powerful new approach to fitting complex statistical models in psychological and educational measurement.

An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop AI algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision-making may be inherently prone to unfairness, even when there is no intention for it. This paper presents an overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms. The paper begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, towards a better understanding of which mechanisms should be used in different scenarios. The paper then describes the most commonly used fairness-related datasets in this field. Finally, the paper ends by reviewing several emerging research sub-fields of algorithmic fairness.