David Tamayo, CIO, DCS CorporationAn old Chinese proverb says, "The best time to plant a tree was 20 years ago. The second-best time is now." This seems to be the thinking of very smart people when it comes to doing something about protecting humanity from the possible dangers of artificial intelligence (AI). Sure, it might be 20, 50 or even 100 years before AI becomes more intelligent than humans, posing an existential problem for today's sapiens. Many luminaries like Elon Musk, Bill Gates and the late Stephen Hawking have warned that failing to prepare for this eventuality will guarantee our demise in some decades to come. Perhaps we can start by noting that advances in artificial intelligence will not stop.

Acid Software, the developer of the school shooting video game is defending the product and vowing to continue selling it online as parents of slain children and other mass shooting victims work to get the game wiped off the internet. The developer for a video game that stimulates a school shooting has found new ways to sell his game after an online gaming platform removed it, following huge backlash from the parents of children killed in school shootings. "Active Shooter" was removed from the platform Steam after anti-gun activists and the parents of students killed during school shootings criticized the game for allowing players to simulate school shootings by playing the role of the shooter. Ryan Petty, the father of Alaina Petty who was killed in the massacre at Marjory Stoneman Douglas High School in Florida, slammed the game, calling it "despicable," and said it was "unacceptable" that Steam allowed games like this to be shared. Anton Makarevskiy, the game's developer, defended it through his entity, Acid Software, citing free expression rights.

In this paper, we propose a novel reinforcement- learning algorithm consisting in a stochastic variance-reduced version of policy gradient for solving Markov Decision Processes (MDPs). Stochastic variance-reduced gradient (SVRG) methods have proven to be very successful in supervised learning. However, their adaptation to policy gradient is not straightforward and needs to account for I) a non-concave objective func- tion; II) approximations in the full gradient com- putation; and III) a non-stationary sampling pro- cess. The result is SVRPG, a stochastic variance- reduced policy gradient algorithm that leverages on importance weights to preserve the unbiased- ness of the gradient estimate. Under standard as- sumptions on the MDP, we provide convergence guarantees for SVRPG with a convergence rate that is linear under increasing batch sizes. Finally, we suggest practical variants of SVRPG, and we empirically evaluate them on continuous MDPs.

This paper develops a computationally efficient posterior sampling algorithm for Bayesian linear regression models with Gaussian errors. Our new approach is motivated by the fact that existing software implementations for Bayesian linear regression do not readily handle problems with large number of observations (hundreds of thousands) and predictors (thousands). Moreover, existing sampling algorithms for popular shrinkage priors are bespoke Gibbs samplers based on case-specific latent variable representations. By contrast, the new algorithm does not rely on case-specific auxiliary variable representations, which allows for rapid prototyping of novel shrinkage priors outside the conditionally Gaussian framework. Specifically, we propose a slice-within-Gibbs sampler based on the elliptical slice sampler of Murray et al. [2010].

In many real-world problems, there is the possibility to configure, to a limited extent, some environmental parameters to improve the performance of a learning agent. In this paper, we propose a novel framework, Configurable Markov Decision Processes (Conf-MDPs), to model this new type of interaction with the environment. Furthermore, we provide a new learning algorithm, Safe Policy-Model Iteration (SPMI), to jointly and adaptively optimize the policy and the environment configuration. After having introduced our approach and derived some theoretical results, we present the experimental evaluation in two explicative problems to show the benefits of the environment configurability on the performance of the learned policy.

Education is moving away from traditional rows of students looking at the same textbook while a teacher lectures from the front of the room. Today's classrooms are not simply evolving to use more technology and digital resources; they are also investing in machine learning. Machine learning is defined as "a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed." For example, in education, we see machine learning in learning analytics and artificial intelligence. Machine learning is essentially mining data.

HARTFORD, CONNECTICUT – The developer of a school-shooting video game is vowing to continue selling it online as parents of slain children and other mass shooting victims work to get the game wiped off the internet. The "Active Shooter" game was created by Anton Makarevskiy, a 21-year-old developer from Moscow, and is being marketed by his entity Acid Software. Acid said in a Twitter posting Tuesday that it will not be censored and cited free expression rights. The game is branded as a "SWAT simulator" that lets players choose between being an active shooter terrorizing a school or the SWAT team responding to the shooting. Players can choose a gun, grenade or knife, and civilian and police death totals are shown on the screen.

Software Engineer - Machine Learning at Bolt San Francisco, California, United States (Posted Jun 2 2018) About the company Welcome to a new era of ecommerce: Amazon-like checkout. What We Do: End-to-End Payment Processing Hyper-Optimized Checkout 100% Fraud Coverage Clear Insights & Analytics What Does that Mean for Merchants? Happier customers who repeat buy No more fraudulent chargebacks Zero order review overhead Full performance transparency Payments completely handled A single dashboard for business analytics 24/7 premium support Job position Permanent Job description Payment infrastructure on the internet is fragmented and broken. Bolt is building a future where sending payments is as easy as sending messages. To do this, we've redesigned payments from the ground up.

In the past year there's been a bit of a careers and job scare when it comes to artificial intelligence, automation, and related technologies. Big consulting firms have conducted studies about the future of jobs and whether they will be lost to artificial intelligence. The consensus is that jobs will be lost, while some jobs will be created. "The development of automation enabled by technologies including robotics and artificial intelligence brings the promise of higher productivity (and with productivity, economic growth), increased efficiencies, safety, and convenience," said McKinsey in a study released last year. "But these technologies also raise difficult questions about the broader impact of automation on jobs, skills, wages, and the nature of work itself."

As the number of people who work on a volunteer basis or try to contribute for good causes increase in Turkey, we look to the future with confidence. Furthermore, if these volunteers comprise of scientists, academicians and youths, supporting such formations mean paving the way of social developments. The Deep Learning Turkey community, which teaches and guides high school students and undergraduates, turning artificial intelligence into a social responsibility project, has reached out to thousands of youths although it was established only in August of last year. The community helps youths who are interested in artificial intelligence and want to have a career in this field as well as providing information sharing on an open platform for scientists. Deep Learning Turkey is the biggest and the most effective artificial intelligence community in Turkey.