DISCOVERING and harnessing fire unlocked more nutrition from food, feeding the bigger brains and bodies that are the hallmarks of modern humans. Google's chief executive, Sundar Pichai, thinks his company's development of artificial intelligence trumps that. "AI is one of the most important things that humanity is working on," he told an event in California earlier this year. "It's more profound than, I don't know, electricity or fire." Hyperbolic analogies aside, Google's AI techniques are becoming more powerful and more important to its business.

As someone whose work crosses so many disciplines, I spend a fair bit of my days skimming new developments across not only computer science, but the humanities, social sciences, arts and many other fields, looking for connections and unexpected new approaches that might benefit my own work. The intensely siloed nature of academia is well known, but equally striking is just how rapidly citation standards are falling in a Google Scholar world filled with explosive growth in available knowledge, in which scholars seem genuinely unaware of developments across the rest of their own field, not to mention the rest of academia. Could machine learning approaches dramatically reform the "related work" and citation review component of peer review and academic publishing? Perhaps the most striking element of modern scholarship is that in an era when much of our modern scholarship is available through web and academic database searches, it takes only a few mouse clicks to compile a cross-section of the recent developments in a given space. Yet, peruse the "related work" or "background" section of a typical academic paper and it is amazing just how discipline-specific and artificially circumscribed the set of references are.

There's no shortage of odes to the ability of athletes and high school sports teams to be the center and glue of a small town. But what if someone paid similar attention to the brainpower -- in this case the high school robotics competition and its ability to capture the hearts of that small town? In a profile in the Star Tribune in April, the Star Tribune's John Reinan said the kids are "Hoosiers with robots," a reference to the movie of an Indiana small town high school team that won a state championship. Greenbush has two of those, winning one in 2016, finishing second last year, and winning the second last month. The film will premiere at the Roso Theater in Roseau, Minn., in August.

However, be warned, it has a lot of maths If you are able to get through it, you will get a very good foundational knowledge on ML. If theory is not your cup of tea, another way to approach ML is to just implement it and learn as you go. You don't need to get a PhD in ML to start implementing it. This is the philosophy behind Jeremy Howard's and Rachel Thomas's http://www.fast.ai. They take you through the implementation steps and introduce you to the theory on a need to know basis, in essence you are doing a top down approach. I am still a few lessons away from finishing the fast.ai

The trash truck rumbles down the street, and its cameras pour video into the city's data lake. An AI-powered application mines that image data looking for graffiti--and advises whether to dispatch a fully equipped paint crew or a squad with just soap and brushes. Meanwhile, cameras on other city vehicles could feed the same data lake so another application detects piles of trash that should be collected. That information is used by an application to send the right clean-up squad. Citizens, too, can get into the act, by sending cell phone pictures of graffiti or litter to the city for AI-driven processing.

"I don't think Homo sapiens-type people will exist in 10 or 20 years' time," Getnet Assefa, 31, speculates as he gazes into the reconstructed eye sockets of Lucy, one of the oldest and most famous hominid skeletons known, at the National Museum of Ethiopia. "Slowly the biological species will disappear and then we will become a fully synthetic species," Assefa says. "Perception, memory, emotion, intelligence, dreams--everything that we value now--will not be there," he adds. Assefa is a computer scientist, a futurist, and a utopian--but a pragmatic one at that. He is founder and chief executive of iCog, the first artificial intelligence (AI) lab in Ethiopia, and a stone's throw from the home of Lucy. Their desks are cluttered with electronic components and dismembered robot body parts, from a soccer-playing bot called Abebe to a miniature robo-Einstein.

It is almost certain that the sub-field of machine learning/artificial intelligence has progressively gained more fame in recent years. As Big Data is in fashion in the tech industry right now, machine learning is staggeringly effective to make predictions or computed recommendations with lot of information. Probably the most well-known cases of machine learning are Netflix or Amazon's algorithms. Machine learning is a type of artificial intelligence (AI) that enables programming applications to be exact in anticipating results without being explicitly modified. The fundamental preface of machine learning is to build algorithms that can get input information and utilize statistical analysis to predict an output value within a worthy range.

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.