The usually sensible people at MIT's Senseable City Lab are looking at the future of the traffic light in the world of the self-driving car, and predict that its days are numbered. Instead, they propose a "slot-based intersections that could replace traditional traffic lights, significantly reducing delays, make traffic patterns more efficient, and lower fuel consumption." It's based on the principle that if all the self-driving cars are communication with each other and know they all are, they can plan speeds and courses so that they essentially pass through each other. Upon approaching an intersection, a vehicle automatically contacts a traffic management system to request access. Each self-driving vehicle is then assigned an individualized time or "slot" to enter the intersection.

Emerging technologies and their possible implications for ethics, security, and even human existence have increasingly gained ground in the past two decades. Some innovations have resulted in obvious security and existential threats: a world with nuclear arms, for example. The potential of other technological shifts, however, has been more mixed. Biotechnologies, genetic engineering, and stems cells have given rise to controversial debates in which advocacy groups on both sides have convincingly put forward pros and cons. The Internet has revolutionized everything from markets to family communication in ways both beneficial and harmful.

It's been an emotional week in the realm of game AI as the world watched the historic five-game showdown between legendary Go world champion Lee Sedol and Google DeepMind's famed deep learning AI AlphaGo. All five games were held at the Four Seasons Hotel in Seoul, South Korea, and as events played out, millions around the world became increasingly captivated. Anticipation for the match began growing in January, when Google's UK-based AI group DeepMind, led by CEO Demis Hassabis, announced their computer algorithm AlphaGo defeated three-time European Go champion Fan Hui 5 games to 0--a victory some experts didn't expect a computer to achieve for a decade. At the end of a Google blog post announcing the win was the promise of a best-of-five face-off between AlphaGo and 18-time international Go champion Lee Sedol, a match equivalent to IBM's Deep Blue defeat of Garry Kasparov in chess in 1997. Notably, Go is inherently more complex than chess and AlphaGo, at least in part, trained itself to play the game.

Predictions about artificial intelligence tend to fall into two scenarios. Others believe it's just a matter of time before software coheres into an army of Terminators that harvest humans for fuel. After spending some time with Tay, Microsoft's new chatbot software, it was easy to see a third possibility: The AI future may simply be incredibly annoying. "I'm a friend U can chat with that lives on the Internets," Tay texted me, adding an emoji shrug. Then: "You walk in on your roomie trying your clothes on, what's the first thing you say." "Didn't realize you liked women's clothes," I texted back, tapping into my iPhone. Tay's reply was a GIF of Macaulay Culkin's Home Alone face. Tay was released on March 23, as a kind of virtual friend on messaging apps Kik, GroupMe, and Twitter.

The Japan eSports Association, which promotes the competitive playing of video games, is nudging the sector toward professional status. JeSPA uses the term e-sport to refer to video games ranging from shootout arcade games to team-based tournaments set on a virtual pitch. It is hard to put a figure on the number of enthusiasts worldwide, but around 100 million are thought to play regularly and seriously. The tournament wound up with finals in five games on March 12 and 13 in Toyosu, Tokyo. The finale was a round of the fighting game "Guilty Gear Xrd -Sign-," which attracted 350 players and roughly 1,000 spectators, while more than 10,000 people followed it on Dwango's Nico Nico Live website.

Note: The reason this post took three weeks to finish is that as I dug into research on Artificial Intelligence, I could not believe what I was reading. It hit me pretty quickly that what's happening in the world of AI is not just an important topic, but by far THE most important topic for our future. So I wanted to learn as much as I could about it, and once I did that, I wanted to make sure I wrote a post that really explained this whole situation and why it matters so much. Not shockingly, that became outrageously long, so I broke it into two parts. This is Part 1--Part 2 is here. We are on the edge of change comparable to the rise of human life on Earth. It seems like a pretty intense place to be standing--but then you have to remember something about what it's like to stand on a time graph: you can't see what's to your right. So here's how it actually feels to stand there: Imagine taking a time machine back to 1750--a time when the world was in a permanent power outage, long-distance communication meant either yelling loudly or firing a cannon in the air, and all transportation ran on hay. When you get there, you retrieve a dude, bring him to 2015, and then walk him around and watch him react to everything. It's impossible for us to understand what it would be like for him to see shiny capsules racing by on a highway, talk to people who had been on the other side of the ocean earlier in the day, watch sports that were being played 1,000 miles away, hear a musical performance that happened 50 years ago, and play with my magical wizard rectangle that he could use to capture a real-life image or record a living moment, generate a map with a paranormal moving blue dot that shows him where he is, look at someone's face and chat with them even though they're on the other side of the country, and worlds of other inconceivable sorcery. This is all before you show him the internet or explain things like the International Space Station, the Large Hadron Collider, nuclear weapons, or general relativity.

"We are absolutely certain that the use of deep-learning machines is going to allow us to have a better understanding of ISIS as a network, and a better understanding of how we can target it precisely and lead to its defeat," Work said March 30 at an event hosted by The Washington Post. Work said he recently met with a firm in Silicon Valley that can crunch vast amounts of social media data to deliver insights. The firm used its analytics capability to recount in real time how a Malaysia Airlines flight was shot down, according to Work. An official investigation concluded that a Russian Buk missile downed the airplane over Ukraine on July 17, 2014, killing 298 people. Courtney Hillson, told FCW the company he referred to is Orbital Insight, a geospatial data firm.

DeepMind was a relatively unknown artificial intelligence (AI) startup in London up until 2014, when it was bought by Google for around 400 million. Today some of the smartest people in the world are queuing up to work at DeepMind, according to an article by Celemency Burton-Hill in The Guardian in February. Interestingly, the same article states that no one has ever left DeepMind, which has created a series of algorithms that can learn for themselves and beat the best humans at games like Go and "Space Invaders." Based in up-and-coming King's Cross, DeepMind now employs around 250 people. However, as Burton-Hill points out, getting a job there is far from easy.

DEDHAM, MA--(Marketwired - Mar 30, 2016) - Rage Frameworks, a provider of knowledge-based automation technology and services, today announced new deployments of its traceable "deep learning" technology known as Rage AI across several global financial services, consumer products and manufacturing firms. The challenges these organizations faced required the understanding and interpretation of complex documents and integration of other transaction data from enterprise resource planning (ERP) systems to identify significant cost efficiencies and compliance conformance. RAGE AI incorporates deep linguistic parsing and proprietary linguistics-based innovations to understand the real meaning of documents and interpret them as a human would, and can operate completely unsupervised or with assistance by human experts. With its traceable, deep learning technology, RAGE AI significantly extends the frontier of deep learning and machine intelligence from "natural language processing" to "natural language understanding." The platform reads and interprets documents within its context, and as a totally transparent solution, RAGE AI enables knowledge workers to move forward confidently knowing the reasoning behind the platform's insights is completely auditable.

Recently, MaxSAT reasoning is shown very effective in computing a tight upper bound for a Maximum Clique (MC) of a (unweighted) graph. In this paper, we apply MaxSAT reasoning to compute a tight upper bound for a Maximum Weight Clique (MWC) of a wighted graph. We first study three usual encodings of MWC into weighted partial MaxSAT dealing with hard clauses, which must be satisfied in all solutions, and soft clauses, which are weighted and can be falsified. The drawbacks of these encodings motivate us to propose an encoding of MWC into a special weighted partial MaxSAT formalism, called LW (Literal-Weighted) encoding and dedicated for upper bounding an MWC, in which both soft clauses and literals in soft clauses are weighted. An optimal solution of the LW MaxSAT instance gives an upper bound for an MWC, instead of an optimal solution for MWC. We then introduce two notions called the Top-k literal failed clause and the Top-k empty clause to extend classical MaxSAT reasoning techniques, as well as two sound transformation rules to transform an LW MaxSAT instance. Successive transformations of an LW MaxSAT instance driven by MaxSAT reasoning give a tight upper bound for the encoded MWC. The approach is implemented in a branch-and-bound algorithm called MWCLQ. Experimental evaluations on the broadly used DIMACS benchmark, BHOSLIB benchmark, random graphs and the benchmark from the winner determination problem show that our approach allows MWCLQ to reduce the search space significantly and to solve MWC instances effectively. Consequently, MWCLQ outperforms state-of-the-art exact algorithms on the vast majority of instances. Moreover, it is surprisingly effective in solving hard and dense instances.