Researchers have developed a system to revolutionize future roadways, doing away with traffic lights to eliminate long waits and reduce emissions. The project, initiated by the MIT Senseable City Lab, proposes'slot-based intersections' for self-driving cars, which designate individual times for each vehicle to enter the roadway. To avoid traffic delays and short stops, autonomous vehicles would use sensors to communicate with those around them while maintaining a safe distance. Researchers have developed a system to revolutionize future roadways, doing away with traffic lights to eliminate long waits and reduce emissions. The project proposes'slot-based intersections' for self-driving cars, which designate individual times for each vehicle to enter the roadway Each autonomous car would be equipped with sensors to'communicate' with the cars around it.

Google made its Cloud Machine Learning platform, which is used by Google Photos, Translate, and Inbox, available to developers today. During the announcement at NEXT 2016, Google's Cloud Platform conference, Alphabet Chairman Eric Schmidt called machine learning "what's next," according to TechCrunch. Machine learning powers features like the Google app's speech recognition and the smart reply feature in the Inbox app, and Google sees the technology as the future of computing. "Cloud Machine Learning will take machine learning mainstream, giving data scientists and developers a way to build a new class of intelligent applications," Fausto Ibarra, Google's director of product management wrote in a blog post. "It provides access to the same technologies that power Google Now, Google Photos, and voice recognition in Google Search as easy to use REST APIs."

There's been a huge amount of progress in machine learning in the last five years, largely due to breakthroughs in deep learning. You might not be directly aware of it, but we're at the beginning of a machine learning boom right now, a neural network renaissance. Google and Facebook are pouring huge amounts of money into deep learning. In the next few years, we're going to see the fruits of these investments. Self-driving cars, automatic closed captions and more accurate machine translation come to mind, but I would argue that the ramifications are going to quickly expand much beyond this.

Robotics companies and academics descended on a resort in Palm Springs this week for an invitation-only conference organized by Amazon.com Inc. to bring together experts in artificial intelligence, robotics, home automation and space exploration. At the event, held at the Parker Palm Springs, there were robotic arms dueling with light sabers from Star Wars, seminars about imbuing machines with human values and a celebrity appearance by film director Ron Howard, known for his portrayal of Richie Cunningham in the sitcom Happy Days. Jeff Bezos mingled with attendees; one said he drank single-malt whiskey with Amazon's chief executive officer. Representatives from companies such as Rethink Robotics, Toyota Motor Corp., and others attended the conference, called MARS, short for Machine-Learning (Home) Automation, Robotics and Space Exploration.

The following is an edited, updated version of an article originally written for IEEE Spectrum. I'm 62, with all that entails. I still play a mean game of hockey, but entropy looms ever larger. So part of me wants very much to believe that we are rapidly approaching "The Singularity." Like heaven, the Singularity comes in many versions, but most involve bionic brain boosting.

Associated Press/Ahn Young-joonTV screens show the live broadcast of the Google DeepMind Challenge Match between Google's artificial intelligence program, AlphaGo, and South Korean professional Go player Lee Sedol, at the Yongsan Electronic store in Seoul, South Korea, Tuesday, March 15, 2016. Humans have been taking a beating from computers lately. The 4-1 defeat of Go grandmaster Lee Se-Dol by Google's AlphaGo artificial intelligence (AI) is only the latest in a string of pursuits in which technology has triumphed over humanity. Self-driving cars are already less accident-prone than human drivers, the TV quiz show Jeopardy! is a lost cause, and in chess humans have fallen so woefully behind computers that a recent international tournament was won by a mobile phone. There is a real sense that this month's human vs AI Go match marks a turning point.

Microsoft has a new artificial intelligence bot, named Taylor, that tries to hold conversations on Twitter, Kik, and GroupMe. And she makes me feel terribly old and out of touch. Tay, as she calls herself, is a chatbot that's targeted at 18- to 24-year-olds in the US. Just tweet at her or message her and she responds with words and occasionally meme pictures. She's meant to be able to learn a few things about you--basic details like nickname, favorite food, relationship status--and is supposed to be able to have engaging conversations.

An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.

Maximizing the speed and precision of communication while minimizing power dissipation is a fundamental engineering design goal. Also, biological systems achieve remarkable speed, precision and power efficiency using poorly understood physical design principles. Powerful theories like information theory and thermodynamics do not provide general limits on power, precision and speed. Here we go beyond these classical theories to prove that the product of precision and speed is universally bounded by power dissipation in any physical communication channel whose dynamics is faster than that of the signal. Moreover, our derivation involves a novel connection between friction and information geometry. These results may yield insight into both the engineering design of communication devices and the structure and function of biological signaling systems.

An increasing number of people are using online social networking services (SNSs), and a significant amount of information related to experiences in consumption is shared in this new media form. Text mining is an emerging technique for mining useful information from the web. We aim at discovering in particular tweets semantic patterns in consumers' discussions on social media. Specifically, the purposes of this study are twofold: 1) finding similarity and dissimilarity between two sets of textual documents that include consumers' sentiment polarities, two forms of positive vs. negative opinions and 2) driving actual content from the textual data that has a semantic trend. The considered tweets include consumers opinions on US retail companies (e.g., Amazon, Walmart). Cosine similarity and K-means clustering methods are used to achieve the former goal, and Latent Dirichlet Allocation (LDA), a popular topic modeling algorithm, is used for the latter purpose. This is the first study which discover semantic properties of textual data in consumption context beyond sentiment analysis. In addition to major findings, we apply LDA (Latent Dirichlet Allocations) to the same data and drew latent topics that represent consumers' positive opinions and negative opinions on social media.