Trying to understand what is *really* going on in the universe.

Chinese Go player Ke Jie reacts during his first match with Google's artificial intelligence program AlphaGo at the Future of Go Summit in Wuzhen, Zhejiang province, China May 23, 2017. Years after Beijing locked out virtually every Alphabet Inc service, executive chairman Eric Schmidt and a cadre of mid-level Chinese government officials kicked off a summit in the canal-laced town of Wuzhen today: A rare instance of the search leader working in tandem with the country's bureaucrats at a high-profile public event. Google experts and prominent local academics will exchange notes and host discussions but the centrepiece will be the 2,500-year-old strategy board game between DeepMind's so-far undefeated AlphaGo system and local champion Ke Jie. Google's absence from China -- a country it initially withdrew from amid fears of censorship and cyber-attacks -- remains the biggest gap in its dominance of global search and video. While Android is the country's most popular mobile software and it sells advertising, other services including search, Gmail, apps and maps are barred by the mainland's firewall.

Nvidia CEO Jen-Hsun Huang unveiled an ambitious new processor for artificial intelligence applications, the Tesla V100. The new chip has 21 billion transistors, and it is an order of magnitude more powerful than the 15-billion transistor Pascal-based processor that Nvidia announced a year ago. It is a huge chip -- 815 square millimeters, or about as big as an Apple Watch face. It has 5,120 CUDA processing cores, and it performs at 7.5 FP64 teraflops. The performance is about three times as fast as last year's product.

A computer wasn't supposed to be able to beat a grandmaster at the ancient game of Go for at least another decade. But AlphaGo, an artificially intelligent system designed by Google-owned DeepMind, did just that. In its public debut last year at a tournament in Seoul, AlphaGo thrashed Lee Sedol, the best player of last decade. Now AlphaGo is back, facing off in China against the world's top player to show just how much further machine-approximated intuition has advanced over the past year, and WIRED is there. Tomorrow morning, AlphaGo is set to play 19-year-old Ke Jie in Wuzhen, a town crisscrossed by canals 80 miles west of Shanghai.

Artificial intelligence and machine learning are often typecast as the futuristic underpinnings for a robot-ruled world -- but in reality, federal agencies are already using more practical applications of the technologies today to improve the way they serve Americans and achieve their missions. The space agency's Jet Propulsion Lab wants to leverage the power of the cloud, machine learning and artificial intelligence to open space voyage to all Americans --whether they're standing on the surface of Mars or in the comfort of their homes. "The idea with this is we're all going to be the future explorers," JPL IT Chief Technology and Innovation Officer Tom Soderstrom said at a recent conference. "Your children are the ones who are one day going to walk on Mars, whether it is virtually through augmented reality or physically as astronauts." Indeed, NASA partnered with Amazon Web Services using its automatic speech recognition and natural language understanding service Lex, which are the same deep-learning technologies that drive the Amazon's Alexa, to develop NASA Mars: an app that allows humans to ask questions about Mars and engage them with NASA's missions.

Cyber attacks have increased in frequency and severity, and financial institutions are particularly interesting targets to cyber criminals. Join this presentation to learn the latest cybersecurity threats and challenges plaguing the financial industry, and the policies and solutions your organization needs to have in place to protect against them. Viewers will learn: • Current trends in Cyber attacks • FFIEC Cyber Assessment Toolkit • NIST Cybersecurity Framework principles • Security Metrics • Oversight of third parties • How to measure cybersecurity preparedness • Automated approaches to integrate Security into DevOps About the Presenter: Ulf Mattsson is the Chief Technology Officer of Security Solutions at Atlantic BT, and earlier at Compliance Engineering. Ulf was the Chief Technology Officer and a founder of Protegrity, He invented the Protegrity Vaultless Tokenization, Data Type Preservation (DTP2) and created the initial architecture of Protegrity's database security technology. Prior to Protegrity, Ulf worked 20 years at IBM in software development and in IBM's Research organization, in the areas of IT Architecture and Security, and received a US Green Card of class'EB 11 – Individual of Extraordinary Ability' after endorsement by IBM.

In deep learning, \textit{depth}, as well as \textit{nonlinearity}, create non-convex loss surfaces. Then, does depth alone create bad local minima? In this paper, we prove that without nonlinearity, depth alone does not create bad local minima, although it induces non-convex loss surface. Using this insight, we greatly simplify a recently proposed proof to show that all of the local minima of feedforward deep linear neural networks are global minima. Our theoretical results generalize previous results with fewer assumptions, and this analysis provides a method to show similar results beyond square loss in deep linear models.

The human reasoning process is seldom a one-way process from an input leading to an output. Instead, it often involves a systematic deduction by ruling out other possible outcomes as a self-checking mechanism. In this paper, we describe the design of a hybrid neural network for logical learning that is similar to the human reasoning through the introduction of an auxiliary input, namely the indicators, that act as the hints to suggest logical outcomes. We generate these indicators by digging into the hidden information buried underneath the original training data for direct or indirect suggestions. We used the MNIST data to demonstrate the design and use of these indicators in a convolutional neural network. We trained a series of such hybrid neural networks with variations of the indicators. Our results show that these hybrid neural networks are very robust in generating logical outcomes with inherently higher prediction accuracy than the direct use of the original input and output in apparent models. Such improved predictability with reassured logical confidence is obtained through the exhaustion of all possible indicators to rule out all illogical outcomes, which is not available in the apparent models. Our logical learning process can effectively cope with the unknown unknowns using a full exploitation of all existing knowledge available for learning. The design and implementation of the hints, namely the indicators, become an essential part of artificial intelligence for logical learning. We also introduce an ongoing application setup for this hybrid neural network in an autonomous grasping robot, namely as_DeepClaw, aiming at learning an optimized grasping pose through logical learning.

In this work, we present the Grounded Recurrent Neural Network (GRNN), a recurrent neural network architecture for multi-label prediction which explicitly ties labels to specific dimensions of the recurrent hidden state (we call this process "grounding"). The approach is particularly well-suited for extracting large numbers of concepts from text. We apply the new model to address an important problem in healthcare of understanding what medical concepts are discussed in clinical text. Using a publicly available dataset derived from Intensive Care Units, we learn to label a patient's diagnoses and procedures from their discharge summary. Our evaluation shows a clear advantage to using our proposed architecture over a variety of strong baselines.

Ridesourcing platforms like Uber and Didi are getting more and more popular around the world. However, unauthorized ridesourcing activities taking advantages of the sharing economy can greatly impair the healthy development of this emerging industry. As the first step to regulate on-demand ride services and eliminate black market, we design a method to detect ridesourcing cars from a pool of cars based on their trajectories. Since licensed ridesourcing car traces are not openly available and may be completely missing in some cities due to legal issues, we turn to transferring knowledge from public transport open data, i.e, taxis and buses, to ridesourcing detection among ordinary vehicles. We propose a two-stage transfer learning framework. In Stage 1, we take taxi and bus data as input to learn a random forest (RF) classifier using trajectory features shared by taxis/buses and ridesourcing/other cars. Then, we use the RF to label all the candidate cars. In Stage 2, leveraging the subset of high confident labels from the previous stage as input, we further learn a convolutional neural network (CNN) classifier for ridesourcing detection, and iteratively refine RF and CNN, as well as the feature set, via a co-training process. Finally, we use the resulting ensemble of RF and CNN to identify the ridesourcing cars in the candidate pool. Experiments on real car, taxi and bus traces show that our transfer learning framework, with no need of a pre-labeled ridesourcing dataset, can achieve similar accuracy as the supervised learning methods.