Fifty years ago this month, in the midst of the Cold War, nations began signing an international treaty to stop the spread of nuclear weapons. Today, as artificial intelligence and machine learning reshape every aspect of our lives, the world confronts a challenge of similar magnitude and it needs a similar response. There is a danger in pushing the parallel between nuclear weapons and AI too far. But the greater risk lies in ignoring the consequences of unleashing technologies whose goals are neither predictable nor aligned with our values. The immediate prelude to the Treaty on Non-Proliferation of Nuclear Weapons was the Cuban missile crisis in 1962.

Facebook Inc. suspended another company that harvested data from its site and said it was investigating whether the analytics firm's contracts with the U.S. government and a Russian nonprofit tied to the Kremlin violate the platform's policies. Crimson Hexagon, based in Boston, has had contracts in recent years to analyze public Facebook data for those and other clients, according to people familiar with the matter and federal procurement data. Crimson Hexagon says it has the largest repository of public social media posts, totaling more than one trillion, from sites that also include Twitter Inc. TWTR -0.05% and Instagram. Crimson Hexagon operates with little oversight from Facebook once it pulls public data from the social-media platform, according to more than a dozen people familiar with the business. The government contracts weren't approved by Facebook in advance, for example, the people said.

Good news for amateur photographers everywhere: You might still be able to salvage all those grainy pics taking up space on your hard drive. On July 9, researchers from NVIDIA, Aalto University, and MIT unveiled a new AI program that can effectively remove the noise from a photograph -- those annoying pixels and specks that show up when you take a pic in low light. And it doesn't even need a "clean" reference image to make it happen. The researchers plan to present their AI at the International Conference on Machine Learning in Stockholm, Sweden, this week. To create their noise-filtering AI, the researchers started by adding noise to 50,000 pairs of clean images.

The World Cup 2018 is all over. Germany was kicked out in the group stages, Brazil was beaten by Belgium, football didn't come home to England, Croatia with its population of four million people reached the final for the first time ever, only to lose to France in the end. Beyond being glued to our screens to watch the action on pitch, we've been looking at what newsrooms are doing off-pitch to cover the competition… with automation and artificial intelligence. Fox Sports (US) teamed up with IBM Watson to make AI-powered highlight videos, French publication Le Figaro created automated visual summaries, and The Times (UK) launched its very own World Cup Alexa Skill. The US didn't qualify for the World Cup this year, but that didn't stop Fox Sports from airing all 64 matches and teaming up with IBM Watson to create the World Cup highlight machine.

The research found that while AI could displace roughly seven million jobs in the country, it could also create 7.2 million roles, resulting in a modest net boost of around 200,000 jobs. It has also estimated that about 20 percent of jobs would be automated over the next 20 years and no sector would be unaffected. Technologies such as robotics, drones and driverless vehicles would replace human workers in some areas, but also create many additional jobs as productivity and real incomes rise and new and better products are developed. In the health and social work sector the number of people employed could rise by almost one million, while jobs in manufacturing could fall by roughly 25 percent, a net loss of almost 700,000 roles. "Major new technologies, from steam engines to computers, displace some existing jobs but also generate large productivity gains," PwC's Chief Economist John Hawksworth said in a press release.

While imitation learning is often used in robotics, this approach often suffers from data mismatch and compounding errors. DAgger is an iterative algorithm that addresses these issues by aggregating training data from both the expert and novice policies, but does not consider the impact of safety. We present a probabilistic extension to DAgger, which attempts to quantify the confidence of the novice policy as a proxy for safety. Our method, EnsembleDAgger, approximates a GP using an ensemble of neural networks. Using the variance as a measure of confidence, we compute a decision rule that captures how much we doubt the novice, thus determining when it is safe to allow the novice to act. With this approach, we aim to maximize the novice's share of actions, while constraining the probability of failure. We demonstrate improved safety and learning performance compared to other DAgger variants and classic imitation learning on an inverted pendulum and in the MuJoCo HalfCheetah environment.

Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification. Moreover, these signatures are often limited to local, contiguous sequences within the data whilst ignoring their context in relation to each other and throughout the malware file as a whole. We present a Deep Learning based malware classification approach that requires no expert domain knowledge and is based on a purely data driven approach for complex pattern and feature identification.

Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at utilizing knowledge from one learning domain (i.e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain. In this paper, we propose an ontology-based approach for human-centric explanation of transfer learning. Three kinds of knowledge-based explanatory evidence, with different granularities, including general factors, particular narrators and core contexts are first proposed and then inferred with both local ontologies and external knowledge bases. The evaluation with US flight data and DBpedia has presented their confidence and availability in explaining the transferability of feature representation in flight departure delay forecasting.

ABSTRACT Incremental improvements in accuracy of Convolutional Neural Networks are usually achieved through use of deeper and more complex models trained on larger datasets. However, enlarging dataset and models increases the computation and storage costs and cannot be done indefinitely. In this work, we seek to improve the identification and verification accuracy of a text-independent speaker recognition system without use of extra data or deeper and more complex models by augmenting the training and testing data, finding the optimal dimensionality of embedding space and use of more discriminative loss functions. Index Terms-- speaker recognition, speaker verification, augmentation, discriminative loss function, convolutional neural networks 1. INTRODUCTION Speaker recognition is an area of research with more than 50 years of history and applications ranging from forensics and security to human-computer interaction in consumer electronics. Speaker recognition can be categorized into two tasks of text-dependent and text-independent speaker recognition with regard to the similarity of the uttered content between utterances.

Ready or not, autonomous robots are leaving laboratories to be tested in real-world contexts. With more and more people living in cities, these technologies offer ways to cope with ageing populations and poorly maintained infrastructures, while promoting safer transport, productive manufacturing and secure energy supplies. Urban "living labs" are one way scientists are trying to understand how autonomous robots – or Robotics and Autonomous Systems (RAS), to give them their full title – will affect our everyday lives. Autonomous robots are interconnected, interactive, cognitive and physical tools, which can perceive their environments, reason about events, make or revise plans and control their own actions. These technologies are designed to draw on big data and connect with the Internet of Things, to make our lives easier by increasing accuracy and efficiency.