A team of researchers in the UK have taught an autonomous car to stay in its own damn lane in just 20 minutes -- an impressive feat considering I know a few human drivers that couldn't achieve that in their lifetime. Road rage aside, the team at Wayve, a company founded by researchers from Cambridge University's Engineering Department detailed their "reinforcement learning" algorithm in a blog post on June 28. The algorithm, in tandem with a human safety driver, taught the car how to remain within a lane over a period of "15-20 minutes." Reinforcement learning for AI has been shown to be highly effective before, with DeepMind Technologies showing it can learn how to play games such as Go or Chess and OpenAI showing that its AI plays 180 days worth of Dota 2 every single day. While defeating human players in incredible complex games like Go or Dota 2 is certainly impressive, teaching a car to drive itself is another wheelhouse altogether.

Born in Poitiers, western France, in 1945, Robuchon rose quickly through apprenticeships, learning techniques from across the country, before taking over as head chef of what was then the Hotel Concorde La Fayette in Paris at the age of 29.

Alex Jones' Infowars podcasts have been removed from Apple's podcasts platform, marking the latest in a series of damaging takedowns. The controversial TV and radio host – who has endorsed conspiracy theories including the idea that many mass shootings are faked – has now been banned from just about every major tech platform, severely limiting his reach. After receiving punishments from YouTube, Facebook, Spotify and more, Apple has removed his podcasts from its platform. That means that he has been removed from the dominant platform for publishing podcasts. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

The driving forces behind one of the UK's leading drone insurance providers has given a peek behind the scenes of the business. Flock's CEO, Ed Leon Klinger, and data scientist, Courtenay Mansel, took to the stage at the Data Science London Festival, to talk about Flock's technology and application. The talk covers the basics of Flock's risk analysis and insurance platform, as well as the data science behind it, and its commercial applicability in the drone industry. The talk can be viewed below.

ADEN, Yemen – Al-Qaida in the Arabian Peninsula, based in Yemen, is considered the most dangerous branch of the terror network after a series of failed attacks on U.S. soil. AQAP has been enmeshed in conflicts in impoverished Yemen for nearly 20 years -- at times working with the government and at times facing crackdown, all the while building ties among tribes in the mountainous countryside to establish refuges and allies. The first anti-American attack in Yemen linked to al-Qaida took place in 1992 when a group called the Islamic Jihad Movement attacked a hotel in the southern city of Aden housing U.S. troops heading to Somalia, killing a Yemeni and an Australian. The group was made up of jihadis who had returned from Afghanistan, where they fought the Soviets alongside Osama bin Laden. The group fell apart after defections spurred by its cozy relationship with ruling authorities as then-President Ali Abdullah Saleh used AQAP fighters to liquidate his top foes, the socialists.

It was part of a brutal day of Japanese government testing for Honda Motor Co. HMC 1.81%, whose vehicle was equipped with a camera and sensors that were supposed to detect obstacles and apply brakes to avoid a collision. The SUV scored 0.2 out of a possible 25 points in the pedestrian portion of the test, the worst among tested vehicles. With its long heritage of technical prowess, Honda was determined to do better--and it did. But Honda engineering didn't get it there. The car maker turned to an off-the-shelf sensing kit from Robert Bosch GmbH, the companies said.

Robots have fascinated and preoccupied human minds for centuries - from ancient tales of stone golems, to modern science fiction. Though the word "robot" was only officially penned in 1921 by Karel Čapek, mankind has endeavored to create autonomous machines since as far back as the 4th Century BCE. Today, robots are widely used across a variety of industries, aiding in the manufacturing of vehicles and more. According to the International Federation of Robotics, in 2015 there were as many as 1.63 million industrial robots in operation worldwide, and that number continues to grow steadily each year. Here's a brief history of how robotics have evolved and grown from the early imaginings of 400 BCE, to the global resource they are today. The earliest beginnings of robotics can be traced back to Ancient Greece. Aristotle was one of the first great thinkers to consider automated tools, and how these tools would affect society at large.

When learning from positive and unlabelled data, it is a strong assumption that the positive observations are randomly sampled from the distribution of $X$ conditional on $Y = 1$, where X stands for the feature and Y the label. Most existing algorithms are optimally designed under the assumption. However, for many real-world applications, the observed positive examples are dependent on the conditional probability $P(Y = 1|X)$ and should be sampled biasedly. In this paper, we assume that a positive example with a higher $P(Y = 1|X)$ is more likely to be labelled and propose a probabilistic-gap based PU learning algorithms. Specifically, by treating the unlabelled data as noisy negative examples, we could automatically label a group positive and negative examples whose labels are identical to the ones assigned by a Bayesian optimal classifier with a consistency guarantee. The relabelled examples have a biased domain, which is remedied by the kernel mean matching technique. The proposed algorithm is model-free and thus do not have any parameters to tune. Experimental results demonstrate that our method works well on both generated and real-world datasets.

Many tasks in machine learning and data mining, such as data diversification, non-parametric learning, kernel machines, clustering etc., require extracting a small but representative summary from a massive dataset. Often, such problems can be posed as maximizing a submodular set function subject to a cardinality constraint. We consider this question in the streaming setting, where elements arrive over time at a fast pace and thus we need to design an efficient, low-memory algorithm. One such method, proposed by Badanidiyuru et al. (2014), always finds a $0.5$-approximate solution. Can this approximation factor be improved? We answer this question affirmatively by designing a new algorithm SALSA for streaming submodular maximization. It is the first low-memory, single-pass algorithm that improves the factor $0.5$, under the natural assumption that elements arrive in a random order. We also show that this assumption is necessary, i.e., that there is no such algorithm with better than $0.5$-approximation when elements arrive in arbitrary order. Our experiments demonstrate that SALSA significantly outperforms the state of the art in applications related to exemplar-based clustering, social graph analysis, and recommender systems.

Mastering the dynamics of social influence requires separating, in a database of information propagation traces, the genuine causal processes from temporal correlation, homophily and other spurious causes. However, most of the studies to characterize social influence and, in general, most data-science analyses focus on correlations, statistical independence, conditional independence etc.; only recently, there has been a resurgence of interest in "causal data science", e.g., grounded on causality theories. In this paper we adopt a principled causal approach to the analysis of social influence from information-propagation data, rooted in probabilistic causal theory. Our approach develops around two phases. In the first step, in order to avoid the pitfalls of misinterpreting causation when the data spans a mixture of several subtypes ("Simpson's paradox"), we partition the set of propagation traces in groups, in such a way that each group is as less contradictory as possible in terms of the hierarchical structure of information propagation. For this goal we borrow from the literature the notion of "agony" and define the Agony-bounded Partitioning problem, which we prove being hard, and for which we develop two efficient algorithms with approximation guarantees. In the second step, for each group from the first phase, we apply a constrained MLE approach to ultimately learn a minimal causal topology. Experiments on synthetic data show that our method is able to retrieve the genuine causal arcs w.r.t. a known ground-truth generative model. Experiments on real data show that, by focusing only on the extracted causal structures instead of the whole social network, we can improve the effectiveness of predicting influence spread.