IN JULY 2011 Sebastian Thrun, who among other things is a professor at Stanford, posted a short video on YouTube, announcing that he and a colleague, Peter Norvig, were making their "Introduction to Artificial Intelligence" course available free online. By the time the course began in October, 160,000 people in 190 countries had signed up for it. At the same time Andrew Ng, also a Stanford professor, made one of his courses, on machine learning, available free online, for which 100,000 people enrolled. Both courses ran for ten weeks. Such online courses, with short video lectures, discussion boards for students and systems to grade their coursework automatically, became known as Massive Open Online Courses (MOOCs).

Google has launched a new feature that could make its search box even more popular than it already is. The company has added a new Personal tab to its search page, which is designed to make it easier for you to track down your own content. Personal is only available when you're logged in to your Google account, and results are taken from other Google services, like Gmail and Google Photos. 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.

Google has redesigned the Chrome app, and Android users can test its new layout now. The company has tweaked the browser's user interface, making its core features easier to access. The address bar has been moved from the top of the screen to the bottom, which is a sensible switch considering that's where your thumbs naturally rest when you hold a smartphone. With phone displays seemingly getting larger and larger by the year, making the top of the screen harder to reach, it's likely that the bottom of your phone will be home to the address bar for the considerable future. Google has also created the Chrome Home Expand Button, an arrow key you can tap for fast access to your favourite websites and news stories Google think you'll be interested in.

Ukrainian authorities earlier this month blocked access to Yandex as well as to several major Russian social media websites. President Petro Poroshenko said the move was made in response to Russia's annexation of the Crimean peninsula and continuing interference in eastern Ukraine.

Stephen Fry has warned we should prepare for the internet of the future to avoid a'nightmare' dystopian existence. The actor and comedian criticised'technophobes' who have been too slow to adapt to what he described as the greatest change in the history of mankind. Speaking at the Hay literary festival in Brecknockshire, Wales on Saturday, he said: 'Whether it is winter that is coming, or a new spring, it is entirely in our hands so long as we prepare. Stephen Fry has warned we should prepare for the internet of the future to avoid a'nightmare' dystopian existence. The actor and comedian criticised'technophobes' who have been too slow to adapt to what he described as the greatest change in the history of mankind'While it's hard to calculate the cascade upon cascade of new developments and their positive effects, we already know the dire consequences and frightening scenarios that threaten to engulf us.' Science fiction writers and dystopians have already laid out the'nightmare' consequences, he added.

Data ethics has exploded into mainstream consciousness in recent weeks, with media coverage of terrorism advertising on YouTube, Cambridge Analytica using Facebook posts to personalise election campaigning, and the endless stream of scandals engulfing taxi-hailing app Uber. The principles and rules are struggling to keep pace with the technological development. A panel of experts assembled by techUK discussed how to ensure principled behaviour. With ethical notions of consent and privacy constantly stretched by the latest advances in tech, a new structure is needed to establish criteria to protect data. "You need standards that give you certainty to innovate," says Royal Statistical Society Executive Director Hetan Shah.

The recently proposed DeGroot-Friedkin model describes the dynamical evolution of individual social power in a social network that holds opinion discussions on a sequence of different issues. This paper revisits that model, and uses nonlinear contraction analysis, among other tools, to establish several novel results. First, we show that for a social network with constant topology, each individual's social power converges to its equilibrium value exponentially fast, whereas previous results only concluded asymptotic convergence. Second, when the network topology is dynamic (i.e., the relative interaction matrix may change between any two successive issues), we show that each individual exponentially forgets its initial social power. Specifically, individual social power is dependent only on the dynamic network topology, and initial (or perceived) social power is forgotten as a result of sequential opinion discussion. Last, we provide an explicit upper bound on an individual's social power as the number of issues discussed tends to infinity; this bound depends only on the network topology. Simulations are provided to illustrate our results.

Developing a dialogue agent that is capable of making autonomous decisions and communicating by natural language is one of the long-term goals of machine learning research. Traditional approaches either rely on hand-crafting a small state-action set for applying reinforcement learning that is not scalable or constructing deterministic models for learning dialogue sentences that fail to capture natural conversational variability. In this paper, we propose a Latent Intention Dialogue Model (LIDM) that employs a discrete latent variable to learn underlying dialogue intentions in the framework of neural variational inference. In a goal-oriented dialogue scenario, these latent intentions can be interpreted as actions guiding the generation of machine responses, which can be further refined autonomously by reinforcement learning. The experimental evaluation of LIDM shows that the model out-performs published benchmarks for both corpus-based and human evaluation, demonstrating the effectiveness of discrete latent variable models for learning goal-oriented dialogues.

We design a new nonparametric method that allows one to estimate the matrix of integrated kernels of a multivariate Hawkes process. This matrix not only encodes the mutual influences of each nodes of the process, but also disentangles the causality relationships between them. Our approach is the first that leads to an estimation of this matrix without any parametric modeling and estimation of the kernels themselves. A consequence is that it can give an estimation of causality relationships between nodes (or users), based on their activity timestamps (on a social network for instance), without knowing or estimating the shape of the activities lifetime. For that purpose, we introduce a moment matching method that fits the third-order integrated cumulants of the process. We show on numerical experiments that our approach is indeed very robust to the shape of the kernels, and gives appealing results on the MemeTracker database.

We show how the discovery of robust scalable numerical solvers for arbitrary bounded linear operators can be automated as a Game Theory problem by reformulating the process of computing with partial information and limited resources as that of playing underlying hierarchies of adversarial information games. When the solution space is a Banach space $B$ endowed with a quadratic norm $\|\cdot\|$, the optimal measure (mixed strategy) for such games (e.g. the adversarial recovery of $u\in B$, given partial measurements $[\phi_i, u]$ with $\phi_i\in B^*$, using relative error in $\|\cdot\|$-norm as a loss) is a centered Gaussian field $\xi$ solely determined by the norm $\|\cdot\|$, whose conditioning (on measurements) produces optimal bets. When measurements are hierarchical, the process of conditioning this Gaussian field produces a hierarchy of elementary bets (gamblets). These gamblets generalize the notion of Wavelets and Wannier functions in the sense that they are adapted to the norm $\|\cdot\|$ and induce a multi-resolution decomposition of $B$ that is adapted to the eigensubspaces of the operator defining the norm $\|\cdot\|$. When the operator is localized, we show that the resulting gamblets are localized both in space and frequency and introduce the Fast Gamblet Transform (FGT) with rigorous accuracy and (near-linear) complexity estimates. As the FFT can be used to solve and diagonalize arbitrary PDEs with constant coefficients, the FGT can be used to decompose a wide range of continuous linear operators (including arbitrary continuous linear bijections from $H^s_0$ to $H^{-s}$ or to $L^2$) into a sequence of independent linear systems with uniformly bounded condition numbers and leads to $\mathcal{O}(N \operatorname{polylog} N)$ solvers and eigenspace adapted Multiresolution Analysis (resulting in near linear complexity approximation of all eigensubspaces).