San Francisco (AFP) - Google took on rivals Apple, Samsung and Amazon in a new push into hardware, launching premium-priced Pixel smartphones and a slew of other devices showcasing artificial intelligence prowess. The unveiling of Google's in-house designed phone came as part of an expanded hardware move by the US company, which also revealed details about its new "home assistant" virtual reality headset and Wi-Fi router system. The San Francisco event marked a shift in strategy for Google, which is undertaking a major drive to make Google Assistant artificial intelligence a futuristic force spanning all kinds of internet-linked devices. "We are evolving from a mobile-first world to an AI-first world," Google chief executive Sundar Pichai said. "Our goal is to build a personal Google for each and every user."

After a several-month trial in which staff used the AI internally, while they dealt with business clients, RBS will let Luvo talk directly to the outside world by the end of 2016. Luvo functions as a chatbot – a program that opens when you access the bank's website – that you can ask typical customer service questions, concerning lost PINs and credit cards that need to be replaced. At first glance, this is nothing extraordinary, and chatbots have become a frequent feature for websites dealing with a large flow of individual queries. But RBS and IBM, which spent millions developing the program together, say that it is revolutionary, with a nuanced understanding of human speech, a "unique" personality, and an ability to learn on the job. "To be helpful it has to understand dialogue," the bank's managing director of digitization, Chris Popple, explained in a presentation earlier this year.

Golden parachutes can't seem to stay out of the news. Last year, Jeff Smisek, the former CEO of United Airlines, received a separation payment of 4.875 million in cash along with additional equity awards and other benefits for a total of close to 37 million after being ousted from his company. Can it also transform the nation? Hillary Clinton was campaigning for her husband in January 1992 when she learned of the race's newest flare-up: Gennifer Flowers had just released tapes of phone calls with Bill Clinton to back up her claim they had had an affair. We tend to associate salads most closely with spring and summer, when fresh produce is at its peak and when we're all in the mood for lighter, fresher-tasting meals.

Artificial intelligence isn't just helping you work more effectively... it can help you find work, too. Source{d} is running a job service that matches programmers with employers by using a "deep neural network" to scan open source code for relevant qualities. And it's not just about understanding whether or not you can write well in a given language, either. The AI can even look for coding styles that match the methods of a given company, so you may land a position simply by fitting in more gracefully than anyone else. Of course, the approach doesn't rely exclusively on algorithms.

Unification in Description Logics has been introduced as a means to detect redundancies in ontologies. We try to extend the known decidability results for unification in the Description Logic $\mathcal{EL}$ to disunification since negative constraints can be used to avoid unwanted unifiers. While decidability of the solvability of general $\mathcal{EL}$-disunification problems remains an open problem, we obtain NP-completeness results for two interesting special cases: dismatching problems, where one side of each negative constraint must be ground, and local solvability of disunification problems, where we consider only solutions that are constructed from terms occurring in the input problem. More precisely, we first show that dismatching can be reduced to local disunification, and then provide two complementary NP-algorithms for finding local solutions of disunification problems.

This paper develops an active sensing method to estimate the relative weight (or trust) agents place on their neighbors' information in a social network. The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents, i.e., agents whose opinions are not influenced by their neighbors. This method can be viewed as a \emph{social RADAR}, where the stubborn agents excite the system and the latter can be estimated through the reverberation observed from the analysis of the agents' opinions. The social network sensing problem can be interpreted as a blind compressed sensing problem with a sparse measurement matrix. We prove that the network structure will be revealed when a sufficient number of stubborn agents independently influence a number of ordinary (non-stubborn) agents. We investigate the scenario with a deterministic or randomized DeGroot model and propose a consistent estimator of the steady states for the latter scenario. Simulation results on synthetic and real world networks support our findings.

Privacy preserving networks can be modelled as decentralized networks (e.g., sensors, connected objects, smartphones), where communication between nodes of the network is not controlled by an all-knowing, central node. For this type of networks, the main issue is to gather/learn global information on the network (e.g., by optimizing a global cost function) while keeping the (sensitive) information at each node. In this work, we focus on text information that agents do not want to share (e.g., text messages, emails, confidential reports). We use recent advances on decentralized optimization and topic models to infer topics from a graph with limited communication. We propose a method to adapt latent Dirichlet allocation (LDA) model to decentralized optimization and show on synthetic data that we still recover similar parameters and similar performance at each node than with stochastic methods accessing to the whole information in the graph.

This paper investigates the effectiveness of factorial speech processing models in noise-robust automatic speech recognition tasks. For this purpose, the paper proposes an idealistic approach for modeling state-conditional observation distribution of factorial models based on weighted stereo samples. This approach is an extension to previous single pass retraining for ideal model compensation which is extended here to support multiple audio sources. Non-stationary noises can be considered as one of these audio sources with multiple states. Experiments of this paper over the set A of the Aurora 2 dataset show that recognition performance can be improved by this consideration. The improvement is significant in low signal to noise energy conditions, up to 4% absolute word recognition accuracy. In addition to the power of the proposed method in accurate representation of state-conditional observation distribution, it has an important advantage over previous methods by providing the opportunity to independently select feature spaces for both source and corrupted features. This opens a new window for seeking better feature spaces appropriate for noisy speech, independent from clean speech features.

As recently as 2013, the [deep learning] space saw fewer than 10 deals. Computer Vision: Startups here are using deep learning for image recognition, analytics, and classification. Aerial image analytics startup Terraloupe was seed-funded this year by Germany-based Bayern Kapital. New York-based Calrifai -- backed by investors including Google Ventures, Lux Capital, and NVidia -- entered the R/GA accelerator this year, after raising 10M in Series A in Q2'15. Captricity, which extracts information from hand-written data, has raised 49M in equity funding so far from investors including Social Capital, Accomplice, White Mountains Insurance Group, and New York Life Insurance Company.

Virginia Tech was the envy of other college students when Alphabet's Project Wing and Chipotle announced plans to deliver burritos to campus by drone. The experiment is now underway and students are receiving stuffed burritos at a designated location from the unmanned aerial vehicles. This testing will generate data on navigational accuracy and vehicle performance to help regulators understand how drones should operate in public airspace. Alphabet's Project Wing and Chipotle have begin testing their pilot project that delivers burritos to campus using drones. Alphabet Inc's Project Wing and Chipotle have started a pilot program that delivers burritos to the students of Virginia Tech in Blacksburg, Virginia.