The expected reward in a linear stochastic bandit model is an unknown linear function of the chosen decision vector. In this paper, we consider the case where the expected reward is an unknown linear function of a projection of the decision vector onto a subspace. We call this the projection reward. Unlike the classical linear bandit problem, we assume that the projection reward is unobservable. Instead, the observed "reward" at each time step is the projection reward corrupted by another linear function of the decision vector projected onto a subspace orthogonal to the first. Such a model is useful in recommendation applications where the observed reward is corrupted by each individual's biases. In the case where there are finitely many decision vectors, we develop a strategy to achieve $O(\log T)$ regret, where $T$ is the number of time steps. In the case where the decision vector is chosen from an infinite compact set, our strategy achieves $O(T^{2/3}(\log{T})^{1/2})$ regret. Simulations verify the efficiency of our strategy.

Does this star have a planet? A new algorithm could help astronomers predict, on the basis of a star's chemical fingerprint, whether that star will host a giant gaseous exoplanet. "It's like Netflix," Natalie Hinkel, a planetary astrophysicist at the Southwest Research Institute in San Antonio, Texas, told Eos. Netflix "sees that you like goofy comedy, science fiction, and kung fu movies--a variety of different patterns" to predict whether you'll like a new movie. Likewise, her team's machine learning algorithm "will learn which elements are influential in deciding whether or not a star has a planet."

Newswire) VSBLTY Groupe Technologies Corp. (CSE: VSBY) (5VS.F) (VSBGF), a leading retail software and technology company, is teaming with Energetika, an international provider of "intelligent lighting" solutions, to install safety lighting and integrated security to Mexico City, and other Latin American cities designated as a "Smart City." Accessibility, habitability, sustainability, air quality, noise levels, energy, health and economic vitality are among the elements necessary to be selected as a "Smart City." Energetika is a leading provider of smart lighting solutions for economically efficient applications that incorporate security. Energetika chose VSBLTY to provide security technology that includes crowd analytics and facial recognition for residential, commercial and governmental applications. VSBLTY technology provides enhanced customer engagement and audience measurement using machine learning and computer vision.

This documentary projects the effects and dangers of Artificial Intelligence (AI) developments for next generations. The video addresses lots of examples in both negative and positive dimensions for using and developing Artificial Intelligence. In my opinion, one of the most important messages of this movie is that speakers in the movie believe that the development of AI is beneficial but it could misuse in lots of malicious areas. Example of that might be within war machines or development of mass destruction weapons which could seriously jeopardize our lives. The message of this movie is clearly states that machines can easily reproduce and duplicate themselves therefore development of full AI could spell the end of the human race.

State of the art music recommender systems mainly rely on either Matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction by learning from temporal sequences of user actions. Despite advances in deep learning for song recommendation, none has taken advantage of the sequential nature of songs by learning sequence models that are based on content. Aside from the importance of prediction accuracy, other significant aspects are important, such as explainability and solving the cold start problem. In this work, we propose a hybrid deep learning structure, called "SeER", that uses collaborative filtering (CF) and deep learning sequence models on the MIDI content of songs for recommendation in order to provide more accurate personalized recommendations; solve the item cold start problem; and generate a relevant explanation for a song recommendation. Our evaluation experiments show promising results compared to state of the art baseline and hybrid song recommender systems in terms of ranking evaluation.

A remarkable feature of human beings is their capacity for creative behaviour, referring to their ability to react to problems in ways that are novel, surprising, and useful. Transformational creativity is a form of creativity where the creative behaviour is induced by a transformation of the actor's conceptual space, that is, the representational system with which the actor interprets its environment. In this report, we focus on ways of adapting systems of learned representations as they switch from performing one task to performing another. We describe an experimental comparison of multiple strategies for adaptation of learned features, and evaluate how effectively each of these strategies realizes the adaptation, in terms of the amount of training, and in terms of their ability to cope with restricted availability of training data. We show, among other things, that across handwritten digits, natural images, and classical music, adaptive strategies are systematically more effective than a baseline method that starts learning from scratch.

A new app from the former head of video-sharing app Vine hopes to repeat the success of the cult social network by making it easier to shoot and edit short clips. Trash hopes that its secret weapon will be "computational cinematography": the app, which entered closed beta on Monday, uses machine learning "to automate the un-fun parts of video editing", automatically processing video to cut together short clips with a consistent mood and feel. A similar approach, computational photography, has already radically changed smartphone photography, enabling features such as the Pixel's Night Sight and iPhone's Portrait Mode. Trash's co-founder, Hannah Donovan, who was Vine's last general manager before the service was shut down by its owner, Twitter, said she hoped the approach would lower the barrier of entry to video editing. "We're analysing the video for a bunch of different things," Donovan said.

They say "behind every good man, there is a woman," but in the world of blockbuster movies, the best allies to have by your side are often of the robotic persuasion. Always ready to dig you out of a rough spot, or march gung-ho into a battle, the movie robot sidekick has become a staple in modern sci-fi and action/adventure. Sure, there have been some bad-ass solo robots over the years like Optimus Prime, Ava of Ex-Machina fame, and even Robocop (although, technically he's a cyborg), but we're here celebrating the sidekick. The robots that make the best partners in crime. Whether it's intergalactic co-pilots, shape-shifting planetary protectors, or time-travelling androids, join us as we count down the 10 best movie robot sidekicks.

Artificial intelligence is a unique part of the emerging tech landscape, with years of science fiction shaping our expectations of AI capabilities. Today, the reality is that AI is much more of an enabling technology than an end user product. Understanding how to use AI requires understanding the data requirements, the programming model, and the nature of the outputs. These elements, which are described further in Emerging Business Opportunities in AI, CompTIA's latest research exploring the adoption of emerging technology, add more complexity to an already complicated IT environment. For solution providers wondering how to best serve their clients, deciding on the right business model and level of investment can be a challenge.

Plate from Muybridge's Animal Locomotion series published in 1887. Deep learning has become the dominate lens through which machines understand video. Yet video files consume huge amounts of storage space and are extremely computationally demanding to analyze using deep learning. Certain use cases can benefit from converting videos to sequences of still images for analysis, enabling full data parallelism and vast reductions in data storage and computation. Representing video as still imagery also presents unique opportunities for non-consumptive analysis similar to the use of ngrams for text.