A number of projects are underway to make smartphones more situationally/contextually aware. Over the last decade, smartphones have ushered in radical and disruptive changes to society and business. They have introduced entirely new ways to interact, consume media, shop, buy products, and even order and pay for food and beverages. They ring, buzz, or beep while we're attending a classical music concert or sitting in church. They are unable to warn us when we exercise too strenuously. They send distracting or unwanted traffic alerts while we are driving, or away on vacation.

And I think it would be hard to argue that any task a human can learn requires NP-hard computation to train, as it would imply pretty massive blowup in learning time. Unless one means maybe something like science or math, where it took thousands of years to figure out and people transferred their model parameters to future generations through a process known as "teaching";)

Newswire) Breaking Data Corp. (TSX VENTURE:BKD) (OTC PINK:BKDCD) (the "Company" or "BKD") is pleased to announce that is has now finalized its new independent Director appointees with key industry veteran leadership and experience. "We are extremely privileged to have this group of experienced and successful directors join our board," said Nick Thain, CEO. "These executives not only bring a vast network of knowledge but also have deep ties in many sports related businesses across the globe, that cross into our key markets. They will, no doubt, be valuable to our strategic and business progress." Greg D'Alba: Mr. D'Alba is currently the Co- founder & CEO of VIDL NEWS LLC, a start-up artificial intelligence news venture focused on real-time personalized news intelligence and reporting.

Artificial intelligence (AI) technologies are being deployed for an increasing variety of use cases across consumer, enterprise, and government markets around the world. AI, which is defined as an information system that is inspired by a biological system, is an umbrella term that includes multiple technologies, such as machine learning, deep learning, computer vision, natural language processing (NLP), machine reasoning, and strong AI. According to a new report from Tractica, interest in implementing AI systems is surging among companies and institutions around the world. The market intelligence firm forecasts that the revenue generated from the direct and indirect application of AI software will grow from $1.4 billion in 2016 to $59.8 billion by 2025. This forecast represents an upgrade of Tractica's previous projection for AI market growth, which was published in 3Q16, owing to a greater than anticipated pace of change and development in the AI sector.

Online reviews provided by consumers are a valuable asset for e-Commerce platforms, influencing potential consumers in making purchasing decisions. However, these reviews are of varying quality, with the useful ones buried deep within a heap of non-informative reviews. In this work, we attempt to automatically identify review quality in terms of its helpfulness to the end consumers. In contrast to previous works in this domain exploiting a variety of syntactic and community-level features, we delve deep into the semantics of reviews as to what makes them useful, providing interpretable explanation for the same. We identify a set of consistency and semantic factors, all from the text, ratings, and timestamps of user-generated reviews, making our approach generalizable across all communities and domains. We explore review semantics in terms of several latent factors like the expertise of its author, his judgment about the fine-grained facets of the underlying product, and his writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii) item facets, and (iii) review helpfulness. Large-scale experiments on five real-world datasets from Amazon show significant improvement over state-of-the-art baselines in predicting and ranking useful reviews.

Current recommender systems exploit user and item similarities by collaborative filtering. Some advanced methods also consider the temporal evolution of item ratings as a global background process. However, all prior methods disregard the individual evolution of a user's experience level and how this is expressed in the user's writing in a review community. In this paper, we model the joint evolution of user experience, interest in specific item facets, writing style, and rating behavior. This way we can generate individual recommendations that take into account the user's maturity level (e.g., recommending art movies rather than blockbusters for a cinematography expert). As only item ratings and review texts are observables, we capture the user's experience and interests in a latent model learned from her reviews, vocabulary and writing style. We develop a generative HMM-LDA model to trace user evolution, where the Hidden Markov Model (HMM) traces her latent experience progressing over time -- with solely user reviews and ratings as observables over time. The facets of a user's interest are drawn from a Latent Dirichlet Allocation (LDA) model derived from her reviews, as a function of her (again latent) experience level. In experiments with five real-world datasets, we show that our model improves the rating prediction over state-of-the-art baselines, by a substantial margin. We also show, in a use-case study, that our model performs well in the assessment of user experience levels.

He enrolled in the NYC Data Science Academy 12-week full time Data Science Bootcamp program taking place between July 5th to September 23rd, 2016. This post is based on their third project - Web Scraping, due on 6th week of the program. The original article can be found here. The average American spends 4 hours a day listening to music (SPIN) and 93% of Americans listen to music daily in some capacity (EDISON). Almost half of that listening is via AM/FM radio, but music streaming services are gaining a greater and greater market share and have even turned around the declining revenues of the music industry.

BootstrapLabs is pleased to announce the return of its annual Applied Artificial Intelligence Conference on May 11, 2017, in San Francisco, the heart of the most advanced AI community in the world. The Applied AI Conference is a must-attend event for people working, researching, building, and investing in Applied Artificial Intelligence technologies and products. The event is focused on practical applications and current commercialization of AI technologies across industries such as Transportation & Logistics, Internet of Things (IoT), Future of Work (FoW), Financial Technologies (FinTech), CyberSecurity, and Healthcare Technologies (HealthTech). It also explores how AI is impacting society, the enterprise, and you! The 2017 conference agenda will provide insights into the present and future impact of AI on your organization, as well as in your daily life.

A hypnotic new video reveals the unearthly beauty of life up-close. Using different colored ink in water, the team at the Macro Room has created a breathtaking short film that could rival the effects of CGI. A hypnotic new video reveals the unearthly beauty of life up-close. Ink In Motion, shared on YouTube by the Macro Room, gives a close-up look at'the hypnotising beauty of colored ink in water and the interaction of this substance with different elements.' It begins with just a tank filled with water, and 3D planet models submerged in the center.

The research arm of the investment bank Citi released a report this morning with seven potential merger and acquisition targets for Apple. Tops on the list is Neflix, at an assessed 40% likelihood, Citi says. Elon Musk's Tesla, on the other hand, is only 5% likely. The full list of acquisition targets includes three media firms, three game developers, and, of course, one car manufacturer. Disney and Hulu are the media firms joining Netflix, while Activision, Electronic Arts, and Take-Two are the gaming companies.