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.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. To celebrate National Robotics Week the Florida Institute for Human and Machine Cognition recently hosted the annual Robotics Open House at the newly constructed Levin Center for IHMC Research. IHMC Researchers demonstrated work across multiple platforms including the Atlas Humanoid robot, MinaV2 Exoskeleton, Planar Elliptical Runner, and Virtual reality stations.

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.

We live in an age where science fiction ever more quickly becomes science fact. Big data and Artificial Intelligence (AI) are revolutionizing industries across the developed world, from retail to finance to domestic and international spying. These technologies are automating functions previously considered tasks only a human could do, and offering detailed, personalized predictions a human could never make. Now these tools are underpinning a new era of content marketing technology: content intelligence. Big data involves computationally analyzing extremely large data sets to reveal patterns, trends, and associations; especially those relating to human behavior and interactions. It is used in everything from predicting stock performance to seasonal buying behavior to helping the NSA know whether your post about "blowing up the joint" refers to your bomb-making or DJing skills. Every human who uses any form of digital communication generates data constantly, both about themselves and about humans in aggregate. Big data refers to the ability to find, sort, and make sense of this ocean of ones and zeroes. It encompasses structured, semi-structured, and unstructured information, both human-generated and from sensors, machines, and public records. Structured data generally means information residing in a fixed field within a record or file, such as that found in spreadsheets and relational databases. Information that's tagged to show some elements within the data, such as metadata in email or photos, is semi-structured data. Unstructured data meanwhile, includes content such as untagged text, images, audio, video, and so on. Big data can also includes demographic or psychographic information about consumers. Think product reviews and commentary, blogs, content on social media sites, and the digital exhaust streamed 24/7 from mobile devices, sensors, and technical devices. The definition of AI is more nebulous because what is considered AI is constantly changing.

At the crux of art and science is humanity. Humans create art and seek the truth through scientific discovery. Man created technology and is shaping its capabilities in his own image. What happens when the technology is capable of not only sensing and learning, but also creating and thinking? Throughout history, art has been humankind's interpretation of the world, translated through applied creativity to produce architecture, painting, drawing, dance, music, stained glass, illuminated manuscripts, tapestry, ceramics, lithography, wood carving, graphics, performance, theater, design, illustration, video, sculpture, photography and writing.