More than 80 per cent of the TV shows people watch on Netflix are discovered through the platform's recommendation system. That means the majority of what you decide to watch on Netflix is the result of decisions made by a mysterious, black box of an algorithm. Netflix uses machine learning and algorithms to help break viewers' preconceived notions and find shows that they might not have initially chosen. To do this, it looks at nuanced threads within the content, rather than relying on broad genres to make its predictions. This explains how, for example, one in eight people who watch one of Netflix's Marvel shows are completely new to comic book-based stuff on Netflix.

IBM (NYSE: IBM) and MIT today announced that IBM plans to make a 10-year, $240 million investment to create the MIT–IBM Watson AI Lab in partnership with MIT. The new lab will be one of the largest long-term university-industry AI collaborations to date, mobilizing the talent of more than 100 AI scientists, professors, and students to pursue joint research at IBM's Research Lab in Cambridge--co-located with the IBM Watson Health and IBM Security headquarters in Kendall Square, in Cambridge, Massachusetts--and on the neighboring MIT campus. In 2016, IBM Research announced a multi-year collaboration with MIT's Department of Brain and Cognitive Sciences to advance the scientific field of machine vision, a core aspect of artificial intelligence. Currently, the Computer Science and Artificial Intelligence Laboratory, the Media Lab, the Department of Brain and Cognitive Sciences, and the MIT Institute for Data, Systems, and Society serve as connected hubs for AI and related research at MIT.

Its mission will include advancing the hardware, software and algorithms used for artificial intelligence. It also will tackle some of the economic and ethical implications of intelligent machines and look at its commercial application for industries ranging from health care to cybersecurity.

Amid the realignment of jobs and changing business models, companies are aligning goals with the world in a quest for a global AI network. This autumn or post-monsoon, depending on which part of the world you live in, the movie Blade Runner will see a return after 35 years. If you remember the first movie, the artificial intelligence character, or the "replicant", the antagonist, played by Rutger Hauer, saves the protagonist, played by Harrison Ford, from dying. After that, the protagonist witnesses the AI character's programmes terminate themselves. Ford then says: "I don't know why he saved my life. Maybe in those last moments he loved life more than he ever had before. All he'd wanted were the same answers the rest of us want. Where did I come from? How long have I got? All I could do was sit there and watch him die."

Can't wait for the next Song of Ice and Fire novel? An AI has generated several chapters based on the previous books in the series, though they tend to not make a lot of sense. When it comes to information processing, computers tend to be way faster than we are. The same thing may be true when it comes to generating new plotlines for A Song of Ice and Fire, the series of novels better known to TV fans as Game of Thrones. After all, with six years having elapsed since his last book, 2011's A Dance With Dragons, was published, author George R.R. Martin certainly appears to be in no rush to publish its follow-up -- which is why the producers of the TV show are currently coming up with their own storylines.

Item cold-start is a classical issue in recommender systems that affects anime and manga recommendations as well. This problem can be framed as follows: how to predict whether a user will like a manga that received few ratings from the community? Content-based techniques can alleviate this issue but require extra information, that is usually expensive to gather. In this paper, we use a deep learning technique, Illustration2Vec, to easily extract tag information from the manga and anime posters (e.g., sword, or ponytail). We propose BALSE (Blended Alternate Least Squares with Explanation), a new model for collaborative filtering, that benefits from this extra information to recommend mangas. We show, using real data from an online manga recommender system called Mangaki, that our model improves substantially the quality of recommendations, especially for less-known manga, and is able to provide an interpretation of the taste of the users.

McGraw-Hill CEO: AI In The Classroom Is Here Video above published Aug 23, 2017: David Levin, CEO of McGraw-Hill Education (domain: mheducation.com),

Artificial intelligence (AI), technically known as "machine intelligence," is a buzzword in the tech industry today.

The program is a result of a $1.25 million gift from the private McCain Foundation.

ARTIFICIAL intelligence (AI) could wipe out thousands of jobs in the country's fast-growing business process outsourcing (BPO) sector, but the industry …