A simple Google image search for'women's professional hairstyles' returns the following: Momentum by TNW is our New York technology event for anyone interested in helping their company grow. That is, until you try searching for'unprofessional women's hairstyles' and find this: In it, you'll find a hodge-podge of hairstyles sported by black women, all of which seem, well, rather normal. In fact, Boing Boing spotted this back in April. In five years, 10 years, 25 years, you can imagine how much of our lives will be dictated by algorithms.

That has been its brand since the days of "It's not television, it's HBO." Television eventually caught up, which meant HBO had to swing bigger; it is not built for, or on, a slate of solid but not spectacular shows. It is a premium cable channel, which means it needs to bring the premium. So when an HBO series fails -- "Vinyl," "Luck," "John From Cincinnati" -- it fails hard. But when it connects, well, hits like "Game of Thrones" and "Veep," "Last Week Tonight With John Oliver" and "Olive Kitteridge" don't just sweep up Emmys, they change the nature of television.

For the 22nd year, Gartner has released its much-discussed hype cycle report on emerging technologies, "providing a cross-industry perspective on the technologies and trends that business strategists, chief innovation officers, R&D leaders, entrepreneurs, global market developers and emerging-technology teams should consider in developing emerging-technology portfolios." Reacting to last year's hype cycle report (see below), I made the following comment: Machine learning is making its first appearance on the chart this year, but already past the peak of inflated expectations. A glaring omission here is "deep learning," the new label for and the new generation of machine learning, and one of the most hyped emerging technologies of the past couple of years. This year, Gartner has moved machine learning back a few notches, putting it at the peak of inflated expectations, still with 2 to 5 years until mainstream adoption. Is machine learning an emerging technology and is there a better term to describe what most of the hype is about nowadays in tech circles?

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications.

Yahoo today announced its latest open-source release: a model that can figure out if images are specifically pornographic in nature. The system uses a type of artificial intelligence called deep learning, which involves training artificial neural networks on lots of data (like dirty images) and getting them to make inferences about new data. The model that's now available on GitHub under a BSD 2-Clause license comes pre-trained, so users only have to fine-tune it if they so choose. The model works with the widely used Caffe open source deep learning framework. The team trained the model using its now open source CaffeOnSpark system.

Bots will become the next version of mobile apps, creating the emotional connection between brands and consumers through artificial intelligence (AI) and search, according to Ryan Gavin, GM at Microsoft for search, cloud and content. During Advertising Week, Gavin told Search Marketing Daily that bots will replace mobile applications. The conversations on bots will occur more often and will become the new app for brands; and natural language will become the use interface, he said. Microsoft this week announced a new group that will help accelerate the use of AI. About 500 computer scientists and engineers will support the team and will include Microsoft Research along with the company's Information Platform Group, Bing and Cortana -- natural language processing -- product groups, along with its Ambient Computing and Robotics teams.

Microsoft is working to infuse all of its products with artificial intelligence capabilities to bring more smarts to its applications and technologies portfolio.

Microsoft Corp. MSFT 0.35 % took advantage of the departure of an influential executive to reorganize efforts around artificial intelligence, one of its strategic priorities. The company announced Thursday that it is concentrating its artificial-intelligence operation in one group. The move comes as Qi Lu, a highly regarded executive in charge of the Office products and Bing search-engine businesses, recovers from a bicycle injury sustained a few months ago. His recovery has been slow, a person familiar with the matter said. Harry Shum, who led the company's research efforts, now will lead artificial intelligence efforts as well, Microsoft said.

Popular literature is replete with scary renditions of Artificial Intelligence. Numerous articles, stories, and novels depict an Eeeeeeeevil AI taking over the world to the detriment of humanity. . From my perspective it appears that all of HollyWeird and most of the legacy publishers to include the Hugo wanna-bes have bought into this meme. The world is doomed if we let the evil machine overlords take over. . I suggest that we have been in a war with a form of artificial intelligence since shortly after the Agricultural Revolution. .

No one would accuse Google Translate, the favored tool of unscholarly high school language students everywhere, of being an inaccurate interpreter. The 10-year-old internet interpreter can fluently translate more than 100 tongues, recognize foreign restaurant menus and signage, and differentiate between dialects in real time. The project is called Google Neural Machine Translation, or GNMT, and it isn't strictly speaking new. It was first employed to improve the efficiency of single-sentence translations, explained Google engineers Quoc V. Le and Mike Schuster, and did so ingesting individual words and phrases before spitting out a translation. But the team discovered that the algorithm was just as effective at handling entire sentences -- even reducing errors by as much as 60 percent.