In this episode of Reinventing Retail, we find out how predictive analytics are giving retailers the power to make data-driven decisions that will forever change assortment planning. Our guest is Richard Wright, VP of merchandising for Predictix: a leading provider of retail machine learning solutions. Infor announced it had completed its acquisition of Predictix on June 28, 2016. Using the artificial intelligence of machine learning technology, retailers are able to forecast potential market fluctuations and trends--with the agility to quickly adapt when disruptions are imminent. Hear what Richard has to say about this next generation retail tech, and subscribe to Reinventing Retail on iTunes for more episodes.

A similar process is done on the target images to mask out the faces and intelligently put your own in their place -- and voila! It's not limited to hairstyles, either: put yourself in a movie, a location, a painting -- as long as there's a similarly positioned face to swap yours with, the software can do it. Kemelmacher-Shlizerman has also created systems that do automated age progression, something that can be useful in missing persons cases. "This is a first step in trying to imagine how a missing person's appearance might change over time."

The author reflects on new research reports on the impact of artificial intelligence (AI) on the workplace. His prediction: AI will make internal politics nastier within management ranks. Artificial intelligence is a hot topic these days. Not surprisingly, news outlets north of the 49th parallel jumped on the release of a new research report cleverly titled "The Talented Mr. Robot: The Impact of Automation on Canada's Workforce." The study was conducted by the Brookfield Institute for Innovation Entrepreneurship, a newly created independent and nonpartisan institute, housed within the Ryerson University of Toronto, dedicated to advancing Canada's innovation and entrepreneurship.

Expert publishing blog opinions are solely those of the blogger and not necessarily endorsed by DBW. Last month, Mike Shatzkin wrote a blog post titled "Full text examination by computer is very unlikely to predict bestsellers," in which he described how the claims of the creation of an algorithm that predicts bestsellers, as outlined in a new book The Bestseller Code: Anatomy of the Blockbuster Novel, are impossible. While I agree in theory with Shatzkin that an algorithm alone cannot predict whether a book will be a bestseller or not, that isn't precisely what The Bestseller Code claims, nor what our experience working with machine learning at Intellogo defines. What we aim to do is identify similar tones, moods, topics and writing styles to those books that are topping bestseller lists--as we can only do through algorithms--and, in this way, better understand the reading audiences' desires. Machine learning allows us to do just that.

In his apartment on March 17, 1970, Shel Dorf displays some posters to be shown at a convention in the U.S. Grant Hotel. The first gathering -- called San Diego's Golden State Comic-Minicon -- was put together by a group of comics, movie and science fiction fans including Shel Dorf, Ken Krueger and Richard Alf. This one-day convention was staged to raise funds and interests for a larger convention. Author Ray Bradbury bought a few items from the dealers at the San Diego Golden State Comics Convention in the U.S. Grant Hotel on Aug. 1, 1970. "I got a few issues of Mad magazine," he said. Called San Diego's Golden State Comic-Con, this was the first full-fledged convention in San Diego with a focus on comic art, film and science fiction. The event included 25 booths and three speakers: comic artist Jack Kirby and authors Ray Bradbury and A.E. van Vogt. Kirk Alyn, left, who portrayed Superman in film serials, signs an autograph for Forrest J Ackerman, editor of Famous Monsters of Filmland magazine.

Ken Tran, Senior Research Engineer at Microsoft Research shares how they are using deep learning to create applications for people who are blind or visually impaired. Using Tesla M40 and TITAN X GPUs with the cuDNN-accelerated Caffe deep learning framework, they have trained their language model to describe images or scenes in natural language. The work is being used for Microsoft's research project called Seeing AI that uses computer vision and natural language processing to describe a person's surroundings, read text, answer questions and even identify emotions on people's faces. Watch more scientists and researchers share how accelerated computing is benefiting their work at http://nvda.ly/X7WpH

Ever wanted a way to easily extract and analyze meaningful data from text? The new Cloud Natural Language API has a feature that lets you extract entities from text -- like people, places and events -- with a single API call. Let's take the following sentence from a recent news article: LONDON -- J. K. Rowling always said that the seventh Harry Potter book, "Harry Potter and the Deathly Hallows," would be the last in the series, and so far she has kept to her word. I could write my own algorithm to find the people and locations mentioned in this sentence, but that would be difficult. And it would be even more difficult if I wanted to gather more data on each of the mentioned entities, or analyze entities across thousands of sentences, while accounting for mentions of the same entity that are phrased differently (e.g., "Rowling" and "J.K. Rowling"). With Cloud Natural Language API, I can analyze the above sentence with the API's analyzeEntities method.

Last month, in collaboration with the folks behind the fantastic Burner app, my company Voxable launched our latest chatbot project, Ghostbot for Burner. Ghostbot runs on top of Burner's "open" phone numbers as an agent inside your text message inbox, allowing you to "ghost" away from fleeting relationships by responding to unwanted or creepy texters on your behalf. Since the launch, Ghostbot has been covered in over one hundred media outlets. Chris Messina was also kind enough to post it to Product Hunt, calling it "the first bot-as-personal-firewall" he had seen. The opportunity to break new ground in the coming era of bot-mediated communication was what initially both excited and terrified us about the project.

Warning: This post contains spoilers for the latest episode of Mr. Robot. If one thing's for certain, it's that nothing is ever what it seems with Mr. Robot. While hordes of Mr. Robot fans are still trying to comprehend the countless theories that have stemmed from last week's two-hour premiere -- Vulture's take may still frustratingly ring true -- this week's episode might have tipped its hat toward a major plot line having to do with one character: Ray. He's the bright neighborhood local/pet-owner trying to lure Elliot out of his retirement from hacktavism. Robot' to debut VR short film on iOS, Oculus, HTC Vive Ray, played by the jovial and beaming Craig Robinson, felt like an odd addition to the otherwise gritty cast of Mr. Robot when his role was announced in March. The premiere did little to clarify his role, presenting him as a comical foil to an otherwise embattled Elliot, one who enlists him in an unnamed task that requires his dark coding expertise.

Word embeddings are ubiquitous in NLP and information retrieval, but it's unclear what they represent when the word is polysemous, i.e., has multiple senses. Here it is shown that multiple word senses reside in linear superposition within the word embedding and can be recovered by simple sparse coding. The success of the method ---which applies to several embedding methods including word2vec--- is mathematically explained using the random walk on discourses model (Arora et al., 2016). A novel aspect of our technique is that each word sense is also accompanied by one of about 2000 discourse atoms that give a succinct description of which other words co-occur with that word sense. Discourse atoms seem of independent interest, and make the method potentially more useful than the traditional clustering-based approaches to polysemy.