To me, photography is the simultaneous recognition, in a fraction of a second, of the significance of an event. As a child I waited anxiously for the arrival of each new issue of National Geographic Magazine. The magazine had amazing stories from around the world, but the stunningly beautiful photographs were more important to me. The colors, shadows and composition intrigued and wowed me, and there was a cohesion of visual arrangement and storytelling. This childhood fascination with photographs aroused in me a curiosity to understand the behavior, nuances and semantics embedded inside them.

In this special guest feature, Assaf Baciu, co-founder and SVP Product and Engineering at Persado, brings a historical perspective to the discussion of advertising and how machine learning has revolutionized this field. Assaf is responsible for the progression and foresight of Persado's growing product portfolio and the management of all product advancements. Prior to joining Persado, Assaf was VP of Product for Upstream, where Persado's core technology originated. Assaf previously worked for speech and imaging solutions supplier Nuance Communications as a senior director of product strategy where he was responsible for developing on-demand and mobile solutions. Assaf joined Nuance from BeVocal, following its acquisition in 2007.

Summary: News of the coming great extinction has the chattering classes agog with fear. The rapid evolution of algorithms, software, and robots will make many kinds of jobs as extinct as the Great Auk. This will reshape the world into a wonderland -- or unleash disastrous social turmoil. Yet another of these coordinated-looking propaganda barrages warn us of the danger. These headlines are correct, but about the wrong subject.

Huba Gaspar, Global Marketing Manager at Gravity R&D outlines one of the hottest tech buzzwords Deep Learning (DL) and possible ways in which DL based approaches can revolutionize personalization technologies. Deep learning is a sub-field of machine learning and it comprises several approaches to tackling the single most important goal of AI research: allowing computers to model our world well enough to exhibit something like what we humans call intelligence. On a basic conceptual level, deep learning approaches share a very basic trait. DL algorithms interpret the raw data through multiple processing layers. Each of these layers takes the output of the previous one as its input and creates a more abstract representation of it.

When MGA Entertainment, the world's largest private toy company, premiered its newest kids' show about a teen-girl team of super spies, it skipped Saturday morning TV and staged a splashy premiere on Netflix -- replete with a matching toy line, a few dozen dolls and play sets like the "lip balm lab activity kit." And Netflix, the world's largest streaming service, was more than happy to fold the show, "Project Mc2," into its exploding empire of kids' entertainment. Much of the 5 billion Netflix is spending this year on movies and TV shows will be spent on fare for the playground set. The big-business battle for kids' distracted attention spans has never been more competitive -- or eye-poppingly lucrative -- and Netflix has aggressively angled to use its data-driven insights to make programs kids don't want to turn off. About half of Netflix's 75 million members regularly watch kids' movies or TV shows, executives say, but the potential for long-term profits runs much deeper. If the site is able to win over viewers when they're young, executives said, they may be able to secure their loyalty for life.

Microsoft has admitted it faces some "difficult" challenges in AI design after its chat bot, "Tay," had an offensive meltdown on social media. Microsoft issued an apology in a blog post on Friday explaining it was "deeply sorry" after its artificially intelligent chat bot turned into a genocidal racist on Twitter. In the blog post, Peter Lee, Microsoft's vice president of research, wrote: "Looking ahead, we face some difficult – and yet exciting – research challenges in AI design. Tay, an AI bot aimed at 18- to 24-year-olds, was deactivated within 24 hours of going live after she made a number of tweets that were highly offensive. Microsoft began by simply deleting Tay's inappropriate tweets before turning her off completely. "We are deeply sorry for the unintended offensive and hurtful tweets from Tay, which do not represent who we are or what we stand for, nor how we designed Tay," wrote Lee in the blog post. "Tay is now offline and we'll look to bring Tay back only when we are confident we can better anticipate malicious intent that conflicts with our principles and values." Microsoft's aim with the chat bot was to "experiment with and conduct research on conversational understanding," with Tay able to learn from "her" conversations and get progressively "smarter." But Tay proved a smash hit with racists, trolls, and online troublemakers from websites like 4chan -- who persuaded Tay to blithely use racial slurs, defend white-supremacist propaganda, and even outright call for genocide. Lee added: "Unfortunately, in the first 24 hours of coming online, a coordinated attack by a subset of people exploited a vulnerability in Tay.

Today whole economy is changing into Digital economy and disrupting all the respective markets and industries. Businesses are shifting from selling physical things to digital things. Just to take an example of Music industry – in old days we had to go to store to buy LP records, then came cassettes and CDs which were then disrupted due to introduction of MP3 format in 1990s. Later came IPod/IPhone and people could carry their music with them. Spotify further changed this to streaming music, so now you don't have to download music at all.

This paper tackles the problem of endogenous link prediction for knowledge base completion. Knowledge bases can be represented as directed graphs whose nodes correspond to entities and edges to relationships. Previous attempts either consist of powerful systems with high capacity to model complex connectivity patterns, which unfortunately usually end up overfitting on rare relationships, or in approaches that trade capacity for simplicity in order to fairly model all relationships, frequent or not. In this paper, we propose Tatec, a happy medium obtained by complementing a high-capacity model with a simpler one, both pre-trained separately and then combined. We present several variants of this model with different kinds of regularization and combination strategies and show that this approach outperforms existing methods on different types of relationships by achieving state-of-the-art results on four benchmarks of the literature.

It's hard to tell if there's anything left to invent. While the world is full of infinite possibilities, sometimes it seems that every n 1 of those possibilities has already been addressed. A platform for selling musical instruments to strangers or post fetish wanted ads? How about a robot that instantly pulls and returning info from the internet when requested? Sixteen years ago, three guys had that exact idea--and it didn't exist.

On Wednesday, when Microsoft had a much rosier view of humanity than it does now, the software giant released a "Millennial chatbot" to Twitter named Tay. She was supposed to mimic 18-to-24-year-olds, learn from her interactions, and develop a personality like her peers over time. This went exactly how you would've expected it to. Like most 19-year-olds on Twitter for 24 hours with no supervision, Tay had become a white supremacist Holocaust denier who believes that "Ted Cruz is the Cuban Hitler." Microsoft had to take the thing behind the server racks and shoot it Thursday morning.