Killer computers, robot uprisings: Hollywood has long had a deep fascination for artificial intelligence. Even off screen, AI is increasingly a key part of the media business -- but thus far, the reality isn't nearly as dramatic as movies like "Her" or "Ex Machina" make it out to be. This story first appeared in the November 08, 2016 issue of Variety. Case in point: You've probably been contacted by AI today without even knowing it. That push notification on your mobile phone, the email newsletter of your favorite website, or the videos recommended to you while binge-watching are being powered by machine-learning algorithms that rely on huge amounts of data to make smart decisions about the media you'd be inclined to consume.

Hedge Funds and money managers have come a long way from the days of handwritten financial models. In fact, stock and commodity exchanges report that more than 80 percent of today's trade decisions are computer driven. But a broader change is underway as Wall Street begins to embrace artificial intelligence. By combining AI with big data, researchers are taking advantage of unusual trading opportunities that, until recently, were too complex or expensive to implement. Some of the largest hedge funds are actively pouring resources into AI-driven analysis and competing to hire the best and brightest talent in the field.

Forrester surveyed business and technology professionals and found that 58% of them are researching AI, but only 12% are using AI systems. This gap reflects growing interest in AI, but little actual use at this time. We expect enterprise interest in, and use of, AI to increase as software vendors roll out AI platforms and build AI capabilities into applications. Enterprises that plan to invest in AI expect to improve customer experiences, improve products and services, and disrupt their industry with new business models. But the burning question is: how can your enterprise use AI today to crush it?

Choosing features to improve a performance of a particular algorithm is a difficult question. Currently here is PCA, which is hard to understand (although it can be used out-of-the-box), is not easy to interpret and requires centralizing and scaling of features. In addition, it does not allow to improve prediction performance for a particular outcome (if its accuracy is lower than for others or it has a particular importance). My method enables to use features without preprocessing. Therefore a resulting prediction is easy to explain.

I find myself being summoned from various directions, as if I've just stepped into a party with my closest friends. Many pins I see are interesting to me -- it's a pleasant feeling. A house with a window lined with dark brown wooden shutters. A shelf made from the back of an iMac. A media console on wheels with iron legs and wooden slats.

Chat applications are becoming a mainstream trend and our preferred way of interacting with colleagues, friends and family. From the early days of SMS to the favorite snaps of our children, real-time online conversations are everywhere and here to stay. The acquisition of WhatAapp by Facebook in 2014 for a hefty $22 Billion price tag made it clear and promising as TechCrunch noticed it one year later. But although TechCrunch saw messaging apps as the future of mobile portal, they remained more or less next to the Internet, without a direct impact, except their increasing audience. The recent surge of interest in Bots and AI is changing the game and we'll be witnessing the second major fragmentation of the Internet.

The O'Reilly Artificial Intelligence Conference call for speakers is open Underneath all the AI hype, real breakthroughs are happening--and obstacles to applied AI are being overcome--allowing AI developers to create software that doesn't just do what it's told, but has the ability to anticipate the needs of its users through a combination of pattern recognition, knowledge, planning, and reasoning. Enterprise bots are emerging to participate in conversations and carry out repetitive tasks. Deep learning toolkits are becoming essential tools for software engineers and data scientists. Frameworks are being developed that promise point-and-click development of intelligent conversational interfaces to relatively unsophisticated developers. There is a growing--and urgent--need for information on applied AI, as opposed to the kind of research presented at academic conferences.

MogIA, an artificial intelligence system and election predictor, has successfully predicted its fourth election in a row. The system, which learns in real-time by examining data on the internet, placed its bets on Donald Trump to win the 2016 US Presidential Election in October after also successfully predicting both the Democratic and Republican party primaries. "If Trump loses, it will defy the data trend for the first time in the last 12 years since Internet engagement began in full earnest," said MogIA's developer Sanjiv Rai in October, who then was still unaware of the certainty of a Trump win. "If you look at the primaries, in the primaries, there were immense amount of negative conversations that happen with regards to Trump," he continued. "However, when these conversations started picking up pace, in the final days, it meant a huge game opening for Trump and he won the Primaries with a good margin."

With visual data, it's sometimes possible to automate experiments that determine which visual features a neural net is responding to. But text-processing systems tend to be more opaque. At the Association for Computational Linguistics' Conference on Empirical Methods in Natural Language Processing, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) will present a new way to train neural networks so that they provide not only predictions and classifications but rationales for their decisions. "In real-world applications, sometimes people really want to know why the model makes the predictions it does," says Tao Lei, an MIT graduate student in electrical engineering and computer science and first author on the new paper. "One major reason that doctors don't trust machine-learning methods is that there's no evidence."

It's one thing to track endangered animals on land, but it's another to follow them when they're in the water. How do you spot individual critters when all you have are large-scale aerial photos? Queensland University researchers have used Google's TensorFlow machine learning to create a detector that automatically spots sea cows in ocean images. Instead of making people spend ages coming through tens of thousands of photos, the team just has to feed photos through an image recognition system that knows to look for the cows' telltale body shapes. An initial version could spot 80 percent of the sea cows that had been confirmed in existing photos.