A slowdown in tech start-up funding has at least one big beneficiary: Walt Disney Co.'s video games division. The entertainment giant is having an easier time finding partners with whom it can develop mobile games, one of its top executives said last week. Why? Up-and-coming companies are losing access to the cash needed to launch games on their own. "As the venture money has dried up and exits have slowed down and valuations have come down, larger game developers that have one or two hits [but not a big stable of them] are now open to work with us in co-development," said Chris Heatherly, senior vice president and general manager at Disney Mobile Games. The comments came during a discussion last week at the L.A. Games Conference with Michael Metzger of investment bank Houlihan Lokey.

Quantitative Trading is the methodical way of trading. It's a 300b industry, and Quantitative Hedge Funds are considered to be the elite of Hedge Funds. Today, with more and better data and software than ever, the application of machine learning methods on financial data becomes increasingly popular in the industry. In his talk Martin will introduce some basic concepts of quantitative trading including indicators, measures, common pitfalls, and best practices to avoid them. Martin worked for many years in the quantitative hedge fund industry - first as a quant, later as the head of quant research team. In 2014 Martin founded Quantiacs - the first marketplace for user-generated trading algorithms. Quantiacs vision is to make algorithmic trading accessible. The company connects user-generted trading algorithms and connects them to capital, so that "free-lance quants" can earn money without investing their own money.

Tech giants are active players at the cognitive technologies scene. Alphabet and Facebook proved themselves active acquirers and investors in cognitive tech. Supercomputers, robots and drones are among themes approached by Internet giants. Microsoft, however, seems to be following a differed path: it integrates cognitive technologies into traditional products and makes them smarter. Bill Gates coined the concept of'digital nervous system (DNS)... that ... [provides] a well-integrated flow of information to the right part of the organization at the right time'.

This snowball effect would lead to an intelligence explosion yielding a superintelligence surpassing all human control, understanding and comprehension. In other words, once we learn how to make machines that can reprogram themselves autonomously and are of course self-aware, they will reach the near infinite nigh-omniscience of the Technological Singularity. Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an'intelligence explosion,' and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control.

As artificial intelligence grows increasingly sophisticated, it also grows increasingly alien.Deep learning algorithms and other A.I. technologies are creating systems capable of solving problems in ways that humans might never consider. But it's important that such systems understand humans as well, lest they inadvertently harm their creators. Accordingly, some researchers have argued that we need to help A.I. grasp human values--and, perhaps, the value of humans--from the start, making our needs a central part of their own development. To better understand some of the thinking around these issues, I spoke with Stuart Russell, a professor of computer science at the University of California, Berkeley. Russell has been involved with A.I. research for decades and is the co-author of one of the field's standard textbooks.

Researchers will pit their A.I. algorithms against the game Doom, to showcase how computers can adapt to visual environments. Video games are a good way to train artificial intelligence algorithms to learn about a visual world--researchers can simulate any situation they want, and it's endlessly repeatable. Google DeepMind is famous for this approach, teaching its A.I. to play Atari. Now researchers are competing to make their algorithms play Doom, the iconic shooting game originally for PC. DeepMind has already trained its algorithm to walk around in a maze based on Doom, but this competition would have the A.I. play death match rounds with rocket launchers.

Where exactly is all the water on Earth's surface? Stand-alone satellite images have their limitations, but using artificial intelligence to examine them can now glean precise levels of water around the world and how they are changing week by week. Palo Alto startup Orbital Insight uses freely available images taken by the U.S. Geological Survey's Landsat 7 and 8 satellites, much like the images you see on Google Maps. The startup feeds the images into a neural network, which pinpoints the exact location and area of surface water. While it's not a new feat to track the Earth's water levels, Orbital Insight is approaching the task with a specially trained neural network that labels water pixel by pixel.

It's a site that only lets in the genetically blessed based on some mysterious beauty metric – and today the personal data of 1.1 million BeautifulPeople.com It's only a slice of data from 2015, and the company says the leak's been patched up, but data once stolen can never be controlled: and so 1.1 million names of self-declared Beautiful People will now begin circulating. Like the Ashley Madison hack – which left 39 million people on a dating site for married people exposed and their names suddenly searchable – there's a joy in shaming people who would sign up for such a thing. "Online dating for beautiful people only," the website announces. "BeautifulPeople.com is the largest internet dating community exclusively for the beautiful," it reads.

OSX is much better than Windows, isn't it? That's a common wisdom, and it seemed to be confirmed once more when I installed XGBoost on both OS. Before I deep dive, let me briefly describe XGBoost. It is a machine learning algorithm that yields great results on recent Kaggle competitions. I decided to install it on my laptops, an old PC running Windows 7, and a brand new Mac Pro running OSX.

Here's a scene from our digital future: You sit down to dinner at a restaurant where your server was selected by a "robo-boss" based on an optimized match of personality and interaction profile, and the angle at which he presents your plate, or how quickly he smiles can be evaluated for further review. Or, perhaps you walk into a store to try on clothes and ask the digital customer assistant embedded in the mirror to recommend an outfit in your size, in stock and on sale. Afterwards, you simply tell it to bill you from your mobile and skip the checkout line. These scenarios describe two predictions in what will be an algorithmic and smart machine driven world where people and machines must define harmonious relationships. In his session at Gartner Symposium/ITxpo 2016 in Orlando, Daryl Plummer, vice president, distinguished analyst and Gartner Fellow, discussed how Gartner's Top Predictions begin to separate us from the mere notion of technology adoption and draw us more deeply into issues surrounding what it means to be human in a digital world.