There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields. AI is the all encompassing umbrella that covers everything from Good Old Fashion AI (GOFAI) all the way to connectionist architectures like Deep Learning. ML is a sub-field of AI that covers anything that has to do with the study of learning algorithms by training with data.

Implementing a Distributed Deep Learning Network over Spark Deep learning & XgBoost: Winning it hands down!

The acquisition will bring Zementis' predictive analytics to Software AG's real-time streaming analytics platform. Software AG has acquired California-based Zementis for an undisclosed sum in a move designed to bolster its internet of things capability. Zementis offers software for'deep learning' which plays a crucial role in the development of machine learning, data science and fundamental technology that drives artificial intelligence (AI) development. According to Software AG, the advances in machine learning and AI are being applied in the next generation Internet of Things (IoT) such as self-driving cars, personal digital assistants, medical diagnosis, predictive maintenance and robotics. Software AG has already employed Adaptive Decision and Predictive Analytics (ADAPA) from Zementis into its Digital Business Platform to offer its clients with comprehensive insights for real time business analytics.

OpenAI, an artificial intelligence research company, wants to let AIs loose in their own universe, where they can learn to play games, use apps and interact with websites. Universe is the name of OpenAI's tool for training AIs on, as it puts it, "any task a human can complete with a computer." Using a VNC (Virtual Network Computing) remote desktop, it allows the AI to control the game or app using a virtual keyboard and mouse, and to see its output by analyzing the pixels displayed on the screen. The source code for Universe posted to Github on Monday, and includes interfaces to a thousand online environments, the company said. Among them are simple browser tasks such as clicking buttons, or copying and pasting items -- but there are also more complex environments, such as the video game Grand Theft Auto V. Training AIs how best to steal cars and shoot bystanders might seem a strange direction to take, given that OpenAI's mission is to build safe AI, and that one of its goals is to enable a general-purpose robot to wield tools -- initially to perform housework.

Alphabet Inc.'s artificial intelligence division Google DeepMind is making the maze-like game platform it uses for many of its experiments available to other researchers and the general public. DeepMind is putting the entire source code for its training environment -- which it previously called Labyrinth and has now renamed as DeepMind Lab -- on the open-source depository GitHub, the company said Monday. Anyone will be able to download the code and customize it to help train their own artificial intelligence systems. They will also be able to create new game levels for DeepMind Lab and upload these to GitHub. The decision to make this AI test bed available to the public is further evidence of DeepMind's decision to embrace more openness around its research.

Only 26 percent of computer professionals were women in 2013, according to a recent review by the American Association of University Women. That figure has dropped 9 percent since 1990. Some say the industry is masculine by design. Others claim computer culture is unwelcoming -- even hostile -- to women. So, while STEM fields like biology, chemistry, and engineering see an increase in diversity, computing does not.

Artificial intelligence got a big push today as both Google and OpenAI announced plans to open-source their deep learning code. Elon Musk's OpenAI released Universe, a software platform that "lets us train a single [AI] agent on any task a human can complete with a computer." At the same time, Google parent Alphabet is putting its entire DeepMind Lab training environment codebase on GitHub, helping anyone train their own AI systems. DeepMind first burrowed into the public consciousness by defeating a world champion at the notoriously difficult game Go. However, to advance deep learning further, Alphabet says that such AI "agents" require highly detailed environments to serve as laboratories for AI research.

Uber has acquired Geometric Intelligence, a two-year-old artificial intelligence startup that vows to surpass the deep learning systems under development at internet giants like Google and Facebook. But as this tiny AI lab slips into Uber's increasingly vast and ambitious operation, the startup is still tight-lipped on what its technology actually looks like. Founded by New York University psychologist Gary Marcus and University of Cambridge professor of information engineering Zoubin Ghahramani, Geometric Intelligence spans thirteen other researchers culled from across the academic world. Fourteen of the startup's fifteen employees will move to San Francisco, where Uber is based, serving as the central AI lab for the ride-hailing company. Ghahramani, the mathematician most responsible for the startup's core technology, will remain at Cambridge while spending half his time working for Uber.

Back in early 2015, Google's mysterious DeepMind unveiled an algorithm that could teach itself to play Atari games. Based on deep neural nets, the AI impressively mastered nostalgic favorites such as Space Invaders and Pong without needing any explicit programming -- it simply learned through millions of examples. But the algorithm had a weakness: memory. Without a memory module, it couldn't store away any information it had already mastered. When faced with problems requiring multi-step reasoning, the algorithm faltered.

Artificial intelligence developed by the likes of Google's DeepMind and Elon Musk's OpenAI is taught within the confines of game worlds – including navigating around mazes, dodging deadly cliffs, playing laser tag and flying through space. In a mission to build a general AI capable of solving any problem put in front of it, DeepMind is open-sourcing its game code to everyone. The software and 14 levels from DeepMind Labs will be put on GitHub later this week. And, not to be outdone, Elon Musk's own OpenAI is also releasing its own'computer training ground' called Universe. Universe is open-source software that supports Gym; OpenAI's toolkit for testing its algorithms which help software play games, for example, using a reward scheme.