OpenAI, the nonprofit artificial intelligence research company established last year with backing from several Silicon Valley figures, today announced its first product: a proving ground for algorithms for reinforcement learning, which involves training machines to do things based on trial and error. OpenAI is releasing tools you can run locally to test out algorithms in various "environments" -- including Atari games like Air Raid, Breakout, and Ms. Pacman -- and a Web service for sharing test results. The system automatically scores evaluations and also seeks to have results reviewed and reproduced by other people. "We originally built OpenAI Gym as a tool to accelerate our own RL research. We hope it will be just as useful for the broader community," OpenAI's Greg Brockman and John Schulman wrote in a blog post. To be sure, there are other online places for showing off algorithms, including Algorithmia.

Bayesian models are rooted in Bayesian statistics, and easily benefit from the vast literature in the field. In contrast, deep learning lacks a solid mathematical grounding. Instead, empirical developments in deep learning are often justified by metaphors, evading the unexplained principles at play. These two fields are perceived as fairly antipodal to each other in their respective communities. It is perhaps astonishing then that most modern deep learning models can be cast as performing approximate inference in a Bayesian setting. The implications of this statement are profound: we can use the rich Bayesian statistics literature with deep learning models, explain away many of the curiosities with these, combine results from deep learning into Bayesian modelling, and much more.

Deep Learning allows computational models composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection, and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large datasets by using the back-propagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about dramatic improvements in processing images, video, speech and audio, while recurrent nets have shone on sequential data such as text and speech. Representation learning is a set of methods that allows a machine to be fed with raw data and to automatically discover the representations needed for detection or classification.

How can we help millions of chronically ill patients lead healthier lives with fewer hospitalizations? Parsing through individual patient remote monitoring data doesn't seem like a scalable solution. But what if artificial intelligence could rapidly analyze this information and predict an individual's health problem, such as worsening heart failure, in time for caregivers to intervene and prevent an emergency department visit or hospital admission? That may sound like a pipe dream, yet the technology to make it happen already exists. This new "prescription" for chronically ill patients would be an algorithm -- a personalized disease model unique to each patient, based on streams of data collected from biosensors worn by patients.

Tesla and SpaceX owner Elon Musk, is now establishing an artificial intelligence'gym' for developers to train their AI systems through a series of games and challenges. Under Musk's 1 billion non-profit OpenAI initiative, the gym is now available as open source code and provides'environments' for developers to test their new AI bots. This includes 59 Atari games including the popular Alien, Air Raid, Asteroids and Pac-Man. The results from these test environments will be compiled by OpenAI into a list of the top performing systems. Instead of ranking the AIs by high scores, the systems will be judged on versatility.

When we think of innovators of the technology space we largely think of blokes like Mark Zuckerberg, Bill Gates, Steve Jobs, Elon Musk – the zeitgeist is largely male. But that hasn't always been the case. As a matter of fact, for the first decade or so programming was a'pink-collar' industry. The vast majority of early coders were women. What's more, it's an industry created by women.

Oussama Khatib held his breath as he swam through the wreck of La Lune, 100 meters below the Mediterranean. The flagship of King Louis XIV sank here in 1664, 20 miles off the southern coast of France, and no human had touched the ruins – or the countless treasures and artifacts the ship once carried – in the centuries since. With guidance from a team of skilled deep-sea archaeologists who had studied the site, Khatib, a professor of computer science at Stanford, spotted a grapefruit-size vase. He hovered precisely over the vase, reached out, felt its contours and weight, and stuck a finger inside to get a good grip. He swam over to a recovery basket, gently laid down the vase and shut the lid.

PA's automation experts, Helena Moren and Simon Andersson, have had an article published in NyTeknik about the rise of smart machines and how Swedish companies are lagging behind that trend. The article outlines how robots and artificial intelligence will have a significant impact on business and finance, with the market for smart machines expected to be worth approximately SEK 130 billion by 2020. While predicting robots' impact on the labour market is more difficult, there is a high probability that the robots will bring new, high-end jobs and greater freedom for mankind. Helena and Simon explain that robotisation can release a comprehensive range of resources and can be used to increase efficiency and productivity. But, as revealed in a report presented at the Davos summit in January, Swedish companies are falling behind in this digitisation, mainly because they are not efficiently resourced to make these developments.

Steve Wozniak tells the innovation summit that artificial intelligence researchers are getting close to the point of understanding "what the brain is". The man who helped bring Apple computers to the world says we're on the verge of an artificial intelligence revolution. Apple co-founder Steve Wozniak told an innovation summit in Brisbane artificial intelligence would be the next big disrupter, separate from the digital revolution that changed the world with personal computers and smartphones. Speaking via video link from the US, "The Woz" described AI as "machines acting like human friends". An attendee at the Brisbane summit films Mr Wozniak's broadcast.

The tech world's spotlight turned to chatbots after Facebook announced the launch of their own bot store on the Messenger platform at the F8 conference in April 2016. A few weeks later, the news has been covered extensively. It has also generated a lot of criticism. The two main arguments against are that this is nothing substantially new and that chatbots give only approximative answers. These hasty criticisms are due to the fact that most people haven't quite grasped the real concept behind the Messenger bot store.