Most of the games that machines can now challenge humans in are strategic, but slow: Chess, Go and poker, unless played in very specific settings, have no time constraints on player moves. That is what has made the work of research group OpenAI, in online team brawler Dota 2 - which requires real-time decision-making between potentially dozens of choices in a single frame - so different. OpenAI's bots, the OpenAI Five, went head-to-head against teams of professional players at Dota 2's annual championship, The International, this August. Although the bots lost, the matches provided an insight into how reinforcement learning is changing the game when it comes to artificial intelligence. It's safe to say that AI has a reputation in gaming: many players consider a match to be an instant loss if they have to play with a bot, and a disconnect is often accompanied by "GG".

Although it was bought by Google in 2014, AI firm DeepMind has always been true to its British roots -- expanding its offices in London, working closely with UK institutions like the NHS, and even teaching in the country's universities. Now, though, the company is opening its "first ever international AI office" -- in Edmonton, Canada. It's a natural fit for DeepMind, which has close links with the AI research community in Edmonton's University of Alberta. The company says nearly a dozen Alberta grads have joined its ranks, and the firm has sponsored the university's machine learning lab for a number of years. Richard Sutton, professor of computing science at Alberta, was also DeepMind's first outside advisor, and will head up the company's new base along with colleagues Michael Bowling and Patrick Pilarski.

Do you have an idea that could make you South Africa's most successful fintech entrepreneur? Are you looking to acquire the skills to launch a data-driven fintech business? A new one-year data science and business skills programme aims to assist aspirant SA fintech entrepreneurs. In an announcement today, the Explore Data Science Academy (Edsa) and Rand Merchant Investments' (RMI) fintech division, AlphaCode, said their Explore 10X programme would assist 20 aspiring SA future fintech entrepreneurs. Successful candidates will go through an intensive six-month data science-training programme, where they will learn how to design a fast-growing business along with the core digital skills needed to build a fintech organisation.

DeepMind is furthering its cancer research efforts with a newly announced partnership. Today, the London-based Google subsidiary said it has been given access to mammograms from roughly 30,000 women that were taken at Jikei University Hospital in Tokyo, Japan between 2007 and 2018. It'll use that data to refine its artificially intelligent (AI) breast cancer detection algorithms. Over the course of the next five years, DeepMind researchers will review the 30,000 images, along with 3,500 images from magnetic resonance imaging (MRI) scans and historical mammograms provided by the U.K.'s Optimam (an image database of over 80,000 scans extracted from the NHS' National Breast Screening System), to investigate whether its AI systems can accurately spot signs of cancerous tissue. The collaboration builds on DeepMind's work with the Cancer Research UK Imperial Center at Imperial College London, where it has already analyzed roughly 7,500 mammograms.

Yann Lecun is the scientist leading Facebook's AI efforts. Facebook is on course to double the size of the Facebook Artificial Intelligence Research (FAIR) division in the next two years, according to the company's chief AI scientist, Yann LeCun. FAIR currently has approximately 180-200 staff, but the division is expected to grow to around 400 people by 2020 as Facebook continues to put AI at the heart of its platforms. "I don't know everybody's name anymore and I don't recognise everybody either," LeCun admitted in an interview at Facebook's New York office. Asked whether FAIR is likely to double in size in the next couple of years, LeCun said: "Yes, probably. Members of FAIR carry out fundamental research in the field of AI. Some of their breakthroughs are applied to Facebook's platforms (Facebook, Instagram, and Whatsapp) by an applied machine learning (AML) team and other engineers, but the majority of their research is purely academic. So far they've developed algorithms that can analyse MRI scans and play games like "Starcraft," among other things. But competition for AI talent is intense. Facebook and Google are locked in a battle to hire the smartest minds in the field, while others like Apple, Amazon, and Microsoft are also trying to poach the best PhD students and other academics, leading to brain drain concerns. Facebook is hiring many of these people through FAIR, while Google, or Alphabet, as Google's parent company is known, is hiring through DeepMind, and Google Brain to some extent. DeepMind's team has swelled from less than 100 to over 700 since it was acquired by Google in 2014 for £400 million. Asked why FAIR hasn't grown at the same rate as some other AI labs, Rob Fergus, head of FAIR in New York, said: "It's a market supply.

Artificial intelligence researchers are reaching out to gamers. We have been watching Alphabet's AI division DeepMind--acquired by Google in 2014--for years, particularly since it unveiled AlphaGo Zero, an AI capable of achieving superhuman intelligence without human assistance. Its latest project partners with Unity, one of the world's leading video game development platforms, and aims to research artificial intelligence agents and machine learning, with hopes of using it to improve costly technologies like robotics and self-driving cars. "The partnership will enable DeepMind to develop virtual environments and tasks in support of their fundamental AI research program," said Danny Lange, the vice president of AI and machine learning at Unity Technologies, in a blog post on Sept. 26. "Games and simulations have been a core part of DeepMind's research programme from the very beginning and this approach has already led to significant breakthroughs in AI research," Demis Hassabis, co-founder and CEO of DeepMind, tells the Daily Dot via email.

Early results from our partnership with the Radiotherapy Department at University College London Hospitals NHS Foundation Trust suggest that we are well on our way to developing an artificial intelligence (AI) system that can analyse and segment medical scans of head and neck cancer to a similar standard as expert clinicians. This segmentation process is an essential but time-consuming step when planning radiotherapy treatment. The findings also show that our system can complete this process in a fraction of the time. More than half a million people are diagnosed each year with cancers of the head and neck worldwide. Radiotherapy is a key part of treatment, but clinical staff have to plan meticulously so that healthy tissue doesn't get damaged by radiation: a process which involves radiographers, oncologists and/or dosimetrists manually outlining the areas of anatomy that need radiotherapy, and those areas that should be avoided.

Researchers at the Alphabet subsidiary DeepMind have spelled out how they will ensure that AI is developed safely. The guidelines aim to make certain that powerful systems capable of learning and figuring out their own solutions to problems don't start to behave in unexpected and unwanted ways. The big issues: The researchers say the key challenges are specifying the intended behavior of a system in a way that avoids unwanted consequences; making it robust even in the face of unpredictability; and providing assurances, or ways to override behavior if necessary. Erratic behavior: This is a growing area of academic research. There are plenty of often amusing examples of machine-learning systems that have started behaving oddly.

Human action recognition refers to automatic recognizing human actions from a video clip, which is one of the most challenging tasks in computer vision. In reality, a video stream is often weakly-annotated with a set of relevant human action labels at a global level rather than assigning each label to a specific video episode corresponding to a single action, which leads to a multi-label learning problem. Furthermore, there are a great number of meaningful human actions in reality but it would be extremely difficult, if not impossible, to collect/annotate video clips regarding all of various human actions, which leads to a zero-shot learning scenario. To the best of our knowledge, there is no work that has addressed all the above issues together in human action recognition. In this paper, we formulate a real-world human action recognition task as a multi-label zero-shot learning problem and propose a framework to tackle this problem. Our framework simultaneously tackles the issue of unknown temporal boundaries between different actions for multi-label learning and exploits the side information regarding the semantic relationship between different human actions for zero-shot learning. As a result, our framework leads to a joint latent embedding representation for multi-label zero-shot human action recognition. The joint latent embedding is learned with two component models by exploring temporal coherence underlying video data and the intrinsic relationship between visual and semantic domain. We evaluate our framework with different settings, including a novel data split scheme designed especially for evaluating multi-label zero-shot learning, on two weakly annotated multi-label human action datasets: Breakfast and Charades. The experimental results demonstrate the effectiveness of our framework in multi-label zero-shot human action recognition.

Today we are announcing our collaboration with DeepMind, a world leader in artificial intelligence research. The partnership will enable DeepMind to develop virtual environments and tasks in support of their fundamental AI research program. DeepMind researchers are addressing huge AI problems, and they have selected Unity as a primary research platform for creating complex virtual environments that will enable the development of algorithms capable of learning to solve complex tasks. We believe the future of AI is being shaped by increasingly sophisticated human-machine interactions, and Unity is proud to be the engine that is enabling these interactions. Unity is no stranger to forging thought-leadership in the AI field.