Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms however have faced great challenges when dealing with high-dimensional environments. The recent development of deep learning has enabled RL methods to drive optimal policies for sophisticated and capable agents, which can perform efficiently in these challenging environments. This paper addresses an important aspect of deep RL related to situations that require multiple agents to communicate and cooperate to solve complex tasks. A survey of different approaches to problems related to multi-agent deep RL (MADRL) is presented, including non-stationarity, partial observability, continuous state and action spaces, multi-agent training schemes, multi-agent transfer learning. The merits and demerits of the reviewed methods will be analyzed and discussed, with their corresponding applications explored. It is envisaged that this review provides insights about various MADRL methods and can lead to future development of more robust and highly useful multi-agent learning methods for solving real-world problems.

Vision algorithms capable of interpreting scenes from a real-time video stream are necessary for computer-assisted surgery systems to achieve context-aware behavior. In laparoscopic procedures one particular algorithm needed for such systems is the identification of surgical phases, for which the current state of the art is a model based on a CNN-LSTM. A number of previous works using models of this kind have trained them in a fully supervised manner, requiring a fully annotated dataset. Instead, our work confronts the problem of learning surgical phase recognition in scenarios presenting scarce amounts of annotated data (under 25% of all available video recordings). We propose a teacher/student type of approach, where a strong predictor called the teacher, trained beforehand on a small dataset of ground truth-annotated videos, generates synthetic annotations for a larger dataset, which another model - the student - learns from. In our case, the teacher features a novel CNN-biLSTM-CRF architecture, designed for offline inference only. The student, on the other hand, is a CNN-LSTM capable of making real-time predictions. Results for various amounts of manually annotated videos demonstrate the superiority of the new CNN-biLSTM-CRF predictor as well as improved performance from the CNN-LSTM trained using synthetic labels generated for unannotated videos. For both offline and online surgical phase recognition with very few annotated recordings available, this new teacher/student strategy provides a valuable performance improvement by efficiently leveraging the unannotated data.

People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: giving adequate success rates to specifically protected groups. In contrast, the alternative paradigm of individual fairness has received relatively little attention, and this paper advances this less explored direction. The paper introduces a method for probabilistically mapping user records into a low-rank representation that reconciles individual fairness and the utility of classifiers and rankings in downstream applications. Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on a variety of real-world datasets. Our experiments show substantial improvements over the best prior work for this setting.

Once a behind-the-scenes technology we hardly noticed, AI is transforming the workforce. This handful of examples make it clear that now's the time to amplify your knowledge on this critical workplace topic. Applications for chatbots alone promise endless possibilities, the tech gurus assure us. So it's perhaps not so surprising that 62 percent of companies anticipated using some form of artificial intelligence by 2018, according to a study by Salary.com, with Gartner predicting that by 2020, AI will eliminate 1.8 million jobs as technology streamlines manual processes, improves workflows and reduces mistakes. The World Economic Forum's pronouncement is even more dire.

The system, named Doctor Alzimov, after the Russian-born science fiction writer, can be installed on any computer and provides images with clearly marked findings for easy interpretation. It was developed by researchers at Peter the Great St. Petersburg Polytechnic University (SPbPU) in St. Petersburg, Russia along with radiologists from the St. Petersburg Oncological Center.

Apex Legends – a brand new, free-to-play battle royale game – is already one of the world's most popular titles. The game has more than a million players just hours after it has launched, and has quickly become the most popular game on streaming service Twitch. The Xbox, PlayStation and PC title is a spin-off from popular series Titanfall, but appeared slightly by surprise overnight. Already, however, it has been the subject of a vast marketing campaign as well as discussion by influencers, which have led to people pouring into the game. The game can be downloaded now over the Origins store for PC, as well as on PlayStation 4 or Xbox One.

On March 23rd 2016, Microsoft released a new artificial intelligence Twitter bot named Tay. "Hellooooooo world!!!" read its cutesy first message. Within hours, however, human users had persuaded Tay to replace its light hearted banter with anti-semitic, sexist, and racist Tweets. The media got hold of the story and pilloried Microsoft and its new CEO, Satya Nadella. While it probably didn't feel like it at the time, Tay represented the start of a significant turnaround in Microsoft's fortunes that would eventually lead the tech giant to reclaim its position as the most valuable company in the world.

Mark Zuckerberg says that Facebook is being criticised too much – and that people dislike it partly because it is empowering people. In a message posted to celebrate his company's 15th birthday, the founder and boss defended his company after a bruising period that has seen it accused of abusing people's most personal data and failing to act to stop deadly misinformation. He admitted the company had more to do around disturbing content but also suggested he does not get enough credit for the positive impact Facebook has had on the world. Mr Zuckerberg claimed he had founded Facebook in response to the fact that it was possible to find many things – such as films and music – on the internet, but not people. He did not make any reference to any of the more sordid uses that the early Facebook was put to, including a central feature that allowed people to rate how attractive other students were.

Despite recent uncertainty in the financial markets, the economic outlook for the banking industry remains positive. Regulatory forces are encouraging innovation and new digital technologies provide opportunities to improve customer experiences. There are strong indications that banking organizations worldwide understand the primary trends impacting the industry as well as the actions that are needed to respond to competitive pressures. The question is whether banks and credit unions will prioritize the deployment of human and financial resources to respond to these changes. Will legacy financial institutions embrace change, take appropriate risks and disrupt themselves to meet the needs of consumers, small businesses and corporate customers?

Artificial intelligence has started -- slowly -- to make its presence felt in payments and commerce, including in fraud prevention, via early deployments of the technology and cutting-edge AI algorithms. And with those deployments comes increasing awareness of what AI can really do, how it can improve upon less sophisticated machine learning technology, and why it promises to play a vital role in the daily lives of consumers in the coming decades. The race to get ahead on the technology is now gaining clarity as well. Fresh data from the U.N. World Intellectual Property Organization, or WIPO, finds that the U.S. and China are building global dominance in AI technology development (along with closely related tech that is finding more use among financial institutions). The study is based on "more than 340,000 AI-related patent applications and 1.6 million scientific papers published since AI first emerged in the 1950s, with the majority of all AI-related patent filings published since 2013."