An artificial intelligence can now play the real-time strategy video game StarCraft II so well that it is better than 99.8 per cent of human players. The AI, called AlphaStar, was developed by tech firm DeepMind, which is owned by the same parent company as Google. AlphaStar played anonymously against human players in a series of online games on the official StarCraft II game server, Battle.net, and ended up ranked in the top 200 players for each of the leagues it competed in. StarCraft II is a popular science-fiction game that involves controlling armies and building infrastructure. Players must compromise between short-term payoffs and long-term gain.

An artificial intelligence (AI) system has reached the highest rank of StarCraft II, the fiendishly complex and wildly popular computer game, in a landmark achievement for the field. DeepMind's AlphaStar outperformed 99.8% of registered human players to attain grandmaster level at the game, which sees opponents build civilisations and battle their inventive, warmongering alien neighbours. The AI system mastered the game after 44 days of training, which involved learning from recordings of the best human players and then going up against itself and versions of the programme that intentionally tested its weaknesses. "AlphaStar has become the first AI system to reach the top tier of human performance in any professionally played e-sport on the full unrestricted game under professionally approved conditions," said David Silver, a researcher at DeepMind. More than $31m in prize money has been handed out from thousands of StarCraft II e-sport tournaments since the game was released in 2010. Players start with a small number of worker units that can gather resources, construct buildings, develop new units and technologies, and embark on scouting missions to gain intelligence on opponents.

Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups in the logistics industry. As there is a large number of startups working on a wide variety of solutions, we decided to share our insights with you. So, let's take a look at promising machine learning solutions for commercial vehicles. For our 5 picks of machine learning startups for commercial vehicles, we used a data-driven startup scouting approach to identify the most relevant solutions globally. The Global Startup Heat Map below highlights 5 interesting examples out of 450 relevant solutions.

Ground-breaking changes are happening on the edge of computing. We're long past the days when all analytics can be centralized in datacenters or even in the cloud. It's an increasingly decentralized world where analytics has to take place in real time right where individual sensors are, or in the fog when there's a need to collect information from multiple devices for fast insights. We recently sat down to talk with renowned technology consultant Marc Staimer about computing in the edge, the fog, and the core. In part two of our conversation, we're going to take a more in-depth look at matching the analytics requirements to the location of the analysis, especially when those analytics need to take place on the edge or in the fog.

A division within the U.S. Food and Drug Administration (FDA), the Center for Drug Evaluation and Research (CDER) has currently seven RPA (Robotic Process Automation) projects in development as it works to free up staff for its core science mission. The center has used RPA for a year with plans to implement bots to Machine Learning and Natural Language Processing (NLP) for applications in regulatory review. CDER ensures safe and effective drugs on the market to improve the health of the people throughout their lifecycle. While the FDA is recognized in the RPA space for automating drug intake forms and work within its chief financial officer's office, CDER has quietly put several RPA use cases into production enterprise-wide. It regulates over-the-counter and prescription drugs, including biological therapeutics and generic drugs.

'Independently I had started to do my research on how we can apply artificial intelligence to investigate some of the challenges that beset major programmes,' said Quang. 'What stood out for me from the course, is how we can use artificial intelligence to tackle both complexity and behavioural decision making. This is now the basis of a multi-year research programme I have the privilege of conducting with Saïd Business School as an Associate Scholar.' The pair leveraged the Oxford network to build the business. Throughout their programme they regularly discussed and refined their ideas with academics and classmates, several of whom are now shareholders.

The SKLearn lab will have a tutorial for sentiment analysis and mnist (via a Google Colab Notebook) with emphasis on how to improve performance, then time for students to try their own classifiers on a separate sentiment analysis task. The PyTorch lab willhave a tutorial on PyTorch and how to build feed-forward nets for the same tasks as in the Sklearn lab (with emphasis on how to improve performance), and time for students to try to build their own network for the separate sentiment analysis task.

World-class thought leaders discuss how emerging technologies are reshaping business and the future of work. With advanced technologies now at the forefront of nearly every major industry, stakeholders from a range of perspectives will discuss the latest innovations in robotics and AI, the pressing questions posed by their adoption and how these trends are transforming the world of work. The symposium will feature executives from some of the world's leading robotics manufacturers, system integrators, end users, technologists, corporate leaders and academics in a series of engaging panel discussions. Robotics manufacturers will also participate in speed networking with University of Michigan students prior to the symposium and will have robots on-site to showcase their capabilities. The following forward-thinking organizations are participating in the speed networking program: ABB; AMT Applied Manufacturing; the Association for Advancing Automation; ATI Industrial Automation; Comau; FANUC America Corporation; Ford Advanced Manufacturing; Honeywell Intelligrated; JR Automation; Littler; Kawasaki Robotics; KUKA Robotics; Universal Robots; and the University of Michigan Robotics Institute.

What is AI? Everything you need to know about Artificial Intelligence Professionals are familiar with economies of scale and learning curves. These concepts were codified in the 1940s. But the scale changes we're talking about are exponential, not linear. Humans are poor at estimating the effect of exponential growth. Scale changes on the order of 10x are exponential and therefore, fall into the human cognitive abyss.

"The time is ripe," said Andrei Iancu, the agency's director. "Our need is high and technology has advanced, so this is a good time to take advantage of these new tools to help our examiners," Mr. Iancu said. The agency's AI expert will advise the CIO on state-of-the-art implementations of smart tools in its internal business units, while creating a road map for using AI, including machine learning, to drive efficiencies in both the patent and trademark examination process, Mr. Iancu said. The goal is to speed up the overall process, he said, in part by automating aspects of the research performed by staff examiners and supervisors, reducing mundane administrative tasks and cutting costs by eking out efficiencies. The agency received about 643,000 patent applications last year, inching down from 2017, but up from about 619,000 in 2014, according to the agency.