Our goal is to pre-train reinforcement learning models on a diverse dataset and then transfer knowledge (either zero-shot or with fine-tuning) to a different test environment. In the last decade, we've seen learning-based systems provide transformative solutions for a wide range of perception and reasoning problems, from recognizing objects in images to recognizing and translating human speech. If fruitful, this line of work could allow learning-based systems to tackle active control tasks, such as robotics and autonomous driving, alongside the passive perception tasks to which they have already been successfully applied. While deep reinforcement learning methods – like Soft Actor Critic– can learn impressive motor skills, they are challenging to train on large and broad data that is not from the target environment. In contrast, the success of deep networks in fields like computer vision was arguably predicated just as much on large datasets, such as ImageNet, as on large neural network architectures.

Reinforcement learning is the study of decision making over time with consequences. The field has developed systems to make decisions in complex environments based on external, and possibly delayed, feedback. "Microsoft Research, works on developing the theory, algorithms and systems for technology that learns from its own successes (and failures), explores the world "just enough" to learn, and can infer which decisions have led to those outcomes. Our primary goal is reinforcement learning in the real world: understanding how to build systems that work, even when simulation is unavailable and samples are scarce." To celebrate hosting the Reinforcement Learning Israel Meetup organized by the talented Shani Gamrian at the Microsoft Reactor here is a list of three Reinforcement Learning Environments every ML enthusiast should know.

What does AI mean for businesses big and small? What key opportunities and challenges does it present? Two experts on the topic weigh in: Rotman School Dean Tiff Macklem and Scotiabank CTO Michael Zerbs. Can you talk a bit about how you're leveraging third-party datasets as part of your AI strategy? MZ: If you work for a large organization, never underestimate the challenge of just getting at the data that you think you've already got.

Match Group, the largest dating app conglomerate in the US, doesn't perform background checks on any of its apps' free users. A ProPublica report today highlights a few incidents in which registered sex offenders went on dates with women who had no idea they were talking to a convicted criminal. These men then raped the women on their dates, leaving the women to report them to the police and to the apps' moderators. These women expected their dating apps to protect them, or at least vet users, only to discover that Match has little to no insight on who's using their apps. The piece walks through individual attacks and argues that the apps have no real case for not vetting their users.

The following keynote speakers have been confirmed for IEEE GLOBECOM 2019. Abstract: We are well into the "Internet of Things" era for the Internet. Billions of devices are expected and it is not uncommon to find a dozen or even a score of Internet-enabled devices in residences and offices around the world. These systems run on software - some of which has not been well tested for safety and security. We need to introduce and promote an ethic of software safety and extended maintenance to protect the users of these devices.

Amazon is leveraging machine learning to fight fraud, audit code, transcribe calls, and index enterprise data. Today during a keynote at its Amazon Web Services (AWS) re:Invent 2019 conference in Las Vegas, the tech giant debuted Amazon Fraud Detector, a fully managed service that detects anomalies in transactions, and CodeGuru, which automates code review while identifying the most "expensive" lines of code. And those are just the tip of the iceberg. With Fraud Detector (in preview), AWS customers provide email addresses, IP addressees, and other historical transaction and account registration data, along with markers indicating which transactions are fraudulent and which are legitimate. Amazon takes that information and uses algorithms -- along with data detectors developed on the consumer business of Amazon's business -- to build bespoke models that recognize things like potentially malicious email domains and IP address formation.

Dr. Ansgar Koene Dr. Ansgar Koene is Global AI Ethics and Regulatory Leader at EY where he supports the AI Lab's Policy activities on Trusted AI. He is also a Senior Research Fellow at the RCUK funded Horizon Digital Economy Research institute (University of Nottingham) where he contributes to the policy impact activities of the institute and leads the policy related stakeholder engagement activities of the ReEnTrust project. As part of this work Ansgar has provided evidence to twelve UK parliamentary inquiries, co-authored a report on Bias in Algorithmic Decision-Making for the Centre for Data Ethics and Innovation, and was lead author of a Science Technology Options Assessment report on a Governance Framework for Algorithmic Accountability and Transparency for the European Parliament. Ansgar chairs the IEEE P7003 Standard for Algorithmic Bias Considerations working group, is the Bias Focus Group leader for the IEEE Ethics Certification Program for Autonomous and Intelligent Systems (ECPAIS), and a trustee for the 5Rgiths foundation for the Rights of Young People Online. Ansgar has a multi-disciplinary research background, having worked and published on topics ranging from Policy and Governance of Algorithmic Systems (AI), data-privacy, AI Ethics, AI Standards, bio-inspired Robotics, AI and Computational Neuroscience to experimental Human Behaviour/Perception studies.

Tony Brooker, who has died aged 94, was a pioneer of computer programming and education. He designed and implemented the world's first high-level programming language, at Manchester University, and was later founding professor of computer science at Essex University. In 1947, when Brooker took up his first academic post, as assistant lecturer in engineering mathematics at Imperial College, University of London, computers were in the air. He joined Professor KD Tocher and another student, Sidney Michaelson, in building the Icce (Imperial College Computing Engine, pronounced "icky"). In 1949 Brooker became a research assistant at the Cambridge University mathematical laboratory and took charge of its differential analyser, a prewar analogue computer.

Description Job Description: The Leidos Innovations Center (LInC) seeks a Machine Learning Research Engineer primarily focused on cognitive signal processing, to work in our Arlington, VA office. The candidate will research & develop new, state-of-the-art machine learning algorithms and implement them across the RF domain (e.g., communications, radar, electronic warfare, spectrum sensing, and signals intelligence [SIGINT]), in both modelling and simulation environments and real time software embed systems. The candidate will also contribute to technology developments in signal processing, optimization, detection & estimation, deep learning, and adaptive decision and control. Requires basic knowledge of and ability to apply machine learning and radar/signal processing principles, theories, and concepts in support of direct programs, IR&D, and marketing efforts. Primary Responsibilities Designs and develops methods, algorithms, and systems that apply machine learning technologies to support advanced signal processing concepts.

Worldpay, in partnership with IntelliQA, has been awarded as a winner in the category of Most Innovative Project by The European Software Testing Awards. Sharad Jain and his team were on hand to collect the award and celebrate: "We looked to robotics to speed up our testing process," says Sharad, "we set up a test lab with four robots doing end to end testing for us and our output increased dramatically...I am delighted to be part of wonderful team Worldpay to see my contribution scaling heights and recognized as the best in Europe. Innovation can't get any sweeter."