Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention thanks to its encouraging performance on a variety of control tasks. Yet, conventional regularization techniques in training neural networks (e.g., $L_2$ regularization, dropout) have been largely ignored in RL methods, possibly because agents are typically trained and evaluated in the same environment. In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks. Interestingly, we find conventional regularization techniques on the policy networks can often bring large improvement on the task performance, and the improvement is typically more significant when the task is more difficult. We also compare with the widely used entropy regularization and find $L_2$ regularization is generally better. Our findings are further confirmed to be robust against the choice of training hyperparameters. We also study the effects of regularizing different components and find that only regularizing the policy network is typically enough. We hope our study provides guidance for future practices in regularizing policy optimization algorithms.

Artificial intelligence (AI) has evolved considerably in the last few years. While applications of AI is now becoming more common in fields like retail and marketing, application of AI in solving problems related to developing countries is still an emerging topic. Specially, AI applications in resource-poor settings remains relatively nascent. There is a huge scope of AI being used in such settings. For example, researchers have started exploring AI applications to reduce poverty and deliver a broad range of critical public services. However, despite many promising use cases, there are many dataset related challenges that one has to overcome in such projects. These challenges often take the form of missing data, incorrectly collected data and improperly labeled variables, among other factors. As a result, we can often end up using data that is not representative of the problem we are trying to solve. In this case study, we explore the challenges of using such an open dataset from India, to predict an important health outcome. We highlight how the use of AI without proper understanding of reporting metrics can lead to erroneous conclusions.

A face spoofing attack occurs when an intruder attempts to impersonate someone who carries a gainful authentication clearance. It is a trending topic due to the increasing demand for biometric authentication on mobile devices, high-security areas, among others. This work introduces a new database named Sense Wax Attack dataset (SWAX), comprised of real human and wax figure images and videos that endorse the problem of face spoofing detection. The dataset consists of more than 1800 face images and 110 videos of 55 people/waxworks, arranged in training, validation and test sets with a large range in expression, illumination and pose variations. Experiments performed with baseline methods show that despite the progress in recent years, advanced spoofing methods are still vulnerable to high-quality violation attempts.

Today's commercial aircraft are typically manufactured in sections, often in different locations -- wings at one factory, fuselage sections at another, tail components somewhere else -- and then flown to a central plant in huge cargo planes for final assembly. But what if the final assembly was the only assembly, with the whole plane built out of a large array of tiny identical pieces, all put together by an army of tiny robots? That's the vision that graduate student Benjamin Jenett, working with Professor Neil Gershenfeld in MIT's Center for Bits and Atoms (CBA), has been pursuing as his doctoral thesis work. It's now reached the point that prototype versions of such robots can assemble small structures and even work together as a team to build up a larger assemblies. The new work appears in the October issue of the IEEE Robotics and Automation Letters, in a paper by Jenett, Gershenfeld, fellow graduate student Amira Abdel-Rahman, and CBA alumnus Kenneth Cheung SM '07, PhD '12, who is now at NASA's Ames Research Center, where he leads the ARMADAS project to design a lunar base that could be built with robotic assembly.

The smart city of Milton Keynes hosted the first edition of the European Robotics League (ERL)- Smart Cities Robotic Challenge (SciRoc Challenge). Ten European teams met in the shopping mall of Centre:mk to compete against each other in five futuristic scenarios in which robots assist humans serving coffee orders, picking products in a grocery shop or bringing medical aid. This robotics competition aims at benchmarking robots using a ranking system that allows teams to assess their performance and compare it with others. The European Robotics League (ERL) was launched in 2016 under the umbrella of SPARC- the Partnership for Robotics in Europe. This pan-European robotics competition builds on the success of the EU-funded projects: RoCKIn, euRathlon, EuRoC and ROCKEU2.

The Machine Learning Summit is designed for everyone from data scientist to business professionals. If you've ever been curious about artificial intelligence and machine learning, whether you're just getting started on your machine learning journey or already a machine learning practitioner, this Summit will provide you with knowledge of what's on the horizon for machine learning. To attend the Machine Learning Summit, purchase a ticket to AWS re:Invent 2019. Once reserved seating opens in the fall, you will be able to register for a seat.

Debate about the role of automation in the workplace has raged for years. The first major study on the subject was conducted in 2013 and it suggested that artificial intelligence and robots could threaten 50% of jobs in the U.S. A few years later in 2018, the OECD released a more detailed report suggesting that just 14% of jobs in OECD countries were "highly automatable". Earlier this year, the Office for National Statistics analysis suggested that this figure was now as low as 7%. Slowly but surely, the fear about automation at work has subsided. In fact, our global research of more than 34,000 people across 18 countries, released this month, has found that workers don't fear technology or automation--69% of them believe it will actually enhance, not replace, their jobs.

LOS ANGELES, CA, Oct. 20, 2019 (GLOBE NEWSWIRE) -- via NEWMEDIAWIRE – iPR Software, the leader in Online Newsrooms, Digital Publishing, Digital Asset Management (DAM) solutions, and customized integrated solutions, announced its largest technology rollout to date at Public Relations Society of America's International Conference in San Diego, California. With the launch of "Metatron," iPR Software's new application empowers Artificial Intelligence (AI) cloud capabilities as well as integrating the power of machine learning into DAM and customized software platforms to increase productivity and corporate asset sharing across multiple customer ecosystems. This latest software release further advances the company's vision for clients to publish their news and information to Traditional and Social media channels and better engage their B2B & B2C audiences while increasing traffic to their branded media and corporate assets. Leading organization's today are utilizing cloud applications to access the latest technology with encryption algorithms they can securely manage, publish, and share rich branded media content. Metatron introduces core, cloud-based software features that enable customers to securely publish and share key digital media and corporate assets, target practical enterprise use cases, increase workflow efficiencies, and automate mundane tasks to reduce data and storage errors.

Artificial intelligence (AI) expert and Flamingo Ai Founder and Executive Director Dr Catriona Wallace is set to share her insights on what we can look forward to in a world with more advanced AI, at this year's CEBIT expo. The keynote, titled'AI: Human Machine: Who gets the upper hand?' will explore developments in AI, how it's being used and how it will transform the business world and life as we know it. "AI, described as the most powerful force equal in impact to the discovery of fire and the invention of electricity, will increasingly become the primary power driving the massive changes that [climate change and disruptive technologies] will bring," Wallace said. "With AI set to replace 40% of jobs and 30% of business interactions in the next five years, and the time of'singularity', where machines may become'smarter' than humans possibly just 20 years away, the onus will be on people to successfully navigate the Fourth Industrial Revolution." NSW Minister for Jobs and Investment Stuart Ayres said CEBIT Australia will provide an international forum for technology companies to do business and discuss the future, including the impact of AI and how it can be harnessed to secure new jobs.

While buzzwords such as predictive maintenance, artificial intelligence, digital twin and augmented reality have promised to enable the fabled digital transformation of manufacturing, when it comes to Industry 4.0, most practical applications start and end with machine connectivity. And when it comes to driving value, look no further than answering these questions; "What's happening?" Simply put, most manufacturers are unable to see what's actually happening on the shop floor in real time because their machines are not connected to any sort of data collection or data visualization system. This inability to both see and use data to drive continuous improvement leads to massive inefficiencies that affect every component of a company's operations, from the shop floor all the way to the C-Suite. That said, as the excitement around the opportunity presented by AI continues to grow, we conducted an interview with our very own Lou Zhang, Chief Data Scientist at MachineMetrics, so he could give us his perspective on where AI lands within the Analytics Journey and its relationship to technologies such as machine monitoring and data collection. How far along is the manufacturing industry as a whole when it comes to taking advantage of AI, for machine monitoring and/or other applications?