Reinforcement Learning (RL) has brought forth ideas of autonomous robots that can navigate real-world environments with ease, aiding humans in a variety of tasks. RL agents have just begun to make their way out of simulation into the real world. Once in the real world, benchmark tasks often fail to transfer into useful skills. We introduce DoorGym, a simulation environment intended to be the first step to move RL from toy environments towards useful atomic skills that can be composed and extended towards a broader goal. DoorGym is an open-source door simulation framework designed to be highly configurable. We also provide a baseline PPO (Proximal Policy Optimization) and SAC (Soft Actor-Critic)implementation, which achieves a success rate of up to 70% for common tasks in this environment. Environment kit available here:https://github.com/PSVL/DoorGym/

The research and development of neural networks is flourishing thanks to recent advancements in computational power, the discovery of new algorithms, and an increase in labelled data. Before the current explosion of activity in the space, the practical applications of neural networks were limited. Much of the recent research has allowed for broad application, the heavy computational requirements for machine learning models still restrain it from truly entering the mainstream. Now, emerging algorithms are on the cusp of pushing neural networks into more conventional applications through exponentially increased efficiency. Neural networks are a prominent focal point in the current state of computer science research.

When most people hear Machine Learning, they picture a robot or some futuristic fantasy. However, it is already here and in fact has been there for almost decades in some specialized areas such as OCR. But, the first breakthrough that took over the world way back in the 1990s was the spam filter. Following this, are now hundreds of ML applications that have now quietly powered hundreds of products and features that you use regularly, say better recommendations on E-commerce portals (or) a voice search. Artificial Intelligence is a technique which allows the machines to act like humans by replicating their behavior.

In the book "Deep Survival", Laurence Gonzales notes that pilots often warn each other against trying to "land the model instead of the plane". This reminds pilots not to get too obsessed with their expected models of the world and, as a result, ignore the most relevant information right in front of them. Statisticians echo a similar fear when they note that all models are wrong, but some are useful. We need to have similar vigilance in Natural Language Processing (NLP) now due to the explosion of new model availability. While these models are indeed incredible and do show unparalleled results, they may not be suited for your NLP task or your business.

Many organizations have started to build infrastructure for AI using IBM Power Systems, which leverage NVIDIA GPUs. Enterprises often focus on building AI solutions that provide high availability, automated orchestration and the like, which can add to the cost of the solution. Educational institutions and research organizations, however, often look for solutions that give them more flexibility in utilizing underlying resources optimally for their machine learning and deep learning (ML/DL) workloads, and with much lower costs. Researchers may require running parallel DL training jobs using different AI runtimes. Professors may require allocating and deallocating AI runtimes to multiple students for AI assignments.

New article says that to take full advantage of deep-learning solutions in healthcare, the US and China should collaborate, not compete. In a new commentary article, titled'It Takes a Planet', Eric Topol, MD, of Scripps Research and Kai-Fu Li, PhD, CEO of the China-based tech investment firm Sinovation Ventures have argued for more collaboration between China and the US on artificial intelligence (AI) development. This comes in the wake of the US government ordering the AI company iCarbonX in China to divest its majority ownership stake in the Massachusetts-based company PatientsLikeMe. "Chinese academics and companies already have unfettered access to personal health data," they write. "To compete in AI health, US companies will need access to clinical data on a similar scale. How will that be possible if the current isolationist policy continues?"

AI procedures are changing and optimizing more and more areas of the economy and society. Especially deep neural networks are characterized by an enormous efficiency and are able to solve various tasks in an intelligent way, which until now has only been possible by humans. Examples are the classification of films according to content, playing poker on a superhuman level or creating deceptively real looking faces. However, it is all too easy to forget that artificial intelligence is fundamentally different from natural intelligence. While the latter inherently uses "worldviews", the former is ultimately based on the highly complex interpolation of countless data. Data are therefore the prerequisite for any AI.

Internet is such a wide thing that there is no need to introduce to you the FaceApp. But for those who don't know it, FaceApp is a photo-morphing app that uses what it calls artificial intelligence to make creepy, hilarious, weird kind of photos. It offers many different filters, but the most popular is the old age filter that makes people old and the second one is the gender-swapping filter that converts male pic into female and vice-versa. But as an AI enthusiastic person, the prime question for us "How does it work?". Well according to the company, the FaceApp uses an "advanced neural portrait editing technology" to automatically generate highly realistic transformations of faces in photographs.

Training deep learning-based models relies on large annotated datasets, which requires lots of resources. Despite achieving state-of-the-art performance in many visual recognition tasks, cross-domain differences still constitute a big challenge. To transfer knowledge across domains, Maximum Classifier Discrepancy for Unsupervised Domain Adaptation uses a novel adversarial learning method for domain adaptation without a need for any labeling information from the target domain. It's observed that minimizing the discrepancy between the probability estimates from two classifiers for samples from a target domain can produce class-discriminative features for various tasks, from classification to semantic segmentation.

Artificial intelligence (AI) researchers are unable to explain exactly how deep learning algorithms arrive at their conclusions. Deep learning is complex by nature, but that does not excuse the pursuit of seeking clarity and understanding of black-box decision making. The quality of a machine learning algorithm requires some level of transparency and an understanding of how a decision was made--this impacts the generalizability of the algorithm and the reliability of the output. Recently in March 2019, researchers from the Fraunhofer Heinrich Hertz Institute, Technische Universität Berlin, Singapore University of Technology and Design, Korea University, and Max Planck Institut für Informatik, published in Nature Communications a method of validating the behavior of nonlinear machine learning in order to better assess the quality of the learning system. The research team of Klaus-Robert Müller, Wojciech Samek, Grégoire Montavon, Alexander Binder, Stephan Wäldchen, and Sebastian Lapuschkin discovered that various AI systems using what psychologists would characterize as a "Clever Hans" type of decision-based on correlation.