Practitioners often rely on compute-intensive domain randomization to ensure reinforcement learning policies trained in simulation can robustly transfer to the real world. Due to unmodeled nonlinearities in the real system, however, even such simulated policies can still fail to perform stably enough to acquire experience in real environments. In this paper we propose a novel method that guarantees a stable region of attraction for the output of a policy trained in simulation, even for highly nonlinear systems. Our core technique is to use "bias-shifted" neural networks for constructing the controller and training the network in the simulator. The modified neural networks not only capture the nonlinearities of the system but also provably preserve linearity in a certain region of the state space and thus can be tuned to resemble a linear quadratic regulator that is known to be stable for the real system. We have tested our new method by transferring simulated policies for a swing-up inverted pendulum to real systems and demonstrated its efficacy.

Artificial intelligence just might be the next major technologic breakthrough to affect health care delivery, with seemingly endless possibilities for the improvement of patient care and optimization of the health care system overall.1 Simply defined, artificial intelligence is a field of computer science focused on enabling computers to complete tasks or generate knowledge that, in the traditional sense, would typically require human intelligence. Within the topic of artificial intelligence, there are the fields of machine learning and deep learning.

Today, with open source machine learning software libraries such as TensorFlow, Keras or PyTorch we can create neural network, even with a high structural complexity, with just a few lines of code. Having said that, the Math behind neural networks is still a mystery to some of us and having the Math knowledge behind neural networks and deep learning can help us understand what's happening inside a neural network. It is also helpful in architecture selection, fine-tuning of Deep Learning models, hyperparameters tuning and optimization. I ignored understanding the Math behind neural networks and Deep Learning for a long time as I didn't have good knowledge of algebra or differential calculus. Few days ago, I decided to to start from scratch and derive the methodology and Math behind neural networks and Deep Learning, to know how and why they work.

Researchers from Rice University have developed a deep learning system that can predict deadly heat waves and winter storms before they happen. The system was trained on hundreds of pairs of maps that showed surface temperatures and air pressures. These included the hot and cold spells that typically lead to extreme weather events. Each pair showed these conditions in the same geographical area, but several days apart. After training, the system was applied to maps that it had never seen before and tasked with making five-day forecasts of extreme weather.

The computer model, which can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs. "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering. "Our approach revealed this amazing molecule which is arguably one of the more powerful antibiotics that has been discovered." In their new study, the researchers also identified several other promising antibiotic candidates, which they plan to test further. They believe the model could also be used to design new drugs, based on what it has learned about chemical structures that enable drugs to kill bacteria.

Artificial Intelligence (AI) is opening up the next phase of technological advances. Riding the AI wave, Google has started six AI-based research projects in India. These projects would focus on addressing social, humanitarian and environmental challenges in sectors such as healthcare, education, disaster prevention and conversation. Google Research India, based in Bengaluru, will provide funding and computational resources besides supporting the efforts with expertise in computer vision, natural language processing, and other deep learning techniques, says Manish Gupta, director of Google Research Team in India. The research team will focus on two pillars: First, advancing fundamental computer science and AI research by building a strong team and partnering with the research community across the country and secondly, applying this research to tackle big problems in fields such as healthcare, agriculture and education while also using it to make apps and services more helpful.

Gaby Ecanow loves listening to music, but never considered writing her own until taking 6.S191 (Introduction to Deep Learning). By her second class, the second-year MIT student had composed an original Irish folk song with the help of a recurrent neural network, and was considering how to adapt the model to create her own Louis the Child-inspired dance beats. "It was cool," she says. "It didn't sound at all like a machine had made it." This year, 6.S191 kicked off as usual, with students spilling into the aisles of Stata Center's Kirsch Auditorium during Independent Activities Period (IAP).

Growing up in Seattle, I was exposed to tech at a pretty young age. Most of my friends' parents worked for Microsoft. I spent a lot of my free time working on little coding projects, and even started my own business developing Video game mods in high school. Movies like 2001: A Space Odyssey captured my imagination, and gave me a sense that AI was going to be an important part of the future, even if it seemed distant at the time. But I really wanted to get involved. In my Senior year of High School, I took an AI summer course at Stanford.

Researchers in the US have used artificial intelligence (AI) to discover a powerful new type of antibiotic capable of killing drug-resistant bacteria. Scientists at MIT trained a machine learning algorithm to analyse the molecular structures of chemical compounds and pick out potential antibiotics. The deep learning model was designed to identify compounds capable of killing bacteria using different mechanisms to those of existing drugs. After analysing some 2,500 different molecules, the AI system identified a new antibiotic compound which, in lab tests, killed many of the world's most problematic disease-causing bacteria, including drug-resistant strains. The new antibiotic compound has been dubbed halicin, named after the the rogue AI system, Hal 9000, from 1968 film 2001: A Space Odyssey.

The Canadian artificial intelligence (AI) industry has been growing fast, and the country has been aiming for more through massive AI research. There are signs all over that Canada is already having an AI-driven digital economy as cities are emerging as hubs for AI labs and deep learning research. There is an increase in the number of AI startups in cities such as Montreal, Vancouver, and Toronto, among others. Canada has become a breeding ground for AI innovations. Inc. (NASDAQ: AMZN), Intel Corp (NASDAQ: INTC), and Uber Technologies (NYSE: UBER) have invested significantly in AI research in the country.