Visitors walk past the showroom of LG Electronics during the Auto China 2016 show in Beijing, China April 26, 2016. SEOUL South Korea's LG Electronics Inc said on Friday it is partnering with Amazon.com LG said in a statement Alexa will work with its SmartThinkQ Hub, an LG device used to connect with home appliances over the internet, to allow users to control the South Korean firm's home appliances via voice-recognition technology. Dash feature on its SmartThinQ Sensors, which enabled so-called "smart" features on appliances that cannot communicate with other devices on their own, to allow users to quickly order household items such as laundry detergent or drinks. "We will work with a wide range of partners to deliver differentiated smart-home solutions to customers," said Jo Seung-jin, head of LG's appliances business, in the statement.

People walk past a LG Electronics logo during the Mobile World Congress in Barcelona, Spain, February 25, 2016. SEOUL South Korea's LG Electronics Inc said on Sunday it will aggressively invest in robots, seeking to capitalize on advancing artificial intelligence that may eventually lead to sophisticated machines performing everyday human tasks. LG, in a statement, said its appliances division is preparing the firm's entry into the robotics industry with the aim to develop products that will work closely with home appliances products such as refrigerators, washers and air conditioning units. "We will prepare for the future by aggressively investing in smart home, robots and key components and strengthen the home appliances business's capabilities," said Jo Seung-jin, head of LG's appliances business, in the statement. Advances in fields such as artificial intelligence and wireless communications are allowing for more sophisticated machines that can talk to each other via the internet and perform more complex tasks.

SHANGHAI/HONG KONG The Chinese government said on Friday it was investigating two high-profile takeover proposals involving U.S. companies, the latest sign of its growing influence on whether deals are approved - even those appearing to have little impact in China.

Artificial Intelligence Is It Real? Artificial intelligence could improve diagnostic power of lung function tests European Lung ...

Artificial intelligence could improve diagnostic power of lung function tests European Lung ...

In a dish of water in Cambridge, Massachusetts, a new kind of robot stirs, its tentacles twitching. Squashy and soft, this robot is different from its technological ancestors – Octobot runs without a power cable or rigid electronics, moving autonomously – if still clumsily – through the world. Soft robots have long been heralded as a new class of machine. But their tethers, and the electronics needed to control their movements, have held them back. Developed by Michael Wehner and colleagues at the Wyss Institute for Bioinspired Engineering, Harvard University, it's a big step towards fulfilling the potential of soft robots.

Advanced robotic technology has allowed researchers to capture unprecedented footage of marine life surrounding the UK's tallest underwater mountains. The remarkable clips reveal a never-before-seen view of deep-sea creatures, including coral, monkfish, and many previously unknown species. Robots obtained high-definition videos from four seamounts in the North East Atlantic Ocean, and have even explored depths more than half a mile below the surface to reveal a deep-water coral that stands over six feet tall. Advanced robotic technology has allowed researchers to capture unprecedented footage of marine life surrounding the UK's tallest underwater mountains. To capture the deep-sea footage, researchers with the Deep Links project used the Isis remotely operated vehicle (ROV).

Tesla's Gigafactory in the Nevada Desert is finally nearing completion. Set to open on July 29th, it will have the largest footprint of any building in the world. The 5 billion structure will produce 500,000 lithium ion batteries each year to meet demand. When it's complete, Tesla's Gigafactory in the Nevada Desert will have the largest footprint of any building in the world. At the Model 3 launch yesterday, founder Elon Musk said the 5 billion structure will produce 500,000 lithium ion batteries annually to meet demand.

The DURUS robot can walk more than a mile in man's shoes. A pair of size 13, Adidas sneakers, to be specific. Engineers at the Georgia Institute of Technology have tackled what they describe as a deceptively difficult challenge: develop a battery-powered robot that mimics the subtle complexity of the human footstep. Aaron Ames, an associate professor of automation and mechatronics, said their feat represents a stride in robotic efficiency and mobility and could allow for robots to function more seamlessly in environments meant for humans. "What drives me a lot is the cool factor, to be honest, but that's my professor hat," Ames said.

Many problems in science and engineering can be formulated as a sequential decision-making problem under uncertainty. A common scenario in such problems that occurs in many different fields, such as online marketing, inventory control, health informatics, and computational finance, is to find a good or an optimal strategy/policy, given a batch of data generated by the current strategy of the company (hospital, investor). Although there are many techniques to find a good policy given a batch of data, only a few of them guarantee that the obtained policy will perform well, when it is deployed. Since deploying an untested policy can be risky for the business, the product (hospital, investment) manager does not usually allow it to happen, unless we provide her/him with some performance guarantees of the obtained strategy, in comparison to the baseline policy (e.g., the policy that is currently in use). In this paper, we focus on the model-based approach to this fundamental problem in the context of infinite-horizon discounted Markov decision processes (MDPs). In this approach, we use the batch of data and build a model or a simulator that approximates the true behavior of the dynamical system, together with an error function that captures the accuracy of the model at each state of the system. Our goal is to compute a safe policy, i.e., a policy that is guaranteed to perform at least as well as the baseline strategy, using the simulator and error function. Most of the work on this topic has been in the model-free setting, where safe policies are computed directly from the batch of data, without building an explicit model of the system [12, 13]. Another class of model-free algorithms are those that use a batch of data generated by the current policy and return a policy that is guaranteed to perform better.