It is the first boat to be showcased at CES in Las Vegas, but the state-of-the-art Sea Ray SLX-R 400e is far from a traditional water vessel. The 40-foot yacht is equip with auto-docking capabilities and allows passengers to communicate with it using gestures and voice commands through a new'Future Helm'. It seats 22 people and comes with a lithium battery pack that can power the craft's high-performance engines in order to save energy. Steve Langlais, Sea Ray president, said: 'CES presents a unique opportunity to debut the new SLX-R 400e in front of an audience that will truly appreciate its beauty, capabilities and incredible suite of new technologies.' 'This pioneering new model, which will be available in 2020, showcases the kind of unique, advanced technologies that are worthy of the world's largest consumer electronics show.'

DOE/Argonne National Laboratory researchers have turned to the power of machine learning and artificial intelligence to dramatically accelerate battery discovery. The press release likens designing new batteries with the best molecular building blocks for battery components to trying to create a recipe for a new kind of cake, when you have billions of potential ingredients. The challenge involves determining which ingredients work best together – or, more simply, produce an edible (or, in the case of batteries, a safe) product. But even with state-of-the-art supercomputers, scientists cannot precisely model the chemical characteristics of every molecule that could prove to be the basis of a next-generation battery material. As described in two new papers, the Argonne researchers first created a highly accurate database of roughly 133,000 small organic molecules that could form the basis of battery electrolytes.

Rest of Europe 8.4 Asia Pacific 8.4.1

Focus is vital to thrive in any career, and data science is no different. Since being a proficient data scientist requires various skills, developers get perplexed and fail to concentrate on the core of the data science. To understand effective ways for flourishing in data science landscape, we interviewed Arihant Jain for our weekly column My Journey In Data Science. Jain is a Staff Data Scientist at General Electric. He has 5 years of experience in the data domain while working at Genpact, RBL Bank, Vodafone, and GE. Jain is a mechanical engineer-turned-data scientist by choice.

Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power gri ds, and transportation systems. Similar to other information systems, a significant threat to indust rial control systems is the attack from cyberspace--the offensive maneuvers launched by "anon ymous" in the digital world that target computer-based assets with the goal of compromising a system's functions or probing for information. Owing to the importance of industrial control systems, and the possibly devastating consequences of being attacked, significant endeavors have been attempted to secure industrial control systems from cyberattacks. Among them are intrusio n detection systems that serve as the first line of defense by monitoring and reporting potenti ally malicious activities. Classical machine-learning-based intrusion detection methods usua lly generate prediction models by learning modest-sized training samples all at once. Such approac h is not always applicable to industrial control systems, as industrial control systems must proces s continuous control commands with limited computational resources in a nonstop way. To satisf y such requirements, we propose using online learning to learn prediction models from the control ling data stream. W e introduce several state-of-the-art online learning algorithms categorical ly, and illustrate their efficacies on two typically used testbeds--power system and gas pipeline. Fur ther, we explore a new cost-sensitive online learning algorithm to solve the class-imbalance pro blem that is pervasive in industrial intrusion detection systems. Our experimental results ind icate that the proposed algorithm can achieve an overall improvement in the detection rate of cybe rattacks in industrial control systems. Modern industrial control systems are microprocessor-equ ipped devices and associated communication networks used to monitor and operate physica l equipment in the industrial environment.

We've all tried Google Street View before, but what if you could explore the world and see faraway places through the eyes of a roving machine? At the recent Combined Exhibition of Advanced Technologies (CEATEC) outside Tokyo, telepresence robots equipped with displays showing their remote users were turning heads on the show floor. These simple machines are basically webcams on wheels, but they formed a striking example of how a system that combines hardware in the physical world with online users and cloud-based artificial intelligence will become part of everyday life. Akira Fukabori, director of ANA HOLDINGS INC.'s Avatar Division, shows off an all-terrain Avatar robot at CEATEC 2019. Developed by OhmniLabs and ANA HOLDINGS INC., the parent company of All Nippon Airways, the newme Avatar telepresence robots are up to 150 cm tall and roll around on a wheeled base at speeds up to 2.9 kph.

Designing the best molecular building blocks for battery components is like trying to create a recipe for a new kind of cake, when you have billions of potential ingredients. The challenge involves determining which ingredients work best together--or, more simply, produce an edible (or, in the case of batteries, a safe) product. But even with state-of-the-art supercomputers, scientists cannot precisely model the chemical characteristics of every molecule that could prove to be the basis of a next-generation battery material. Instead, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory have turned to the power of machine learning and artificial intelligence to dramatically accelerate the process of battery discovery. As described in two new papers, Argonne researchers first created a highly accurate database of roughly 133,000 small organic molecules that could form the basis of battery electrolytes.

Oral-B has launched its Genius X toothbrush which uses artificial intelligence to help you brush your teeth better for US$220. The Oral-B 10000 Genius X is available from their website for US$220 is the follow up to the Genius 9000, which sold from the Shavershop for AU$349. Unfortunately, there is no word on whether the Oral-B Genius X will make its way down under for Christmas. Featuring wireless Bluetooth connection, the Oral-B Genius X links to a dedicated companion app on your phone to time how long you brush your teeth for, how to pressure your applying, where you have been brushing and where you should brush more next time. Utilising sensors within the toothbrush, the device can detect pressure and its location within your mouth, something a reviewer from Forbes was most impressed about. It does this through the "Genius X AI algorithm" which provides a better brush guide, with a full rating as well.

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. One of the worst parts of pet ownership is keeping up with the sheer amount of fur your dogs or cats shed on a daily basis. If you agree, maybe it's time to get a robot vacuum cleaner designed to keep up with your pet's constant shedding. These automated cleaners can be set to run on a schedule, so the only thing you have to do is occasionally empty its dust bin.