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.

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. Whether you just like the idea of letting a robot handle cleaning up your floors or you just don't like to vacuum, a robot vacuum cleaner can be a real help. But with so many companies making robot vacuums, how do you know if any of them are actually worth the money? Luckily, we've done the hard work for you. We have a specially built obstacle course in our labs that tests how well robot vacuums pick up dirt, navigate around ytour furniture, and deal with floor types from hardwood floors to low- and high-pile carpets.

What is this article about? This article provides an overview of the main models available for modelling time series and forecasting their evolution. The models were developed in R and Python. The related code is available here. Time series forecasting is a hot topic which has many possible applications, such as stock prices forecasting, weather forecasting, business planning, resources allocation and many others.

We use data on 124 batteries released by Stanford University to first try to solve the binary classification problem of determining if a battery is "good" or "bad" given only the first 5 cycles of data (i.e., will it last longer than a certain threshold of cycles), as well as the prediction problem of determining the exact number of cycles a battery will last given the first 100 cycles of data. We approach the problem from a purely data-driven standpoint, hoping to use deep learning to learn the patterns in the sequences of data that the Stanford team engineered by hand. For both problems, we used a similar deep network design, that included an optional 1-D convolution, LSTMs, an optional Attention layer, followed by fully connected layers to produce our output. For the classification task, we were able to achieve very competitive results, with validation accuracies above 90%, and a test accuracy of 95%, compared to the 97.5% test accuracy of the current leading model. For the prediction task, we were also able to achieve competitive results, with a test MAPE error of 12.5% as compared with a 9.1% MAPE error achieved by the current leading model (Severson et al. 2019).

Ibrahim Gokcen, the digital chief technology officer for industrial giant Schneider Electric, has some words of caution about deep learning--the latest craze in artificial intelligence. Sometimes, conventional data crunching works just fine. All of the technology sold by Schneider that warns corporate customers when their industrial equipment may fail uses basic analytics or statistical analysis to make predictions. Although the software incorporates machine learning, it doesn't use deep learning, a technology that has led to breakthroughs in image and language translation. But that's okay, Gokcen explained.