Tesla is looking into new ways to swap out the battery packs in its electric cars – a move that would slash the amount of time it takes for drivers to get a full charge. The firm recently filed a patent for a battery-swapping system that can lift up a vehicle and change out its battery pack for a new one in just 15 minutes. According to the patent, the rig could even be installed on a trailer with ramps built on either side, allowing cars to drive right in and out. The patent, filed in May, relies on a mechanical lift that can raise the vehicle to a predetermined height. The patent, filed in May, is designed for Model S and Model X vehicles, and relies on a mechanical lift that can raise the vehicle to a predetermined height.

It's been a week since the Apple Watch Series 3 was revealed, and reviews of the wearable device have poured in -- pointing out the good and bad. The Apple Watch Series 3's major boost from previous models is its support for cellular LTE. However, the Wall Street Journal's Joanna Stern said there were issues with connectivity during the week in which she tested out the new models. Stern also said there were problems with Series 3's battery life. "You're lucky if the battery allows you to roam on cellular for longer than half a day--especially if you're making calls. And only a limited number of third-party apps work without the phone close by. Most worryingly, my colleague Geoffrey Fowler and I experienced cellular connectivity issues on three separate pre-production models, in two different states, on two different 4G LTE carriers. On the AT&T -connected models, the cellular connection dropped, calls were often choppy and Siri sometimes failed to connect. On the one that ran on T-Mobile, I experienced several dropped connections."

Libratone's Zipp and Zipp Mini wireless speakers support Wi-Fi as well as Bluetooth, they have touch-sensitive controls, and they're equipped with microphones. Libratone is now leveraging those features to tap Amazon's Alexa Voice Service and transform the speakers into battery-powered Echo clones that sound much better than the originals. This fall, Zipp and Zipp Mini owners will be able to download a free firmware update that activates Alexa. Once that's installed, they'll be able to use voice commands to control their speakers as well as their smart-home devices. They'll also be able to ask Alexa for news, sports, and traffic updates and anything else an Amazon Echo, Echo Dot, or Amazon Tap can do. Unlike Amazon's speakers, they'll be able to deploy the Zipp and Zipp Mini in multiple rooms--just like Sonos speakers.

Amazon's Echo Dot is the most cost-effective way to add voice-activated control to your smart home. There's an Alexa skill for just about every smart-home platform, DIY or custom installed, and second-generation Echo Dot's cost just $50 each. I have six deployed in my own house, connected to a Vivint Smart Home system. The Vaux speaker dock changes that and delivers a bonus: A rechargeable battery that lets you take Alexa all over your house--and out onto the porch, too. The Vaux costs $50, so it and an Echo Dot still cost less than Amazon's own portable Alexa solution, the Amazon Tap. The Vaux's speaker not only sounds better than the Tap, it doesn't force you to press a button to get Alexa's attention like the Tap does.

As technology tries to maintain its dizzying ascent, one dead weight has kept its altitude in check: the battery. Our chips keep getting faster and our data rates keep climbing, but at the end of the day--or worse, by mid-afternoon--those power meters on our screens inevitably turn to red. Every great device, gadget, electric car, and robot would be even greater if batteries didn't suck so badly. Despite a steady flow of rumors that transformative breakthroughs are just around the corner, progress has moved at the pace of a tar flow. Steven Levy is Backchannel's founder and Editor in Chief. Sign up to get Backchannel's weekly newsletter.

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradation within an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner.

Ring's second-generation video doorbell adds a quick release rechargeable battery and boosts video resolution from 720p to 1080p. It remains one of the only video doorbells that can provide its own power instead of relying on your home's existing wiring. Like the previous version we tested, we set up the Ring Video Doorbell 2 in its wireless configuration, mounting the unit in the same, somewhat awkward, location outside our front door. While much of the setup process remains the same, one key difference is the addition of a removable lithium ion battery. This means you can physically install the doorbell while waiting for the battery to charge.

South Korean tech powerhouse LG Electronics Inc. said Sunday it has set up two research centers to develop technologies related to artificial intelligence, Yonhap News Agency reported. One center will focus on developing AI and the other robotics, it said. The AI center will focus on developing technologies applicable to LG Electronics' home appliance lineup, smartphones and automobile parts, while the robotics center will focus on developing "core technologies of smart robotics," Yonhap reported. A division within the AI center "will be devoted to R&D for deep learning, a new area of AI research where a computer emulates the way the human brain creates patterns from data and processes them," the report said.

Logitech has announced a pair of new computer mice with the MX Master 2S and the MX Anywhere 2S. Both are updated versions of Logitech's previous MX models, and both come with a new "Flow" feature for multiple computer support. Logitech's MS Master and MX Anywhere were both considered to be the company's two best mice, and it just seems appropriate that both have new, upgraded models. Like with its previous iteration, the MX Master 2S's thumb wheel can be customized with additional functions through the Logitech Options software. As for the MX Anywhere 2S, it comes with the same speed adaptive scroll wheel feature.

Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent of cloud-connected devices, data from cells in various applications is becoming increasingly available, and the feasibility of data-driven methods for battery prognostics is increasing. Here we propose Gaussian process (GP) regression for forecasting battery state of health, and highlight various advantages of GPs over other data-driven and mechanistic approaches. GPs are a type of Bayesian non-parametric method, and hence can model complex systems whilst handling uncertainty in a principled manner. Prior information can be exploited by GPs in a variety of ways: explicit mean functions can be used if the functional form of the underlying degradation model is available, and multiple-output GPs can effectively exploit correlations between data from different cells. We demonstrate the predictive capability of GPs for short-term and long-term (remaining useful life) forecasting on a selection of capacity vs. cycle datasets from lithium-ion cells.