Alexa-enabled devices like the Echo or Echo Dot can be used to control smart home products such as the Ring Alarm Kit. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. Our picks and opinions are independent from USA TODAY's newsroom and any business incentives. Prime Day is a great opportunity to find money-saving deals on smart home products that can be controlled using Amazon Alexa on your Echo device. From smart doorbells to pressure cookers, here are the best Alexa-compatible Prime Day deals of 2019.

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

The aluminum-bodied robot weighs in at 16.5 kg (36 lb), and can run for a claimed five hours on one eight-hour charge of its user-swappable 28.8-V/7.2-Ah And while the prototype that we saw in Montreal at the 2019 International Conference on Robotics and Automation had six dual-purpose composite "legs," more swimming-efficient vinyl/steel-spring flippers can be substituted if it's going to only be used underwater – a place where it should be more eco-friendly than traditional remote-operated vehicles (ROVs).

Last year RISC-V cores made it into low-cost hardware with neural network and audio accelerator to speed up artificial intelligence workloads at the edge such as object recognition, and speech processing. More precisely, Kendryte K210 dual-core RISC-V processor was found in Sipeed MAIX modules and boards going for $5 and up. Since then a few other variants and kits have been made available including Seeed Studio Grove AI HAT that works connected to a Raspberry Pi or in standalone mode. Seeed Studio has now released another board with Kendryte K210 RISC-V AI processor, but based on Arduino UNO form factor and equipped with an ESP32 module for WiFi and Bluetooth connectivity. Typical applications would include smart home (robot cleaners or smart speakers), medical devices, factory 4.0 (intelligent sorting or monitoring of electrical equipment), as well as agriculture, and education.

Visual inspection of underwater structures by vehicles, e.g. remotely operated vehicles (ROVs), plays an important role in scientific, military, and commercial sectors. However, the automatic extraction of information using software tools is hindered by the characteristics of water which degrade the quality of captured videos. As a contribution for restoring the color of underwater images, Underwater Denoising Autoencoder (UDAE) model is developed using a denoising autoencoder with U-Net architecture. The proposed network takes into consideration the accuracy and the computation cost to enable real-time implementation on underwater visual tasks using end-to-end autoencoder network. Underwater vehicles perception is improved by reconstructing captured frames; hence obtaining better performance in underwater tasks. Related learning methods use generative adversarial networks (GANs) to generate color corrected underwater images, and to our knowledge this paper is the first to deal with a single autoencoder capable of producing same or better results. Moreover, image pairs are constructed for training the proposed network, where it is hard to obtain such dataset from underwater scenery. At the end, the proposed model is compared to a state-of-the-art method.

This post was done in partnership with Wirecutter. When readers choose to buy Wirecutter's independently chosen editorial picks, Wirecutter and Engadget may earn affiliate commission. If you want to see who's on the other side of your door without having to get up and look yourself, then the Ring Video Doorbell 2 is the best choice for most everyone. It lets you screen (and record) visitors and keep an eye out for package deliveries. Motion and ring alerts to a smartphone are typically fast, audio and 1080p video are clear, and the Ring 2 can be powered by either standard doorbell wiring or a removable rechargeable battery. The Ring Video Doorbell 2 performs like a cross between a modestly aggressive guard dog and a trusty digital butler. In addition to notifying you--audibly and via smartphone--of activity, it records all motion events to the cloud, letting you view those recordings (as well as live video) on your phone or computer any time. It's also compatible with a good number of smart-home devices, platforms, and monitored security systems. Though video recording and storage require a subscription, the $30 annual fee (a mere 8¢ per day) for 60 days of unlimited video storage is downright cheap compared with the competition. We like the Ring Video Doorbell Pro for all the reasons we like the Ring 2. Additionally, it has a much slimmer and sleeker design that will fit in more doorframes and includes the option for customized motion-detection zones.

In this paper, we develop a multi-agent reinforcement learning (MARL) framework to obtain online power control policies for a large energy harvesting (EH) multiple access channel, when only the causal information about the EH process and wireless channel is available. In the proposed framework, we model the online power control problem as a discrete-time mean-field game (MFG), and leverage the deep reinforcement learning technique to learn the stationary solution of the game in a distributed fashion. We analytically show that the proposed procedure converges to the unique stationary solution of the MFG. Using the proposed framework, the power control policies are learned in a completely distributed fashion. In order to benchmark the performance of the distributed policies, we also develop a deep neural network (DNN) based centralized as well as distributed online power control schemes. Our simulation results show the efficacy of the proposed power control policies. In particular, the DNN based centralized power control policies provide a very good performance for large EH networks for which the design of optimal policies is intractable using the conventional methods such as Markov decision processes. Further, performance of both the distributed policies is close to the throughput achieved by the centralized policies. The work in this paper will appear in part at IEEE ICASSP 2019 [1] and IEEE WiOpt 2019 [2]. This research has been partly supported by the ERC-PoC 727682 CacheMire project. I. INTRODUCTION Internet-of-things (IoT) [3] networks connect a large number of low power sensors whose lifespan is typically limited by the energy that can be stored in their batteries. In this context, the advent of the energy harvesting (EH) technology [4] promises to prolong the lifespan of IoT networks by enabling the nodes to operate by harvesting energy from environmental sources, e.g., the sun, the wind, etc.

This post was done in partnership with Wirecutter. When readers choose to buy Wirecutter's independently chosen editorial picks, Wirecutter and Engadget may earn affiliate commission. Wireless TV headphones allow you to enjoy TV shows, movies, and video games without disturbing people around you. After spending dozens of hours researching the available options and testing 20 systems, we're confident that the Sennheiser RS 165 is the best one available today. It's easy to set up, sounds much better than the competition, and produces almost no latency between the audio and video (a major problem with many systems). The Sennheiser RS 165 is the best-sounding wireless TV headphone system we tested, and unlike with most of the competition, we didn't detect any noticeable delay between audio and the video we watched, making for the best experience. The lightweight headphones are comfortable to wear, easy to charge, and easy to add to most existing TVs or home theater setups. The rechargeable batteries last long enough to make it through several movies.

Robots have a tough job making their way in the world. Life throws up obstacles, and it takes a lot of computing power to avoid them. At the IEEE International Solid-State Circuits Conference last month in San Francisco, engineers presented some ideas for lightening that computational burden. That's a particularly good thing if you're a compact robot, with a small battery pack and a big job to do. Engineers at Intel are experimenting with robot-specific accelerators as part of a collaborative multirobot system.

Bassam Mohammed Abdelnabi adjusts a combustion test rig in a GE lab in Niskayuna, N.Y., on which researchers have run tests to validate simulations used to develop an AI model that they say will radically cut the time it takes to design products.Courtesy of General Electric Artificial intelligence is helping computers drive cars, recognize faces in a crowd and hold lifelike conversations. General Electric engineers now say they've used the data-intensive technology to develop tools that could cut the industrial giant's design process for jet engines and power turbines in at least half, speeding up its next generation of products. Today, it might take two days for engineers to run a computational analysis of the fluid dynamics of a single design for a turbine blade or an engine component. Scientists at General Electric's research center in Niskayuna, New York, say they've leveraged machine learning to train a surrogate model so that it can evaluate a million variations of a design in just 15 minutes. "This is, we think, a huge breakthrough," Robert Zacharias, technology director of thermosciences at GE Research, tells Forbes.