Energy

While most buildings get occasional walk-through energy usage audits, Verdigris' digital system uploads electricity consumption data to the cloud 24/7. It can even integrate the data with building management systems to automate electricity usage controls. Chung estimates this helps Verdigris train models 20 times as fast as on CPUs. Eventually, Chung said he'd like Verdigris to expand beyond smart building optimization and into enabling smart cities.

Job growth has continued because there has been a rapid rise in service sector physical jobs like home health aide or short order cook. A UBI is thus both less targeted and more expensive that the EITC, but the real problem is that a UBI doesn't give people any clear reason to get off the sidelines of the economy. Tech progress has changed our economy a lot over the past generation, and will change it even more quickly in the years to come. Instead of trying to prepare for a jobless future, we should instead be preparing for one that's a turbocharged version of what we already have: a job creation engine that has shifted into a lower gear, and a large number of people tempted to sit on the sidelines rather than contributing their skills to the economy.

Commit to building the world's best AI ecosystem: The winning AI cluster will create many high-paying jobs and create spillover effects for the middle class – but the also-rans will not. Create at-scale AI training programs: Industry can form coalitions to collect data, oversee curriculum development and rapidly retrain workers in the skills needed to succeed in nascent AI applications. Launch innovative new training models: The government could launch and fund a "venture capital lab" to create innovative training programs, so new training ideas can be tested, validated and scaled up (as recommended by the Advisory Council on Economic Growth). Build real links between companies and research schools: Large companies could partner with universities and vocational schools to provide equipment, facilities and expertise to prepare students for AI.

Commit to building the world's best AI ecosystem: The winning AI cluster will create many high-paying jobs and create spillover effects for the middle class – but the also-rans will not. Create at-scale AI training programs: Industry can form coalitions to collect data, oversee curriculum development and rapidly retrain workers in the skills needed to succeed in nascent AI applications. Launch innovative new training models: The government could launch and fund a "venture capital lab" to create innovative training programs, so new training ideas can be tested, validated and scaled up (as recommended by the Advisory Council on Economic Growth). Build real links between companies and research schools: Large companies could partner with universities and vocational schools to provide equipment, facilities and expertise to prepare students for AI.

Commit to building the world's best AI ecosystem: The winning AI cluster will create many high-paying jobs and create spillover effects for the middle class – but the also-rans will not. Create at-scale AI training programs: Industry can form coalitions to collect data, oversee curriculum development and rapidly retrain workers in the skills needed to succeed in nascent AI applications. Launch innovative new training models: The government could launch and fund a "venture capital lab" to create innovative training programs, so new training ideas can be tested, validated and scaled up (as recommended by the Advisory Council on Economic Growth). Build real links between companies and research schools: Large companies could partner with universities and vocational schools to provide equipment, facilities and expertise to prepare students for AI.

Using historical data from compressors with maintenance problems, the software pinpointed patterns and put online a system to inform the operator when there would be problems, Beck said. The technology has been used by a large oil and gas company to gain insight about the drillbit downhole. That's where column analytics--software tools that meld predictive models with collected data--come into play. Looking at the investor presentation slide decks of some of industry's large independents and integrated oil companies, Beck said companies are talking about how they are using data analytics to improve efficiency in the oil field, including in the Permian Basin.

But machine learning systems, looking at that data, can tell something else about your home besides its energy use--they can tell if you are home, or if you are not. In a recent paper, Jin and his colleagues demonstrated that machine learning systems can be trained to detect occupancy without any initial information from a home owner. Using this assumption, the machine learning algorithms were able to tease out more detailed characteristics about power consumption when a home is occupied; they then are able to tell when someone is home or not, even when that person's patterns are outside the norm. "Right now, meters are sending accurate information about energy consumption.

The following figure shows how the quality of the transformed image decreases when compared to the original image, when an nxn LPF is applied and how the quality (measured in terms of PSNR) degrades as n (LPF kernel width) increases. As we go on increasing the kernel size, the quality fo the final image obtained by down/up sampling the original image decreases as n increases, as shown in the following figure. The first one is the video of some students working on a university corridor, as shown below (obtained from youtube), extract some consecutive frames, mark a face in one image and use that image to mark all thew faces om the remaining frames that are consecutive to each other, thereby mark the entire video and estimate the motion using the simple block matching technique only. The following figure shows the frame with the face marked, now we shall use this image and block matching technique to estimate the motion of the student in the video, by marking his face in all the consecutive frames and reconstructing the video, as shown below.. As can be seen from the following figure, the optimal median filter size is 5 5, which generates the highest quality output, when compared to the original image.

In a picnic area at General Electric Co.'s Global Research Center, a group of scientists and engineers are working on a new industrial revolution that will involve robots, drones and artificial intelligence. On Tuesday, a team supervised by John Lizzi, director of robotics at GE Global Research, and Judy Guzzo, a project leader, were performing drone testing on a simulated oil rig flare stack. And GE's software creates so-called digital twins of industrial equipment that can predict when the actual equipment will break down or need servicing. The technology is currently being targeted for customers of GE's oil and gas business.

Context-aware inference apps have become pervasive as a result of the Internet of Things (IoT). However, most of these apps run continuously on a single device, resulting in limited sensor coverage and high energy consumption. Recent advances in IoT devices, specifically hardware heterogeneity, can be leveraged to improve the accuracy and energy efficiency of context inferences.