If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
At the 73rd Annual Conference of Indian Radiological & Imaging Association (IRIA) 2020, Gandhinagar, Siemens Healthineers launched the ACUSON Redwood Ultrasound System. The system is built on the company's new platform architecture and features advanced applications for greater clinical confidence, AI-powered tools for smart workflows and has shared services cardiology features used by different hospital departments. "We are seeing an increased demand for premium medical imaging services being driven by the growing healthcare needs of a population with varied requirements, particularly in regard to chronic diseases," says Vivek Kanade, Executive Director, Siemens Healthineers India. We worked together with inputs from users to transform care delivery with the ACUSON Redwood and meet these challenges." The ACUSON Redwood's advanced applications, including Contrast Enhanced Ultrasound (CEUS) and Shear wave Elastography, are available for the first time from the company in this segment and support precise lesion detection and characterisation as well as potentially reduces the need for invasive procedures.
Artificial Intelligence in Energy Market research report is the new statistical data source added by A2Z Market Research. "Artificial Intelligence in Energy Market is growing at a High CAGR during the forecast period 2020-2026. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market". Artificial Intelligence in Energy Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors.
To demonstrate a practical use of an ML pipeline, this example uses the sample HVAC.csv data file that comes pre-loaded on the default storage for your HDInsight cluster, either Azure Storage or Data Lake Storage. HVAC.csv contains a set of times with both target and actual temperatures for HVAC (heating, ventilation, and air conditioning) systems in various buildings. The goal is to train the model on the data, and produce a forecast temperature for a given building.
The design of building heating, ventilation, and air conditioning (HVAC) system is critically important, as it accounts for around half of building energy consumption and directly affects occupant comfort, productivity, and health. Traditional HVAC control methods are typically based on creating explicit physical models for building thermal dynamics, which often require significant effort to develop and are difficult to achieve sufficient accuracy and efficiency for runtime building control and scalability for field implementations. Recently, deep reinforcement learning (DRL) has emerged as a promising data-driven method that provides good control performance without analyzing physical models at runtime. However, a major challenge to DRL (and many other data-driven learning methods) is the long training time it takes to reach the desired performance. In this work, we present a novel transfer learning based approach to overcome this challenge. Our approach can effectively transfer a DRL-based HVAC controller trained for the source building to a controller for the target building with minimal effort and improved performance, by decomposing the design of neural network controller into a transferable front-end network that captures building-agnostic behavior and a back-end network that can be efficiently trained for each specific building. We conducted experiments on a variety of transfer scenarios between buildings with different sizes, numbers of thermal zones, materials and layouts, air conditioner types, and ambient weather conditions. The experimental results demonstrated the effectiveness of our approach in significantly reducing the training time, energy cost, and temperature violations.
Easy to set up and incredibly responsive, the Cielo Breez Plus is one of two Wi-Fi-connected air-conditioner controllers this company manufactures. Like many similar devices we've tested, the simpler (and less-expensive, at a street price of $68) Breez Eco relies entirely on the Cielo Home app. The pricier Breez Plus, reviewed here, has a street price of $109, but it features a display and onboard controls, so you can program a connected air conditioner without needing to pull out your mobile device. The Breez Plus, like all Wi-Fi A/C controllers, enables your generally unconnected window, portable, mini-split, and various other types of air conditioner (or dehumidifier) to be a lot smarter. Not only do you gain more control over your appliance, you also stand to save money by reducing your energy consumption. These controllers operate much like a smart thermostat for a whole-house HVAC system, but they manage a single device in one room.
To take one example from the above, there is enormous potential in using industrial knowledge graphs to enhance AI models by combining different datasets. "Knowledge graphs add context to the data you're analyzing," explains Norbert Gaus, Head of R&D in Digitalization and Automation at Siemens. "For example, machine data can be analyzed in the context of design data, including the tasks the machine is made for, the temperatures it should operate at, the key thresholds built into the parts, and so forth. To this we could add the service history of similar machines, including faults, recalls and expected inspection outcomes throughout the machine's operational life. Knowledge graphs make it much easier to augment the machine data we use to train AI models, adding valuable contextual information."
Let's face it, things are getting hotter. The planet is warming, and it is resulting in hotter summers for many people around the world. Solving that problem is going to take a lot of work, and it's still not going to spare you from the record heat that continues to plague us today. The problem is, most of the tools we currently have to keep us cool in this heat contribute to the emissions that just make things worse. That is where EvaSMART 2 comes in.
Artificial intelligence technology is about collecting vast amounts of data from devices around the world and making sense out of it. Data is only useful, though, if you can do something with it. Artificial intelligence is how companies can analyze and learn from massive amounts of data to make their companies operate with greater efficiency and to offer their customers a better user experience. Machine learning in insurance and artificial intelligence in manufacturing and banking is already proving its worth as financial institutions are using this technology for fraud detection. There is no industry that can't benefit from implementing artificial intelligence; the three industries we discuss in our video, "How Can Companies Better Use AI Technology," include the financial, manufacturing and insurance sectors.
The evolution of technology is taking artificial intelligence (AI) to the fore in nearly every industry. As AI gradually becomes mature, it is being applied in the energy management sector. A number of Internet of Things (IoT) companies are using AI to help businesses reduce energy consumption and expenses. U.S.-based BuildingIQ is one of these companies that aim to improve energy efficiency in large, complex building structures. BuildingIQ's Predictive Energy Optimization (PEO) service uses cloud-based software to calculate heating, ventilation and air conditioning (HVAC) related utility expenses.