The company teamed up with popular thermostat maker Johnson Controls to create GLAS, a sleek new touchscreen wall thermostat that promises to do much more than just turn up the heat. GLAS will run on the Windows 10 IoT Core OS, which is made specifically for smart devices. The thermostat offers Cortana voice services, so you'll be able to interact with the AI directly on the wall. GLAS will likely look to stake its place as a high-end competitor to other smart thermostats like Google's Nest and Ecobee, that latter of which also offers voice control through Amazon's Alexa.
FILE - In this May 17, 2017 file photo, an Amazon Alexa device is switched on for a demonstration of its use in a ballpark suite before a Seattle Mariners baseball game in Seattle. Struggling retailer Sears is looking to get a hand from Amazon, announcing that it will start offering its Kenmore products on the online powerhouse's website. Sears, which runs Kmart and its namesake stores, said that Kenmore Smart appliances will also be fully integrated with Amazon's Alexa. This will allow consumers to control products, like Kenmore Smart air conditioners, by making a request to Alexa.
Because it's getting harder to find humans who want to or can do those kinds of jobs, says Jagannath Rao, the company's SVP for data services at its campus outside Atlanta. "We've gone through a period of time when many people were steered away from traditional trades, apprenticeships, college-level education for some of these practical trades, in favor of going to university," says Jeff Bonnell, a VP at Coresystems in Vancouver, which provides software for diagnosing problems with industrial machinery. Coresystems uses Siemens's MindSphere service, which gathers data from machines for tasks like predicting breakdowns, something that was once done by humans. These networks collect the data that machine learning AI can munch on to learn the early signs of an impending breakdown--replacing those old techs who could diagnose a machine by placing an ear up to it.
This report is a summary of graph analysis of engagements and conversations including retweets, mentions and replies of tweets related to the subject of'Industrial IoT'. Compared to the previous IIoT Report published in April, PageRank brings up Evan Kirstel (@evankirstel) on the top position, followed by Bill McCabe (@IoTRecruiting) and Carol Rudinschi (@IIoT_World). Some big players in the industrial market like Rockwell Automation (ROKAutomation), @Schneider Electric, @PTC, @ThingWorx (a PTC business and an award winning IoT solution) or @Avnet are in this top too. Insights include flocks, top trending terms, top hashtags, top Users/accounts, RR topics, top tweets and several other measures.
IoTI.com Content Director Brian Buntz wrote recently about the resources Siemens is throwing at software, and while that's significant, I'm more interested in Siemens' AI and machine learning work. For the past decade, Siemens' AI efforts have been focused on improving control of industrial processes using deep learning and reinforcement learning. An example of this technology is Siemens' "self-optimizing" gas turbines that leverage reinforcement learning. You can extend the classical control loop with a machine-learning loop using neural networks, making it dynamic and thus creating a new control policy.
Some of you may recall that back in October 2014, Hong Kong startup Ambi Labs unveiled its Ambi Climate as a gateway between your smartphone and your dumb air conditioner at home. But it isn't just about replacing your infrared remote control; what makes Ambi Climate unique is its machine learning capability, so that over time it learns your comfort preferences by way of various sensors, while also saving up to 20-percent energy according to user feedback. For instance, users in New York have a wider range of preferred temperatures, whereas users in Singapore peaked at around 77 degrees Fahrenheit (about 25 degrees Celsius) -- which is actually very close to what I prefer here in Hong Kong. On top of Comfort Mode, Ambi Climate also offers a Temperature Mode, an Away Mode (it only turns on the air conditioner to suit your settings) and a Manual Mode, as well as a timer and a scheduling feature.
The technology could also help human-driven and automated vehicles stay safe, for example by listening for emergency sirens or sounds indicating road surface quality. OtoSense has developed machine-learning software that can be trained to identify specific noises, including subtle changes in an engine or a vehicle's brakes. Under a project dubbed AudioHound, OtoSense has developed a prototype tablet app that a technician or even car owner could use to record audio for automated diagnosis, says Guillaume Catusseau, who works on vehicle noise in PSA's R&D department. Tests have shown that the system can identify unwanted noises from the engine, HVAC system, wheels, and other components.
More complex industrial equipment likely communicates over OPC or proprietary socket communications, but with all of their complexity, these machines still can't connect to the internet. Companies building IoT devices are solving this challenge by using gateways, also known as edge-based processing, to connect to cloud-based IoT platforms. However, connecting devices isn't as easy as updating software; instead, it's an investment in retrofitting old machines, replacing existing equipment, and enabling a workforce to leverage this equipment. After connecting devices and sending information to a data lake, we have to wait and observe what happens.
For example, the company's Software Environment for Neural Networks (SENN) is being continuously refined and adapted to new and evolving applications, including the optimization of gas turbines and wind turbines. Siemens Power Generation Services and CT have developed a system that continuously optimizes the operation and control of combustion in gas turbines. Based on AI from CT, the system, which is known as a Gas Turbine Autonomous Control Optimizer (GT-ACO), is currently being installed at a top customer in Asia. Improvements in overall gas turbine operation can be difficult to achieve because lower emissions characteristically result in shorter service life.
Falkner, Andreas (Siemens AG Austria) | Friedrich, Gerhard (University of Klagenfurt) | Haselböck, Alois (Siemens AG Austria) | Schenner, Gottfried (Siemens AG Austria) | Schreiner, Herwig (Siemens AG Austria)
The development of problem solvers for configuration tasks is one of the most successful and mature application areas of artificial intelligence. The provision of tailored products, services, and systems requires efficient engineering and design processes where configurators play a crucial role. For more than 25 years the application of constraint-based methods has proven to be a key technology in order to realize configurators at Siemens. This article summarizes the main aspects and insights we have gained looking back over this period.