PointGrab is an Israel based business that offers a platform which includes an image sensing a cloud and hardware unit management program called CogniPoint, which they say might help building maintenance managers reduce operational costs by using AI to automate as well as enhance facility management. PointGrab claims computer users can integrate the CogniPoint formula of theirs into current building automation systems. Furthermore the Cognipoint sensor is actually installed to certain rooms in the structure to monitor the amount of occupants. The sensor could be hooked up to the buildings' present local area network (LAN), Power over Ethernet (POE) or perhaps WiFi connections. The company claims each of the sensor products of theirs are able to cover up to forty eight square meters (or maybe 520 sq ft).

Artificial intelligence is quickly growing in importance in the'smart building' sector. Paul Skelton looks at the road ahead for a complex technology. When Mark Chung received an unexpectedly high $500 monthly electricity bill, he turned to his utility for help and answers. However, despite'smart' meters being installed in his home, they were no help. So Mark – an electrical engineer trained at Stanford University – took matters into his own hands.

Among the young companies covered in the report, a number of them are innovatively bringing AI into our buildings in a way only start-ups can. These new entrants are currently exploiting the technology across a range of applications, from BIoT, energy management, security, real estate and property management and smart building to smart grid applications. B2B IoT startup Verdigris is one such company. The firm applies a rudimentary form of AI to building management, according to CEO Mark Chung. Verdigris' system listens to electrical signals to identify the type of equipment in the building, it can then create algorithms to offer predictive analysis and anomaly detection. "The main value proposition is to use the platform around energy efficiency and data layer to get better feedback around energy performance in a building," said Chung.

So when his Silicon Valley utility couldn't explain it -- despite "smart meters" installed throughout its system -- he took matters into his own hands. The Stanford-trained electrical engineer hacked some inexpensive meters from his local hardware store to be wi-fi enabled, and then built an electrical map of his home. They showed that a small failure in his pool pump was causing a massive current overload, which couldn't have been detected with traditional tools. More importantly, he learned how hard it is to get information from buildings, which typically lack any kind of computerized management. Thus was born the idea for Verdigris, a startup that wants to help conserve energy in buildings using GPU-powered artificial intelligence.

Mark Chung was a chip guy. He spent nearly 15 years in the semiconductor industry since getting his master's and bachelor's degrees in electrical engineering at Stanford in 1999. He'd been an engineer at AMD for nearly six years designing chips like the Athlon and Opteron, then at startup PA Semi working on microprocessors that, he anticipated, would go into Apple computers (Apple later purchased the company and the designs would end up in iPhones). In 2008 Chung was a principal engineer at RMI, a company that later merged with Netlogic and was acquired by Broadcom. One month that year--a month he and his family had mostly spent out of town, he received a surprisingly large electrical bill: $560, when his typical bill was around $100.

The smart energy startup Verdigris announced today that it has raised $6.7 million to scale production of its Einstein smart sensor and frequency detectors. The sensors are used to predict the failure of machines and improve energy efficiency. Factories, manufacturing facilities, and other large buildings using Verdigris technology reduce energy use 8 to 22 percent, CEO Mark Chung told VentureBeat in a phone interview. The Einstein frequency detector from Verdigris made its debut in August. "Rather than take a big data approach where we study thousands of motors and this is the failure pattern, we instead take a physics based model which is looking at a signal through our sensors," Chung said.

The smart energy startup Verdigris announced today that it has raised 6.7 million to scale production of its Einstein smart sensor and frequency detectors. The sensors are used to predict the failure of machines and improve energy efficiency. Factories, manufacturing facilities, and other large buildings using Verdigris technology reduce energy use 8 to 22 percent, CEO Mark Chung told VentureBeat in a phone interview. The Einstein frequency detector from Verdigris made its debut in August. "Rather than take a big data approach where we study thousands of motors and this is the failure pattern, we instead take a physics based model which is looking at a signal through our sensors," Chung said.

We hear a lot about the Internet of Things on the consumer side. The oft trotted out example of the'smart' refrigerator that tells consumers when they've run out of the milk, and so on. But more serious potential for IoT -- and potentially seriously big wins -- are likely to be on the enterprise side where connected sensors can be deployed to automate at scale. The company took in a 6 million Series A round in December, which it's just announcing now -- and which includes, on top of that, a 3 million convertible seed, bringing its total raised to date to 9 million. Investors in the business include Jabil Circuit, Stanford StartX Fund, Founder.org