Last year Intel acquired two startups working on chips to power machine learning in the cloud and for computer vision, Nervana and Movidius. The AI startups Intel acquired created chips to accelerate artificial neural networks. Some leading AI researchers, including Facebook's Yann LeCun, have expressed skepticism about neuromorphic chips, noting that spiking silicon neurons have not yet proved as powerful or flexible as machine-learning software running on conventional chips. The recent rush to make and use chips designed to support AI software suggests companies are no longer happy to just rely on improvements to conventional chip technology.
Switching to efficient artificial intelligence systems has already saved Google a ton of money on its energy bills. With power theft costing the industry roughly $96 billion in losses per year, companies could start looking to AI to help identify pilferers. The firm claims that 22 percent of all Brazilian energy generated is siphoned off by thieves. But, Brazil isn't alone -- countries like the UK estimate that similar activity results in losses of £440 million ($596 million) each year.
The ML model an email provider might use to detect spam is the naive bayes classifier (but other applicable models exist as well). With the model sufficiently trained, they can use it to classify incoming emails as spam or not spam with high accuracy. No data, no quality data, no machine data, no coalesced data out of 19 different databases into a single data store … no machine learning. They can help you put together a complete ML solution -- from data retrieval, to data storage, to actually training the ML model -- and deliver powerful functionality to your product or company.
Brazil has a big electricity theft problem. Who is responsible for the theft in Brazil isn't always clear – sending meter readers to check whether meters and overhead cabling have been tampered with is dangerous work, says Adrian Grilli of the Joint Radio Company in London, which does telecommunications for global energy companies. The most accurate versions of the system were able to identify problem cases just over 65 per cent of the time, which the team believes outperforms similar tools. Similar false positives may also be leading to erroneous billing by energy companies.
General Electric Co. is working on a way to use artificial intelligence in electricity grids, a technology that it expects will save $200 billion globally by improving efficiency. "We're also putting a lot into the machine learning side, a lot," said Steven Martin, chief digital officer at GE's energy connections business, at an interview at the Bloomberg New Energy Finance summit in London. This is expected to significantly increase the efficiency of the grid and save consumers money. Researchers are looking into how so-called machine learning can be integrated into businesses from healthcare to computing, and now energy.
To comprehensively study, understand and inform policy around these complex systems, the next generation of researchers in the physical, social and biological sciences will need fluency with data analysis methods that transverse traditional academic boundaries. A new interdisciplinary curriculum will train graduate students from geosciences, economics, computer science, public policy and other programs in computational and data science techniques critical for modern science. The program will build upon successful UChicago training initiatives such as the Executive Program in Applied Data Analytics, the Computational Analysis and Public Policy curriculum at the Harris School of Public Policy and the Data Science for Social Good Summer Fellowship. Instruction and mentorship will be provided by several UChicago research groups, including the Center for Robust Decision-Making on Climate and Energy Policy (climate and agricultural modeling), Knowledge Lab (text mining), the Energy Policy Institute at UChicago (environmental and energy economics), the Center for Data Science and Public Policy (data analytics and project management) and the Center for Spatial Data Science (spatial analysis).
The study is important because many of the most promising applications for these aircraft - including package delivery, public safety and traffic management - entail flights over people and raise the chance, however unlikely, of an impact between a drone and a human. So researchers with Virginia Tech's injury biomechanics group took advantage of their FAA-approved drone test site to obtain more data on the subject, releasing the first peer-reviewed study to offer numerical data on injury risk associated with drone collisions. Studying human-drone impact's is important because many of the most promising applications for these aircraft - including package delivery, public safety and traffic management - entail flights over people and raise the chance, however unlikely, an impact'In some instances it was low, and in some instances it was high, and there are lessons we can take away from that to reduce injury risk in a deliberate way through product design.' Researchers with Virginia Tech's injury biomechanics group took advantage of their FAA-approved drone test site to obtain data, releasing the first peer-reviewed study to offer numerical data on injury risk associated with drone collisions Injury risk was also reduced when the aircraft deformed upon impact or when pieces broke off, because those deformations absorb some of the energy of the crash and offer another route for risk mitigation.
The U.S. Department of Energy will explore whether artificial intelligence could help electric grids handle power fluctuations, avoid failures, resist damage, and recover faster from major storms, cyberattacks, solar flares and other disruptions. GRIP is the first project to use artificial intelligence (AI) to help power grids deal with disturbances, says Sila Kiliccote, GRIP's principal investigator and director of the Grid Integration, Systems and Mobility lab at the SLAC National Accelerator Laboratory in Menlo Park, Calif. GRIP will develop algorithms to learn how power grids work by analyzing smart meter data, utility-scale SCADA (supervisory control and data acquisition) data, electric vehicle charging data, and even satellite and street-view imagery. Some of the first places the project will test its data analytics platform are Southern California Edison, a leader in smart metering, and Packetized Energy, which helps grids manage distributed energy resources.
The Engine initially raised $150 million for its first fund, but later tacked on the additional $50 million. MIT launched The Engine almost a year ago to provide resources to startups whose technologies might get stranded in the research lab because they would take more time and money to develop than most venture capitalists are willing to invest--think biotech, medical devices, robotics, advanced manufacturing, materials science, and energy. The Engine combines a venture fund and access to work space, expert advisors, educational workshops and events, and business services. The companies' founders include MIT professors Bob Langer, a prolific life sciences inventor, and Yet-Ming Chiang, a materials science and engineering expert who previously helped start A123 Systems, American Superconductor, and Desktop Metal.
SAE International has created the now-standard definitions for the six distinct levels of autonomy, from Level 1 representing only minor driver assistance (like today's cruise control) to Level 6 being the utopian dream of full automation: naps and movie-watching permitted. Many of the features of AI-assisted driving center around increased safety, like automatic braking, collision avoidance systems, pedestrian and cyclists alerts, cross-traffic alerts, and intelligent cruise control. A connected vehicle could also share performance data directly with the manufacturer (called "cognitive predictive maintenance"), allowing for diagnosis and even correction of performance issues without a stop at the dealer. Although it may not at first appear directly tied to automotive AI, the health and medical industry stands to experience some significant disruptions as well.