Artificial intelligence is helping computers drive cars, recognize faces in a crowd, and hold life-like conversations. General Electric engineers now say they've used the data-intensive technology to develop tools that could cut the industrial giant's design process for jet engines and power turbines in at least half, speeding up its next generation of products. Today, it might take two days for engineers to run a computational analysis of the fluid dynamics of a single design for a turbine blade or an engine component. Scientists at General Electric's research center in Niskayuna, New York, say they've leveraged machine learning to train a surrogate model so that it can evaluate a million different variations of a design in just 15 minutes. "This is, we think, a huge breakthrough," says Robert Zacharias, technology director of thermosciences at GE Research.

Zion Market Research (ZMR) has recently published the comprehensive and insightful report, Artificial Intelligence in Manufacturing Market: Global Industry Analysis, Size, Share, Growth, Trends, and Forecasts 2016–2024. The global Artificial Intelligence in Manufacturing Market research report is an output of a brief assessment and an extensive analysis of practical data collected from the global Artificial Intelligence in Manufacturing Market. The data are collected on the basis of industrial drifts and demands related to the services & products. The meticulously collected data offers for the process of effortless strategic planning. It also helps in creating promising business alternatives.

Raytheon is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, creed, sex, sexual orientation, gender identity, national origin, disability, or protected Veteran status.

Machines are learning faster than ever. Artificial intelligence systems run algorithms and make decisions faster than humans. They excel at performing tasks, but they don't have the ability to tell users why one decision is better than another, making some of their recommendations seem arbitrary or unreliable. That poses a risk when AI is used in military situations. Operators demand accountability from their machines, especially when they are used to understand massive amounts of images, far more than any human could analyze.

After months of delays, Johnson Controls' GLAS smart thermostat hit the pre-order stage this week. The thermostat, which is available on both Amazon and Johnson Controls' sites, costs $319 and will ship starting August 24, as reported by MSPowerUser.com. Microsoft and Johnson Controls originally hinted about the coming Windows 10 IoT/Cortana-powered GLAS thermostat in the summer of 2017. In January 2018, Johnson Controls said the device would be available for pre-order in March 2018 (which didn't happen). The GLAS thermostat, which uses a Snapdragon 410E embedded platform, has a translucent OLED touchscreen display for controlling temperature, monitoring indoor and outdoor air quality, and checking the weather.

Kimberly-Clark is a Fortune 500 company. Through Kimberly-Clark Professional the company offers products and solutions to create healthier, safer and more productive workplaces in a variety of industries including food services, healthcare, manufacturing, office buildings and more. As an industry leader, Kimberly-Clark is committed to driving digital innovations to improve operations and customer experiences in the fast-moving consumer goods category. Here are a few ways Kimberly-Clark is using big data, the Internet of Things (IoT) and artificial intelligence (AI) in their operations. Kimberly-Clark sponsors the annual K-Challenge that invites entrepreneurs, start-ups and other inventors to develop innovations for the consumer packaged goods category via the Kimberly-Clark Digital Innovation Lab (D'Lab).

General Electric (GE) was born when several electrical companies owned by Thomas Edison were merged under a single name - Edison General Electric Company - in 1889. Fast forward to 1892 when Edison General Electric Company merged with Thomson-Houston Electric Company, and both became united under a single name - General Electric. Today, GE is a multinational conglomerate corporation incorporated in New York with its headquarters in Boston, Massachusetts. The company has hundreds of interests, which cater to the needs of the financial services, medical devices, life sciences, pharmaceutical, automotive, software development and engineering industries. GE has revenues of $126,661 million, which places it at #13 on the Fortune 500 list.

The Otis Elevator Company was started by Elisha Otis, who invented the first safety elevator. Today, Otis is a subsidiary of United Technologies. I spoke with the chief information officer of the Otis Elevator Company to learn about the company's digital transformation. Marcus Galafassi, the company's CIO, was my guest on episode 292 of the CXOTalk series of conversations with the world's top innovators. This episode offers an inside look into the hidden world of elevators and the people who build and service them. The company's digital transformation is a fascinating story that involves modern technologies such as sensors, internet of things, and even building interfaces between Amazon's Alexa and elevators. Watch our conversation in the video embedded above and read the complete transcript. Below are edited excerpts from the transcript. And, if you want to learn whether the "close door" button on elevators works, then read on! We have done a lot of apps for our mechanics.

Given how rife 2017 was for controversy in the marketing industry, AI winning out as the marketing word of the year against topics like transparency is surprising. This year has seen a handful of big-name advertisers, including Procter & Gamble and Unilever, trim back their spending on digital media as the channel's proved rife with non-transparency and murky business practices. P&G's Chief Brand Officer Marc Pritchard also sparked industry-wide discussions that have extended throughout the year about these subjects and also the need for ad vendors to receive third-party media accreditation from watchdog groups like the Media Rating Council.

The goal of variable clustering is to partition a random vector ${\bf X} \in R^p$ in sub-groups of similar probabilistic behavior. Popular methods such as hierarchical clustering or K-means are algorithmic procedures applied to observations on ${\bf X}$, while no population-level target is defined prior to estimation. We take a different view in this paper, where we propose and investigate model based variable clustering. We identify variable clusters with a partition G of the variable set, which is the target of estimation. Motivated by the potential lack of identifiability of the G-latent models, which are currently used in problems involving variable clustering, we introduce the class of G-block correlation models and show that they are identifiable. The new class of models allows the unknown number of the clusters K to grow linearly with p, which itself can depend, and be larger, than the sample size. Moreover, the minimum size of a cluster can be as small as 1, and the maximum size can grow as p. In this context, we introduce MCord, a new cluster separation metric, tailored to G-block correlation models. The difficulty of any clustering algorithm is given by the size of the cluster separation required for correct recovery. We derive the minimax lower bound on MCord below which no algorithm can estimate the clusters exactly, and show that its rate is $\sqrt{log(p)/n}$. We accompany this result by a simple, yet powerful, algorithm, CORD, and show that it recovers exactly the clusters of variables, with high probability, at the minimax optimal MCord separation rate. Our new procedure is available on CRAN and has computational complexity that is polynomial in p. The merits of our model and procedure are illustrated via a data analysis.