American Sign Language (ASL) has long enabled real-time conversations for English-speaking people who are deaf and hard-of-hearing. But discussions often face significant lags when one or more conversants aren't fluent in the language system. But by combining deep learning artificial intelligence and micro-sonar technologies, researchers at Cornell University are developing a new wearable to help overcome the communication barriers. With further refinement, SpellRing may one day facilitate entire conversations regardless of your ASL comprehension skills. ASL's earliest iterations developed in the early 18th century at the American School for the Deaf in Hartford, Connecticut.

HOUSTON, Texas – The Johnson Space Center welcomed 12 new astronauts – 10 Americans and two from the United Arab Emirates – after the class completed a two-year training program through NASA. These astronauts will be assigned missions to the International Space Station and future commercial space stations, and will also focus on missions to the moon in preparation for Mars. Luke Delaney, a retired United States Marine Corps major from DeBary, Florida, said graduating from the program was a dream – for some, a dream that was decades in the making. Ten American astronauts and two United Arab Emirates astronauts recently graduated after completing a two-year training through NASA. When putting on his spacesuit, Delaney said he felt like he made it.

HARTFORD, Conn.--(BUSINESS WIRE)--The Travelers Companies, Inc. (NYSE: TRV) today announced a strategic partnership with Groundspeed Analytics, Inc. to simplify its new business and policy renewal processes through the use of artificial intelligence (AI). The two companies will also collaborate on the design of additional AI capabilities that can provide increased efficiencies through the automation of commercial insurance analytics. Quote requests often require manual effort to extract information from submitted documents before an underwriter can fully evaluate and price the risk. The use of AI will augment the company's underwriting capabilities by enhancing risk selection and increasing efficiency while also allowing agents and brokers to write business more quickly. "Using Groundspeed's AI capabilities will optimize productivity for both our underwriters and our agent and broker partners," said Bill Devine, Senior Vice President, Business Insurance, Travelers.

SS&C Technologies Holdings, Inc. (Nasdaq: SSNC) today announced the results of its study detailing innovative technology adoption by investment operations and accounting users in the insurance sector. SS&C's "2019 Insurance Asset Management Technology Outlook" revealed that three quarters of insurance asset managers surveyed are actively deploying or considering deploying innovative technologies such as Robotic Process Automation (RPA), Machine Learning (ML) and Artificial Intelligence (AI). Within this group, 38 percent are using third-party disruptive applications in conjunction with existing investment systems, while another 37 percent are considering using these technologies in the future for recoded or new systems. "Insurance firms are relying more on bank data without their own independent calculation, reconciliation and valuation. Key internal control functions, particularly, independently reconciling positions and cash, are enhanced by artificial intelligence, machine learning and robotic process automation," said Christy Bremner, Senior Vice President and General Manager, SS&C Institutional and Investment Management.

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outcomes that go beyond current resource capabilities. Relevant applications include new product architecture design, rapid material characterization, and life-cycle management tied with a digital strategy that will enable efficient development of products from cradle to grave. In addition, there are also challenges to overcome that must be addressed through a major, sustained research effort that is based solidly on both inferential and computational principles applied to design tailoring of functionally optimized structures. Current applications of structural materials in the aerospace industry demand the highest quality control of material microstructure, especially for advanced rotational turbomachinery in aircraft engines in order to have the best tailored material property. In this paper, deep convolutional neural networks were developed to accurately predict processing-structure-property relations from materials microstructures images, surpassing current best practices and modeling efforts. The models automatically learn critical features, without the need for manual specification and/or subjective and expensive image analysis. Further, in combination with generative deep learning models, a framework is proposed to enable rapid material design space exploration and property identification and optimization. The implementation must take account of real-time decision cycles and the trade-offs between speed and accuracy.

Denise Garcia knows that her students sometimes cheat, but the situation she unearthed in February seemed different. A math teacher in West Hartford, Connecticut, Garcia had accidentally included an advanced equation in a problem set for her AP Calculus class. Yet somehow a handful of students in the 15-person class solved it correctly. Those students had also shown their work, defeating the traditional litmus test for sussing out cheating in STEM classrooms. Garcia was perplexed, until she remembered a conversation from a few years earlier.

We consider the optimization of cost functionals on manifolds and derive a variational approach to accelerated methods on manifolds. We demonstrate the methodology on the infinite-dimensional manifold of diffeomorphisms, motivated by registration problems in computer vision. We build on the variational approach to accelerated optimization by Wibisono, Wilson and Jordan, which applies in finite dimensions, and generalize that approach to infinite dimensional manifolds. We derive the continuum evolution equations, which are partial differential equations (PDE), and relate them to simple mechanical principles. Our approach can also be viewed as a generalization of the $L^2$ optimal mass transport problem. Our approach evolves an infinite number of particles endowed with mass, represented as a mass density. The density evolves with the optimization variable, and endows the particles with dynamics. This is different than current accelerated methods where only a single particle moves and hence the dynamics does not depend on the mass. We derive the theory, compute the PDEs for acceleration, and illustrate the behavior of this new accelerated optimization scheme.

We consider the optimization of cost functionals on manifolds and derive a variational approach to accelerated methods on manifolds. We demonstrate the methodology on the infinite-dimensional manifold of diffeomorphisms, motivated by registration problems in computer vision. We build on the variational approach to accelerated optimization by Wibisono, Wilson and Jordan, which applies in finite dimensions, and generalize that approach to infinite dimensional manifolds. We derive the continuum evolution equations, which are partial differential equations (PDE), and relate them to simple mechanical principles. Our approach can also be viewed as a generalization of the $L^2$ optimal mass transport problem. Our approach evolves an infinite number of particles endowed with mass, represented as a mass density. The density evolves with the optimization variable, and endows the particles with dynamics. This is different than current accelerated methods where only a single particle moves and hence the dynamics does not depend on the mass. We derive the theory, compute the PDEs for acceleration, and illustrate the behavior of this new accelerated optimization scheme.

By using the viewpoint of modern computational algebraic geometry, we explore properties of the optimization landscapes of the deep linear neural network models. After clarifying on the various definitions of "flat" minima, we show that the geometrically flat minima, which are merely artifacts of residual continuous symmetries of the deep linear networks, can be straightforwardly removed by a generalized $L_2$ regularization. Then, we establish upper bounds on the number of isolated stationary points of these networks with the help of algebraic geometry. Using these upper bounds and utilizing a numerical algebraic geometry method, we find all stationary points of modest depth and matrix size. We show that in the presence of the non-zero regularization, deep linear networks indeed possess local minima which are not the global minima. Our computational results clarify certain aspects of the loss surfaces of deep linear networks and provide novel insights.

Chinese billionaire investor and media mogul Bruno Wu is planning on building a $300 million blockchain, cryptocurrency, and artificial intelligence (AI) hub in Hartford, Connecticut for talent development. According to Business Insider, the Seven Stars Cloud (SSC) group is the main investment entity in the project and is owned by Bruno. It announced plans to build a "Fintech Village" in the area in July. The center will serve as a hub for the company and others to collaborate on tech projects, mostly involving cryptocurrencies, robotics, and machine learning. According to documents obtained about the hub by Business Insider, a related FinTech college project is also set to be launched at the Hartford campus.