NTT Research, Inc., a division of NTT (TYO:9432), today announced that it has named Dr. Brent Waters as Director of its Cryptography and Information Security (CIS) Lab. Dr. Waters joined NTT Research in 2019 as Distinguished Scientist and has been instrumental in building the CIS Lab into one of the world’s premier cryptography organizations. Dr. Waters, who is also a professor in the Department of Computer Science at the University of Texas at Austin, was the 2015 recipient of the Association for Computing Machinery (ACM) Grace Murray Hopper Award and is recognized as a founder of Functional Encryption and Attribute-based Encryption. He assumed his new role on June 1, 2022, succeeding Dr. Tatsuaki Okamoto, the CIS Lab’s first director, whose contributions to the field of cryptography include hundreds of academic articles and service as president of the Japan Society for Industrial and Applied Mathematics (JSIAM) and director of the International Association of Cryptology Research (IACR). NTT Research Names Brent Waters as CIS Lab Director | AI-TechPark

If your organization or department has a transformative case study on the latest AI and data solutions to share, there’s still time to apply to be a sponsor at Transform 2022.Join fellow sponsors such as Intel, Lumen, Cognigy, Dell Technologies, Labelbox, Coro, Aviso, Skillsoft, NTT Research and Sama, which will be participating at the in-person Executive Summit on July 19 at The Palace Hotel in San Francisco, or taking to the virtual stage during The Data Week (July 20-22) or The AI & Edge Week (July 26-28). There’s still time to join leading AI and data thought leaders on stage at Transform 2022 | VentureBeat

Inside a soundproofed crate sits one of the world’s worst neural networks. After being presented with an image of the number 6, it pauses for a moment before identifying the digit: zero. Peter McMahon, the physicist-engineer at Cornell University who led the development of the network, defends it with a sheepish smile, pointing out that the handwritten number looks sloppy. Logan Wright, a postdoc visiting McMahon’s lab from NTT Research, assures me that the device usually gets the answer right, but acknowledges that mistakes are common. “It’s just this bad,” he said. https://www.quantamagazine.org/how-to-make-the-universe-think-for-us-20220531/

NTT Research, Inc., a division of NTT, appointed Dr. Al Emondi as its head of partner strategy. Emondi joined NTT Research in March 2022 on a contract basis as part of its leadership advisory team. He is also president and chief executive officer of Piontier, LLC, a technology development and neurotech product line consultancy. https://www.hpcwire.com/2022/05/02/hpc-career-notes-may-2022-edition/

An organizational and technology leader, Dr. Emondi joined NTT Research last month on a contract basis as part of its leadership advisory team, with responsibilities that include growth and research partner opportunities, strategic project and program support, partner enablement and value, and research investments. Dr. Emondi is also President and CEO of Piontier, LLC, a technology development and neurotech product line consultancy. He was previously a program manager for the Defense Advanced Research Projects Agency (DARPA) and CTO of the U.S. Naval Information Warfare Center (NAVWAR) Atlantic. Dr. Emondi received his M.S. in Electrical Engineering and Ph.D. in neuroscience from Syracuse University. https://insidehpc.com/2022/04/former-darpa-program-manager-dr-al-emondi-named-ntt-head-of-partner-strategy/

The MIT AI Hardware Program is a new academia and industry collaboration aimed at defining and developing translational technologies in hardware and software for the AI and quantum age. A collaboration between the MIT School of Engineering and MIT Schwarzman College of Computing, involving the Microsystems Technologies Laboratories and programs and units in the college, the cross-disciplinary effort aims to innovate technologies that will deliver enhanced energy efficiency systems for cloud and edge computing. https://scitechdaily.com/mit-ai-hardware-program-aims-to-lead-in-artificial-intelligence-technology-development/

Deep-learning models have become pervasive tools in science and engineering. However, their energy requirements now increasingly limit their scalability. Deep-learning accelerators aim to perform deep learning energy-efficiently, usually targeting the inference phase and often by exploiting physical substrates beyond conventional electronics. Approaches so far have been unable to apply the backpropagation algorithm to train unconventional novel hardware in situ. The advantages of backpropagation have made it the de facto training method for large-scale neural networks, so this deficiency constitutes a major impediment.  https://www.nature.com/articles/s41586-021-04223-6