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Erie County

Professor's perceptron paved the way for AI – 60 years too soon Cornell Chronicle


In July 1958, the U.S. Office of Naval Research unveiled a remarkable invention. An IBM 704 – a 5-ton computer the size of a room – was fed a series of punch cards. After 50 trials, the computer taught itself to distinguish cards marked on the left from cards marked on the right. It was a demonstration of the "perceptron" – "the first machine which is capable of having an original idea," according to its creator, Frank Rosenblatt '50, Ph.D. '56. At the time, Rosenblatt – who later became an associate professor of neurobiology and behavior in Cornell's Division of Biological Sciences – was a research psychologist and project engineer at the Cornell Aeronautical Laboratory in Buffalo, New York.

Fingerprints, Face Recognition, Move Aside: Heart Scan Latest Biometric Security

International Business Times

While the new Apple iPhone X might boast of advanced biometrics with its Face Unlock facial recognition system, there are new authentication systems in the fray -- researchers at the University of Buffalo, New York, have developed a new authentication system that can scan a user's heart's shape and size from a distance and use it to authenticate devices. The authentication system uses cardiac scans to form an authentication system that does not require any contact with the user. "Logging-in and logging-out are tedious," Wenyao Xu, assistant professor at the University of Buffalo's department of computer science and engineering and the lead author on the paper said in press release on the University of Buffalo website. He also stated that the research's mechanism would be the first device to be a non-contact one characterizing heart geometry. The device uses a low-level Doppler radar to continuously scan a user's heart's shape and size and can even authenticate people over distances of up to 98 feet.

Robots fill new roles at work

AITopics Original Links

When Christian Johnson began his summer 2012 internship at the information management branch of NASA's Langley Research Center in Hampton, Virginia, he little suspected that he'd soon be virtually tooling around the center via a vaguely humanoid robot on wheels. Once classes began in the fall, the 18-year-old had to finish up his senior year of high school in Buffalo, New York and needed to telecommute to continue his work as data analytics specialist at the research center. One of his co-workers had heard about a company called VGo Communications that makes a wheeled personal avatar, or what it calls a "productivity improvement solution," that lets people see and hear--and be seen and be heard--from far away. The co-worker wrote a proposal urging Langley's CIO to buy a VGo unit, and the CIO's office approved the purchase of one of the robotic avatars so that Johnson could use it to move virtually through the building and attend meetings--just one of the new ways robots are making their mark in business today. Industrial robots have been around since the early 1960s and have been used mainly in automotive plants.

Centric Models of the Orientation Map in Primary Visual Cortex

Neural Information Processing Systems

Centric Models of the Orientation Map in Primary Visual Cortex William Baxter of Computer Science, S.U.N.Y. at Buffalo, NY 14620Department Bruce Dow Department of Physiology, S.U.N.Y. at Buffalo, NY 14620 Abstract the visual cortex of the monkey the horizontal organization of the preferredIn of orientation-selective cells follows two opposing rules: 1) neighbors tendorientations Several orientation models which satisfy these constraints are found in the spacing and the topological index of their singularities. Using the rateto differ of orientation change as a measure, the models are compared to published experimental results. Introduction It has been known for some years that there exist orientation-sensitive neurons in the visual cortex of cats and mOnkeysl,2. These cells react to highly specific patterns of light occurring in narrowly circumscribed regiOns of the visual field, i.e., the cell's receptive field. The best patterns for such cells are typically not diffuse levels of but elongated bars or edges oriented at specific angles.