Cornell Researchers Train Physical Systems, Revolutionize Machine Learning

A Cornell research group led by Prof. Peter McMahon, applied and engineering physics,has successfully trained various physical systems to perform machine learning computations in the same way as a computer. The researchers have achieved this by turning physical systems, such as an electrical circuit or a Bluetooth speaker, into a physical neural network — a series of algorithms similar to the human brain, allowing computers to recognize patterns in artificial intelligence. Machine learning is at the forefront of scientific endeavors today. It is used for a host of real-life applications, from Siri to search optimization to Google translate. However, chip energy consumption constitutes a major issue in this field, since the execution of neural networks, forming the basis of machine learning, uses an immense amount of energy.

Cornell Professor Creates AI-Interfaced Photoluminescent Fiber Installation to Facilitate Dialogue Through Technology

In Building 99 on Microsoft’s Redmond, Washington campus, Prof. Jenny Sabin, architecture, unveiled her latest project: an AI interface called Ada that translates people’s facial reactions into color by using a network of a dozen cameras designed to collect people’s facial expressions. Sabin, who was invited to participate in Microsoft’s Artist in Residence program, hoped to “explore artificial intelligence in ways that would make it more human centered — would provide bridges to understanding the technology.” Through Ada, she hopes to bring more people closer to artificial intelligence in a more friendly, approachable manner. Ada was named after gifted mathematician and early computer programmer Ada Lovelace, who was cited to have written instructions for the first computer program in the mid-1800s. According to Sabin, the system functions as an interface for “expressing sentiment data that’s been picked up by cameras and reveals the data through light and color.”

Beyond the 12 cameras within the room, there is also an additional sensor and camera contained inside the project that can override the other cameras. These sensors and cameras read “the collective sentiment of the building [facial expressions] from individuals,” according to Sabin.

DELGADO | Artificial Intelligence’s Exclusivity Issue

Humans have created systems to simplify global problem-solving and expedite learning for almost a century. Artificial intelligence is cited by some industry leaders as the next big breakthrough in human technological evolution. Detractors claim that AI poses a unique range of challenges. Tesla CEO Elon Musk expressed the potential dangers of AI and how future overreliance on AI could lead to the downfall of human creativity. Musk referred to humanity as the “biological boot loader” for computer programming.

WANG | On Yesterday, Song-Writing and Artificial Intelligence

By now, you’ve probably heard about the upcoming film Yesterday. It follows a struggling musician who gets the break of a lifetime when he’s rudely waylaid by a truck, and he awakens to a world suddenly forgetful of The Beatles. Through sheer bashfulness and chutzpah, he starts to “write” hit song after hit song from the Beatles catalog for a girl he’s after. We can guess where the movies goes: He gets the girl, writes the hit song, rides off into the sunset. The whole movie is a sundae in cinematic form: Sweet and reliable with a pleasant aftertaste.