They didn't want to put their children in the middle--so they put a machine there instead. Sol Kennedy used to ask his assistant to read the messages his ex-wife sent him. After the couple separated in 2020, Kennedy says, he found their communications "tough." An email, or a stream of them, would arrive--stuff about their two kids mixed with unrelated emotional wallops--and his day would be ruined trying to reply. Kennedy, a serial tech founder and investor in Silicon Valley, was in therapy at the time.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Meta announced today that its artificial intelligence (AI) research framework, PyTorch, has a new home. It is moving to an independent PyTorch Foundation, which will be part of the nonprofit Linux Foundation, a technology consortium with a core mission of collaborative development of open-source software. According to Aparna Ramani, VP of engineering at Meta, over the next year the focus will be on making a seamless transition from Meta to the foundation.

A new database would help engineers and manufacturers to apply machine learning to mechanical parts. Computer vision researchers use machine learning to train computers in visually recognizing objects--but very few apply machine learning to mechanical parts such as gearboxes, bearings, brakes, clutches, motors, nuts, bolts and washers. A team of Purdue University mechanical engineers has created the first comprehensive open-source annotated database of more than 58,000 3-D mechanical parts, designed to help researchers apply machine learning to those parts in actual machines. "We are in the deep learning era, using computers to search for things visually," said Karthik Ramani, Purdue's Donald W. Feddersen Distinguished Professor of Mechanical Engineering. "But no one is focusing on the parts that go into machines: pipes, bearings, motors, washers, nuts and bolts, etc. Those are the things that are important to us as engineers and manufacturers. We want to be able to point a camera at a real-world part, and have the computer tell us everything about that part or design."

WEST LAFAYETTE, Ind. -- If any factory worker could program low-cost robots, then more factories could actually use robotics to increase worker productivity. This is because workers would be able to shift to taking on more varied and higher-level tasks, and factories could produce a greater variety of products. That's the idea behind a prototype smartphone app Purdue University researchers have developed that allows a user to easily program any robot to perform a mundane activity, such as picking up parts from one area and delivering them to another. The setup could also take care of household chores – no more plants dying because you forgot to water them. Purdue researchers present their research on the embedded app, called VRa, on June 23 at DIS 2019 in San Diego.

This is because workers would be able to shift to taking on more varied and higher-level tasks, and factories could produce a greater variety of products. That's the idea behind a prototype smartphone app Purdue University researchers have developed that allows a user to easily program any robot to perform a mundane activity, such as picking up parts from one area and delivering them to another. The setup could also take care of household chores -- no more plants dying because you forgot to water them. Purdue researchers present their research on the embedded app, called VRa, on June 23 at DIS 2019 in San Diego. The platform is patented through the Purdue Research Foundation Office of Technology Commercialization, with plans to make it available for commercial use.

A new technique that uses the artificial intelligence methods of machine learning and deep learning is able to create 3-D shapes from 2-D images, such as photographs, and is even able to create new, never-before-seen shapes. Karthik Ramani, Purdue's Donald W. Feddersen Professor of Mechanical Engineering, says that the "magical" capability of AI deep learning is that it is able to learn abstractly. "If you show it hundreds of thousands of shapes of something such as a car, if you then show it a 2-D image of a car, it can reconstruct that model in 3-D," he says. "It can even take two 2-D images and create a 3-D shape between the two, which we call'hallucination.'" When fully developed, this method, called SurfNet, could have significant applications in the fields of 3-D searches on the Internet, as well as helping robotics and autonomous vehicles better understand their surroundings.

Research at Purdue University funded through a National Science Foundation grant has led to development of a new kind of toy-building kit that allows children to create robots and control them remotely like a puppeteer. The new kit, called Ziro, was developed in research led by Karthik Ramani, the Donald W. Feddersen Professor of Mechanical Engineering and co-founder and chief scientist of the company ZeroUI, with locations at the Purdue Research Park and in San Jose, California. Ziro is the first commercial application of ZeroUI's gesture-based Natural User Interface technology platform. Sensors in a "smart glove" communicate with wireless motorized modules, enabling users to direct the robotic creations with the lift of a finger or flick of a wrist in real-time. Research funding was provided as part of the NSF grant to both the university as well as through the Small Business Innovation Research program, designed to move innovations from discovery to commercialization. The NSF is nurturing a national innovation ecosystem through development of technologies, products and processes that benefit society.