RSS feed source: National Science Foundation

The United States semiconductor industry is projected to have between 60,000 and 100,000 unfilled jobs by 2030. As the need for semiconductor technicians, engineers and scientists continues to increase, there is also a growing demand for innovative ways to train this anticipated workforce. But such training typically requires expensive clean rooms and advanced equipment, resources that many schools don’t have access to.

A team of researchers, which included high school and community college students, found a solution to this challenge by using artificial intelligence-powered virtual reality (VR) to create simulations as a cost-effective alternative for people to learn about the process of semiconductor fabrication. The results of their research, which is supported by the U.S. National Science Foundation Advanced Technological Education Micro Nano Technology Education Center at Pasadena City College (PCC), in collaboration with the University of California, Irvine (UCI), are available in the Journal of Advanced Technological Education.

“Many students, especially those at underfunded schools, never get to see or touch the real semiconductor fabrication tools,” said Kristal Hong, a member of the research team and a computer science major at UCI. “I, myself, was a community college student without access to a cleanroom, so I know how that gap can dampen student enthusiasm.”

By using AI-powered VR to create cleanroom simulations, the team is offering a learning channel outside of traditional classrooms and

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RSS feed source: National Science Foundation

At Brown University, an innovative new project is revealing that teaching artificial intelligence to perceive things more like people may begin with something as simple as a game. The project invites participants to play an online game called Click Me, which helps AI models learn how people see and interpret images. While the game is fun and accessible, its purpose is more ambitious: to understand the root causes of AI errors and to systematically improve how AI systems represent the visual world.

Over the past decade, AI systems have become more powerful and widely used, particularly in tasks like recognizing images. For example, these systems can identify animals, objects or diagnose medical conditions from images. However, they sometimes make mistakes that humans rarely do. For instance, an AI algorithm might confidently label a photo of a dog wearing sunglasses as a completely different animal or fail to recognize a stop sign if it’s partially covered by graffiti. As these models become larger and more complex, these kinds of errors become more frequent, revealing a growing gap between how AI and humans perceive the world.

Recognizing this challenge, researchers funded in part by the U.S. National Science Foundation propose to combine insights from psychology and neuroscience with machine learning to create the next generation of human-aligned AI. Their goal is to understand how people process

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