Microsoft boss troubled by rise in reports of ‘AI psychosis’ | BBC News

There are increasing reports of people suffering “AI psychosis”, Microsoft’s head of artificial intelligence (AI), Mustafa Suleyman, has warned.

In a series of posts on X, he wrote that “seemingly conscious AI” are keeping him “awake at night” and said they have societal impact even though the technology is not conscious in any human definition of the term.

“There’s zero evidence of AI consciousness today. But if people just perceive it as conscious, they will believe that perception as reality,” he wrote.

Related to this is the rise of a new condition called “AI psychosis”: a non-clinical term describing incidents where people increasingly rely on AI chatbots such as ChatGPT, Claude and Grok and then become convinced that something imaginary has become real.

Understanding AI psychosis

For some people who are developing relationships with AI tools, the technology is inducing delusions or distorted beliefs, a phenomenon referred to as “AI psychosis.” Dr. John Torous, psychiatrist for Beth Israel Deaconess Medical Center, joined “The Takeout” to discuss.

Research Advances in AI-Assisted Material Generation for Physical AI

Digital twins are vital for the training and testing of physical AI that can understand and perform complex actions in the real world. But building digital twins is slow and tedious. NVIDIA’s latest generative AI and rendering research showcases the potential to dramatically accelerate and scale these workflows.

These breakthroughs combine NVIDIA Cosmos World Foundation Models and advanced rendering technologies to allow developers to pair AI assistants with experts at the heart of digital twin and physical AI development. In these future workflows, experts will engage with AI assistants using simple language to quickly generate realistic, physically accurate base materials for 3D models. They can then fine-tune generated materials, adjusting properties such as roughness and texture to better reflect the real world.

This research, along with the workflows it enables, showcases the potential to quickly transform 3D models into large-scale, simulation-ready digital twins that can be used to train safe and efficient physical AI.