The intersection of quantum computing and AI represents one of the most promising frontiers in computational science. As AI models grow in complexity and data requirements, classical computing approaches are reaching their practical limits. Quantum computing, leveraging the principles of quantum mechanics, offers a fundamentally new way to process information. 

This week Jonathan Kao and his UCLA-based team published exciting new findings in Nature Machine Intelligences that were also summarized in a companion article in Nature. 

The convergence of genomics, computational mathematics, and artificial intelligence (AI) is rapidly transforming the landscape of biomedical research and clinical practice.

The integration of BCIs (Brain-Computer Interfaces) with AI is reshaping the boundaries of human-machine interaction. AI enhances BCIs by processing and interpreting complex neural data, enabling more precise, adaptive, and efficient control of external devices. This convergence has the potential to revolutionize healthcare, augment human cognition, and redefine the interface between humans and technology.

Being a cautious AI optimist, in INSTANT AI UPDATE 9, I wrote about AI hallucinations and the risks associated with AI providing “false but probable-sounding information.” This week’s headline about AI in med schools led me to research the growth of AI in medical education and identify the risks associated with this shift.