The Download: AI “mind-reading” and creative uses for small batteries

A new artificial‑intelligence tool has shown it can infer the image a person is looking at from brain scans, and can also generate the expected neural activity when a specific image is shown. Researchers call the system a “mind reader” that reconstructs visual content with remarkable accuracy. Potential applications include better understanding of cognitive processes and helping patients with locked‑in syndrome communicate by interpreting their brain patterns. Experts caution that the same technique could be used to extract thoughts or mental images without consent, raising serious privacy and ethical concerns.

At the same time, the expansion of large‑scale battery installations has faced pushback in densely populated areas such as New York, after fire incidents. In response, several startups are exploring smaller, distributed batteries integrated into everyday devices like induction cooktops or food carts. While each unit is limited, their combined capacity can be substantial, and as costs fall they could be incorporated into more aspects of daily life, easing the strain on the power grid. Dataqbs, for example, is developing modular storage solutions that take advantage of this decentralized approach.

These developments are significant because advances in reading brain activity could transform medical communication and spark debates over mental privacy, while distributed battery adoption offers a path toward a more resilient and community‑friendly energy infrastructure.

Read the original article on MIT Technology Review

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