# Brain-IT Trains on Brain Scans It Generates Itself by Peter Diamandis > Brain-IT reconstructs the image a person is viewing from fMRI activity by learning structure and meaning separately. Its reverse encoder predicts how a brain will respond to images no person has seen in a scanner, generating synthetic brain scans that allow the model to build its own training dataset. > **— Adapted from Peter Diamandis**, *MOONSHOTS Live, October 2026* ## Sources and Context - **Recording or publication:** [MOONSHOTS Live at 23:22](https://www.youtube.com/watch?v=Blyb1D927pM&t=1402s) — The poster text is an explicit non-verbatim adaptation assembled from the source excerpts below. It preserves the speaker's argument while removing spoken-language filler, restoring the subject, and completing the mechanism or consequence needed for independent use. - **Exact transcript excerpt at [23:22](https://www.youtube.com/watch?v=Blyb1D927pM&t=1402s):** “Uh, I'm gonna jump into three fun breaking stories this week, two on science and one on AI. Uh, and Richard, they, you know, relate back to your work and your book. Uh, our first story was published in MIT Tech Review today. Uh, it tells the story of researchers at Israel's Weizmann Institute who built a system called Brain-IT. The system reconstructs the image a person is looking at while inside a functional MRI machine. I, I'm just post-- put up the slide here. So take a look at this image. You know, it's pretty extraordinary. On one side is the image that a person is looking at inside the fMRI machine. On the other is the AI reconstruction. You know, so earlier brain image decoders could tell you were looking at a dog or a clock tower, but it lost the color, the composition, and the details. So Brain-IT learns structure and meaning separately and then puts the picture back together. Uh, and here's the clever part: They also can run the model in reverse, an encoder that predicts how a brain will respond to an image. So they can feed it images no human has ever seen into the scanner, uh, generate predicted brain scans, and then train on those. The model effectively builds its own dataset. So Richard, you know, you say in your book that AI is superhuman in any domain you can simulate or verify, uh, and nowhere else. So is a brain simulatable do-- Is the brain a simulatable domain, or is this something different?” - **Reconciled source dossier:** [[research/ASI and RSI Timeline Research Moonshots|ASI and RSI Timeline Research Moonshots]] — preserves broadcast order, speaker reconciliation, editorial conventions, and the surrounding argument from which this Reminder was promoted. ## Related Articles and Collections - **Collection:** [[collections/Neurotech|Neurotech]] - **Collection:** [[collections/Consciousness Continuity|Consciousness Continuity]] - **Article:** [[articles/2026 Annual Report on Brain-Computer Interfaces|2026 Annual Report on Brain-Computer Interfaces]] - **Article:** [[articles/The Organic-Synthetic Brain Atlas|The Organic-Synthetic Brain Atlas]] ## Related Topics - [[wiki/Peter Diamandis|Peter Diamandis]] - [[wiki/Brain-IT|Brain-IT]] - [[wiki/Synthetic fMRI Training Data|Synthetic fMRI Training Data]] ## Share on Social Media ``` Brain-IT reconstructs the image a person is viewing from fMRI activity by learning structure and meaning separately. Its reverse encoder predicts how a brain will respond to images no person has seen in a scanner, generating synthetic brain scans that allow the model to build its own training dataset. — Adapted from Peter Diamandis, MOONSHOTS Live, October 2026 https://bryantmcgill.com/simple-reminders-peter-diamandis-brain-it-trains-on-brain-scans-it-generates-itself ```