# Biology May Reveal a Learning Algorithm Far Beyond Gradient Descent by Dave Blundin > Artificial neural networks are trained through gradient descent, yet biology has not revealed anything clearly equivalent in the brain. Detailed neural imaging may uncover the actual algorithm that changes synaptic weights—and that discovery could make machine learning ten, a hundred, a thousand, or even a million times more efficient. > **— Adapted from Dave Blundin**, *MOONSHOTS Live, October 2026* ## Sources and Context - **Recording or publication:** [MOONSHOTS Live at 31:38](https://www.youtube.com/watch?v=Blyb1D927pM&t=1898s) — 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 [31:38](https://www.youtube.com/watch?v=Blyb1D927pM&t=1898s):** “Oh, no, the, the result I really am looking forward to is, uh, how does the brain train itself without gradient descent? Everything going on in AI right now, e- everything going on for the last 20 years in AI is driven by gradient descent algorithms, and the biologists can't find anything even vaguely like that in actual biology. And so that, that's, you know, I think with, with really detailed imaging, we might finally crack the code on what is the fundamental learning algorithm that changes the synaptic weights. It's not, it's not what we use for artificial neural nets. It's, it's something different. And if we discover that, we might find that neural net training can be 10, 100, 1,000, a million times more efficient, and so hopefully that'll come out-” - **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/Dave Blundin|Dave Blundin]] - [[wiki/Biological Learning Algorithm|Biological Learning Algorithm]] - [[wiki/Gradient Descent|Gradient Descent]] ## Share on Social Media ``` Artificial neural networks are trained through gradient descent, yet biology has not revealed anything clearly equivalent in the brain. Detailed neural imaging may uncover the actual algorithm that changes synaptic weights—and that discovery could make machine learning ten, a hundred, a thousand, or even a million times more efficient. — Adapted from Dave Blundin, MOONSHOTS Live, October 2026 https://bryantmcgill.com/simple-reminders-dave-blundin-biology-may-reveal-a-learning-algorithm-far-beyond-gradient-descent ```