If the brain is our most important organ, why do most brain disorders such as depression and post-traumatic stress disorder (PTSD) still rely on subjective diagnosis and treatment based on questionnaires and a taxing trial-and-error process?

For over six years, the co-inventor of Apple’s FaceID recognition system and an Israeli-American computational neuroscientist pursued this question as their quest to build an artificial intelligence model, which they say promises to decode and understand the complex language of the brain.

After working under the radar, Gidi Littwin and Hagai Lalazar, the co-founders of Hemispheric have now come out with Descartes, which they say will be able to decipher the electrical activity of the human brain to help diagnose neurological and psychological conditions such as depression and PTSD.

Speaking to The Times of Israel, Littwin, and Lalazar, respectively CTO and CEO of the Israeli startup, said that the Descartes AI model is trained to interpret the brain’s electrical signals, or language, in the same way large language models process the semantic meaning of text or computer vision systems interpret images.

“The problem that we are coming to solve is really paramount to humanity, which is that our brain is the most complicated organ in our body, the largest disease burden, but we have no quantitative, non-invasive measurement of its function, of its health, and as a neuroscientist — that really bothered me,” said Lalazar. “When you visit psychiatrists, they still ask you how you feel, and neurologists still ask you to touch your nose with a finger, so with all the advancements we haven’t gotten to the accuracy needed to improve people’s lives.”

Littwin developed deep learning solutions for Apple’s augmented reality device Vision Pro, but left the Cupertino tech giant in 2020 and teamed up with Lalazar. The two sought to harness AI to build a generalized machine learning model designed to help infer brain function from electrical activity within the skull without invasive surgical procedures, such as implanting electrodes or chips. But they encountered a major issue: Lack of brain data to feed such an AI model.

“In recent years, large language models have been trained on massive amounts of data to power applications like ChatGPT, but there is no available brain data to train deep learning models to decode brain activity,” said Littwin. “Over the past seven years, we built the world’s largest lab network........

© The Times of Israel