Armed with some Python and a white-hot sense of injustice, one medical student spent six months trying to figure out whether an algorithm trashed his job application. It was mid-October, peak leaf-peeping season in Hanover, New Hampshire, and Chad Markey was on a rare break between clinical rotations during his last year of medical school. He should have been inhaling Green Mountain air and gossiping with his Dartmouth classmates about life after graduation. In a few months, they'd all be going their separate ways to start residency training at hospitals around the country. Instead, Markey was alone in his apartment, deep down a rabbit hole, preparing to go to war. He'd wake each morning, eat breakfast, open his laptop at the kitchen table or settle into the tan armchair with the good back support, and start coding . Some days, he wouldn't notice the sun had gone down until one of his roommates came home and asked why the lights weren't on. For days, Markey had been scrolling through a Discord group about medical residency, a font of crowdsourced knowledge where students report back to their peers on every stage of the application and selection process. He'd watched as other students, lots of them, posted about the interview invitations they'd received.

Transcranial focused ultrasound is a noninvasive way to stimulate the brain and see how it functions. How does the physical matter in our brains translate into thoughts, sensations, and emotions? It's hard to explore that question without neurosurgery. But in a recent paper, MIT philosopher Matthias Michel, Lincoln Lab researcher Daniel Freeman, and colleagues outline a strategy for doing so with an emerging tool called transcranial focused ultrasound. This noninvasive technology reaches deeper into the brain, with greater resolution, than techniques such as EEG and MRI. It works by sending acoustic waves through the skull to focus on an area of a few millimeters, allowing specific brain structures to be stimulated so the effects can be studied.

Bayesian brain theory suggests that the brain employs generative models to understand the external world.

Over time, the two branches interact recurrently to build a scene representation from moving fixations.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/