Using brain scans and direct neuron recording from macaque monkeys, the team found specialized "face patches" that respond to specific combinations of facial features. In the early 2000s, while recording from epilepsy patients with electrodes implanted into their brains, Quian Quiroga and colleagues found that face cells are particularly picky. In a stroke of luck, Tsao and team blew open the "black box" of facial recognition while working on a different problem: how to describe a face mathematically, with a matrix of numbers. In macaque monkeys with electrodes implanted into their brains, the team recorded from three "face patches"--brain areas that respond especially to faces--while showing the monkeys the computer-generated faces.

Picture this: you're sitting in a police interrogation room, struggling to describe the face of a criminal to a sketch artist. You pause, wrinkling your brow, trying to remember the distance between his eyes and the shape of his nose. Suddenly, the detective offers you an easier way: would you like to have your brain scanned instead, so that machines can automatically reconstruct the face in your mind's eye from reading your brain waves? After decades of work, scientists at Caltech may have finally cracked our brain's facial recognition code. Using brain scans and direct neuron recording from macaque monkeys, the team found specialized "face patches" that respond to specific combinations of facial features.

When we look at a selection of faces, our brains can single out the familiar ones with no effort at all. This smooth process comes so naturally that most people never give it a second thought. But someone who does give this phenomenon a second thought is Doris Tsao, a professor of biology and biological engineering at the California Institute of Technology in Pasadena. Over recent years, Prof. Tsao has conducted a range of experiments that have attempted to get to the bottom of facial perception. In earlier studies, Prof. Tsao and her colleagues used functional MRI scans to search for relevant brain areas in humans and other primates.

Scientists in the US have accurately reconstructed images of human faces by monitoring the responses of monkey brain cells. The brains of primates can resolve different faces with remarkable speed and reliability, but the underlying mechanisms are not fully understood. The researchers showed pictures of human faces to macaques and then recorded patterns of brain activity. The work could inspire new facial recognition algorithms, they report. In earlier investigations, Professor Doris Tsao from the California Institute of Technology (Caltech) and colleagues had used functional magnetic resonance imaging (fMRI) in humans and other primates to work out which areas of the brain were responsible for identifying faces.

It is becoming increasingly clear that there is an infinite number of definitions of intelligence. Machines that are intelligent in different narrow ways have been built since the 50s. We are entering now a golden age for the engineering of intelligence and the development of many different kinds of intelligent machines. At the same time there is a widespread interest among scientists in understanding a specific and well defined form of intelligence, that is human intelligence. For this reason we propose a stronger version of the original Turing test. In particular, we describe here an open-ended set of Turing++ Questions that we are developing at the Center for Brains, Minds and Machines at MIT — that is questions about an image. Questions may range from what is there to who is there, what is this person doing, what is this girl thinking about this boy and so on.  The plural in questions is to emphasize that there are many different intelligent abilities in humans that have to be characterized, and possibly replicated in a machine, from basic visual recognition of objects, to the identification of faces, to gauge emotions, to social intelligence, to language and much more. The term Turing++ is to emphasize that our goal is understanding human intelligence at all Marr’s levels — from the level of the computations to the level of the underlying circuits. Answers to the Turing++ Questions should thus be given in terms of models that match human behavior and human physiology — the mind and the brain. These requirements are thus well beyond the original Turing test. A whole scientific field that we call the science of (human) intelligence is required to make progress in answering our Turing++ Questions. It is connected to neuroscience and to the engineering of intelligence but also separate from both of them.