However, it remains elusive whether and how object percepts alone, or concomitantly a nonphysical attribute of the objects ("learned"), are decoded from perirhinal activities. By combining monkey psychophysics with optogenetic and electrical stimulations, we found a focal spot of memory neurons where both stimulations led monkeys to preferentially judge presented objects as "already seen." In an adjacent fringe area, where neurons did not exhibit selective responses to the learned objects, electrical stimulation induced the opposite behavioral bias toward "never seen before," whereas optogenetic stimulation still induced bias toward "already seen." These results suggest that mnemonic judgment of objects emerges via the decoding of their nonphysical attributes encoded by perirhinal neurons.
To tackle my aunt's puzzle, the expert systems approach would need a human to squint at the first three rows and spot the following pattern: The human could then instruct the computer to follow the pattern x * (y 1) z. Even when machines teach themselves, the preferred patterns are chosen by humans: Should facial recognition software infer explicit if/then rules, or should it treat each feature as an incremental piece of evidence for/against each possible person? And so they designed deep neural networks, a machine learning technique most notable for its ability to infer higher-level features from more basic information. These questions have constrained efforts to apply neural networks to new problems; a network that's great at facial recognition is totally inept at automatic translation.
Familiarity alters face recognition: Familiar faces are recognized more accurately than unfamiliar ones and under difficult viewing conditions when unfamiliar face recognition fails. Using whole-brain functional magnetic resonance imaging, we found that personally familiar faces engage the macaque face-processing network more than unfamiliar faces. Familiar faces also recruited two hitherto unknown face areas at anatomically conserved locations within the perirhinal cortex and the temporal pole. These two areas, but not the core face-processing network, responded to familiar faces emerging from a blur with a characteristic nonlinear surge, akin to the abruptness of familiar face recognition.
In 1851, a Florida doctor named John Gorrie received a patent for the first ice machine. He'd been trying to alleviate high fevers in malaria patients with cooled air. To this end, he designed an engine that could pull in air, compress it, then run it through pipes, allowing the air to cool as it expanded. It wasn't until the pipes on Gorrie's machine unexpectedly froze and began to develop ice that he found a new opportunity.
He kept odd hours, played music too loud, and relished the New York jazz scene. John Pierce was another of the Bell Labs friends whose company Shannon shared in the off hours. It turns out that there were three certified geniuses at BTL [Bell Telephone Laboratories] at the same time, Claude Shannon of information theory fame, John Pierce, of communication satellite and traveling wave amplifier fame, and Barney. If people didn't believe in them, he ignored those people," McMillan told Gertner.
As Uber battles taxis and other ride-hailing apps in cities across the world, the company is beginning to move quickly into a much larger transportation market: trucking. This spring, Uber unveiled Uber Freight, a brokerage service connecting shippers and truckers through a new app. Since then, the teams have split up into self-driving research and development, managed by Alden Woodrow, formerly of Google X, and the Uber Freight team. Even in trucking, Uber's acquisition of Otto has led to a lawsuit filed by Alphabet's self-driving car division, Waymo, related to the alleged theft of sensor technology.
To maintain Moore's law, the semiconductor industry decided a decade ago that a new transistor was imperative. That silver bullet has yet to materialize, but computer design innovations are now maintaining or even exceeding expected scaling progress. This theme issue gives a cross-sectional view of these new scaling drivers.
If both our brains and our neurons were 10 times bigger, we'd have 10 times fewer thoughts during our lifetimes. We can argue, then, that, it is difficult to imagine any life-like entities with complexity rivaling the human brain that occupy scales larger than the stellar size scale. Conversely, a planet with 10 times lower gravity than Earth's could potentially have animals that are 10 times bigger. As was first pointed out in the 1930s by Max Kleiber, the metabolic rate per kilogram of Earth's animals decreases in proportion to the mass of the animal raised to the power of 0.25.2 Indeed, if this heating rate didn't decrease, large animals would literally cook themselves (as recently and vividly illustrated by Aatish Batia and Robert Krulwich).