Someone taps your shoulder. The organized touch receptors in your skin send a message to your brain, which processes the information and directs you to look left, in the direction of the tap. Now, Penn State and U.S. Air Force researchers have harnessed this processing of mechanical information and integrated it into engineered materials that ‘think.’
Using electrocorticography (ECoG), we probed the neurocognitive substrates of mentalizing at the level of neuronal populations. We found that mentalizing about the self and others recruited near-identical cortical sites (Fig. 5a, b) in a common spatiotemporal sequence (Figs. 5 c and 6). Within our ROIs, activations began in visual cortex, followed by temporoparietal DMN regions (TPJ, ATL, and PMC), and lastly in mPFC regions (amPFC, dmPFC, and vmPFC; Fig. 3e, f). Critically, regions with later activations exhibited greater functional specialization for mentalizing as measured by three metrics: functional specificity for mentalizing versus arithmetic (Figs. 3 c, d and 4b), self/other differentiation in activation timing (Fig. 5c, d), and temporal associations with behavioral responses (Fig. 4D and Table 1). Taken together, these results reveal a common neurocognitive sequence28,29,30,31 for self-and other-mentalizing, beginning in visual cortex (low specialization), ascending through temporoparietal DMN areas (intermediate specialization), then reaching its apex in mPFC regions (high specialization).
Our results are consistent with gradient-based models of brain function, which posit that concrete sensorimotor processing in unimodal regions (e.g., visual cortex) gradually yields to increasingly abstract and inferential processing in ‘high-level’ transmodal regions like mPFC41,42. We found that the strength of self/other differences in activation timing increased along a gradient from visual cortex to mPFC. Specifically, other-mentalizing evoked slower (Fig. 5c) and lengthier (Fig. 5d) activations than self-mentalizing throughout successive DMN ROIs. These self/other functional differences corresponded with self/other differences in RTBehav (Supplementary Fig. 4), although the two were often dissociable (Table 1). Thus, perhaps because we know ourselves better than others, other-mentalizing may require lengthier processing at more abstract and inferential levels of representation, ultimately resulting in slower behavioral responses.
What might our results imply about extant fMRI findings? Hundreds of fMRI studies consistently suggest that: TPJ and dmPFC are most crucial for mentalizing6,8,11,12,43,44,45,46, and dmPFC is selective for thinking about others over oneself 32,33,34,35. However, when examined with ECoG, we found that both pieces of received wisdom are not what they seem. Below, we discuss both issues before moving onto our peculiar vmPFC results, and then conclude with systems-level discussion.
WEST LAFAYETTE, Ind. — When the human brain learns something new, it adapts. But when artificial intelligence learns something new, it tends to forget information it already learned.
As companies use more and more data to improve how AI recognizes images, learns languages and carries out other complex tasks, a paper published in Science this week shows a way that computer chips could dynamically rewire themselves to take in new data like the brain does, helping AI to keep learning over time.
“The brains of living beings can continuously learn throughout their lifespan. We have now created an artificial platform for machines to learn throughout their lifespan,” said Shriram Ramanathan, a professor in Purdue University’s School of Materials Engineering who specializes in discovering how materials could mimic the brain to improve computing.
When some materials are cooled to a certain temperature, they lose electric resistance, becoming superconductors.
In this state, an electric charge can course through the material indefinitely, making superconductors a valuable resource for transmitting high volumes of electricity and other applications. Superconductors ferry electricity between Long Island and Manhattan. They’re used in medical imaging devices such as MRI machines, in particle accelerators and in magnets such as those used in maglev trains. Even unexpected materials, such as certain ceramic materials, can become superconductors when cooled sufficiently.
But scientists previously have not understood what occurs in a material to make it a superconductor. Specifically, how high-temperature superconductivity, which occurs in some copper-oxide materials, works hasn’t been previously understood. A 1966 theory examining a different type of superconductors posited that electrons which spin in opposite directions bind together to form what’s called a Cooper pair and allow electric current to pass through the material freely.
It doesn’t involve magic but mirrors and lenses.
Energy can be trapped in the form of electric charge and heat, but until now, it has been impossible to absorb it in the form of light using traditional methods. Now a team of researchers from the Hebrew University of Jerusalem and Vienna University of Technology (TU Wien) claims to have developed the perfect setup to trap light, according to a press release published by EurekAlert.
Although this isn’t the first time scientists have come up with a way to absorb light energy, it is probably the only “light trap” method using which light energy can be absorbed even by very thin and weak mediums.
Recent developments like DALLE-2 and LaMDA are impressive and seem ready for impact. Is AI ready to change the world?
Whether you love, fear, or have mixed feelings about the future of artificial intelligence, the cultural fixation on the subject over the past decade has made it feel like the technology’s meteoric impact is just around the corner. The problem is that it is always just around the corner, yet never seems to arrive. Many hype-filled years have passed us by since the releases of Ex Machina (2014) and Westworld (2016), but it feels like we are still waiting on AI’s big splash. However, a handful of recent developments—specifically, OpenAI’s unveiling of GPT-3 and DALLE-2, and Google’s LaMDA controversy—have unleashed a new wave of excitement—and terror—around the possibility that AI’s game-changing moment is finally here.
There are several reasons why it feels it has taken a long time for AI projects to bear fruit. One is that pop culture seems almost exclusively focused on the possible endgames of the technology, rather than its broader journey. This isn’t much of a surprise. When we stream the latest sci-fi movie or binge Black Mirror episodes, we want to see killer robots and computer chip brain implants. No one is buying a ticket to see a movie about the slow, incremental rollout of existing technology—not unless it mutates and starts killing within the first 30 minutes. But while AI’s more futuristic forms are naturally the most entertaining, and provide an endless source of material for screenwriters, anyone who based their expectations for AI off of Bladerunner has got to be feeling disappointed by now.
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Vanadium dioxide marks the first material EPFL researchers have discovered that identified as possessing this property.
It can also solve the carbon intensity problem in the construction industry.
Researchers at the Nanyang Technological University (NTU) in Singapore have invented an invisible coating that can be applied to wood to make it fireproof.
Researchers from École Polytechnique Fédérale de Lausanne (EPFL) have discovered that vanadium dioxide (VO2) is capable of “remembering” the entire history of previous external stimuli.
Vanadium dioxide marks the first material EPFL researchers have discovered that identified as possessing this property.
