Brain-computer interfaces (BCIs) can translate a person’s brainwaves into action, but these devices are vulnerable to hacking.
Artificial neural networks—algorithms inspired by biological brains—are at the center of modern artificial intelligence, behind both chatbots and image generators. But with their many neurons, they can be black boxes, their inner workings uninterpretable to users.
Researchers have now created a fundamentally new way to make neural networks that in some ways surpasses traditional systems. These new networks are more interpretable and also more accurate, proponents say, even when they’re smaller. Their developers say the way they learn to represent physics data concisely could help scientists uncover new laws of nature.
Now, scientists in China have developed robots that give human-like realistic expressions.
The humanoid robot with highly expressive facial features is developed by Liu Xiaofeng, a professor at Hohai University in east China’s Jiangsu Province, and his research team.
For the development of this robot, the research team developed a new algorithm for generating facial expressions on humanoid robots.
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He writes that AI is now exceeding the human brain at several cognitive tasks and that it will eventually do all things far better than even the most expert humans.
These new machines can learn, reason, plan and act with intention, and they are becoming far smarter far faster than most people, save Kurzweil, could have predicted.
Soon, he forecasts, they will be indistinguishable from human brains, before accelerating past them in nearly every way.
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But the technology for using bridge RNA in genome editing is still in the early stages. Hsu’s team has demonstrated this system only in bacteria, though Hsu says he is optimistic that efforts to adapt the approach to work in mammalian cells will succeed. Chen says this system’s efficiency may not be as good as CRISPR’s is now but that improvements will come with time.
The wearables market has been dominated, so far, by smartwatches and fitness trackers. The first Apple Watch was launched in April 2015, and wearable technology now includes jewelry that tracks your steps and notifies you of an incoming call, VR headsets for gamers, earbuds, smart glasses with Internet access, smart clothing integrated with electronic devices and a range of health monitors.
But the world’s first eyelid wearable device opens up a whole new world of opportunity.
Blink Energy’s device weighs just 0.4 grams (0.014 ounces) — less than half the weight of a paperclip – and is fitted to one eyelid. You barely notice it, says Bar-On. “After two minutes you forget it’s there.”
Electronic waste, or e-waste, is a rapidly growing global problem, and it’s expected to worsen with the production of new kinds of flexible electronics for robotics, wearable devices, health monitors, and other new applications, including single-use devices.
A new kind of flexible substrate material developed at MIT, the University of Utah, and Meta has the potential to enable not only the recycling of materials and components at the end of a device’s useful life, but also the scalable manufacture of more complex multilayered circuits than existing substrates provide.
The development of this new material is described in the journal RSC Applied Polymers (“Photopatternable, Degradable, and Performant Polyimide Network Substrates for E-Waste Mitigation”), in a paper by MIT Professor Thomas J. Wallin, University of Utah Professor Chen Wang, and seven others.
Researchers led by the University of California, Irvine have discovered how the TREM2 R47H genetic mutation causes certain brain areas to develop abnormal protein clumps, called beta-amyloid plaques, associated with late-onset Alzheimer’s disease. Leveraging single-cell Merfish spatial transcriptomics technology, the team was able to profile the effects of the mutation across multiple cortical and subcortical brain regions, offering first-of-their-kind insights at the single-cell level.
The study, published in Molecular Psychiatry, compared the brains of normal mice and special mouse models that undergo changes like those in humans with Alzheimer’s.
Findings revealed that the TREM2 mutation led to divergent patterns of beta-amyloid plaque accumulation in various parts of the brain involved in higher-level functions such as memory, reasoning and speech. It also affected certain cell types and their gene expression near the plaques.
