Japanese researches control Gundam robot using their mind.
Japanese scientists have created a device that controls a mini toy Gundam robot using the human mind, turning one of the anime’s most exciting technological concepts into reality.
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Real-time health monitoring and sensing abilities of robots require soft electronics, but a challenge of using such materials lie in their reliability. Unlike rigid devices, being elastic and pliable makes their performance less repeatable. The variation in reliability is known as hysteresis.
Guided by the theory of contact mechanics, a team of researchers from the National University of Singapore (NUS) came up with a new sensor material that has significantly less hysteresis. This ability enables more accurate wearable health technology and robotic sensing.
The research team, led by Assistant Professor Benjamin Tee from the Institute for Health Innovation & Technology at NUS, published their results in the prestigious journal Proceedings of the National Academy of Sciences on 28 September 2020.
DiCarlo and Yamins, who now runs his own lab at Stanford University, are part of a coterie of neuroscientists using deep neural networks to make sense of the brain’s architecture. In particular, scientists have struggled to understand the reasons behind the specializations within the brain for various tasks. They have wondered not just why different parts of the brain do different things, but also why the differences can be so specific: Why, for example, does the brain have an area for recognizing objects in general but also for faces in particular? Deep neural networks are showing that such specializations may be the most efficient way to solve problems.
Neuroscientists are finding that deep-learning networks, often criticized as “black boxes,” can be good models for the organization of living brains.
Hello World.
I’m Imagination.
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Researchers at Stanford University have developed a CRISPR-based “lab on a chip” to detect COVID-19, and are working with automakers at Ford to develop their prototype into a market-ready product.
This could provide an automated, hand-held device designed to deliver a coronavirus test result anywhere within 30 minutes.
In a study published this week in the Proceedings of the National Academy of Sciences, the test spotted active infections quickly and cheaply, using electric fields to purify fluids from a nasal swab sample and drive DNA-cutting reagents within the system’s tiny passages.
Boeing has hired a former SpaceX and Tesla executive with autonomous technology experience to lead its software development team.
Effective immediately, Jinnah Hosein is Boeing’s vice-president of software engineering, a new position that includes oversight of “software engineering across the enterprise”, Boeing says.
“Hosein will lead a new, centralised organisation of engineers who currently support the development and delivery of software embedded in Boeing’s products and services,” the Chicago-based airframer says. “The team will also integrate other functional teams to ensure engineering excellence throughout the product life cycle.”
Another argument for government to bring AI into its quantum computing program is the fact that the United States is a world leader in the development of computer intelligence. Congress is close to passing the AI in Government Act, which would encourage all federal agencies to identify areas where artificial intelligences could be deployed. And government partners like Google are making some amazing strides in AI, even creating a computer intelligence that can easily pass a Turing test over the phone by seeming like a normal human, no matter who it’s talking with. It would probably be relatively easy for Google to merge some of its AI development with its quantum efforts.
The other aspect that makes merging quantum computing with AI so interesting is that the AI could probably help to reduce some of the so-called noise of the quantum results. I’ve always said that the way forward for quantum computing right now is by pairing a quantum machine with a traditional supercomputer. The quantum computer could return results like it always does, with the correct outcome muddled in with a lot of wrong answers, and then humans would program a traditional supercomputer to help eliminate the erroneous results. The problem with that approach is that it’s fairly labor intensive, and you still have the bottleneck of having to run results through a normal computing infrastructure. It would be a lot faster than giving the entire problem to the supercomputer because you are only fact-checking a limited number of results paired down by the quantum machine, but it would still have to work on each of them one at a time.
Continue reading “Will Quantum Computing Supercharge Artificial Intelligence?” »
Looks like inventory robots won’t be replacing humans in Walmart for now. 😃
I’m a bit sad for the supplier of the robots. But I’m glad that people will keep their jobs in Walmart.
Bitter Reality
Continue reading “Walmart Is Giving Up on Its Inventory Robots” »
Military observers said the disruptive technologies – those that fundamentally change the status quo – might include such things as sixth-generation fighters, high-energy weapons like laser and rail guns, quantum radar and communications systems, new stealth materials, autonomous combat robots, orbital spacecraft, and biological technologies such as prosthetics and powered exoskeletons.
Speeding up the development of ‘strategic forward-looking disruptive technologies’ is a focus of the country’s latest five-year plan.
EPFL engineers have developed a computer chip that combines two functions—logic operations and data storage—into a single architecture, paving the way to more efficient devices. Their technology is particularly promising for applications relying on artificial intelligence.
It’s a major breakthrough in the field of electronics. Engineers at EPFL’s Laboratory of Nanoscale Electronics and Structures (LANES) have developed a next-generation circuit that allows for smaller, faster and more energy-efficient devices—which would have major benefits for artificial-intelligence systems. Their revolutionary technology is the first to use a 2-D material for what’s called a logic-in–memory architecture, or a single architecture that combines logic operations with a memory function. The research team’s findings appear today in Nature.
Until now, the energy efficiency of computer chips has been limited by the von Neumann architecture they currently use, where data processing and data storage take place in two separate units. That means data must constantly be transferred between the two units, using up a considerable amount of time and energy.
