Anthrobots: These remarkable spheroid-shaped multicellular biological robots, or biobots, are not the products of advanced robotics laboratories but are instead born from the inherent potential of adult human somatic progenitor seed cells.
Advanced Science is a high-impact, interdisciplinary science journal covering materials science, physics, chemistry, medical and life sciences, and engineering.
For example, the New York Times states: “The AI industry this year is set to be defined by one main characteristic: A remarkably rapid improvement of the technology as advancements build upon one another, enabling AI to generate new kinds of media, mimic human reasoning in new ways and seep into the physical world through a new breed of robot.”
Ethan Mollick, writing in his One Useful Thing blog, takes a similar view: “Most likely, AI development is actually going to accelerate for a while yet before it eventually slows down due to technical or economic or legal limits.”
The year ahead in AI will undoubtedly bring dramatic changes. Hopefully, these will include advances that improve our quality of life, such as the discovery of life saving new drugs. Likely, the most optimistic promises will not be realized in 2024, leading to some amount of pullback in market expectations. This is the nature of hype cycles. Hopefully, any such disappointments will not bring about another AI winter.
AI tools like ChatGPT can draft letters, tell jokes and even give legal advice – but only in the form of computerized text.
Now, scientists have created an AI that can imitate human handwriting, which could herald fresh issues regarding fraud and fake documents.
Amazingly, the results are almost indistinguishable from the real thing drafted by human hands.
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BrainChip, a neuromorphic computing device provider, will present a demonstration featuring its Akida neuromorphic processor operating on Microchips’ embedded platform at CES 2024. This will utilize two evaluation boards, namely Microchip’s SAMv71 Ultra board and SAMA7G54-EK board, with a particular focus on showcasing the efficiency of the Akida neuromorphic processor when integrated with a 32-bit microprocessor unit. BrainChip aims to highlight its capabilities in always-on machine learning tasks, including keyword spotting and visual wake words.
“We look forward to demonstrating the potential and ease of integrating Akida for always-on machine learning applications on embedded devices at CES,” says Rob Telson, vice president of Ecosystem and Partnerships at BrainChip.
Neuromorphic computing systems are designed to execute parallel and distributed processing, mimicking the neural structure and functioning of the human brain. BrainChip Akida is an example of such a neuromorphic computing processor, which is designed for edge applications. It operates on an event-based principle, remaining dormant until activated, thereby reducing power consumption.
New advancements in technology frequently necessitate the development of novel materials – and thanks to supercomputers and advanced simulations, researchers can bypass the time-consuming and often inefficient process of trial-and-error.
The Materials Project, an open-access database founded at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) in 2011, computes the properties of both known and predicted materials. Researchers can focus on promising materials for future technologies – think lighter alloys that improve fuel economy in cars, more efficient solar cells to boost renewable energy, or faster transistors for the next generation of computers.
To back up the decision, Waymo pointed to its safety record and history building and operating self-driving trucks on highways. (The company shuttered its self-driving truck project last year to focus on taxis.) Including highways should also decrease route times for riders—especially from the airport—with some rides taking half the time.
Although highways are simpler to navigate than city streets—where cars contend with twists, turns, signs, stoplights, pedestrians, and pets—the stakes are higher. A crash at 10 or 20 miles per hour is less likely to cause major injury than one at highway speeds. And while it’s relatively straightforward (if less than ideal) for a malfunctioning robotaxi to stop or pull to the side of the road and await human help in the city, such tactics won’t do on the highway, where it’s dangerous for cars to suddenly slow or stop.
But learning to drive on the highway will be a necessary step if robotaxis are to become an appealing, widely used product. After years of testing, the question of whether companies can build a sustainable business out of all that investment is increasingly pressing.
Some technologies are so cool they make you do a double take. Case in point: robots being controlled by rat brains. Kevin Warwick, once a cyborg and still a researcher in cybernetics at the University of Reading, has been working on creating neural networks that can control machines. He and his team have taken the brain cells from rats, cultured them, and used them as the guidance control circuit for simple wheeled robots. Electrical impulses from the bot enter the batch of neurons, and responses from the cells are turned into commands for the device. The cells can form new connections, making the system a true learning machine. Warwick hasn’t released any new videos of the rat brain robot for the past few years, but the three older clips we have for you below are still awesome. He and his competitors continue to move this technology forward – animal cyborgs are real.
The skills of these rat-robot hybrids are very basic at this point. Mainly the neuron control helps the robot to avoid walls. Yet that obstacle avoidance often shows clear improvement over time, demonstrating how networks of neurons can grant simple learning to the machines. Whenever I watch the robots in the videos below I have to do a quick reality check – these machines are being controlled by biological cells! It’s simply amazing.