The Defense Advanced Research Projects Agency (DARPA), the Pentagon’s cutting-edge research and development branch, is funding one of the oddest robotic concepts yet: a robot that mimics an earthworm to dig underground tunnels. It’s all part of an effort to demonstrate robotic tunneling technologies that will provide a secure way of resupplying U.S. Army troops in battle zones.
A machine-learning algorithm has been developed by scientists in Japan to breathe new life into old molecules. Called BoundLess Objective-free eXploration, or Blox, it allows researchers to search chemical databases for molecules with the right properties to see them repurposed. The team demonstrated the power of their technique by finding molecules that could work in solar cells from a database designed for drug discovery.
Chemical repurposing involves taking a molecule or material and finding an entirely new use for it. Suitable molecules for chemical repurposing tend to stand apart from the larger group when considering one property against another. These materials are said to be out-of-trend and can display previously undiscovered yet exceptional characteristics.
‘In public databases there are a lot of molecules, but each molecule’s properties are mostly unknown. These molecules have been synthesised for a particular purpose, for example drug development, so unrelated properties were not measured,’ explains Koji Tsuda of the Riken Centre for Advanced Intelligence and who led the development of Blox. ‘There are a lot of hidden treasures in databases.’
Sirtuins, telomeres, A.I. experiment with vitamin A and personalized medicine, a bit of everything here.
https://facebook.com/LongevityFB https://instagram.com/longevityyy https://twitter.com/Longevityyyyy https://linkedin.com/company/longevityy
- Please also subscribe and hit the notification bell and click “all” on these YouTube channels:
https://youtube.com/Transhumania
https://youtube.com/BrentNally
https://youtube.com/EternalLifeFan
https://youtube.com/MaxEternalLife
https://youtube.com/LifespanIO
https://youtube.com/LifeXTenShow
https://youtube.com/BitcoinComOfficialChannel
https://youtube.com/RogerVer
https://youtube.com/RichardHeart
https://youtube.com/sciVive
Brian Kennedy’s Lab Website:
Researchers at Toshiba Corporate R&D Center and Kioxia Corporation in Japan have recently carried out a study exploring the feasibility of using nonlinear ferroelectric tunnel junction (FTJ) memristors to perform low-power linear computations. Their paper, published in Nature Electronics, could inform the development of hardware that can efficiently run artificial intelligence (AI) applications, such as artificial neural networks.
“We all know that AI is slowly becoming an important part of many business operations and consumers’ lives,” Radu Berdan, one of the researchers who carried out the study, told TechXplore. “Our team’s long-term objective is to develop more efficient hardware in order to run these very data-intensive AI applications, especially neural networks. Using our expertise in novel memory development, we are targeting (among others) memristor-based in-memory computing, which can alleviate some of the efficiency constraints of traditional computing systems.”
Memristors are non-volatile electrical components used to enhance the memory of computer systems. These programmable resistors can be packed neatly into small but computationally powerful crossbar arrays that can be used to compute the core operations of artificial neural networks, acting as a memory and reducing their access to external data, thus ultimately enhancing their energy efficiency.
Advances in artificial intelligence could soon make creating convincing fake audio and video – known as “deepfakes” – relatively easy. Making a person appear to say or do something they did not has the potential to take the war of disinformation to a whole new level. Scroll down for more on deepfakes and what the US government is doing to combat them.
The ability to estimate the physical properties of objects is of key importance for robots, as it allows them to interact more effectively with their surrounding environment. In recent years, many robotics researchers have been specifically trying to develop techniques that allow robots to estimate tactile properties of objects or surfaces, which could ultimately provide them with skills that resemble the human sense of touch.
Building on previous research, Matthew Purri, a Ph.D. student specializing in Computer Vision and AI at Rutgers University, recently developed a convolutional neural network (CNN)-based model that can estimate tactile properties of surfaces by analyzing images of them. Purri’s new paper, pre-published on arXiv, was supervised by Kristin Dana, a professor of Electrical Engineering at Rutgers.
“My previous research dealt with fine-grain material segmentation from satellite images,” Purri told TechXplore. “Satellite image sequences provide a wealth of material information about a scene in the form of varied viewing and illumination angles and multispectral information. We learned how valuable multi-view information is for identifying material from our previous work and believed that this information could act as a cue for the problem of physical surface property estimation.”