When tuned up, old algorithms can match the abilities of their successors.
Category: robotics/AI – Page 1,947
AI And The Digital Mine
When you think of the words “data” and “mine”, no doubt the idea of data mining comes first. However, just as much as we find value in mining the rich resources of data, so too can we apply the advanced techniques for dealing with data to real-world mining — that is, extracting natural resources from the earth. The world is just as dependent on natural resources as it is data resources, so it makes sense to see how the evolving areas of artificial intelligence and machine learning have an impact on the world of mining and natural resource extraction.
Mining has always been a dangerous profession, since extracting minerals, natural gas, petroleum, and other resources requires working in conditions that can be dangerous for human life. Increasingly, we are needing to go to harsher climates such as deep under the ocean or deep inside the earth to extract the resources we still need. It should come as little surprise then that mining and resource extraction companies are looking to robotics, autonomous systems, and AI applications of all sorts to minimize risk, maximize return, and also lessen the environmental impact that mining has on our ecosystem.
On a recent AI Today podcast episode, Antoine Desmet of mining technology and equipment company Komatsu shared how they’re using advanced forms of AI, automation, and robotics to make an impact on the organization’s operations. Antoine has an interesting background, starting his career as a telecom engineer and receiving a Ph.D in neural network engineering. After getting his Ph.D, he returned to Komatsu and started working in surface analytics. He states that at the time there was a lot of data to work with, but very few analytics in place. He decided to start implementing machine learning and in the last few years his company has seen significant growth through this approach, with his data science team growing from just one person to ten people.
Why the Military Is Building a Tunneling Earthworm
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.
Algorithm tracks down buried treasure among existing compounds
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.’
BRIAN KENNEDY — Reversing Human Aging (#0004)
Sirtuins, telomeres, A.I. experiment with vitamin A and personalized medicine, a bit of everything here.
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Executing low-power linear computations using nonlinear ferroelectric memristors
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.
