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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.

Day 4: The kids learnt how to build a robotic arm using breadboard, servo motor, batteries and sensor in the Artificial Intelligence Hub boot camp tagged Introduction to Robotics 1.0 #TakeOver.

Kelvin Dafiaghor added a new photo.

Google and Stanford University researchers propose a technique that has AI predict the steps required to reach a goal state.

It is common knowledge that neural networks are very powerful and they can be used for almost any statistical learning problem with great results. But have you thought about why is this the case? Why is this method more powerful in most scenarios than many other algorithms?

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.”

Eric Klien

A liquid metal lattice that can be crushed but returns to its original shape on heating has been developed by Pu Zhang and colleagues at Binghamton University in the US. The material is held together by a silicone shell and could find myriad uses including soft robotics, foldable antennas and aerospace engineering. Indeed, the research could even lead to the creation of a liquid metal robot evoking the T-1000 character in the film Terminator 2.

The team created the liquid metal lattice using a special mixture of bismuth, indium and tin known as Field’s alloy. This alloy has the relatively unusual property of melting at just 62 °C, which means it can be liquefied with just hot water. Field’s alloy already has several applications – including as a liquid-metal coolant for advanced nuclear reactors.

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Oxford and Cambridge are carefully assessing the threat presented by intelligent machines of the future.