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Category: robotics/AI – Page 1,512

Computer Chips That Imitate the Brain

A multi-institutional collaboration, which includes the U.S. Department of Energy’s (DOE) Argonne National Laboratory, has created a material that can be used to create computer chips that can do just that. It achieves this by using so-called “neuromorphic” circuitry and computer architecture to replicate brain functions. Purdue University professor Shriram Ramanathan led the team.

“Human brains can actually change as a result of learning new things,” said Subramanian Sankaranarayanan, a paper co-author with a joint appointment at Argonne and the University of Illinois Chicago. “We have now created a device for machines to reconfigure their circuits in a brain-like way.”

With this capability, artificial intelligence-based computers might do difficult jobs more quickly and accurately while using a lot less energy. One example is analyzing complicated medical images. Autonomous cars and robots in space that might rewire their circuits depending on experience are a more futuristic example.

Written all over your face: An improved AI model for recognizing facial expression

When it comes to our state of mind and emotions, our faces can be quite telling. Facial expression is an essential aspect of nonverbal communication in humans. Even if we cannot explain how we do it, we can usually see in another person’s face how they are feeling. In many situations, reading facial expressions is particularly important. For example, a teacher might do it to check if their students are engaged or bored, and a nurse may do it to check if a patient’s condition has improved or worsened.

Thanks to advances in technology, computers can do a pretty good job when it comes to recognizing faces. Recognizing facial expressions, however, is a whole different story. Many researchers working in the field of artificial intelligence (AI) have tried to tackle this problem using various modeling and classification techniques, including the popular convolutional (CNNs). However, facial expression recognition is complex and calls for intricate neural networks, which require a lot of training and are computationally expensive.

In an effort to address these issues, a research team led by Dr. Jia Tian from Jilin Engineering Normal University in China has recently developed a new CNN model for facial expression recognition. As described in an article published in the Journal of Electronic Imaging, the team focused on striking a good balance between the training speed, memory usage, and recognition accuracy of the model.

Astronauts on Space Station Explore Artificial Intelligence and Human Nervous System

On Tuesday, July 5, space physics and human studies dominated the science agenda aboard the International Space Station. The Expedition 67 crew also reconfigured a US airlock and put a new 3D printer through its paces.

The lack of gravity in space impacts a wide range of physics revealing new phenomena that researchers are studying to improve life for humans on and off the Earth. One such project uses artificial intelligence to adapt complicated glass manufacturing processes in microgravity with the goal of benefitting numerous Earth-and space-based industries. On Tuesday afternoon, NASA

Established in 1958, the National Aeronautics and Space Administration (NASA) is an independent agency of the United States Federal Government that succeeded the National Advisory Committee for Aeronautics (NACA). It is responsible for the civilian space program, as well as aeronautics and aerospace research. Its vision is “To discover and expand knowledge for the benefit of humanity.” Its core values are “safety, integrity, teamwork, excellence, and inclusion.”

Machines with Minds? The Lovelace Test vs. the Turing Test

Selmer Bringsjord, and his colleagues have proposed the Lovelace test as a substitute for the flawed Turing test. The test is named after Ada Lovelace.

Bringsjord defined software creativity as passing the Lovelace test if the program does something that cannot be explained by the programmer or an expert in computer code.2 Computer programs can generate unexpected and surprising results.3 Results from computer programs are often unanticipated. But the question is, does the computer create a result that the programmer, looking back, cannot explain?

When it comes to assessing creativity (and therefore consciousness and humanness), the Lovelace test is a much better test than the Turing test. If AI truly produces something surprising which cannot be explained by the programmers, then the Lovelace test will have been passed and we might in fact be looking at creativity. So far, however, no AI has passed the Lovelace test.4 There have been many cases where a machine looked as if it were creative, but on closer inspection, the appearance of creative content fades.

Nation emerging as global pioneer in AI technology

China is emerging as a pioneer in artificial intelligence as it makes strides in filing AI patents and experimenting with the latest AI technology to power industrial applications, industry experts said.

Their comments came after a Stanford University report that shows China filed more than half of all the world’s AI patent applications last year and Chinese researchers produced about one-third of AI journal papers and AI citations in 2021.

Wu Hequan, an academician at the Chinese Academy of Engineering, said China has been working to build a solid foundation to support its AI economy and is making significant contributions to AI globally.

New AI-powered app could boost smartphone batteries by 30 per cent

A cutting-edge AI development that could boost smartphone battery life by 30 percent and shave countless kilowatts from energy bills will be unveiled to technology giants. The ground-breaking University of Essex-developed work has been rolled into an app called EOptomizer—which will be demonstrated to expert researchers and designers as well as major manufacturing companies like Nokia and Huawei.

It is hoped the EOptomizer app will be adapted across the industry and help drive down , by making consumers’ goods last longer.

It will do this by using software to dramatically increasing efficiency and reliability in phones, tablets, cars, smart fridges and computers’ batteries—delaying when consumers need to buy carbon-footprint-producing replacements. The event—which takes place in Robinson College, in Cambridge, on 11July—will showcase the impact EOptomizer could have across the globe.

Photonic synapses with low power consumption and high sensitivity

Neuromorphic photonics/electronics is the future of ultralow energy intelligent computing and artificial intelligence (AI). In recent years, inspired by the human brain, artificial neuromorphic devices have attracted extensive attention, especially in simulating visual perception and memory storage. Because of its advantages of high bandwidth, high interference immunity, ultrafast signal transmission and lower energy consumption, neuromorphic photonic devices are expected to realize real-time response to input data. In addition, photonic synapses can realize non-contact writing strategy, which contributes to the development of wireless communication.

The use of low-dimensional materials provides an opportunity to develop complex brain-like systems and low-power memory logic computers. For example, large-scale, uniform and reproducible transition metal dichalcogenides (TMDs) show great potential for miniaturization and low-power biomimetic device applications due to their excellent charge-trapping properties and compatibility with traditional CMOS processes. The von Neumann architecture with discrete memory and processor leads to high power consumption and low efficiency of traditional computing. Therefore, the sensor-memory fusion or sensor-memory-processor integration neuromorphic architecture system can meet the increasingly developing demands of big data and AI for and high performance devices. Artificial synaptic devices are the most important components of neuromorphic systems. The performance evaluation of synaptic devices will help to further apply them to more complex artificial neural networks (ANN).

Chemical vapor deposition (CVD)-grown TMDs inevitably introduce defects or impurities, showed a persistent photoconductivity (PPC) effect. TMDs photonic synapses integrating synaptic properties and optical detection capabilities show great advantages in neuromorphic systems for low-power visual information perception and processing as well as brain memory.

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