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Engineers Create AI From Only a Sheet of Glass

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A group of engineers and scientists at the University of Wisconsin-Madison have developed AI that is made from only a piece of glass with no sensors, circuits, or power sources. This is based on the same technology used for facial recognition in today’s smartphones, but it is much more simple. Professor of Electrical and Computer Engineering, Zongfu Yu, commented on the new research that has been published in Photonics Research. 

“We’re using optics to condense the normal setup of cameras, sensors, and deep neural networks into a single piece of thin glass.” 

As of now, AI uses a lot of computational resources and energy for things like facial recognition. With this new technology, the simple sheet of glass could possibly do the same thing but with absolutely no power source. 

This exciting new AI is multiple pieces of glass that look like translucent squares. Within this glass are bubbles and other small impurities that are spread throughout certain locations. These work by bending light so that it can detect certain images. Instead of using codes, this glass is able to work with analog material only. 

“We’re accustomed to digital computing, but this has broadened our view…The wave dynamics of light propagation provide a new way to perform analog artificial neural computing.” says Zongfu Yu. 

Since this new type of AI glass does not rely on any type of power, circuits, or internet, it can last much longer. According to the engineers and scientists, there’s no reason to believe that one piece of glass couldn’t last for thousands of years.

According to Yu, “We could potentially use the glass as a biometric lock, tuned to recognize only one person’s face…Once built, it would last forever without needing power or internet, meaning it could keep something safe for you even after thousands of years.” 

If implemented into something like a smartphone, this technology would drastically improve the battery life since the phone would no longer have to dedicate large amounts of energy to things like facial recognition. To top it all off, the piece of glass is an inexpensive creation. 

The engineers and researchers at the University of Wisconsin-Madison figured out a way to take glass pieces and use them to identify written numbers. Light came from one of the written numbers and into the sheet of glass On the other side, there were nine spots that correspond to different digits. The light then focused on one of those as it moved through the glass. The engineers and scientists kept adjusting the location of the bubbles and impurities by small movements. After thousands of alterations, the glass was able to detect when a handwritten #3 was changed to a #8. 

The engineers now want to push this further and try to get it to work with things like facial recognition. This technology could really change the way AI operates, and it could play a big role in the development of more complex systems in the future. 

According to Ming Yuan, a Professor of Statistics at Columbia University who worked with the researchers, “The true power of this technology lies in its ability to handle much more complex classification tasks instantly without an energy consumption…These tasks are the key to create artificial intelligence; to teach driverless cars to recognize a traffic signal, to enable voice control in consumer devices, among numerous other examples.” 

If this technology keeps getting developed further, it could really change the way some of our AI operates. The possibilities are endless when you have a simple sheet of glass that can perform the complex actions of something like facial recognition technology. 

 

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Alex McFarland is a historian and journalist from the United States who covers developments in AI around the world.

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Intel’s New Neuromorphic Chips are 1,000 Times Faster Than Normal CPUs

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Intel’s new system codenamed Pohoiki Beach will be at the Consumer Electronics Show (CES) in Las Vegas. The device is built from 64 Loihi research chips, and the goal is for it to simulate the human brain when it comes to learning ability and energy efficiency. These neuromorphic chips are a simpler version of the way neurons and synapses function in the brain. 

Rich Uhlig, managing director of Intel Labs, spoke on the new technology. 

“We are impressed with the early results demonstrated as we scale Loihi to create more powerful neuromorphic systems. Pohoiki Beach will now be available to more than 60 ecosystem partners, who will use this specialized system to solve complex, compute-intensive problems.” 

The new AI neuromorphic chip can perform data-crunching tasks 1,000 times faster than normal processors like CPUs and GPUs while using a lot less power. 

The way it is based on brain neurons is not something entirely new. Many AI algorithms simulate neural networks in their programs. They use parallel processing for recognizing objects in images and words in speech. The new neuromorphic chips put these neural networks into silicon. While they are less flexible and powerful than some of the best general-purpose chips, they really perform when specialized in specific tasks. The new AI chip from Intel is 10,000 times more efficient than general processors. Since they are so energy efficient, the technology will be ideal for mobile devices, vehicles, industrial equipment, cybersecurity, and smart homes. AI researchers have already begun to use the system for things like improving prosthetic limbs so that they can adapt better to uneven ground, as well as creating digital maps to be used by self-driving cars. 

Chris Eliasmith, co-CEO of Applied Brain Research and professor at the University of Waterloo, is one of the several researchers using the new technology. 

“With the Loihi chip we’ve been able to demonstrate 109 times lower power consumption running a real-time deep learning benchmark compared to a GPU, and 5 times lower power consumption compared to specialized IoT interface hardware…Even better, as we scale the network up by 50 times, Loihi maintains real-time performance results and uses only 30 percent more power, whereas the IoT hardware uses 500 percent more power and is no longer real-time,” Chris Eliasmith said. 

Konstantinos Michmizos is a professor of Rutgers University, and his lab does work with SLAM which will be presented at the International Conference on Intelligent Robots and Systems (IROS) in November. 

“Loihi allowed us to realize a spiking neural network that imitates the brain’s underlying neural representations and behavior. The SLAM solution emerged as a property of the network’s structure. We benchmarked the Loihi-run network and found it to be equally accurate while consuming 100 times less energy than a widely used CPU-run SLAM method for mobile robots,” he said. 

As of right now, Pohoiki Beach is an 8 million neuron system. Rich Uhlig, head of Intel Labs, thinks that the company will be able to create a system that is able to simulate 100 million neurons by the end of 2019. This new technology will be able to be used by researchers for a wide range of things such as improvement of robot arms. These new developments and research are leading to what will likely be the commercialization of neuromorphic technology. 

According to the company, “Later this year, Intel will introduce an even larger Loihi system named Pohoiki Springs, which will build on the Pohoiki Beach architecture to deliver an unprecedented level of performance and efficiency for scaled-up neuromorphic workloads.” 

 

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AI Brings New Potential for Prosthetics with 3D-Printed Hand

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A new 3D-printed prosthetic hand paired with AI has been developed by Biological Systems Lab at Hiroshima University in Japan. This new technology can dramatically change the way prosthetics work. It is another step in the direction of combining both the physical human body with artificial intelligence, something that we are most definitely heading towards. 

The 3D-printed prosthetic hand has been paired with a computer interface to create the lightest and cheapest model yet. This version is the most reactive to motion intent that we have seen. Before the current model, they were normally made from metal which caused them to be both heavier and more expensive. The way this new technology works is by a neural network that is trained to recognize certain combined signals, these signals have been named “muscle synergies” by the engineers working on the project. 

The prosthetic hand has five independent fingers that can make complex movements. Compared to previous models, these fingers are able to move around more as well as all at the same time. These developments make it possible for the hand to be used for tasks like holding items such bottles and pens. Whenever the user of the technology wants to move the hand or fingers in a certain way, they only have to imagine it. Professor Toshio Tsuji of the Graduate School of Engineering at Hiroshima University explained the way a user can move the 3D-printed hand. 

“The patient just thinks about the motion of the hand and then the robot automatically moves. The robot is like a part of his body. You can control the robot as you want. We will combine the human body and machine like one living body.”

The 3D-printed hand works when electrodes in the prosthetic measures electrical signals that come from nerves through the skin. It can be compared to the way ECG and heart rates work. The measured signals are then sent to a computer within five milliseconds at which point the computer recognizes the desired movement. The computer then sends the signal back to the hand. 

There is a neural network that helps the computer learn the different complex movements, it has been named Cybernetic Interface. It can differentiate between the 5 fingers so that there can be individual movements. Professor Tsuji also spoke on this aspect of the new technology.

“This is one of the distinctive features of this project. The machine can learn simple basic motions and then combine and then produce complicated motions.”

The technology was tested among seven people, and one of the seven was an amputee who has been wearing a prosthesis for 17 years. The patients performed daily tasks, and they had a 95% accuracy rate for single simple motion and a 93% rate for complex movements. The prosthetics that were used in this specific test were only trained for 5 different movements with each finger; there could be many more complex movements in the future. With just these 5 trained movements, the amputee patient was able to pick up and put down things like bottles an notebooks. 

There are numerous possibilities for this technology. It could decrease cost while providing extremely functional prosthetic hands to amputee patients. There are still some problems like muscle fatigue and the capability of software recognizing many complex movements. 

This work was completed by Hiroshima University Biological Systems Engineering Lab along with patients from the Robot Rehabilitation Center in the Hygo Institute of Assistive Technology, Kobe. The company Kinki Gishi was responsible for creating the socket which was used on the arm of the amputee patient. 

 

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In a First, Facebook’s Superhuman Poker Beats World-Class Poker Professionals

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Can a machine or automated systems bluff?

A few months ago, the answer would probably be a negation. But not anymore!

In another incredible invention, an AI system can bluff better than any human. And, this time it’s Facebook and CMU’s superhuman AI which has bettered human professionals in the challenging game of poker.

Since, its inception, artificial intelligence robots have beaten humans in the game of chess, checkers and two-player poker. But, the AI bot developed by researchers at Carnegie Mellon University and Facebook AI lab has managed to defeat human professionals in a multi-player game of poker – a task which earlier was considered unachievable. Their bot called as Pluribus defeated some of the top players in the world in a series of no limit six-person poker game.

In 12 days and 10,000 hands, Pluribus played against top 12 poker players in two different environments. In one environment, the AI bot was up against 5 pros human players and in the second one, 5 AI bots played against one human player (the AI bots were unable to reach to a decisive conclusion in the second environment). In an unbelievable turn of events, Pluribus made around $1000 per hour, which is a considerable margin victory.

“It’s safe to say we’re at a superhuman level and that’s not going to change,” said Noam Brown, a research scientist at Facebook AI Research and co-creator of Pluribus.

Pluribus win is seen as a revolutionary landmark in AI because the earlier poker programs were designed with an altogether different algorithm. The previous bots focused only on two players, where it was easy to understand the strategy to beat the other player. But, the six-player poker game involves hell lot of variables and Pluribus outperforming them is a landmark achievement for the tech sector. Irrespective of the number of players, poker is probably one of the toughest game to program because unlike chess there is a lot of hidden information.

“Pluribus achieved superhuman performance at multiplayer poker, which is a recognized milestone in artificial intelligence and in game theory that has been open for decades,” said Tuomas Sandholm, a professor at Carnegie Mellon School of Computer Science, who helped develop the system.

What is more surprising is that during the game, Pluribus came out with completely unexpected moves. Because the program was built from scratch, it had no access to historical data or strategies.  The intelligent program picked up the game and made bluffs bigger than anyone else. During the entire game, Pluribus kept changing its strategy even when it was dealt with the same hand. This is surprising because picking up a pattern in a game of poker has always been impossible for humans.

To stay in the game, Pluribus initially followed the limping strategy, which is making the smallest bet. As the bot became a strong player, it eventually stopped limping – a move which has surprised many world-class poker players.

With professionals being beaten by the bots, the entire poker community is excited because the AI program is likely to teach new strategies to the players. The penetration of these strategies is expected to change the whole landscape of the online poker community.

The superhuman level capabilities displayed by the AI bot are likely to be transferrable to other situations such as cyber-security, financial negotiation and even fraud prevention. “It can even help navigate traffic with self-driving cars,” said Brown.

This groundbreaking invention is likely to fuel many revolutionary AI programs. The future of AI is definitely on the sunny side.

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