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Newly Developed Cameras Use Light to See Around Corners

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Newly Developed Cameras Use Light to See Around Corners

David Lindell, a graduate student in electrical engineering at Stanford University, along with his team, developed a camera that can watch moving objects around corners. When they tested the new technology, Lindell wore a high visibility tracksuit as he moved around an empty room. They had a camera that was aimed at a blank wall away from Lindell, and the team was able to watch all of his movements with the use of a high powered laser. The laser reconstructed the images through the use of single particles of light that were reflected onto the walls around Lindell. The newly developed camera used advanced sensors and a processing algorithm. 

Gordon Wetzstein, assistant professor of electrical engineering at Stanford, spoke about the newly developed technology. 

“People talk about building a camera that can see as well as humans for applications such as autonomous cats and robots, but we want to build systems that go well beyond that,” he said. “We want to see things in 3D, around corners and beyond the visible light spectrum.” 

The camera system that was tested will be presented at the SIGGRAPH 2019 conference on August 1. 

The team has already developed similar around-the-corner cameras in the past, but this one is able to capture more light from more surfaces. It can also see wider and farther as well as monitor out-of-sight movement. They are hoping that these “superhuman vision systems” will be able to be used in autonomous cars and robots so that they will operate more safely than when controlled by a human. 

One of the team’s main goals is to keep the system practical. They use hardware, scanning and image processing speeds, and styles of imaging that are already used in autonomous car vision systems. One difference is that the new system is able to capture light bouncing off of a variety of different surfaces with different textures. Before, the systems that were used to see things outside of a camera’s line of sight were only able to do so with objects that reflected even and strong light. 

One of the developments that helped them create this technology was a laser that is 10,000 times more powerful than the one they used last year. It scans a wall on the opposite side of the point of interest. The light bounces off the wall, hits the objects in the scene, and returns back to the wall and camera sensors. The sensor is then able to pick up small specks of the laser light and sends them to an algorithm that was also developed by the team. The algorithm deciphers the specks to reconstruct the images. 

“When you’re watching the laser scanning it out, you don’t see anything,” Lindell said. “With this hardware, we can basically slow down time and reveal these tracks of light. It almost looks like magic.” 

The new system is able to scan at four frames per second and reconstruct scenes up to 60 frames per second with a computer graphics processing unit that enhance the capabilities. 

The teams drew inspiration from other fields such as seismic imaging systems. Those bounce soundwaves off underground layers of Earth, and they are able to see what’s beneath the surface. The algorithm is reconfigured to decipher light that bounces off of hidden objects. 

Matthew O’Toole, assistant professor at Carnegie Mellon University and previous postdoctoral fellow in Wetzstein’s lab, spoke about the new technology. 

“There are many ideas being used in other spaces — seismology, imaging with satellites, synthetic aperture radar — that are applicable to looking around corners,” he said. We’re trying to take a little bit from these fields and we’ll hopefully be able to give something back to them at some point.” 

The team’s next step is testing the system on autonomous research cars. They also want to see if it will be applicable in other areas such as medical imaging and to help combat problems of visual conditions that drivers encounter such as fog, rain, sandstorms, and snow. 

 

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Super-Compressible Material Developed Through AI

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Super-Compressible Material Developed Through AI

A new super-compressible material developed through AI by researchers at TU Delft can transform many of our everyday objects while still staying strong. The researchers did not conduct any experimental tests, and they created the material using only artificial intelligence and machine learning.

Miguel Bessa is the first author of the publication which appeared in Advanced Materials on October 14. 

“AI gives you a treasure map, and the scientist needs to find the treasure,” he said. 

Transforming Everyday Objects

Miguel Bessa, an assistant professor in materials science at TU Delft, got the inspiration to create this material after spending time at the California Institute of Technology. It was there, at the Space Structures Lab, where he observed a satellite structure that was able to open long solar sails from a small package. 

After seeing this, Bessa wanted to know if it was possible to design a super-compressible yet strong material and compress it into a small fraction of its volume. 

“If this was possible, everyday objects such as bicycles, dinner tables and umbrellas could be folded into your pocket,” he said. 

The Next Generation of Materials 

Bessa believes it is important that the next generation of materials be adaptive and multi-purpose with the capability to be altered. The way to do this is through structure-dominated materials, which are metamaterials that are able to exploit new geometries. This will allow the materials to have certain properties and functionalities that did not exist before. 

“However, metamaterial design has relied on extensive experimentation and a trial-and-error approach,” Bessa says. “We argue in favor of inverting the process by using machine learning for exploring new design possibilities, while reducing experimentation to an absolute minimum.”

“We follow a computational data-driven approach for exploring a new metamaterial concept and adapting it to different target properties, choice of base materials, length scales and manufacturing processes.

New Possibilities

Using machine learning, Bessa developed two designs that were different length scales for the super-compressible material developed through AI. They transformed brittle polymers into metamaterials which were a lot more lightweight and recoverable. The most important and impressive aspect of these new metamaterials is that they are super-compressible. The macro-scale design focuses on maximum compressibility, while the micro-scale is best for high strength and stiffness. 

Bessa argues that the most important part of the work is not the actual developed material, but it’s the new way of designing through the use of machine learning and artificial intelligence. This could open up possibilities that were unknown before. 

“The important thing is that machine learning creates an opportunity to invert the design process by shifting from experimentally guided investigations to computationally data-driven ones, even if the computer models are missing some information. The essential requisites are that ‘enough’ data about the problem of interest is available, and that the data is sufficiently accurate.”

Bessa believes in data-driven research in materials science and its ability to revolutionize and transform our way of life. 

“Data-driven science will revolutionize the way we reach new discoveries, and I can’t wait to see what the future will bring us.”

Taking Over From Start to Finish

These new developments show that there are areas that can be transformed by AI and machine learning that are not well-known. While it is proven that artificial intelligence will revolutionize machines, technologies, and almost every other aspect of society, it is not often acknowledged that it can also develop these completely on their own. There will be a point at which machine learning and AI will take over the design and development process from start to finish. It will be up to humans to instill certain mechanisms in these technologies so that they are compatible with our ways of life. 

 

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Company Using AI To Automate Pizza Production

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Company Using AI To Automate Pizza Production

The Seattle-based food tech company Picnic is in the process of utilizing AI to create pizzas for their customers. The deep learning algorithms used by Picnic are capable of running a pizza production line with very little oversight, analyzing the pizza at different stages with a computer-vision system.

Picnic was once called Vivid Robotics and the company has created what it dubs the first every all-purpose, automated system designed for the creation of food in the hospitality and foodservice sectors. According to TechXplore, the system is integrated with an app that customers can download and order pizzas with, customizing their toppings. The orders are given directly to the system, and the AI can oversee the creation of up to 300 12-inch or 180 18-inch pizzas every hour.

As reported by TechXplore, although the current version of the pizza production AI is just a prototype, the company expects that it will be able to create industry-ready versions of their product by the end of 2020. Picnic currently has clients lining up for their production run. The CEO of Picnic, Clayton Wood, explained that Picnic’s pizza system should cut down on labor-intensive and repetitious work, as it only requires one worker to place the dough onto the assembly line and to occasionally refill the toppings.

As many restaurants shift from dine-in operations to take-out, more food needs to be produced in a short amount of time. People are frequently frustrated and upset if their pizza takes much longer than 30 minutes. Wood thinks that Picnic can help pizza places cope with high-demand and a staffing shortage that are both creating a “great deal of stress”.

The vice president of sales at CHD Expert, a research company, Charles Chuman explained to Techxplore that there were two major factors driving the labor shortage in the food industry. One factor is the immigration constraints pushed forward by the Trump administration. The other factor is the general disruption of traditional restaurants by catering, online ordering and delivery, robotics, and AI.

Pizza itself is experiencing a partial decline in popularity in recent years as new low-carb diets have become popular and access to more diverse cuisines has expanded. While the pizza market definitely isn’t going away anytime soon, Chuman explained that the pizza industry isn’t seeing the kind of rapid growth that it was a decade ago.

Picnic has acknowledged the changing landscape of food and it is currently aiming to expand its system of automated food production out into other kinds of meals. Picnic wants to start the automated production of various pasta bowls, salads, and Mexican food using their AI-driven system.

“We think that when they’re delivering consistent, high-quality product, they’ll see increased demand for the product, as well as the savings from food waste,” explained Wood to TechXplore.

Picnic is intending to operate and upgrade their AI-driven food production system for no upfront cost, instead- charging clients a monthly fee that scales depending on the complexity of the operation. Wood expects clients to be attracted by the ease of producing constantly high-quality items.

Chuman predicts that as food production and delivery becomes quicker and easier thanks to automated systems, restaurants will need to innovate and offer different experiences or services in order to get customers to dine-in.

Picnic is far from the only company investigating how AI can be used to transform the landscape of food production and delivery. Companies like Instacart use machine-learning to predict the availability of groceries in real-time, while the start-up Sure aims to help customers decide what kind of food they should have when eating out at night. Companies like Gastrograph AI are even doing research on customer’s tastes to try and determine what kind of food and beverages they will like. Finally, companies like the Japanese start-up Calbee are using artificial intelligence to combat food waste and extend the lifespan of their products.

As AI becomes more ubiquitous and versatile, the myriad of ways it will transform the food industry will expand as well.

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China Enters the AI Chip Race with Alibaba’s Hanguang 800

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California Start-Up Cerebras Has Developed World’s Biggest Chip For AI

Alibaba announced on Tuesday that they have released their first artificial intelligence (AI) chip called the Hanguang 800. This new chip will be able to process and compute certain tasks at a faster rate. This new development brings to light China’s pursuit to get involved in the semiconductor industry and artificial intelligence. 

According to the company, they are able to complete certain tasks in five minutes that used to take one hour. They are using it within their business operations and in areas such as product search, automatic translation, and advertisement. The chip brings a new level of efficiency to these tasks that require high computing. After being used within business operations, it will eventually be available to Alibaba Cloud customers. 

According to Jeff Zhang, Alibaba’s chief technology officer, “In the near future, we plan to empower our clients by providing access through our cloud business to the advanced computing that is made possible by the chip, anytime and anywhere.”

The new chip can be critical in reducing Chinese companies’ dependence on U.S. technology. This comes at a time of heightened tensions between the United States and China. The current trade war is complicating relationships and business partnerships between tech companies from each nation.

The new chip could play a role in expanding Alibaba Cloud into other markets outside of China, where it is the current leader. Other companies such as Microsoft, Amazon, and Google are the leaders in areas like the Asia-Pacific region. 

T-Head is responsible for creating the new Hanguang 800. They are a group within Alibaba DAMO Academy, which is a global research and development initiative. Alibaba has invested more than $15 billion into the initiative. 

“The launch of Hanguang 800 is an important step in our pursuit of next-generation technologies, boosting computing capabilities that will drive both our current and emerging businesses while improving energy-efficiency,” Jeff Zhang said. 

As reported by the Financial Times, Alibaba is now joining the chip-making industry as a non-traditional creator. Others in that group include Chinese tech companies like Baidu and Tencent, along with Google and Facebook. All of these companies are in a worldwide race to develop the most powerful chips. These will play a huge role in increasing computing power and the new AI developments which are coming.

According to He Wei, a researcher at Tsinghua University’s department of precision instruments, “There’s a trend now for non-traditional chip companies to start developing chips, especially for AI chips, where there isn’t a clear leader.” He continued to say, “There will of course be some encouragement from the [Chinese] government for companies doing this.”

China is not on par with the United States when it comes to the development of high-end processor chips, but they are closer with AI chips. The Chinese government has been focusing resources on developing chips and manufacturing them within their own borders. Their goal is to create semiconductor self-sufficiency. 

One of the big forces behind these developments in chips is the use of open-source chip architecture such as RISC-V. This makes it cheaper, and there is no need to pay big licensing fees to companies that design the chips. 

These new developments show that China is going to be a major player in the chip industry. With the increasing tensions brought on by the trade war, one can expect more developments like these in the future. Whichever companies are able to develop the most powerful computing chips are going to have the upper hand in an array of industries such as artificial intelligence.

 

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