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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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Artificial Intelligence Used to Prevent Icebergs from Disrupting Shipping

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Artificial Intelligence Used to Prevent Icebergs from Disrupting Shipping

Experts at the University of Sheffield have developed a combination of control systems and artificial intelligence (AI) forecasting models to prevent icebergs from drifting into busy shipping regions. 

Through the use of a recently published control systems model, experts were able to predict the movement of icebergs. In 2020, between 479 and 1,015 icebergs are expected to drift into waters south of 48°, an area that sees great shipping movement between Europe and north-east North America. Last year, there were a total of 1,515 observed in that same area.

The team relied on experimental artificial intelligence analysis in order to independently support the number of icebergs predicted. They also discovered a rapid early rise in the number of icebergs present in this area during the ice season, which runs from January to September. 

The International Ice Patrol (IIP) is supplied with the findings, and they use the information to figure out the best use of resources for better ice forecasts during the season. According to the seasonal forecast, ships in the north-west Atlantic will be less likely to encounter an iceberg compared to last year.

Icebergs cause serious problems and shipping risks in the north-west Atlantic. Records show that there have been collisions and sinkings dating back to the 17th century. The IIP was established in 1912 after the sinking of the Titanic, and its job is to observe sea ice and conditions in the north-west Atlantic and warn of potential dangers.

The risk of icebergs to shipping changes each year. One year can see no icebergs crossing the area, while another year can see over 1,000. This makes it difficult to predict, but in general, there has been a higher amount detected since the 1980s. 

2020 is the first year that artificial intelligence is being used to forecast the icebergs in the area, as well as the rate of change across the season.

The model was developed by a team led by Professor Grant Bigg at the University of Sheffield, and it was funded by insurance firm AXA XL’s Ocean Risk Scholarships Programme. There is a control systems model as well as two machine learning tools that are used. 

Data related to the surface temperature of the Labrador Sea is analyzed, as well as variations in atmospheric pressure in the North Atlantic and the surface mass balance of the Greenland ice sheet.

The foundation control systems approach had an 80 percent accuracy level when tested against data on iceberg numbers for the seasons between 1997 and 2016. 

According to some of Professor Bigg’s earlier research, the variation of the number of icebergs drifting into the region was due to variable calving rates from Greenland. However, the regional climate and ocean currents are the biggest factors. Higher numbers of icebergs appear when there are colder sea surface temperatures and stronger northwesterly winds. 

Grant Brigg is a Professor of Earth System Science at the University of Sheffield.

“We have issued seasonal ice forecasts to the IIP since 2018, but this year is the first time we have combined the original control system model with two artificial intelligence approaches to specific aspects of the forecast. The agreement in all three approaches gives us the confidence to release the forecast for low iceberg numbers publicly this year—but it is worth remembering that this is just a forecast of iceberg conditions, not a guarantee, and that collisions between ships and icebergs do occur even in low ice years.”

According to Mike Hicks of the International Ice Patrol,  “The availability of a reliable prediction is very important as we consider the balance between aerial and satellite reconnaissance methods.”

Dr. John Wardman is a Senior Science Specialist in the Science and Natural Perils team at AXA XL. 

“The impact of sea level rise on coastal exposure and a potential increase in Arctic shipping activity will require a greater number and diversity of risk transfer solutions through the use of re/insurance products and other ‘soft’ mitigation strategies. The insurance industry is keeping a keen eye on the Arctic, and this model is an important tool in helping the industry identify how or when the melting Greenland Ice Sheet will directly impact the market.”

 

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Cerebras Has the “World’s Fastest AI Computer”

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Cerebras Has the “World’s Fastest AI Computer”

According to the startup Cerebras Systems, the CS-1 is the world’s most powerful AI computer system. It is the latest attempt to create the best supercomputer, and it has been accepted by the U.S. federal government’s supercomputing program. 

The CS-1 uses an entire wafer instead of a chip, and their computer design has many little cores across the wafer. There are over 1.2 trillion transistors across the cores of one wafer, which is a lot more than the 10 million that are often on one chip of a processor. If that wasn’t enough, the CS-1 supercomputer has six of the Cerebras wafers in one system. They are called a Wafer Scale Engine. 

Cerebras’ first CS-1 was sent to the U.S. Department of Energy’s Argonne National Laboratory. The 400,000 cores will be used to work on extremely difficult AI computing problems like studying cancer drug interactions. The Argonne National Lab is one of the world’s top buyers of supercomputers. 

The CS-1

The CS-1 is programmable with the Cerebras Software Platform and can be used with existing infrastructure, according to the startup. The Wafer Scale Engine (WSE) has more silicon area than the biggest graphics processing unit, and the 400.000 Sparse Linear Algebra Compute (SLAC) cores are flexible, programmable, and optimized for neural networks. 

The CS-1 has a copper-colored block, or cold plate, that conducts heat away from the giant chip. Pipes of cold water are responsible for cooling, and fans blow cold air to carry heat away from the pipes. 

According to many, the big breakthrough is the dashboard. Argonne has been constantly working on spreading out a neural net over large numbers of individual chips, making them better to program compared to other supercomputer machines like Google’s Pod. 

The Cerebras CS-1 is basically one giant, self-contained chip where the neural network can be placed. A program has been developed to optimize the way math operations of a neural network are spread across the WSE’s circuits. 

According to Rick Stevens, Argonne’s associate laboratory director for computing, environment, and life sciences, “We have tools to do this but nothing turnkey the way the CS-1 is, [where] it’s all done automatically.”

Built From the Ground Up

According to Cerebras, they are the only startup to build a dedicated system from the ground up. In order to achieve its amazing performance, Cerebras optimized every aspect of chip design, system design, and software of the CS-1 system. This allows the CS-1 to complete AI tasks that normally take months in minutes. 

The supercomputer machine also greatly reduces training time, and single image classification can be completed in microseconds. 

According to an interview given to the technology website VentureBeat, CEO of Cerebras Andrew Feldman said, “This is the largest square that you can cut out of a 300 millimeter wafer.” he continued, “Even though we have the largest and fastest chip, we know that an extraordinary processor is not necessarily sufficient to deliver extraordinary performance. If you want to deliver really fast performance, you need to build a system. And you can’t take a Ferrari engine and put it in a Volkswagen to get Ferrari performance. What you do is you move the bottlenecks if you want to get a 1,000 times performance gain.”

After the introduction of the CS-1 system, Cerebras have positioned themselves as one of the leaders in the supercomputer industry. Their contribution will undoubtedly have a major impact in solving some of the world’s most pressing AI challenges. These systems are drastically decreasing the time it will take to tackle many problems.

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Microsoft Partners with Startup Graphcore to Develop AI Chips

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Microsoft Partners with Startup Graphcore to Develop AI Chips

Microsoft hopes that its Azure cloud platform will catch up in popularity with Amazon and Google, so as Wired reports, it has partnered with a British startup Graphcore to come up with a new computer chip that would be able to sustain all-new artificial intelligence developments.

As Wired notes, Bristo, UK startup Graphcore “has attracted considerable attention among AI researchers—and several hundred million dollars in investment—on the promise that its chips will accelerate the computations required to make AI work.” Since its inception in 2016, this is the first time that the company is publicly coming up with its chips and testing results.

Microsoft’s invested in Graphcore in December 2018 “as a part of a $200 million funding round”, as it wants to stimulate the use of its cloud services to a growing number of customers that use AI applications.

Graphcore itself designed its chips from scratch “to support the calculations that help machines to recognize facesunderstand speechparse languagedrive cars, and train robots.” The company expects that its chips will be used by “companies running business-critical operations on AI, such as self-driving car startups, trading firms, and operations that process large quantities of video and audio, as well as those working on next-generation AI algorithms.”

According to the benchmarks published by Microsoft and Graphcore on November 13, 2019, “the chip matches or exceeds the performance of the top AI chips from Nvidia and Google using algorithms written for those rival platforms. Code is written specifically for Graphcore’s hardware maybe even more efficient.”

The two companies also stated that “certain image-processing tasks work many times faster on Graphcore’s chips,” and that “ they were able to train a popular AI model for language processing, called BERT, at rates matching those of any other existing hardware.”

Moor Insights AI chip specialist Karl Freund is of the opinion that the results of the new chip show that it is “cutting-edge but still flexible,”  and that “they’ve done a good job making it programmable,” an extremely hard thing to do.

Wired further adds that Nigel Toon, co-founder, and CEO of Graphcore, says the companies began working together a year after his company’s launch, through Microsoft Research Cambridge in the UK. He also told the publication that his company’s chips are especially well-suited to tasks that involve very large AI models or temporal data. Also, one customer in finance supposedly saw a 26-fold performance boost in an algorithm used to analyze market data thanks to Graphcore’s hardware.

Some other, smaller companies used this occasion to announce that “they are working with Graphcore chips through Azure.” This includes Citadel, which will use the chips to analyze financial data, and Qwant, a European search engine that wants the hardware to run an image-recognition algorithm known as ResNext.

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