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.“
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.
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 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.
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 faces, understand speech, parse language, drive 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.
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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