A Machine Learning Solution for Designing Materials with Desired Optical Properties

a machine learning solution for designing materials with desired optical properties


Understanding how matter interacts with light – its optical properties – is critical in a myriad of energy and biomedical technologies, such as targeted drug delivery, quantum dots, fuel combustion, and cracking of biomass. But calculating these properties is computationally intensive, and the inverse problem – designing a structure with desired optical properties – is even harder.

Now Berkeley Lab scientists have developed a machine learning model that can be used for both problems – calculating optical properties of a known structure and, inversely, designing a structure with desired optical properties. Their study was published in Cell Reports Physical Science.
“Our model performs bi-directionally with high accuracy and its interpretation qualitatively recovers physics of how metal and dielectric materials interact with light,” said corresponding author Sean Lubner.

Lubner notes that understanding radiative properties (which includes optical properties) is equally important in the natural world for calculating the impact of aerosols such as black carbon on climate change.

The machine learning model proposed in this study was trained on spectral emissivity data from nearly 16,000 particles of various shapes and materials that can be experimentally fabricated.
“Our machine learning model speeds up the inverse design process by at least two to three orders of magnitude as compared …



Amazon brings machine learning to industry, developers and contact centers

amazon brings machine learning to industry, developers and contact centers


Amazon Web Services Inc. said today it’s trying to embed more intelligence into the industrial sector with a number of new machine learning tools and services aimed at helping customers monitor their workers, the machines and equipment they operate, and the environments they work in.
The new machine learning services, plus a range of others, were introduced during the virtual AWS re:Invent 2020 event in a keynote by Chief Executive Andy Jassy (pictured).
Possibly the most important of the new services is Amazon Monitron. As the name suggests, it’s designed to monitor equipment and machines and send alerts to the engineering team if it shows any signs of breaking down. When industrial firms know their equipment is failing, they can perform maintenance before it does, at a time that suits them best, rather than having to shut down operations at an inappropriate time when it stops working.
Jassy said during his presentation that experienced engineers often know when a piece of machinery is breaking down by a change in its sound or vibration. But teams could benefit from an earlier warning, he said.
“A lot of companies either don’t have sensors, they’re not modern powerful sensors, …


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New machine learning services boost AWS RoboMaker innovation

new machine learning services boost aws robomaker innovation


Whether robots are improving the efficiency of industries, helping with medical surgeries or enabling Earth and space exploration, these devices are increasingly attracting the attention of enterprises and their software developers.
For companies interested in further developing these devices, Amazon Web Services Inc. is boosting its RoboMaker — its cloud solution for robotic developers — with new industrial machine learning services, announced this week during the virtual AWS re:Invent event.
“We continue to see the story of how processing is moving to the edge and cloud services are augmenting that processing at the edge with unique and new services,” said Roger Barga (pictured), general manager of AWS robotics and autonomous services at AWS. “Andy [Jassy, chief executive of AWS] talked about five new industrial machine learning services yesterday, which are very relevant to exactly what we’re trying to do with AWS RoboMaker.”
Barga spoke with John Furrier, host of theCUBE, SiliconANGLE Media’s livestreaming studio, during AWS re:Invent. They discussed how new ML services improve the AWS RoboMaker, the importance of testing in simulation to enable dynamic robots, and some interesting use cases. (* Disclosure below.)
Testing in simulation makes dynamic robots possible
Amazon Monitron, an end-to-end system that uses …


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AWS DeepRacer trains and engages tens of thousands of people on machine learning

aws deepracer trains and engages tens of thousands of people on machine learning


Ever since the launch of Amazon Web Services Inc.’s DeepRacer in 2018, tens of thousands of developers from around the world have been getting hands-on experience with reinforcement learning in the AWS Management Console by building their AWS DeepRacer models and competing in the AWS DeepRacer League for a chance to be crowned the AWS DeepRacer League Champion at Amazon’s yearly re:Invent conference.
“COVID has restricted our ability to have our in-person races, so we’ve really gone gangbusters with our virtual league,” said Mike Miller (pictured), general manager of AWS AI devices at AWS. “So we have monthly races for competitors that culminate in a championship at re:Invent. So this year, we’ve got over 100 competitors who have qualified and who are racing virtually with us.”
Miller spoke with Lisa Martin, host of theCUBE, SiliconANGLE Media’s livestreaming studio, during AWS re:Invent. They discussed DeepRacer’s growing contributions to machine learning, as well as new machine learning services in AWS. (* Disclosure below.)
DeepRacer helps train and engage all skill levels on machine learning
Not only has DeepRacer gotten many developers participating in the league, but AWS continues to see great traction and adoption amongst its …


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Machine Learning Might Be the Future of Dyslexia Diagnosis

machine learning might be the future of dyslexia diagnosis


Dyslexia is a congenital disorder that, according to some sources, affects about 5 to 16% of the population. Left undiagnosed or unaddressed, this condition can seriously impact a person’s quality of life.While the condition has certain treatment options that address the issue with varying degrees of success, it is not completely curable and can seriously affect a person’s opportunities in life if serious enough. To this end, it is critical to identify potential dyslexics as soon as possible to help provide the support they need to get the most out of formal educational programs.While there are some very robust and mature diagnostic tests currently available, they rely on the skill and experience of trained experts in the field to carry out. Could there be a way to automate the process using tools like machine learning? Could such systems prove to be more accurate and expedient in identifying potential dyslexics?Let’s find out. RELATED: TOP AI TRENDS TO WATCH IN 2021How is dyslexia detected?Dyslexia is a learning disorder that primarily affects a person’s ability to read and write. It is estimated that around 5 to 10% of Americans show signs of dyslexia, with symptoms including being slow to learn how to read, …


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MetricStream’s New M7 Integrated Risk Platform Leverages AWS Machine Learning

metricstream’s new m7 integrated risk platform leverages aws machine learning


SAN JOSE, Calif., Dec. 2, 2020 /PRNewswire/ — MetricStream, the independent market leader in enterprise cloud applications for Governance, Risk, and Compliance (GRC), announced enhancements to its cloud-native M7 Integrated Risk Platform that is intelligent by design, and audit, compliance, enterprise risk, third-party risk and cyber security products leveraging the power of Amazon Web Services (AWS). Learn more.
A recent IDC report states that enterprises have increased their cloud usage by 60% in 2020. The increased volume and velocity of risks, cybersecurity incidents, increasing number of compliance regulations and updates have made it critical for organizations to gain a more holistic view of governance, risk, compliance, and cyber programs. The reality of a business environment where risk has permeated all levels of the organization has prompted enterprises to enable all lines of the business to flag risk and compliance anomalies. Adoption of a cloud-native Integrated Risk Platform enables GRC practitioners to move away from a siloed approach and achieve seamless collaboration across the organization.
MetricStream’s latest innovations with AWS provide a complete lifecycle for machine learning (ML) programs by supporting Amazon SageMaker and offer a native Machine Learning Framework to build, train, and deploy ML models. MetricStream customers are leveraging Enterprise Issue Analytics and …


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New machine learning approach promises breakthrough performance for mi

new machine learning approach promises breakthrough performance for mi


The U.S. Army has reported that it has worked with the Virginia Polytechnic Institute and State University on machine learning algorithms to improve military radar systems.
According to a recent service news release, researchers from the U.S. Army Combat Capabilities Development Command, now known as DEVCOM, developed an automatic way for radars to seamlessly operate in congested and limited spectrum environments created by commercial 4G LTE and future 5G communications systems.
“Future implementations of this algorithm into Army legacy and developmental radars will provide unprecedented spectrum dominance for Soldiers,” said Army researcher Dr. Anthony Martone. “This will enable Soldiers to use their radars for problems such as tracking incoming targets while mitigating interference to maximize target detection range.”


The researchers examined how future DOD radar systems will share the spectrum with commercial communications systems. The team used machine learning to learn the behavior of ever-changing interference in the spectrum and find clean spectrum to maximize the radar performance. Once clean spectrum is identified, waveforms can be modified to best fit into the spectrum.
This research is part of a larger defense program to implement adaptive signal processing and machine learning algorithms …


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Machine learning: The new language of data and analytics

machine learning: the new language of data and analytics


Machine learning is all the rage in today’s analytical market. According to Kenneth Research, the value of machine learning is growing sharply and is expected to reach over $23B by 2023 – an annual growth rate of 43 percent between 2018-2023. IDC enforces this point predicting that worldwide spend on cognitive & AI systems, which includes machine learning, will reach $110B by 2024. Likewise, Gartner believes the business value machine learning and AI will create will be about $3.9T in 2022. With these kinds of predictions, it’s no surprise organizations want to incorporate these popular (and lucrative) methods into their analytical processes. Machine learning for data preparationMachine learning is not a new concept in the analytical lifecycle – data scientists have been using machine learning to help facilitate analytical processes and drive insights for decades. What is new is the use of machine learning for data preparation tasks to accelerate data processes and expedite analytical efforts. Here are four ways data preparation efforts can leverage machine learning for more effective and faster data reconditioning efforts:1. Data transformation recommendations built into solutions suggest how data needs to be standardized and converted to meet analytical needs. This feature can proactively look at the quality of the data …


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Trust in the Machine: The Exponential Rise of Human AI in Banking

trust in the machine: the exponential rise of human ai in banking


By Justin Bercich, PhD, Head of AI, Lucinity

Our entire lives, both inside and outside work, are dictated by the decisions that we make. In the main, we’re hardwired to subconsciously learn from our mistakes, to avoid bad decisions and to question how we’d improve our decision-making if faced with similar scenarios in the future.
And that’s exactly the same concept for machine learning (ML). AI (artificial intelligence) brains are, by and large, programmed the same way as a human brain. Advanced AI and deep learning are built to learn from human decisions, ask the same questions and reinforce the same principles. And the more seamlessly human that AI becomes, the more we can connect and relate to this incredible technology and the more we can trust it to sharpen and improve our decision-making and, ultimately, our lives.
Open source will level the playing field.
Put simply, a machine-learning algorithm watches—identifying the good decisions and learning from the bad ones—just like a human brain. And that’s the exhilarating maturity curve we now find ourselves accelerating along, one built on ML algorithms and AI models that are beginning to operate, inexorably, in the same way …


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