I have two Linux Foundation open source releases today. We connected through the EdgeX Foundry IoT platform. Then we discovered we had many common interests. One of the releases touches on a fundamental element of commerce and collaboration—trust. The Linux Foundation is an open source project. Open source is a powerful software development model. But, it takes many dedicated and talented people to accomplish the task. Why do people work on open source? LF conducted a survey to get an idea.
The Janssen Project Takes on World’s Most Demanding Digital Trust Challenges at Linux Foundation
New Janssen Project seeks to build the world’s fastest and most comprehensive cloud native identity and access management software platform
The Linux Foundation announced the Janssen Project, a cloud native identity and access management software platform that prioritizes security and performance for our digital society. Janssen is based on the Gluu Server and benefits from a rich set of signing and encryption functionalities. Engineers from IDEMIA, F5, BioID, Couchbase, and Gluu will make up the Technical Steering Committee.
Online trust is a fundamental challenge to our digital society. The Internet has connected us. But at the same time, it has undermined trust. Digital identity starts with a connection between a person and a digital device. Identity software conveys the integrity of that connection from the user’s device to a complex web of backend services. Solving the challenge of digital identity is foundational to achieving trustworthy online security.
While other identity and access management platforms exist, the Janssen Project seeks to tackle the most challenging security and performance requirements. Based on the latest code that powers the Gluu Server–which has passed more OpenID self-certification tests then any other platform–Janssen starts with a rich set of signing and encryption functionality that can be used for high assurance transactions. Having shown throughput of more than one billion authentications per day, the software can also handle the most demanding requirements for concurrency thanks to Kubernetes auto-scaling and advances in persistence.
“Trust and security are not competitive advantages–no one wins in an insecure society with low trust,” said Mike Schwartz, Chair of the Janssen Project Technical Steering Committee. “In the world of software, nothing builds trust like the open source development methodology. For organizations who cannot outsource trust, the Janssen Project strives to bring transparency, best practices and collective governance to the long term maintenance of this important effort. The Linux Foundation provides the neutral and proven forum for organizations to collaborate on this work.”
The Gluu engineering teams chose the Linux Foundation to host this community because of the Foundation’s priority of transparency in the development process and its formal framework for governance to facilitate collaboration among commercial partners.
New digital identity challenges arise constantly, and new standards are developed to address them. Open source ecosystems are an engine for innovation to filter and adapt to changing requirements. The Janssen Project Technical Steering Committee (“TSC”) will help govern priorities according to the charter. The initial TSC includes:
Michael Schwartz, TSC Chair, CEO Gluu
Rajesh Bavanantham, Domain Architect at F5 Networks/NGiNX
Rod Boothby, Head of Digital Trust at Santander
Will Cayo, Director of Software Engineering at IDEMIA Digital Labs
Ian McCloy, Principal Product Manager at Couchbase
Alexander Werner, Software Engineer at BioID
New Open Source Contributor Report from Linux Foundation and Harvard Identifies Motivations and Opportunities for Improving Software Security
New survey reveals why contributors work on open source projects and how much time they spend on security
The Linux Foundation’s Open Source Security Foundation (OpenSSF) and the Laboratory for Innovation Science at Harvard (LISH) announced release of a new report, “Report on the 2020 FOSS Contributor Survey,” which details the findings of a contributor survey administered by the organizations and focused on how contributors engage with open source software. The research is part of an ongoing effort to study and identify ways to improve the security and sustainability of open source software.
The FOSS (Free and Open Source Software) contributor survey and report follow the Census II analysis released earlier this year. This combined pair of works represents important steps towards understanding and addressing structural and security complexities in the modern-day supply chain where open source is pervasive but not always understood. Census II identified the most commonly used free and open source software (FOSS) components in production applications, while the FOSS Contributor Survey and report shares findings directly from nearly 1,200 respondents working on them and other FOSS software.
Key findings from the FOSS Contributor Survey include:
The top three motivations for contributors are non-monetary. While the overwhelming majority of respondents (74.87 percent) are already employed full-time and more than half (51.65 percent) are specifically paid to develop FOSS, motivations to contribute focused on adding a needed feature or fix, enjoyment of learning and fulfilling a need for creative or enjoyable work.
There is a clear need to dedicate more effort to the security of FOSS, but the burden should not fall solely on contributors. Respondents report spending, on average, just 2.27 percent of their total contribution time on security and express little desire to increase that time. The report authors suggest alternative methods to incentivizing security-related efforts.
As more contributors are paid by their employer to contribute, stakeholders need to balance corporate and project interests. The survey revealed that nearly half (48.7 percent) of respondents are paid by their employer to contribute to FOSS, suggesting strong support for the stability and sustainability of open source projects but drawing into question what happens if corporate interest in a project diminishes or ceases.
Companies should continue the positive trend of corporate support for employees’ contribution to FOSS. More than 45.45 percent of respondents stated they are free to contribute to FOSS without asking permission, compared to 35.84 percent ten years ago. However, 17.48 percent of respondents say their companies have unclear policies on whether they can contribute and 5.59 percent were unaware of what policies – if any – their employer had.
The report authors are Frank Nagle, Harvard Business School; David A. Wheeler, the Linux Foundation; Hila Lifshitz-Assaf, New York University; and Haylee Ham and Jennifer L. Hoffman, Laboratory for Innovation Science at Harvard.
Apple co-founder rolls out Efforce to enable any investor to help the planet and participate in the massive $250 billion energy efficiency market.
This is not specifically manufacturing or even technology news. What we have here is a unique financing instrument to help companies achieve energy savings. Energy savings were a part of my portfolios over my years in product development. I view it as a Lean initiative in that it is a process for eliminating waste. Not to mention side benefits of both helping a company’s bottom line as well as helping the planet’s bottom line.
Here is the press release I received last week.
Apple co-founder, Steve Wozniak, is rolling out his second company, Efforce, to transform and disrupt the energy efficiency market, 45 years after starting the computer company that changed technology. His new business may be on track to do the same with a token listing December 6, 2020 that sent its market capitalization to $950M in the first 13 minutes,10 times the listing price. The company had received an initial valuation of $80M by investors in private sales.
Efforce is a marketplace that enables companies to undertake energy efficiency measures at no cost so that they can invest their liquidity in more critical tasks. With Efforce, the energy efficiency market is accessible to large and small investors alike who can then monetize the transferable energy savings.
Currently, financing energy efficiency measures can be a complex mix of financial and regulatory challenges that limit the speed of growth. Efforce uses an innovative web-based platform to leverage the blockchain, and tokens called WOZX, as the mechanisms to create a seamless platform to spur global energy efficiency. Efforce’s WOZX tokens were listed December 6, 2020 on bithumb.pro.
When Wozniak started Apple his goal was to build smaller, more efficient machines that one day could be accessible to anyone. Through his involvement in Efforce, Wozniak continues to focus on efficiency, broadening business access to energy improvements as well as public access to energy efficiency investments.
“Energy consumption and CO2 emissions worldwide have grown exponentially, leading to climate change and extreme consequences to our environment. We can improve our energy footprint and lower our energy consumption without changing our habits. We can save the environment simply by making more energy improvements,” said Wozniak. “We created Efforce to be the first decentralized platform that allows everyone to participate and benefit financially from worldwide energy efficiency projects, and create meaningful environmental change.”
“In these difficult times, many small companies are struggling,” said Jacopo Visetti, project lead and co-founder, Efforce. “They can’t afford to switch to LED lighting, streamline production processes, or even insulate to conserve heat, all of which could save them money in the long term. Efforce allows business owners to safely register their energy upgrade project on the web and secure funding from all types of investors around the world. The companies will then have more available cash to use for other critical projects such as infrastructure or hiring.”
The Energy Efficiency Market
The market for energy efficiency projects has reached a staggering $250 billion.* Not only is private industry contributing to the booming market, but governments including the EU and China are investing heavily in energy efficiency funding. However, in order to achieve the International Energy Agency’s Efficient World Scenario, the sector still must double the size of investments to $580 billion by 2025.
Today, investor groups called energy services companies (ESCOs) must have access to large amounts of capital (typically $200,000 minimum) to undertake energy efficiency improvements. They often are unable to turn to traditional banking channels as banks lack the technical expertise to properly assess the return on investment.
In contrast, the Efforce platform democratizes the market. “We have created a business model that allows anyone to participate in the greater good of making the world cleaner and healthier, all by leveraging efficiency for economic growth,” said Visetti. “Energy efficiency is a way to create a sustainable future, and this is a way to help counter climate change, reduce carbon — and make money while you do it.”
The Efforce Business Model
Using the Efforce platform, the process of financing and undertaking projects is streamlined:
ESCOs register an intended energy efficiency project which is then validated by the Efforce team.
Efforce develops the project with the company, including evaluating the investment need, calculating the anticipated return, and creating an Energy Performance Contract (EPC) that details the savings and the duration of the returns for the company and investors.
The platform then lists the project for crowd contribution. The participants may buy into the project using fractional or whole WOZX tokens.
Efforce measures energy savings on these projects through smart meters attached to the blockchain. The savings data are loaded to the investor’s profile as an energy credit for use or sale by the investor. Energy credits are distributed in megawatt-hours.
The company is run by veteran executives highly familiar with the energy efficiency sector, who after a decade of experience with the less efficient but still-profitable ESCO model began to develop the Efforce business model and platform. Visetti previously founded Milan-based AitherCO2, with annual revenues of $240 million and no outside investment funding. Wozniak was attracted to Efforce for its unique approach to democratizing energy efficiency, and this is the only company he has participated in as a co-founder since Apple.
About Efforce
Efforce has created the first platform leveraging the power of blockchain technology to democratize access to energy efficiency projects and investment opportunities. Co-founded by Apple co-founder Steve Wozniak, Jacopo Visetti, Jacopo Vanetti, and Andrea Castiglione who have more than a decade of experience in this field, Efforce believes in a world where sustainability actually generates outsized benefits without consumers needing to change their energy behavior. For more information, visit www.Efforce.io.
Microsoft Azure had its day on Dec. 3 just as I was digesting the news from rival Amazon Web Services (AWS). The theme was “all about data and analytics.” The focus was on applications Microsoft has added to its Azure services. Anyone who ever thought that these services stopped at being convenient hosts for your cloud missed the entire business model.
Industrial software developers have been busily aligning with Microsoft Azure. Maybe that is why there was no direct assault on their businesses like there was with the AWS announcements. But… Microsoft’s themes of breaking silos of information and combining advanced analytics have the possibility of rendering moot some of the developers’ own tools—unless they just repackage those from Microsoft.
The heart of the meaning of the virtual event yesterday was summed up by Julia White, Corporate Vice President, Microsoft Azure, on a blog post.
Over the years, we have had a front-row seat to digital transformation occurring across all industries and regions around the world. And in 2020, we’ve seen that digitally transformed organizations have successfully adapted to sudden disruptions. What lies at the heart of digital transformation is also the underpinning of organizations who’ve proven most resilient during turbulent times—and that is data. Data is what enables both analytical power—analyzing the past and gaining new insights, and predictive power—predicting the future and planning ahead.
To harness the power of data, first we need to break down data silos. While not a new concept, achieving this has been a constant challenge in the history of data and analytics as its ecosystem continues to be complex and heterogeneous. We must expand beyond the traditional view that data silos are the core of the problem. The truth is, too many businesses also have silos of skills and silos of technologies, not just silos of data. And, this must be addressed holistically.
For decades, specialized technologies like data warehouses and data lakes have helped us collect and analyze data of all sizes and formats. But in doing so, they often created niches of expertise and specialized technology in the process. This is the paradox of analytics: the more we apply new technology to integrate and analyze data, the more silos we can create.
To break this cycle, a new approach is needed. Organizations must break down all silos to achieve analytical power and predictive power, in a unified, secure, and compliant manner. Your organizational success over the next decade will increasingly depend on your ability to accomplish this goal.
This is why we stepped back and took a new approach to analytics in Azure. We rearchitected our operational and analytics data stores to take full advantage of a new, cloud-native architecture. This fundamental shift, while maintaining consistent tools and languages, is what enables the long-held silos to be eliminated across skills, technology, and data. At the core of this is Azure Synapse Analytics—a limitless analytics service that brings together data integration, enterprise data warehousing, and Big Data analytics into a single service offering unmatched time to insights. With Azure Synapse, organizations can run the full gamut of analytics projects and put data to work much more quickly, productively, and securely, generating insights from all data sources. And, importantly, Azure Synapse combines capabilities spanning the needs of data engineering, machine learning, and BI without creating silos in processes and tools. Customers such as Walgreens, Myntra, and P&G have achieved tremendous success with Azure Synapse, and today we move to the global generally availability, so every customer can now get access.
But, just breaking down silos is not sufficient. A comprehensive data governance solution is needed to know where all data resides across an organization. An organization that does not know where its data is, does not know what its future will be. To empower this solution, we are proud to deliver Azure Purview—a unified data governance service that helps organizations achieve a complete understanding of their data.
Azure Purview helps discover all data across your organization, track lineage of data, and create a business glossary wherever it is stored: on-premises, across clouds, in SaaS applications, and in Microsoft Power BI. It also helps you understand your data exposures by using over 100 AI classifiers that automatically look for personally identifiable information (PII), sensitive data, and pinpoint out-of-compliance data. Azure Purview is integrated with Microsoft Information Protection which means you can apply the same sensitivity labels defined in Microsoft 365 Compliance Center. With Azure Purview, you can view your data estate pivoting on classifications and labeling and drill into assets containing sensitive data across on-premises, multi-cloud, and multi-edge locations.
Yesterday, Microsoft announced that the latest version of Azure Synapse is generally available, and the company also unveiled a new data governance solution, Azure Purview.
In the year since Azure Synapse was announced, Microsoft says the number of Azure customers running petabyte-scale workloads – or the equivalent of 500 billion pages of standard printed text – has increased fivefold.
Azure Purview, now available in public preview, will initially enable customers to understand exactly what data they have, manage the data’s compliance with privacy regulations and derive valuable insights more quickly.
Just as Azure Synapse represented the evolution of the traditional data warehouse, Azure Purview is the next generation of the data catalog, Microsoft says. It builds on the existing data search capabilities, adding enhancements to help customers comply with data handling laws and incorporate security controls.
The service includes three main components:
Data discovery, classification and mapping: Azure Purview will automatically find all of an organization’s data on premises or in the cloud and evaluate the characteristics and sensitivity of the data. Beginning in February, the capability will also be available for data managed by other storage providers.
Data catalog: Azure Purview enables all users to search for trusted data using a simple web-based experience. Visual graphs let users quickly see if data of interest is from a trusted source.
Data governance: Azure Purview provides a bird’s-eye view of a company’s data landscape, enabling data officers to efficiently govern data use. This enables key insights such as the distribution of data across environments, how data is moving and where sensitive data is stored.
Microsoft says these improvements will help break down the internal barriers that have traditionally complicated and slowed data governance.
As a companion to my Amazon Web Services (AWS) product release post, several company spokespeople have discussed their applications of the new AWS predictive maintenance and quality products.
“Industrial and manufacturing customers are constantly under pressure from their shareholders, customers, governments, and competitors to reduce costs, improve quality, and maintain compliance. These organizations would like to use the cloud and machine learning to help them automate processes and augment human capabilities across their operations, but building these systems can be error prone, complex, time consuming, and expensive,” said Swami Sivasubramanian, Vice President of Amazon Machine Learning for AWS. “We’re excited to bring customers five new machine learning services purpose-built for industrial use that are easy to install, deploy, and get up and running quickly and that connect the cloud to the edge to help deliver the smart factories of the future for our industrial customers.”
Fender Musical Instruments Corporation is an iconic brand and the world’s foremost manufacturer of guitars, basses, amplifiers, and related equipment. “Over the past year we worked with AWS to help develop the critical but sometimes overlooked part of running a successful manufacturing business, knowing your equipment condition. For manufacturers worldwide, maintaining equipment uptime is the only way to remain competitive in a global market. Unplanned downtime is costly both in loss of production and labor due to the fire-fighting nature of breakdowns,” said Bill Holmes, Global Director of Facilities at Fender. “Amazon Monitron can give both large industry manufacturers as well as small ‘mom and pop shops’ the ability to predict equipment failures, giving us the opportunity to preemptively schedule equipment repairs.”
RS Components is a leading player in the industrial components and predictive maintenance space. “We are constantly trying to innovate how we serve the maintenance needs of our customers. With the emergence of IoT, we have seen our customers looking to bring real-time condition monitoring capabilities into the factory environment to reduce reactive maintenance and improve asset reliability,” said Richard Jeffers, Technical Director at RS Components. “We are excited to be working with AWS to bring Amazon Monitron to our customers because it allows them to deploy a cost effective, easy to use, continuously improving condition monitoring solution and enable predictive maintenance across a broader set of equipment in their asset base. Although we stock over 500,000 products from 2,500 different suppliers, this is the first end-to-end wireless vibration and temperature condition monitoring solution in our portfolio. We plan to make Amazon Monitron available to our customers via our e-commerce platform, and leverage it to deliver condition-based monitoring and reliability services through RS Monition, our data led, reliability services business. Working with AWS will enable us to support our customers’ efforts to adopt IoT and machine learning as emerging technologies and accelerate their Industry 4.0 strategies.”
GS EPS is a South Korean Industrial Conglomerate. “We have been generating data across our assets for over a decade now but have only been using physics and rules based methods to gain insights into our data,” said Kang Bum Lee, Executive Vice President of GS EPS. “Amazon Lookout for Equipment is enabling our plant operation teams to build models on our equipment with no ML expertise required. We are leading the transformation of our organization into a data-driven work culture with AWS and Amazon Lookout for Equipment.”
Doosan Infracore is a leading global manufacturer of heavy duty equipment and engines. “Leveraging AI is critical in advancing Doosan’s next generation equipment, so we are working with AWS to develop use cases where automated and scalable machine learning could be leveraged,” said Mr. Jae Yeon Cho, Vice President of Doosan Infracore. “Based on this, we are excited to continue to work with AWS to leverage Amazon Lookout for Equipment in our next generation IoT platform.”
OSIsoft is a manufacturer of application software for real-time data management, called the PI System. “Today, there are more than 2 billion sensor-based data streams inside OSIsoft PI Systems, and thousands of customers relying on the PI System daily to run their operations. These customers are constantly looking for methods to easily serve up insights for improving their competitiveness. OSIsoft products can be integrated with AWS services to help customers unlock additional value from their data. Amazon Lookout for Equipment expands the scope of services and insights available to customers by delivering automated machine learning built specifically for equipment monitoring,” said Michael Graves, Director of Strategic Alliances at OSIsoft.
“Every month, millions of trucks enter Amazon facilities so creating technology that automates trailer loading, unloading, and parking is incredibly important,” said Steve Armato, VP Middle Mile Production Technology at Amazon.com. “Amazon’s Middle Mile Products & Technology (MMPT) has begun using AWS Panorama to recognize license plates on these vehicles and automatically expedite entry and exit for drivers. This enables safe and fast visits to Amazon sites, ensuring faster package delivery for our customers.”
BP is a global energy company, providing customers with fuel for transport, energy for heat and light, lubricants to keep engines moving, and the petrochemicals products used to make everyday items as diverse as paints, clothes, and packaging. The organization has 18,000 service stations and more than 74,000 employees worldwide. “Our engineering teams here at bpx are working very closely with AWS to build an IoT and cloud platform that will enable us to continuously improve the efficiency of our operations,” said Grant Matthews, Chief Technology Officer at BP America. “One of the areas we have explored as part of this effort is the use of computer vision to help us further improve security and worker safety. We want to leverage computer vision to automate the entry and exit of trucks to our facility and verify that they have fulfilled the correct order. Additionally, we see possibilities for computer vision to keep our workers safe in a number of ways, from monitoring social distancing, to setting up dynamic exclusion zones, and detecting oil leaks. AWS Panorama offers an innovative approach to delivering all of these solutions on a single hardware platform with an intuitive user experience. Our teams are excited to work with AWS on this new technology and expect it to help us address many new use cases.”
Siemens Mobility offers intelligent and efficient mobility solutions for urban, interurban, and freight transportation. “Siemens Mobility has been a leader for seamless, sustainable, and secure transport solutions for more than 160 years. The Siemens ITS Digital Lab is the innovation team in charge of bringing the latest digital advances to the traffic industry and uniquely positioned to provide data analytics and AI solutions to public agencies,” said Laura Sanchez, Innovation Manager, Siemens Mobility ITS Digital Lab. “As cities face new challenges, municipalities have turned to us to innovate on their behalf. Cities would like to understand how to effectively manage their assets and improve congestion and direct traffic. We want to use AWS Panorama to bring computer vision to existing security cameras to monitor traffic and intelligently allocate curbside space, help cities optimize parking and traffic, and improve quality of life for their constituents.”
ADLINK Technology offers hardware/software platforms enabling customers to implement edge AI solutions for real-time delivery of actionable data in industrial markets such as manufacturing, transportation, healthcare, energy, and communications. “The integration of AWS Panorama on ADLINK’s industrial vision systems makes for truly plug-and-play computer vision at the edge,” said Elizabeth Campbell, CEO at ADLINK USA. “In 2021, we will be making AWS Panorama-certified ADLINK NEON cameras powered by NVIDIA Jetson AGX Xavier available to customers to drive high-quality computer vision powered outcomes much, much faster. This allows ADLINK to deliver ML digital experiments and time to value for our customers more rapidly across logistics, manufacturing, energy, and utilities use cases.”
INDUS.AI is the world’s most advanced construction intelligence solution that enables real estate investors, owners, developers, and general contractors to have real-time visibility and actionable insights into all activities, productivity, and risks at their construction sites. INDUS.AI seeks to make construction sites and projects safer, more efficient, and completely transparent. “Construction zones are dynamic environments. At any given time you’ve got hundreds of deliveries and subcontractors sharing the site with heavy equipment and it’s changing every day. INDUS.AI is focused on delivering construction intelligence for general contractors,” said Matt Man, CEO of INDUS.AI. “Computer vision is an especially valuable tool for this because of its ability to handle multiple tasks at once. We are looking forward to delivering real-time insights on job-site management and safety in a SaaS-like experience for AWS Panorama customers.”
Dafgards is a household name in Sweden, manufacturing a broad assortment of foods. One of their most successful brands is Billys Pan Pizza, a microwaveable pizza baked and packed at a speed of 2 pizzas per second. “To uphold our brand and deliver the freshest and tastiest customer experience, we want to ensure that all our pizzas are adequately covered in cheese and with the correct toppings. Previously, we installed a machine vision system to detect proper coverage of cheese across a pizza’s surface. While this system served well for our original inspection requirement, it was unable to detect defects on new product types that include multiple toppings,” said Fredrik Dafgård, Head of Operational Excellence and Industrial IoT at Dafgards. “Amazon Lookout for Vision automates and scales inspection of diverse product types such as a cheese pizza with vegetables. We successfully expanded our quality assurance for new product types with minimal impact to operations.”
GE Healthcare is a leading global medical technology and digital solutions innovator that develops, manufactures, and distributes diagnostic imaging agents, radiopharmaceuticals, medical diagnostic equipment, including CT and MRI machines, and intelligent devices supported by its Edison intelligence platform. “Today, we use human inspection to verify the quality of our medical equipment. To uphold our brand and deliver best-in-class products trusted by healthcare professionals, we’re excited about the possibility of using Amazon Lookout for Vision to programmatically improve the speed, consistency, and accuracy of detecting product defects across our factories in Japan and potentially in other plants globally in the near future,” said Kozaburo Fujimoto, Operating Officer, General Manager, Manufacturing Division, Plant Manager at GE Healthcare Japan.
Nukon, a SAGE Group company, is a digital transformation consultancy and delivery company that delivers custom-designed solutions which combine strategy, analysis, and technology to give visibility into key business processes so they can be optimized. “We are excited about how Amazon Lookout for Vision will help us detect product defects in real-time within the manufacturing facility for our sister company, SAGE Automation, in line with their stringent quality control program. We are excited to now apply this technology to other manufacturers and integrate it into their quality systems,” said Rafael Amaral, Chief Technology Officer at Nukon.
As usual, there is more than one conference this week. I have two more to report on. This one is a bit of a surprise. I was providing thoughts about potential disruption of the mainstream automation market from below when I received this news from Amazon Web Services (AWS). This is a lengthy report, because there is a lot of news here. And it’s worth pausing to consider. I worked with another IT company a few years ago about predictive maintenance as a possible market. That company did not see the sales possibilities and bailed.
AWS is not just a remote server farm for cloud services. It has packed tons of tech into the company. Imagine your possibilities using this.
Amazon Monitron provides customers an end-to-end machine monitoring solution comprised of sensors, gateway, and machine learning service to detect abnormal equipment conditions that may require maintenance
Amazon Lookout for Equipmentgives customers with existing equipment sensors the ability to use AWS machine learning models to detect abnormal equipment behavior and enable predictive maintenance
AWS Panorama Appliance enables customers with existing cameras in their industrial facilities with the ability to use computer vision to improve quality control and workplace safety
AWS Panorama Software Development Kit (SDK) allows industrial camera manufacturers to embed computer vision capabilities in new cameras
Amazon Lookout for Visionuses AWS-trained computer vision models onimages and video streams to find anomalies and flaws in products or processes
Axis, ADLINK Technology, BP, Deloitte, Fender, GE Healthcare, and Siemens Mobility among customers and partners using new AWS industrial machine learning services
This week at AWS re:Invent, Amazon Web Services Inc. (AWS), an Amazon.com company, announced Amazon Monitron, Amazon Lookout for Equipment, the AWS Panorama Appliance, the AWS Panorama SDK, and Amazon Lookout for Vision. Together, these five new machine learning services help industrial and manufacturing customers embed intelligence in their production processes in order to improve operational efficiency, quality control, security, and workplace safety.
The services combine sophisticated machine learning, sensor analysis, and computer vision capabilities to address common technical challenges faced by industrial customers. To learn more about AWS’s new industrial machine learning services, visit https://aws.amazon.com/industrial/
Companies are increasingly looking to add machine learning capabilities to industrial environments, such as manufacturing facilities, fulfillment centers, and food processing plants. For these customers, data has become the connective tissue that holds their complex industrial systems together. With these evolving needs and opportunities, industrial companies have asked AWS to help them leverage the cloud, the industrial edge, and machine learning together to get even more value from the vast amounts of data being generated by their equipment.
Amazon Monitron and Amazon Lookout for Equipment enable predictive maintenance powered by machine learning
In order to make predictive maintenance work, companies have historically needed skilled technicians and data scientists to piece together a complex solution from scratch. This included identifying and procuring the right type of sensors for the use case and connecting them together with an IoT gateway. Companies then had to test the monitoring system and transfer the data to on-premises infrastructure or the cloud for processing. Only then could the data scientists on staff build machine learning models to analyze the data for patterns and anomalies or create an alerting system when an outlier was detected.
Some companies have invested heavily in installing sensors across their equipment and the necessary infrastructure for data connectivity, storage, analytics, and alerting. But even these companies typically use rudimentary data analytics and simple modeling approaches that are expensive and often ineffective at detecting abnormal conditions compared to advanced machine learning models. Most companies lack the expertise and staff to build and refine the machine learning models that would enable highly accurate predictive maintenance. As a result, few companies have been able to successfully implement predictive maintenance, and those that have done it are looking for ways to further leverage their investment, while also easing the burden of maintaining their homegrown solutions.
Here’s how the new AWS machine learning services can help:
For customers who do not have an existing sensor network, Amazon Monitron offers an end-to-end machine monitoring system comprised of sensors, a gateway, and a machine learning service to detect anomalies and predict when industrial equipment will require maintenance. Amazon Monitron detects when machines are not operating normally based on abnormal fluctuations in vibration or temperature, and notifies customers when to examine machinery in order to determine if preventative maintenance is needed. The end-to-end system includes IoT sensors to capture vibration and temperature data, a gateway to aggregate and transfer data to AWS, and a machine learning cloud service that can detect abnormal equipment patterns and deliver results in minutes with no machine learning or cloud experience required. Amazon Monitron also includes a mobile app for a customer’s onsite maintenance technicians to monitor equipment behavior in real time https://aws.amazon.com/monitron.
For customers that have existing sensors but don’t want to build machine learning models, Amazon Lookout for Equipment provides a way to send their sensor data to AWS to build models for them and return predictions to detect abnormal equipment behavior. To get started, customers upload their sensor data to Amazon Simple Storage Service (S3) and provide the S3 location to Amazon Lookout for Equipment. Amazon Lookout for Equipment can also pull data from AWS IoT SiteWise, and works seamlessly with other popular machine operations systems like OSIsoft. Amazon Lookout for Equipment analyzes the data, assesses normal or healthy patterns, and then uses the learnings from all of the data on which it is trained to build a model that is customized for the customer’s environment. Amazon Lookout for Equipment can then use the machine learning model to analyze incoming sensor data and identify early warning signs for machine failure. https://aws.amazon.com/lookout-for-equipment.
AWS Panorama uses computer vision to improve industrial operations and workplace safety
Many industrial and manufacturing customers want to be able to use computer vision on live video feeds of their facility and equipment to automate monitoring or visual inspection tasks and to make decisions in real time. However, the typical monitoring methods used today are manual, error prone, and difficult to scale.
Customers could build computer vision models in the cloud to monitor and analyze their live video feeds, but industrial processes typically need to be physically located in remote and isolated places, where connectivity can be slow, expensive, or completely non-existent. This type of video feed could be automatically processed in the cloud using computer vision, but video feeds are high bandwidth and can be slow to upload. Most customers end up running unsophisticated models that can’t be programmed to run custom code that integrates into the industrial machines. Here’s how AWS can now help:
The AWS Panorama Appliance provides a new hardware appliance that allows organizations to add computer vision to existing on-premises cameras that customers may already have deployed. Customers start by connecting the AWS Panorama Appliance to their network, and the device automatically identifies camera streams and starts interacting with the existing industrial cameras. The AWS Panorama Appliance is integrated with AWS machine learning services and IoT services that can be used to build custom machine learning models or ingest video for more refined analysis.
The AWS Panorama Software Development Kit (SDK) enables hardware vendors to build new cameras that can run meaningful computer vision models at the edge. Cameras that are built with the AWS Panorama SDK run computer vision models for use cases like detecting damaged parts on a fast-moving conveyor belt or spotting when machinery is outside of a designated work zone. Customers can train their own models in Amazon SageMaker and deploy them on cameras built with the AWS Panorama SDK with a single click. Customers can also add Lambda functions to cameras built with the AWS Panorama SDK to be alerted to potential issues via text or email. AWS also offers pre-built models for tasks like PPE detection and social distancing and can deploy these models in minutes without doing any machine learning work or special optimizations.
https://aws.amazon.com/panorama.
Amazon Lookout for Vision offers automated fast and accurate visual anomaly detection for images and video at a low cost
One use case where AWS customers are excited to deploy computer vision with their cameras is for quality control. Machine learning-powered visual anomaly systems remain out of reach for the vast majority of companies. Here’s how AWS can now help these companies:
Amazon Lookout for Vision offers customers a high accuracy, low-cost anomaly detection solution that uses machine learning to process thousands of images an hour to spot defects and anomalies. Customers send camera images to Amazon Lookout for Vision in batch or in real-time to identify anomalies, such as a crack in a machine part, a dent in a panel, an irregular shape, or an incorrect color on a product. Amazon Lookout for Vision then reports the images that differ from the baseline so that appropriate action can be taken. Amazon Lookout for Vision also runs on Amazon Panorama appliances. Customers can run Amazon Lookout for Vision in AWS starting today, and beginning next year, customers will be able to run Amazon Lookout for Vision on AWS Panorama Appliances and other AWS Panorama devices so customers will be able to use Amazon Lookout for Vision in locations where Internet connectivity is limited or non-existent. https://aws.amazon.com/lookout-for-vision.
Notwithstanding that some people in my little home town back in the late 60s when I was a college student labelled me “The Protestor”, I have some sympathy for these students. After all, the woman who wrote to me is an engineering student. I graduated from university with some debt. The next generation graduated with substantially more. And now, it’s ridiculous–and for essentially the same education. I don’t have any answers. But I do like seeing students not just sit back and take it. From the civil rights and anti-war movements, I have seen the limits of protest–but also the chance to move the discussion. Anyway–something different for this blog.
1,300 students at Columbia University commit to tuition strike, calling for “a democratic vision of the university where students, workers, and the Harlem community are valued above profits”
Over 1,300 students at Columbia University have committed to withholding their tuition payments for next semester if the University continues to ignore student demands to reduce the cost of attendance, increase financial aid, and concede on a number of key demands relating to labor, investments, and the surrounding community.
The tuition strike demands letter calls on the University to recognize these movements and give students a more democratic say over the cost of tuition and how it is spent. In addition to calling for tuition reduction and increased aid in light of the economic crisis caused by the pandemic, the letter stipulates that these demands “should not come at the expense of instructor or worker pay, but rather at the expense of bloated administrative salaries, expansion projects, and other expenses that don’t benefit students and workers.”
The following demands focus on changing how tuition payments are spent: calling on the University to invest in the West Harlem community, end expansion projects, replace Public Safety with community- and student-controlled safety solutions, bargain in good-faith with campus labor unions on key demands, and provide students greater transparency and control over investment decisions.
Students involved in the tuition strike view their efforts as part of a wider struggle against unaffordable tuition and student debt and in support of College for All. A statement by Columbia-Barnard YDSA (Young Democratic Socialists of America), the group organizing the tuition strike, noted that tuition at Columbia is between 51-84% more expensive than the average U.S. four-year private university and stated that “it is unacceptable that students should go tens of thousands of dollars into debt for an education when this university possesses such vast wealth.” Columbia students hold an average of $21,979 in student debt after graduation. The university’s endowment is $11.26 billion, including a $310 million increase during the pandemic.
