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Category Archives: Machine Learning
The apex power sector regulator, the Central Electricity Regulatory Commission (CERC), is planning to set up an artificial intelligence (AI)-based regulatory expert system tool (REST) for improving access to information and assist the commission in discharge of its duties. So far, only the Supreme Court (SC) has an electronic filing (e-filing) system and is in the process of building an AI-based back-end service.
The CERC will be the first such quasi-judicial regulatory body to embrace AI and machine learning (ML). The decision comes at a time when the CERC has been shut for four ...
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First Published: Fri, January 15 2021. 06:10 IST
New research project will use machine learning to advance metal alloys for aerospace – Metal Additive Manufacturing magazine
Ian Brooks, AM Technical Fellow, AMRC North West with Renishaws RenAM 500Q metal Additive Manufacturing machine (Courtesy Renishaw/ AMRC North West)
UK-based Intellegens, a University of Cambridge spin-out specialising in artificial intelligence; the University of Sheffield Advanced Manufacturing Research Centre (AMRC) North West, Preston, Lancashire, UK; and Boeing will collaborate on Project MEDAL: Machine Learning for Additive Manufacturing Experimental Design.
The project aims to accelerate the product development lifecycle of aerospace components by using a machine learning model to optimise Additive Manufacturing processing parameters for new metal alloys at a lower cost and faster rate. The research will focus on metal Laser Beam Powder Bed Fusion (PBF-LB), specifically on key parameter variables required to manufacture high density, high strength parts.
Project MEDAL is part of the National Aerospace Technology Exploitation Programme (NATEP), a 10 million initiative for UK SMEs to develop innovative aerospace technologies funded by the Department for Business, Energy and Industrial Strategy and delivered in partnership with the Aerospace Technology Institute (ATI) and Innovate UK. Intellegens was a startup in the first group of companies to complete the ATI Boeing Accelerator last year.
We are very excited to be launching this project in conjunction with the AMRC, stated Ben Pellegrini, CEO of Intellegens. The intersection of machine learning, design of experiments and Additive Manufacturing holds enormous potential to rapidly develop and deploy custom parts not only in aerospace, as proven by the involvement of Boeing, but in medical, transport and consumer product applications.
James Hughes, Research Director for University of Sheffield AMRC North West, explained that the project will build the AMRCs knowledge and expertise in alloy development so it can help other UK manufacturers.
Hughes commented, At the AMRC we have experienced first-hand, and through our partner network, how onerous it is to develop a robust set of process parameters for AM. It relies on a multi-disciplinary team of engineers and scientists and comes at great expense in both time and capital equipment.
It is our intention to develop a robust, end-to-end methodology for process parameter development that encompasses how we operate our machinery right through to how we generate response variables quickly and efficiently. Intellegens AI-embedded platform Alchemite will be at the heart of all of this.
There are many barriers to the adoption of metallic AM but by providing users, and maybe more importantly new users, with the tools they need to process a required material should not be one of them, Hughes continued. With the AMRCs knowledge in AM, and Intellegens AI tools, all the required experience and expertise is in place in order to deliver a rapid, data-driven software toolset for developing parameters for metallic AM processes to make them cheaper and faster.
Sir Martin Donnelly, president of Boeing Europe and managing director of Boeing in the UK and Ireland, reported that the project shows how industry can successfully partner with government and academia to spur UK innovation.
Donnelly noted, We are proud to see this project move forward because of what it promises aviation and manufacturing, and because of what it represents for the UKs innovation ecosystem. We helped found the AMRC two decades ago, Intellegens was one of the companies we invested in as part of the ATI Boeing Accelerator and we have longstanding research partnerships with Cambridge University and the University of Sheffield.
He added, We are excited to see what comes from this continued collaboration and how we might replicate this formula in other ways within the UK and beyond.
Aerospace components have to withstand certain loads and temperature resistances, and some materials are limited in what they can offer. There is also simultaneous push for lower weight and higher temperature resistance for better fuel efficiency, bringing new or previously impractical-to-machine metals into the aerospace material mix.
One of the main drawbacks of AM is the limited material selection currently available and the design of new materials, particularly in the aerospace industry, requires expensive and extensive testing and certification cycles which can take longer than a year to complete and cost as much as 1 million to undertake.
Pellegrini explained that experimental design techniques are extremely important to develop new products and processes in a cost-effective and confident manner. The most common approach is Design of Experiments (DOE), a statistical method that builds a mathematical model of a system by simultaneously investigating the effects of various factors.
Pellegrini added, DOE is a more efficient, systematic way of choosing and carrying out experiments compared to the Change One Separate variable at a Time (COST) approach. However, the high number of experiments required to obtain a reliable covering of the search space means that DOE can still be a lengthy and costly process, which can be improved.
The machine learning solution in this project can significantly reduce the need for many experimental cycles by around 80%. The software platform will be able to suggest the most important experiments needed to optimise AM processing parameters, in order to manufacture parts that meet specific target properties. The platform will make the development process for AM metal alloys more time and cost-efficient. This will in turn accelerate the production of more lightweight and integrated aerospace components, leading to more efficient aircrafts and improved environmental impact, concluded Pellegrini.
Intellegens will produce a software platform with an underlying machine learning algorithm based on its Alchemite platform. It has reportedly already been used successfully to overcome material design problems in a University of Cambridge research project with a leading OEM where a new alloy was designed, developed and verified in eighteen months rather than the expected twenty-year timeline, saving approximately $10 million.
Research papers come out far too rapidly for anyone to read them all, especially in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect the most relevant recent discoveries and papers particularly in but not limited to artificial intelligence and explain why they matter.
This week has a bit more basic research than consumer applications. Machine learning can be applied to advantage in many ways users benefit from, but its also transformative in areas like seismology and biology, where enormous backlogs of data can be leveraged to train AI models or as raw material to be mined for insights.
Were surrounded by natural phenomena that we dont really understand obviously we know where earthquakes and storms come from, but how exactly do they propagate? What secondary effects are there if you cross-reference different measurements? How far ahead can these things be predicted?
A number of recently published research projects have used machine learning to attempt to better understand or predict these phenomena. With decades of data available to draw from, there are insights to be gained across the board this way if the seismologists, meteorologists and geologists interested in doing so can obtain the funding and expertise to do so.
The most recent discovery, made by researchers at Los Alamos National Labs, uses a new source of data as well as ML to document previously unobserved behavior along faults during slow quakes. Using synthetic aperture radar captured from orbit, which can see through cloud cover and at night to give accurate, regular imaging of the shape of the ground, the team was able to directly observe rupture propagation for the first time, along the North Anatolian Fault in Turkey.
The deep-learning approach we developed makes it possible to automatically detect the small and transient deformation that occurs on faults with unprecedented resolution, paving the way for a systematic study of the interplay between slow and regular earthquakes, at a global scale, said Los Alamos geophysicist Bertrand Rouet-Leduc.
Another effort, which has been ongoing for a few years now at Stanford, helps Earth science researcher Mostafa Mousavi deal with the signal-to-noise problem with seismic data. Poring over data being analyzed by old software for the billionth time one day, he felt there had to be better way and has spent years working on various methods. The most recent is a way of teasing out evidence of tiny earthquakes that went unnoticed but still left a record in the data.
The Earthquake Transformer (named after a machine-learning technique, not the robots) was trained on years of hand-labeled seismographic data. When tested on readings collected during Japans magnitude 6.6 Tottori earthquake, it isolated 21,092 separate events, more than twice what people had found in their original inspection and using data from less than half of the stations that recorded the quake.
Image Credits: Stanford University
The tool wont predict earthquakes on its own, but better understanding the true and full nature of the phenomena means we might be able to by other means. By improving our ability to detect and locate these very small earthquakes, we can get a clearer view of how earthquakes interact or spread out along the fault, how they get started, even how they stop, said co-author Gregory Beroza.
The last decadehas seen a relentless push to deliver software faster. Automated testing has emerged as one of the most important technologies for scaling DevOps, companies are investing enormous time and effort to build end-to-end software delivery pipelines, and containers and their ecosystem are holding up on their early promise.
The combination of delivery pipelines and containers has helped high performers to deliver software faster than ever.That said, many organizations are stillstruggling to balance speed and quality. Many are stuck trying to make headway with legacy software, large test suites, and brittle pipelines. So where do yougofrom here?
In the drive to release quickly, end users have become software testers. But theyno longer want to be your testers, and companies are taking note. Companies now want to ensure that quality is not compromised in the pursuit of speed.
Testing is one of the top DevOps controls that organizations can leverage to ensure that their customers engage with a delightful brand experience. Othersinclude access control, activity logging, traceability, and disaster recovery. Our company'sresearch over the past year indicates that slow feedback cycles, slow development loops, and developer productivity will remain the top priorities over the next few years.
Quality and access control are preventative controls, while others are reactive. There will be an increasing focus on quality in the future because it prevents customers from having a bad experience. Thus, delivering value fastor better yet, delivering the right value at the right quality level fastis the key trend that we will see this year and beyond.
Here are the five key trends to watch.
Test automation efforts will continue to accelerate. A surprising number of companiesstill have manual tests in their delivery pipeline, but you can't deliver fast if you have humans in the critical path of the value chain, slowing things down. (The exception isexploratory testing, where humans are a must.)
Automating manual tests is a long process that requires dedicated engineering time. While many organizations have at least some test automation, there's more that needs to be done. That's why automatedtesting willremain one of the top trends going forward.
As teams automate tests and adopt DevOps, quality must become part of the DevOps mindset. That means quality will become a shared responsibility of everyone in the organization.
Figure 2. Top performers shift tests around to create new workflows. They shift left for earlier validation and right to speed up delivery. Source: Launchable
Teams will need to become more intentional about where tests land. Should they shift tests left to catch issues much earlier, or should they add more quality controls to the right? On the "shift-right"side of the house, practices such as chaos engineering and canary deployments are becoming essential.
Shifting large test suites left is difficult because you don't want to introduce long delays while running tests in an earlier part of your workflow. Many companies tag some tests from a large suite to run in pre-merge, but the downside is that these tests may or may not be relevant to a specific change set. Predictive test selection (see trend 5 below) provides a compelling solution for running just the relevant tests.
Over the past six to eightyears, the industry has focused on connecting various tools by building robust delivery pipelines. Each of those tools generates a heavy exhaust of data, but that data is being used minimally, if at all. We have moved from "craft" or "artisanal" solutions to the "at-scale" stage in the evolution of tools in delivery pipelines.
The next phase is to bring smartsto the tooling.Expect to see an increased emphasis by practitioners onmakingdata-driven decisions.
There are two key problems in testing: not enough tests, and too many of them. Test-generation tools take a shot at the first problem.
To create a UI test today, you either must write a lot of code or a tester has to click through the UI manually, which is an incredibly painful and slow process. To relieve this pain, test-generation tools use AI to create and run UI tests on various platforms.
For example, one tool my team exploreduses a "trainer"that lets you record actions on a web app to create scriptless tests. While scriptless testing isnt a new idea, what is new is that this tool "auto-heals"tests in lockstep with the changes to your UI.
Another tool that we explored has AI bots that act like humans. They tap buttons, swipe images, type text, and navigate screens to detect issues. Once they find an issue, they create a ticket in Jira for the developers to take action on.
More testing tools that use AI willgain traction in 2021.
AI has other uses for testing apart from test generation. For organizations struggling with runtimes of large test suites, an emerging technology calledpredictive test selectionis gaining traction.
Many companies have thousands of tests that run all the time. Testing a small change might take hours or even days to get feedback on. While more tests are generally good for quality, it also means that feedback comes more slowly.
To date, companies such as Google and Facebook have developed machine-learning algorithms that process incoming changes and run only the tests that are most likely to fail. This is predictive test selection.
What's amazing about this technology is that you can run between 10% and 20% of your tests to reach 90% confidence that a full run will not fail. This allows you to reduce a five-hour test suite that normally runs post-merge to 30 minuteson pre-merge, running only the tests that are most relevant to the source changes. Another scenario would be to reduce a one-hour run to six minutes.
Expect predictive test selection to become more mainstream in 2021.
Automated testing is taking over the world. Even so, many teams are struggling to make the transition. Continuous quality culture will become part of the DevOps mindset. Tools will continue to become smarter. Test-generation tools will help close the gap between manual and automated testing.
But as teams add more tests, they face real problems with test execution time. While more tests help improve quality, they often become a roadblock to productivity. Machine learning will come to the rescue as we roll into 2021.
See the original post here:
The future of software testing: Machine learning to the rescue - TechBeacon
Experts predict artificial intelligence (AI) and machine learning will enter a golden age in 2021, solving some of the hardest business problems.
Machine learning trains computers to learn from data with minimal human intervention. The science isnt new, but recent developments have given it fresh momentum, said Jin-Whan Jung, Senior Director & Leader, Advanced Analytics Lab at SAS. The evolution of technology has really helped us, said Jung. The real-time decision making that supports self-driving cars or robotic automation is possible because of the growth of data and computational power.
The COVID-19 crisis has also pushed the practice forward, said Jung. Were using machine learning more for things like predicting the spread of the disease or the need for personal protective equipment, he said. Lifestyle changes mean that AI is being used more often at home, such as when Netflix makes recommendations on the next show to watch, noted Jung. As well, companies are increasingly turning to AI to improve their agility to help them cope with market disruption.
Jungs observations are backed by the latest IDC forecast. It estimates that global AI spending will double to $110 billion over the next four years. How will AI and machine learning make an impact in 2021? Here are the top five trends identified by Jung and his team of elite data scientists at the SAS Advanced Analytics Lab:
Canadas Armed Forces rely on Lockheed Martins C-130 Hercules aircraft for search and rescue missions. Maintenance of these aircraft has been transformed by the marriage of machine learning and IoT. Six hundred sensors located throughout the aircraft produce 72,000 rows of data per flight hour, including fault codes on failing parts. By applying machine learning, the system develops real-time best practices for the maintenance of the aircraft.
We are embedding the intelligence at the edge, which is faster and smarter and thats the key to the benefits, said Jung. Indeed, the combination is so powerful that Gartner predicts that by 2022, more than 80 per cent of enterprise IoT projects will incorporate AI in some form, up from just 10 per cent today.
Computer vision trains computers to interpret and understand the visual world. Using deep learning models, machines can accurately identify objects in videos, or images in documents, and react to what they see.
The practice is already having a big impact on industries like transportation, healthcare, banking and manufacturing. For example, a camera in a self-driving car can identify objects in front of the car, such as stop signs, traffic signals or pedestrians, and react accordingly, said Jung. Computer vision has also been used to analyze scans to determine whether tumors are cancerous or benign, avoiding the need for a biopsy. In banking, computer vision can be used to spot counterfeit bills or for processing document images, rapidly robotizing cumbersome manual processes. In manufacturing, it can improve defect detection rates by up to 90 per cent. And it is even helping to save lives; whereby cameras monitor and analye power lines to enable early detection of wildfires.
At the core of machine learning is the idea that computers are not simply trained based on a static set of rules but can learn to adapt to changing circumstances. Its similar to the way you learn from your own successes and failures, said Jung. Business is going to be moving more and more in this direction.
Currently, adaptive learning is often used fraud investigations. Machines can use feedback from the data or investigators to fine-tune their ability to spot the fraudsters. It will also play a key role in hyper-automation, a top technology trend identified by Gartner. The idea is that businesses should automate processes wherever possible. If its going to work, however, automated business processes must be able to adapt to different situations over time, Jung said.
To deliver a return for the business, AI cannot be kept solely in the hands of data scientists, said Jung. In 2021, organizations will want to build greater value by putting analytics in the hands of the people who can derive insights to improve the business. We have to make sure that we not only make a good product, we want to make sure that people use those things, said Jung. As an example, Gartner suggests that AI will increasingly become part of the mainstream DevOps process to provide a clearer path to value.
Responsible AI will become a high priority for executives in 2021, said Jung. In the past year, ethical issues have been raised in relation to the use of AI for surveillance by law enforcement agencies, or by businesses for marketing campaigns. There is also talk around the world of legislation related to responsible AI.
There is a possibility for bias in the machine, the data or the way we train the model, said Jung. We have to make every effort to have processes and gatekeepers to double and triple check to ensure compliance, privacy and fairness. Gartner also recommends the creation of an external AI ethics board to advise on the potential impact of AI projects.
Large companies are increasingly hiring Chief Analytics Officers (CAO) and the resources to determine the best way to leverage analytics, said Jung. However, organizations of any size can benefit from AI and machine learning, even if they lack in-house expertise.
Jung recommends that if organizations dont have experience in analytics, they should consider getting an assessment on how to turn data into a competitive advantage. For example, the Advanced Analytics Lab at SAS offers an innovation and advisory service that provides guidance on value-driven analytics strategies; by helping organizations define a roadmap that aligns with business priorities starting from data collection and maintenance to analytics deployment through to execution and monitoring to fulfill the organizations vision, said Jung. As we progress into 2021, organizations will increasingly discover the value of analytics to solve business problems.
SAS highlights a few top trends in AI and machine learning in this video.
Jim Love, Chief Content Officer, IT World Canada
Across government, IT managers are looking to harness the power of artificial intelligence and machine learning techniques (AI/ML) to extract and analyze data to support mission delivery and better serve citizens.
Practically every large federal agency is executing some type of proof of concept or pilot project related to AI/ML technologies. The governments AI toolkit is diverse and spans the federal administrative state, according to a report commissioned by the Administrative Conference of the United States (ACUS). Nearly half of the 142 federal agencies canvassed have experimented with AI/ML tools, the report, Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies, states.
Moreover, AI tools are already improving agency operations across the full range of governance tasks, including regulatory mandate enforcement, adjudicating government benefits and privileges, monitoring and analyzing risks to public safety and health, providing weather forecasting information and extracting information from the trove of government data to address consumer complaints.
Agencies with mature data science practices are further along in their AI/ML exploration. However, because agencies are at different stages in their digital journeys, many federal decision-makers still struggle to understand AI/ML. They need a better grasp of the skill sets and best practices needed to derive meaningful insights from data powered by AI/ML tools.
Understanding how AI/ML works
AI mimics human cognitive functions such as the ability to sense, reason, act and adapt, giving machines the ability to act intelligently. Machine learning is a component of AI, which involves the training of algorithms or models that then give predictions about data it has yet to observe. ML models are not programmed like conventional algorithms. They are trained using data -- such as words, log data, time series data or images -- and make predictions on actions to perform.
Within the field of machine learning, there are two main types of tasks: supervised and unsupervised.
With supervised learning, data analysts have prior knowledge of what the output values for their samples should be. The AI system is specifically told what to look for, so the model is trained until it can detect underlying patterns and relationships. For example, an email spam filter is a machine learning program that can learn to flag spam after being given examples of spam emails that are flagged by users and examples of regular non-spam emails. The examples the system uses to learn are called the training set.
Unsupervised learning looks for previously undetected patterns in a dataset with no pre-existing labels and with a minimum of human supervision. For instance, data points with similar characteristics can be automatically grouped into clusters for anomaly detection, such as in fraud detection or identifying defective mechanical parts in predictive maintenance.
Supervised, unsupervised in action
It is not a matter of which approach is better. Both supervised and unsupervised learning are needed for machine learning to be effective.
Both approaches were applied recently to help a large defense financial management and comptroller office resolve over $2 billion in unmatched transactions in an enterprise resource planning system. Many tasks required significant manual effort, so the organization implemented a robotic process automation solution to automatically access data from various financial management systems and process transactions without human intervention. However, RPA fell short when data variances exceeded tolerance for matching data and documents, so AI/ML techniques were used to resolve the unmatched transactions.
The data analyst team used supervised learning with preexisting rules that resulted in these transactions. The team was then able to provide additional value because they applied unsupervised ML techniques to find patterns in the data that they were not previously aware of.
To get a better sense of how AI/ML can help agencies better manage data, it is worth considering these three steps:
Data analysts should think of these steps as a continuous loop. If the output from unsupervised learning is meaningful, they can incorporate it into the supervised learning modeling. Thus, they are involved in a continuous learning process as they explore the data together.
It is important for IT teams to realize they cannot just feed data into machine learning models, especially with unsupervised learning, which is a little more art than science. That is where humans really need to be involved. Also, analysts should avoid over-fitting models seeking to derive too much insight.
Remember: AI/ML and RPA are meant to augment humans in the workforce, not merely replace people with autonomous robots or chatbots. To be effective, agencies must strategically organize around the right people, processes and technologies to harness the power of innovative technologies such as AI/ML to achieve the performance they need at scale.
About the Author
Samuel Stewart is a data scientist with World Wide Technology.
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Harnessing the power of machine learning for improved decision-making - GCN.com