Algorithmia, a provider of enterprise machine learning operations (MLOps) software, has been selected by Raytheon Intelligence & Space, a Raytheon Technologies (NYSE: RTX) business to support the teams development of the U.S. Armys Tactical Intelligence Targeting Access Node (TITAN) program. TITAN is a tactical ground station that finds and tracks threats to support long-range precision targeting.

Algorithmia, along with other leaders in artificial intelligence and machine learning, will enable Raytheon TechnologiesTITAN team to deliver easily digestible data to Army operators. TITAN will ingest data from space and high-altitude, aerial and terrestrial sensors to provide targetable data to defense systems. It also provides multi-source intelligence support to targeting, and situational awareness and understanding for commanders.

Algorithmias MLOps platform has been used by over 130,000 data scientists in a wide range of organizations. Its customers include large and midsize enterprises, Fortune 500 companies, the United Nations and multiple government intelligence agencies. The companys momentum is a product of growing interest in AI-based applications and the need organizations have to efficiently manage cost and security for machine learning models.

Machine learning significantly accelerates the process by which organizations can uncover important data points and respond to critical issues, said Diego Oppenheimer, CEO of Algorithmia. Our platform streamlines the deployment of machine learning models into production while providing important oversight, including review for ethical standards, to ensure models operate when and how they should, which makes Algorithmia a natural fit for sensitive applications. We are excited to join Raytheon in supporting its work with the U.S. Army.

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Algorithmia Machine Learning Operations Selected for Use by Raytheon Technologies Homeland Security Today - HSToday

KINGSTON, R.I. July 1, 2021 Was Socrates a man or a god? How can you remove societal biases from machine learning? How should solitary confinement in prisons be reformed?

Those are just a few of the 11 research projects being tackled this summer by College of Arts and Science Fellows at the University of Rhode Island.

The summer fellowship program funds undergraduates in an Arts and Sciences major to participate in research, scholarly or creative projects under the supervision of a faculty member for up to 10 weeks. This year, the program is awarding $28,000 in stipends supporting approximately 2,400 hours of research for students majoring in such fields as criminology and criminal justice, political science, computer science, and philosophy.

In addition to support from the College of Arts and Sciences RhodyNow Fund and its Deans Excellence Endowment, the fellowship program is supported by a generous gift from Bob and RenamarieDiMuccio in honor of President David M. Dooley. As President Dooley retires at the end of July, the DiMuccios wished to recognize his leadership in transforming URI over the last 12 years with a gift to support undergraduate research experiences that visibly impact students and build a pathway for their future success.

Hannah Beaucaire 22, a political science and criminology and criminal justice major from Gardiner, Maine, will spend the summer researching solitary confinement practices in U.S. prisons. Working with Assistant Professor Natalie Pifer, Beaucaire will examine large-scale reforms that her home state is enacting to determine if the reforms should be adopted nationally.

One of the reasons I was interested in studying solitary confinement was the extreme physiological consequences it has been known to cause, she said. For such an extreme practice, I find solitary confinement to be under-regulated.

The end result of her research will be an online platform that will include short videos providing a history of solitary confinement, its consequences and the reforms Maine is attempting. She plans to use social media to attract interest in the site, which she hopes will serve as an educational and advocacy tool.

Without the monetary award I would have spent most of my time working a summer job, she said, but now I get to use that time to study something I find really exciting.

For John Mancini 22, the summer will be spent reading the dialogues of the ancient Greek philosopher Plato to determine if Plato viewed fellow contemporary philosopher Socrates as a god or a mortal. Plato, who lived from about 428 to 348 B.C., wrote about 35 dialogues. Socrates was a main character in many.

How do you determine someones divinity?

Mancini, a philosophy and political science major from Westerly, will look at Platos writings to determine what he considered gods and the characteristics of his Forms, his theory of the metaphysical structure of the universe. He will also look at secondary sources to answer what makes a person divine, godlike, or a Form.

The Forms are what make things the way they are and so explain what things are in themselves, he said. For example, the Form of Beauty is responsible for all things that contain beauty; the Form of Tallness is the reason that some things are considered tall. Platos theory basically answers the why question: I am beautiful because I partake in the Beautiful; I am tall because I partake in Tallness.

Mancini will conduct research and discuss his conclusions with Professor Doug Reed, who specializes in ancient philosophy, and plans to write a paper explaining his findings.

When my findings are published, other philosophers will be able to offer me pushback and constructive criticism, he said. This will allow me to better develop my positionshould I need to. Philosophy is very much a discussion, and after drawing my conclusions, someone is bound to disagree with me. I welcome any opposition so philosophers can gain a fuller understanding of Platonic dialogues.

This summer, Jacob Afonso 22, a computer science major, will be researching fairness and bias in machine learning models, under the supervision of Assistant Professor Sarah Brown. The goal of the project is to test and find ways to eliminate biases from the models.

In the data used to create machine learning models, societal biases are often present, said Afonso, who lives in Smithfield. When using biased data, the resulting model used for any sort of predicting will have those underlying biases.

I wanted to research this because I believe this is one of the largest areas of machine learning that makes people skeptical of its effectiveness, he added. It is also important for the future of equality of all groups of people as the use of machine learning continues to grow.

Afonsos research will include reading papers on the topic and learning code libraries, which hold the code for the different machine learning algorithms. He will use those to create and test fair models. Eventually, the outcome of his research will provide different ideas for removing biases, along with an analysis of the best and worst of them, he said.

Other 2021 Arts & Science Fellows are:

Mia Giglietti 23, of West Hartford, Connecticut, who is majoring in political science and economics with a minor in Spanish, will analyze economic literature over the 20th century to look at the elite interconnections among corporate boards and their links with governmental bodies to see how those connections benefit those corporations.

I wanted to participate in this because Ive always wanted to learn more about how corporations and economic/political corruption work to maintain the power of major corporations and the wealthy, said Giglietti, who is working with Assistant Professor Nina Eichacker. I think it is crucial to understand those concepts in the era of severe income inequality that we are currently living through.

Samantha Murphy 22, of Cumberland, who is studying applied economics with a minor in music, will work with Associate Professor Smita Ramnarain to compare public health disasters with other types of disasters, looking at how health disasters, such as the COVID-19 pandemic, interact with other crises and social inequalities, and how they have gendered impacts.

I wanted to do this research project because of my growing interest in heterodox economics, said Murphy. The fellowship is giving me the opportunity to do research under the guidance of a faculty member who is well-versed in the fields that I am interested in further studying after my undergraduate time at URI.

Jason Phillips 23, of Barrington, Illinois, who is majoring in English, journalism and writing and rhetoric, will be looking at how aware students are of the way colleges and universities treat adjunct faculty. Phillips plans to interview students around the country over the summer on their understanding and feelings about the issues with plans to publish a research paper.

I chose to research this because, for a large part, adjunct faculty are treated poorly, yet students do not often understand the problem, said Phillips, who is working with Professor Carolyn Betensky. I am passionate about understanding how students truly feel about how their professors are treated at their universities.

Abigail Dodd 22, of Wakefield, Rhode Island, a history and gender and women studies major, will identify primary sources from URI Distinctive Collections to document the changing role of womenstudents and facultyat URI between 1950 and 1980. Her research will create a content module on women that will be used in the course The URI Campus: A Walk Through Time. Faculty mentor: Senior Lecturer Catherine DeCesare, with assistance from Karen Morse, director of Distinctive Collections.

Kevin Hart 22, of Wakefield, Massachusetts, who is majoring in political science and history with a minor in economics, is researching right-wing terrorism in the U.S. and the motivation behind it. I wanted to research the topic because it is an under-researched case of political violence but an important one with important implications, he said. Working with Assistant Professor Brendan Skip Mark will give me the guidance and experience I need to develop a worthwhile and academically sound project.

Sierra Obi 21, of Danville, New Hampshire, who is majoring in computer science and Spanish, is working on a project exploring computer authentication difficulty faced by people with upper extremity impairment part of National Science Foundation-funded research being conducted by Assistant Professor Krishna Venkatasubramanian. Obi is working to understand the reasons and circumstances around who people with the impairment share their personal computing devices and credentials with in an effort to improve login security.

Alfred Timperley 23, of East Greenwich, who is majoring in computer science and data science, will be working on a project to develop a novel tool that will enable future research into program classification source code authorship, plagiarism detection, malware identification, and others. Faculty mentor: Assistant Professor Marco Alvarez.

Ethan Wyllie 22, of North Kingstown, who is majoring in political science and Spanish, is researching racial inequality in welfare participation. He will track participation rates by different groupsWhites, Blacks, Hispanics, and immigrantsat the state level over the past 20 years to determine racial disparity in U.S. social safety net coverage. Faculty mentor: Associate Professor Ping Xu.

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From Socrates to machine learning: Arts and Sciences fellows spend the summer on research projects - URI Today

The Earth and space sciences present ideal use cases for machine learning (ML) applications because the problems being addressed are globally important and the data are often freely available, voluminous, and of high quality.Machine learning (ML), loosely defined as the ability of computers to learn from data without being explicitly programmed, has become tremendously popular in technical disciplines over the past decade or so, with applications including complex game playing and image recognition carried out with superhuman capabilities. The Earth and space sciences (ESS) community has also increasingly adopted ML approaches to help tackle pressing questions and unwieldy data sets. From 2009 to 2019, for example, the number of studies involving ML published in AGU journals approximately doubled.

In many ways, ESS present ideal use cases for ML applications because the problems being addressedlike climate change, weather forecasting, and natural hazards assessmentare globally important; the data are often freely available, voluminous, and of high quality; and computational resources required to develop ML models are steadily becoming more affordable. Free computational languages and ML code libraries are also now available (e.g., scikit-learn, PyTorch, and TensorFlow), contributing to making entry barriers lower than ever. Nevertheless, our experience has been that many young scientists and students interested in applying ML techniques to ESS data do not have a clear sense of how to do so.

An ML algorithm can be thought of broadly as a mathematical function containing many free parameters (thousands or even millions) that takes inputs (features) and maps those features into one or more outputs (targets). The process of training an ML algorithm involves optimizing the free parameters to map the features to the targets accurately.

There are two broad categories of ML algorithms relevant in most ESS applications: supervised and unsupervised learning (a third category, reinforcement learning, is used infrequently in ESS). Supervised learning, which involves presenting an ML algorithm with many examples of input-output pairs (called the training set), can be further divided, according to the type of target that is being learned, as either categorical (classification; e.g., does a given image show a star cluster or not?) or continuous (regression; e.g., what is the temperature at a given location on Earth?). In unsupervised learning, algorithms are not given a particular target to predict; rather, an algorithms task is to learn the natural structure in a data set without being told what that structure is.

Supervised learning is more commonly used in ESS, although it has the disadvantage that it requires labeled data sets (in which each training input sample must be tagged, or labeled, with a corresponding output target), which are not always available. Unsupervised learning, on the other hand, may find multiple structures in a data set, which can reveal unanticipated patterns and relationships, but it may not always be clear which structures or patterns are correct (i.e., which represent genuine physical phenomena).

Books and classes about ML often present a range of algorithms but leave people to imagine specific applications of these algorithms on their own.Books and classes about ML often present a range of algorithms that fall into one of the above categories but leave people to imagine specific applications of these algorithms on their own. However, in practice, it is usually not obvious how such approaches (some seemingly simple) may be applied in a rich variety of ways, which can create an imposing obstacle for scientists new to ML. Below we briefly describe various themes and ways in which ML is currently applied to ESS data sets (Figure 1), with the hope that this listnecessarily incomplete and biased by our personal experienceinspires readers to apply ML in their research and catalyzes new and creative use cases.

One of the simplest and most powerful applications of ML algorithms is pattern identification, which works particularly well with very large data sets that cannot be traversed manually and in which signals of interest are faint or highly dimensional. Researchers, for example, applied ML in this way to detect signatures of Earth-sized exoplanets in noisy data making up millions of light curves observed by the Kepler space telescope. Detected signals can be further split into groups through clustering, an unsupervised form of ML, to identify natural structure in a data set.

Conversely, atypical signals may be teased out of data by first identifying and excluding typical signals, a process called anomaly or outlier detection. This technique is useful, for example, in searching for signatures of new physics in particle collider experiments.

An important and widespread application of supervised ML is the prediction of time series data from instruments or from an index (or average value) that is intended to encapsulate the behavior of a large-scale system. Approaches to this application often involve using past data in the time series itself to predict future values; they also commonly involve additional inputs that act as drivers of the quantities measured in the time series. A typical example of ML applied to time series in ESS is its use in local weather prediction, with which trends in observed air temperature and pressure data, along with other quantities, can be predicted.

In many instances, however, predicting a single time series of data is insufficient, and knowledge of the temporal evolution of a physical system over regional (or global) spatial scales is required. This spatiotemporal approach is used, for example, in attempts to predict weather across the entire globe as a function of time and 3D space in high-capacity models such as deep neural networks.

Physics-based simulations can take days or weeks to run on even the most powerful computers. An alternate solution is to train ML models to act as emulators for physics-based models.Traditional, physics-based simulations (e.g., global climate models) are often used to model complex systems, but such models can take days or weeks to run on even the most powerful computers, limiting their utility in practice. An alternate solution is to train ML models to act as emulators for physics-based models or to replicate computationally intensive portions within such models. For example, global climate models that run on a coarse grid (e.g., 50- to 100-kilometer resolution) can include subgrid processes, like convection, modeled using ML-based parameterizations. Results with these approaches are often indistinguishable from those produced by the original model alone but can run millions or billions of times faster.

Many physics-based simulations proceed by integrating a set of partial differential equations (PDEs) that rely on time-varying boundary conditions and other conditions that drive interior parts of the simulation. The physics-based model then propagates information from these boundary and driver conditions into the simulation spaceimagine, for example, a 3D cube being heated at its boundary faces with time-varying heating rates or with thermal conductivity that varies spatiotemporally within the cube. ML models can be trained to reflect the time-varying parameterizations both within and along the simulation boundaries of a physical model, which again may be computationally cheaper and faster.

If a spatiotemporal ML model of a physical system can be trained to produce accurate results under a variety of input conditions, then the implication is that the model implicitly accounts for all the physical processes that drive that system, and thus, it can be probed to gain insights into how the system works. Certain algorithms (e.g., random forests) can automatically provide a ranking of feature importance, giving the user a sense of which input parameters affect the output most and hence an intuition about how the system works.

More sophisticated techniques, such as layerwise relevance propagation, can provide deeper insights into how different features interact to produce a given output at a particular location and time. For example, a neural network trained to predict the evolution of the El NioSouthern Oscillation (ENSO), which is predominantly associated with changes in sea surface temperature in the equatorial Pacific Ocean, revealed that precursor conditions for ENSO events occur in the South Pacific and Indian Oceans.

A ubiquitous challenge in ESS is to invert observations of a physical entity or process into fundamental information about the entity or the causes of the process (e.g., interpreting seismic data to determine rock properties). Historically, inverse problems are solved in a Bayesian framework requiring multiple runs of a forward model, which can be computationally expensive and often inaccurate. ML offers alternative methods to approach inverse problems, either by using emulators to speed up forward models or by using physics-informed machine learning to discover hidden physical quantities directly. ML models trained on prerun physics-based model outputs can be used for rapid inversion.

Satellite observations often provide global, albeit low-resolution and sometimes indirect (i.e., proxy-based), measurements of quantities of interest, whereas local measurements provide more accurate and direct observations of those quantities at smaller scales. A popular and powerful use for ML models is to estimate the relationship between global proxy satellite observations and local accurate observations, which enables the creation of estimated global observations on the basis of localized measurements. This approach often includes the use of ML to create superresolution images and other data products.

Typically, uncertainty in model outputs is quantified using a single metric such as the root-mean-square of the residual (the difference between model predictions and observations). ML models can be trained to explicitly predict the confidence interval, or inherent uncertainty, of this residual value, which not only serves to indicate conditions under which model predictions are trustworthy (or dubious) but can also be used to generate insights about model performance. For instance, if there is a large error at a certain location in a model output under specific conditions, it could suggest that a particular physical process is not being properly represented in the simulation.

Domain experts analyzing data from a given system, even in relatively small quantities, are often able to extrapolate the behavior of the systemat least conceptuallybecause of their understanding of and trained intuition about the system based on physical principles. In a similar way, laws and relationships that govern physical processes and conserved quantities can be explicitly encoded into neural network algorithms, resulting in more accurate and physically meaningful models that require less training data.

In certain applications, the values of terms or coefficients in PDEs that drive a systemand thus that should be represented in a modelare not known. Various ML algorithms were developed recently that automatically determine PDEs that are consistent with the available physical observations, affording a new and powerful discovery tool.

In still newer work, ML methods are being developed to directly solve PDEs. These methods offer accuracy comparable to traditional numerical integrators but can be dramatically faster, potentially allowing large-scale simulations of complex sets of PDEs that have otherwise been unattainable.

The Earth and space sciences are poised for a revolution centered around the application of existing and rapidly emerging ML techniques to large and complex ESS data sets being collected. These techniques have great potential to help scientists address some of the most urgent challenges and questions about the natural world facing us today. We hope the above list sparks creative and valuable new applications of ML, particularly among students and young scientists, and that it becomes a community resource to which the ESS community can add more ideas.

We thank the AGU Nonlinear Geophysics section for promoting interdisciplinary, data-driven research, for supporting the idea of writing this article, and for suggesting Eos as the ideal venue for dissemination. The authors gratefully acknowledge the following sources of support: J.B. from subgrant 1559841 to the University of California, Los Angeles, from the University of Colorado Boulder under NASA Prime Grant agreement 80NSSC20K1580, the Defense Advanced Research Projects Agency under U.S. Department of the Interior award D19AC00009, and NASA/SWO2R grant 80NSSC19K0239 and E.C. from NASA grants 80NSSC20K1580 and 80NSSC20K1275. Some of the ideas discussed in this paper originated during the 2019 Machine Learning in Heliophysics conference.

Jacob Bortnik ([emailprotected]), University of California, Los Angeles; and Enrico Camporeale, Space Weather Prediction Center, NOAA, Boulder, Colo.; also at Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder

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Ten Ways to Apply Machine Learning in Earth and Space Sciences - Eos

TROY Artificial intelligence and machine learning are revolutionizing the ways in which we live, work, and spend our free time, from the smart devices in our homes to the tasks our phones can carry out. This transformation is being made possible by a surge in data and computing power that can help machine learning algorithms not only perform device-specific tasks, but also help them gain intelligence or knowledge over time.

In the not-so-distant future, artificial intelligence and machine learning tasks will be carried out among connected devices through wireless networks, dramatically enhancing the capabilities of future smartphones, tablets, and sensors, and achieving whats known as distributed intelligence. As technology stands right now, however, machine learning algorithms are not efficient enough to be run over wireless networks and wireless networks are not yet ready to transmit this type of intelligence.

With the support of a National Science Foundation Faculty Early Career Development Program grant, Tianyi Chen, an assistant professor of electrical, computer, and systems engineering at Rensselaer Polytechnic Institute and member of the Rensselaer-IBM Artificial Intelligence Research Collaboration (AIRC), is exploring how to make such knowledge-sharing tools a reality.

I think in the future, the main terminal of intelligence will be our phones. Our phones will be able to control our computers, our cars, our meeting rooms, our apartments, Chen said. This will be powered by resource-efficient machine learning algorithms and also the support of future wireless networks.

Through his collaboration with the Lighting Enabled Systems and Applications Center at Rensselaer, Chen will validate the algorithms he develops using the centers smart conference room.

The conference room is equipped with devices that are capable of sensing the environment, processing that information, and efficiently sharing it with other devices on the network the same framework the algorithms are being designed to function within.

We need to redesign our wireless networks to support not only traditional traffic, like video and voice, but to support new traffic such as transmittable intelligence, Chen said. We need to design more efficient learning algorithms that are suitable for running on the wireless network.

Chen also stressed the importance of ensuring that knowledge-sharing algorithms only extract anonymized information in order to maintain data privacy as our devices and daily lives become increasingly networked. While the goals of this research are foundational in nature.

Chen said the potential for future applications is wide-ranging from power grids to urban transportation systems.

Founded in 1824, Rensselaer Polytechnic Institute is Americas first technological research university. Rensselaer encompasses five schools, 32 research centers, more than 145 academic programs, and a dynamic community made up of more than 7,600 students and more than 100,000 living alumni. Rensselaer faculty and alumni include more than 145 National Academy members, six members of the National Inventors Hall of Fame, six National Medal of Technology winners, five National Medal of Science winners, and a Nobel Prize winner in Physics. With nearly 200 years of experience advancing scientific and technological knowledge, Rensselaer remains focused on addressing global challenges with a spirit of ingenuity and collaboration.

This feature was originally published on the Rensselaer website.

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Companies everywhere are pouring resources into artificial intelligence (A.I.) and machine learning (ML) initiatives. Many technologists believe that apps smartened with A.I. and ML tools will eventually offer better customer personalization; managers hope that A.I. will lead to better data analysis, which in turn will power better business strategies.

But which industries are actually hiring A.I. specialists? If you answer that question, it might give you a better idea of where those resources are being deployed. Fortunately,CompTIAs latest Tech Jobs Reportoffers a breakdown of A.I. hiring, using data from Burning Glass, which collects and analyzes millions of job postings from across the country. Check it out:

Perhaps its no surprise that manufacturing tops this list; after all, manufacturers have been steadily automating their production processes for years, and it stands to reason that they would turn to A.I. and ML to streamline things even more. In theory, A.I. will also help manufacturers do everythingfrom reducing downtime to improving supply chainsalthough it may take some time to get the models right.

The presence of healthcare, banking, and public administration likewise seem logical.These three industries have the money to invest in A.I. and ML right now and have the greatest opportunity to see the investment pay off, fast, Gus Walker, director of product at Veritone, an A.I. tech company based in Costa Mesa, California,told Dicelate last year.That being said, the pandemic has caused industries hit the hardest to take a step back and look at how they can leverage AI and ML to rebuild or adjust in the new normal.

Compared to overall tech hiring, the number of A.I.-related job postings is still relatively small. Right now, mastering and deploying A.I. and machine learning is something of a specialist industry; but as these technologies become more commodified, and companies develop tools that allow more employees to integrate A.I. and ML into their projects, the number of job postings for A.I. and ML positions could increase over the next several years. Indeed, one IDC report from 2020 found three-quarters of commercial enterprise applications could lean on A.I. in some way by2021.

Its also worth examining where all that A.I. hiring is taking place; its interesting that Washington DC tops this particular list, with New York City a close second; Silicon Valley and Seattle, the nations other big tech hubs, are somewhat further behind, at least for the moment. Washington DC is notable not only for federal government hiring, but the growing presence of companies such as Amazon that hunger for talent skilled in artificial intelligence:

Jobs that leverage artificial intelligence are potentially lucrative, with a current median salary (according to Burning Glass)of $105,000. Its also a skill-set thatmore technologists may need to become familiar with, especially managers and executives.A.I. is not going to replace managers but managers that use A.I. will replace those that do not, Rob Thomas, senior vice president of IBMscloudand data platform,recently told CNBC. If you mention A.I. or ML on your resume and applications, make sure you know your stuff before the job interview; chances are good youll be tested on it.

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Which Industries are Hiring AI and Machine Learning Roles? - Dice Insights

AIOps and MLOps are terms that might appear to have a similar meaning, given that the acronyms on which they are based -- AI and ML -- are often used in similar contexts. However, AIOps and MLOps mean radically different things.

A team or company might use both AIOps and MLOps at the same time but not for the same purposes. Let's dig into what each is individually and then whether they can be used together.

AIOps, which stands for artificial intelligence for IT operations, is the use of AI to help perform IT operations work.

For example, a team that uses AIOps might use AI to analyze the alerts generated by its monitoring tools and then prioritize the alerts so that the team knows which ones to focus on. Or an AIOps tool could automatically find and fix an application that has crashed, using AI to determine the cause of the problem and the proper remediation.

Short for machine learning IT operations, MLOps is a technique that helps organizations optimize their use of machine learning and AI tools.

The core idea behind MLOps is that the stakeholders involved in making decisions about machine learning and AI are typically siloed from each other. Data scientists know how AI and machine learning algorithms work. But they don't usually collaborate closely with IT engineers, responsible for deploying AI and machine learning tools, or with compliance officers, who manage security and regulatory aspects of machine learning and AI use.

Put another way, MLOps is like DevOps in that it seeks to break down the silos that separate different types of teams. But, whereas DevOps is all about encouraging collaboration between developers and IT operations teams, MLOps focuses on collaboration between everyone who plays a role in choosing or managing machine learning and AI resources.

It's tempting to assume that AIOps and MLOps basically mean the same thing, given that AI and machine learning mean similar -- albeit not identical -- things.

But, in fact, the terms are not closely related at all. You could argue that a healthy MLOps practice would help organizations choose and deploy AIOps tools, but that's only one possible goal of MLOps. Beyond that, AIOps and MLOps don't intersect.

This is a sign that the tech community has overused the -Ops construction. When you can take any noun, add the -Ops suffix and invent a new buzzword -- without logical consistency to unite it with similarly formed buzzwords -- it might be time to move onto new techniques for labeling buzzwords.

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Battle of the buzzwords: AIOps vs. MLOps square up - TechTarget

New research using machine learning on images of everyday items is improving the accuracy and speed of detecting respiratory diseases, reducing the need for specialist medical expertise.

In a study by researchers at Edith Cowan University (Perth, Australia), the results of this technique, known as transfer learning, achieved a 99.24% success rate when detecting COVID-19 in chest X-rays. The study tackles one of the biggest challenges in image recognition machine learning: algorithms needing huge quantities of data, in this case images, to be able to recognize certain attributes accurately.

According to the researchers, this was incredibly useful for identifying and diagnosing emerging or uncommon medical conditions. The key to significantly decreasing the time needed to adapt the approach to other medical issues was pre-training the algorithm with the large ImageNet database. The researchers hope that the technique can be further refined in future research to increase accuracy and further reduce training time.

"Our technique has the capacity to not only detect COVID-19 in chest x-rays, but also other chest diseases such as pneumonia. We have tested it on 10 different chest diseases, achieving highly accurate results," said ECU School of Science researcher Dr. Shams Islam. "Normally, it is difficult for AI-based methods to perform detection of chest diseases accurately because the AI models need a very large amount of training data to understand the characteristic signatures of the diseases. The data needs to be carefully annotated by medical experts, this is not only a cumbersome process, it also entails a significant cost. Our method bypasses this requirement and learns accurate models with a very limited amount of annotated data. While this technique is unlikely to replace the rapid COVID-19 tests we use now, there are important implications for the use of image recognition in other medical diagnoses."

Related Links:Edith Cowan University

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Machine Learning Algorithm Trained on Images of Everyday Items Detects COVID-19 in Chest X-Rays with 99% Accuracy - HospiMedica