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Plagiarism Detection Tools Offer a False Sense of Accuracy The Markup – The Markup
When Katherine Pickering Antonova became a history professor in 2008, she got access to the plagiarism detection software tools Turnitin and SafeAssign. At first blush, she thought the technology would be great. She had just finished a graduate program where she had manually graded papers as a teaching assistant, meticulously checking students suspect phrases to see if any showed up elsewhere.
But her first use of the plagiarism checkers gave her a jolt. The software suggested the majority of her students had copied portions of their essays.
Soon she realized the lie in how the tools were described to her. Its not tracking plagiarism at all, Pickering Antonova said. Its just flagging matching text. Those two concepts have different standards; plagiarism is a subjective assessment of misconduct, but scholars may have matching words in their academic articles for a variety of legitimate reasons.
Plagiarism checkers are built into The City University of New Yorks learning management system, where faculty members post assignments and students submit them. As at many colleges throughout the country, scanning for plagiarism in submitted assignments is the default. But fed up with false flags and the countless hours required to check potentially plagiarized passages against the source material Turnitin and SafeAssign highlight, Pickering Antonova gave up on the tools entirely a couple years ago.
The bots are literally worse than useless, she said. They do harm, and they dont find anything I couldnt find by myself.
Some experts agree that Claudine Gay, Harvards ousted president and a widely respected political scientist, recently became the latest victim of this technology. She was forced to step down from the presidency after an accuser flagged nearly 50 examples from her writing that they called plagiarism. But many of the examples looked a lot like what Pickering Antonova considered a waste of her time when she was grading student work.
The Voting Rights Act of 1965 is often cited as one of the most significant pieces of civil rights legislation passed in our nations history, Gay wrote in one paper. Her accuser says she plagiarized David Canons description of the landmark lawbut as the Washington Free Beacon reported in publishing the allegations, Canon himself disagrees, arguing Gay had done nothing wrong.
The controversy over Gays alleged plagiarism has roiled the academic community, and while much of the attention has been on the political maneuvering behind her ouster and the definition of plagiarism, some scholars have commented on the detection software that was likely behind it. The fact is, however, that students, not academics, bear the brunt of the tools shoddy analyses. Turnitin is the industry leader in marshaling text analysis tools to assess academic integrity, boasting partnerships with more than 20,000 institutions globally and a repository of over 1.8 billion student paper submissions (and still counting).
The companies that are marketing plagiarism detection tools tend to acknowledge their limitations. While they may be referred to as plagiarism checkers, the products are described as highlighting text similarities or duplicate content. They scan billions of webpages and scholarly articles looking for those matches and surface them for a reviewer. Some, like Grammarlys, are marketed to writers and offer to help people add proper citations where they may have forgotten them. It isnt meant to police plagiarism, but rather help writers avoid it. Turnitin specifically says its Similarity Report does not check for plagiarism.
Still, the tools are frequently used to justify giving students zeroes on their assignmentsand the students most likely to get such dismissive grading are those at less-selective institutions, where faculty are overstretched and underpaid.
For her part, Pickering Antonova came to feel guilty about putting students through the stress of seeing their Turnitin results.
They see their paper is showing up 60 percent plagiarized, and they have a heart attack, she said.
Plagiarism does not carry a legal definition. Institutions create their own plagiarism policies, and academic fields have norms about how to credit and cite sources in scholarly text. Plagiarism checkers are not designed with such nuance. It is up to users to follow up their algorithmic output with good, human judgment.
Jo Guldi, a professor of quantitative methods at Emory University, recently published The Dangerous Art of Text Mining: A Methodology for Digital History and jumped into the Gay plagiarism controversy with a now-deleted post on X before Christmas. She pointed out that computers can search for five-word overlaps in text but argued that such repetition does not equal plagiarism: the technology of text mining can be used to destroy the career of any scholar at any time, she wrote.
By phone, Guldi said that while she didnt cover plagiarism detection in her book, the parallel is clear. Her book traces bad conclusions reached because people fail to critically analyze the data. She, too, has used Turnitin in her classes and recognized the findings cannot be taken at face value.
You look at them and you see you have to apply judgment, she said. Its always a judgment call.
Many scholars, including those Gay is supposed to have plagiarized, have come to Gays defense over the course of the last month, arguing the text similarities highlighted do not rise to the level of plagiarism.
Machine Learning
Stanford study found AI detectors are biased against non-native English speakers
Yet her accuser has identified nearly 50 examples of overlap, pairing her writing with that of other scholars and insisting there is a pattern of academic misconduct. The sheer number of examplesand promise of more to comehelped seal Gays fate. And some scholars worry anyone with enemies could be next.
Ian Bogost, a professor at Washington University in St. Louis, mulled in The Atlantic what a full-bore plagiarism war could look like, running his own dissertation through iThenticate, a checker run by the same company as Turnitin that is marketed to researchers, publishers, and scholars.
Bill Ackman, a billionaire Harvard megadonor, signaled his commitment to participating in such a war after Business Insider launched its own grenade, publishing an analysis last week that accused his wife, Neri Oxman, of plagiarizing parts of her dissertation. Oxman got her Ph.D. at MIT in 2010 before joining the faculty and then leaving to become an entrepreneur. Suspecting someone from MIT encouraged Business Insider to take a closer look at her dissertation, Ackman posted on X that he was going to begin a review of the work of all current @MIT faculty members, President Kornbluth, other officers of the Corporation, and its board members for plagiarism.
He later added, Why would we stop at MIT? Dont we have to do a deep dive into academic integrity at Harvard as well? What about Yale, Princeton, Stanford, Penn, Dartmouth? You get the point.
Its unclear which tool Gays accuser used to identify their examples, but experts agree the accusations seem to come from a text comparison algorithm. A Markup analysis of five of Gays papers in the Grammarly and EasyBib plagiarism checkers did not turn up any of the plagiarism accusations that have surfaced in recent months. Grammarlys tool did flag instances of text overlap between Gays writing and other scholars, sometimes because they were citing her paper, but sometimes because the two authors were simply describing similar things. Gays 2017 political science paper A Room for Ones Own? is the subject of more than half a dozen accusations of plagiarism that Grammarly didnt flagbut the tool did, for example, suggest her line The estimated coefficients and standard errors from the may have been plagiarized from an article about diabetes in Bali.
Analyzing the same paper, Turnitin ignored several of the lines included in complaints against her but it did flag four from two academic papers. It also found other similarities, suggesting, for example, that the phrase receive a 10-year stream of tax credits warranted review.
Credit:Turnitin
David Smith, an associate professor of computer science at Northeastern University, has studied natural language processing and computational linguistics. He said plagiarism detection tools tend to start with what is called a null model. The algorithm is given very few assumptions and simply told to identify matching words across texts. To find examples in Gays writing, he said, it basically took people looking through the really low-precision output of these models.
Machine Learning
A Markup examination of a typical college shows how students are subject to a vast and growing array of watchful tech, including homework trackers, test-taking software, and even license platereaders
Somebody could have trained a better model that had higher precision, Smith said. That doesnt seem to be how it went in this case.
The result was a long list of plagiarism accusations most scholars found baffling.
Turnitin introduced its similarity check in 2000. Since then, plagiarism analyses have become the norm for editors of some academic journals as well as many college and university faculty members. Yet the tool is not universal. Many users, like Pickering Antonova, have decided the software isnt worth the time and dont align with their teaching goals. This has created two distinct classes of people: those who are subjected to plagiarism checkers and those who are not. For professional academics, Gays case highlights the concern that anyone with a high profile who makes the wrong enemy could quickly become part of the former group.
For students, its often just a matter of their schools norms. Plagiarism checkers can seem like a straightforward assessment of the originality of student work, reporting a percentage of the paper that may have been plagiarized. For faculty members who dont have the time to look at the dozens of false flags, it can be easy to rely on the total percentage and grade accordingly.
This behavior worries Smith, the computer scientist. Getting a quantification makes it easier to just judge a lot of student papers at scale, he said. Thats not whats going on in the Claudine Gay case but is troubling about whats going on with students subjection to these methods.
Tech companies have produced a steady stream of new tools for educators concerned with students cheating, including AI detectors that followed the widespread adoption of ChatGPT. With each new tool comes a promise of scientific accuracy and cutting-edge analysis of unbiased data.
But as Claudine Gays case demonstratesand the threat of the plagiarism wars promisesplagiarism detection is far from precise.
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Minimizing the Reality Gap in Quantum Devices with Machine Learning – AZoQuantum
A major obstacle facing quantum devices has been solved by a University of Oxford study that leveraged machine learning capabilities. The results show how to bridge the reality gap, or the discrepancy between expected and observed behavior from quantum devices, for the first time. Physical Review X has published the findings.
Image Credit:metamorworks/Shutterstock.com
Numerous applications, such as drug development, artificial intelligence, financial forecasting, and climate modeling, might be significantly improved by quantum computing. However, this will necessitate efficient methods for combining and scaling separate quantum bits (also known as qubits). Inherent variability, which occurs when even seemingly similar units display distinct behaviors, is a significant obstacle to this.
It is assumed that nanoscale flaws in the materials utilized to create quantum devices are the source of functional variability. This internal disorder cannot be represented in simulations since these cannot be measured directly, which accounts for the discrepancy between expected and observed results.
The study team addressed this by indirectly inferring certain disease traits through the use of a physics-informed machine learning technique. This was predicated on how the devices intrinsic instability impacted the electron flow.
As an analogy, when we play crazy golf the ball may enter a tunnel and exit with a speed or direction that doesnt match our predictions. But with a few more shots, a crazy golf simulator, and some machine learning, we might get better at predicting the balls movements and narrow the reality gap.
Natalia Ares, Study Lead Researcher and Associate Professor, Department of Engineering Science, University of Oxford
One quantum dot device was used as a test subject, and the researchers recorded the output current across it at various voltage settings. A simulation was run using the data to determine the difference between the measured current and the theoretical current in the absence of an internal disturbance.
The simulation was forced to discover an internal disorder arrangement that could account for the results at all voltage levels by monitoring the current at numerous distinct voltage settings. Deep learning was combined with statistical and mathematical techniques in this method.
Ares added, In the crazy golf analogy, it would be equivalent to placing a series of sensors along the tunnel, so that we could take measurements of the balls speed at different points. Although we still cant see inside the tunnel, we can use the data to inform better predictions of how the ball will behave when we take the shot.
The novel model not only identified appropriate internal disorder profiles to explain the observed current levels, but it also demonstrated the ability to precisely forecast the voltage settings necessary for particular device operating regimes.
Most importantly, the model offers a fresh way to measure the differences in variability between quantum devices. This could make it possible to predict device performance more precisely and aid in the development of ideal materials for quantum devices. It could guide compensatory strategies to lessen the undesirable consequences of material flaws in quantum devices.
Similar to how we cannot observe black holes directly but we infer their presence from their effect on surrounding matter, we have used simple measurements as a proxy for the internal variability of nanoscale quantum devices. Although the real device still has greater complexity than the model can capture, our study has demonstrated the utility of using physics-aware machine learning to narrow the reality gap.
David Craig, Study Co-Author and PhD Student, Department of Materials, University of Oxford
Craig, D. L., et. al. (2023) Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning. Physical Review X. doi:10.1103/PhysRevX.14.011001
Source: https://www.ox.ac.uk/
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Machine Learning for Predicting Oliguria in Intensive Care Units | Healthcare News – Medriva
Intensive care units (ICUs) are critical environments that deal with high-risk patients, where early detection of complications can significantly improve patient outcomes. Oliguria, a condition characterized by low urine output, is a common concern in ICUs and often signals acute kidney injury (AKI). Early prediction of oliguria can lead to timely intervention and better management of patients. Recent studies have shown that machine learning, a branch of artificial intelligence, can be effectively used to predict the onset of oliguria in ICU patients.
A retrospective cohort study aimed to develop and evaluate a machine learning algorithm for predicting oliguria in ICU patients. The study used electronic health record data from 9,241 patients admitted to the ICU between 2010 and 2019. The machine learning model demonstrated high accuracy in predicting the onset of oliguria at 6 hours and 72 hours with Area Under the Curve (AUC) values of 0.964 and 0.916, respectively. This suggests that the machine learning model can be a valuable tool for early identification of patients at risk of developing oliguria, enabling prompt intervention and optimal management of AKI.
The machine learning model identified several important variables for predicting oliguria. These included urine values, severity scores (SOFA score), serum creatinine, oxygen partial pressure, fibrinogen, fibrin degradation products, interleukin 6, and peripheral temperature. By taking into account these variables, the model was able to provide accurate predictions. The use of machine learning also allows for the continuous update and improvement of the model as more data becomes available, increasing its predictive accuracy over time.
Interestingly, the models accuracy varied based on several factors, including sex, age, and furosemide administration. This highlights the complex nature of predicting oliguria and the need for personalized, patient-specific models. It also underlines the potential of machine learning to adapt and learn from varying patient characteristics, providing more precise and individualized predictions.
The utilization of machine learning is not limited to predicting oliguria. Another study aimed to develop a machine learning model for early prediction of adverse events and treatment effectiveness in patients with hyperkalemia, a condition characterized by high levels of potassium in the blood. This study, too, achieved promising results, underscoring the potential of machine learning to revolutionize various aspects of patient care in the ICU setting.
The use of machine learning models in healthcare, and particularly in intensive care units, is a promising avenue for improving patient outcomes. By predicting the onset of conditions like oliguria, these models can provide critical early warnings that allow healthcare providers to intervene promptly. However, its crucial to remember that these models are tools to assist clinicians and not replace their judgment. As research continues and more data becomes available, these models are expected to become even more accurate and valuable in the future.
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Machine Learning in Business: 5 things a Data Science course won’t teach you – Towards Data Science
The author shares some important aspects of Applied Machine Learning that can be overlooked in formal Data Science education.
If you feel that I used a clickbaity title for this article, Id agree with you but hear me out! I have managed multiple junior data scientists over the years and in the last few years I have been teaching an applied Data Science course to Masters and PhD students. Most of them have great technical skills but when it comes to applying Machine Learning to real-world business problems, I realized there were some gaps.
Below are the 5 elements that I wish data scientists were more aware of in a business context:
Im hoping that reading this will be helpful to junior and mid-level data scientists to grow their career!
In this piece, I will focus on a scenario where data scientists are tasked with deploying machine learning models to predict customer behavior. Its worth noting that the insights can be applicable to scenarios involving product or sensor behaviors as well.
Lets start with the most critical of all: the What that you are trying to predict. All subsequent steps data cleaning, preprocessing, algorithm, feature engineering, hyperparameters optimization become futile unless you are focusing on the right target.
In order to be actionable, the target must represent a behavior, not a data point.
Ideally, your model aligns with a business use case, where actions or decisions will be based on its output. By making sure the target you are using is a good representation of a customer behavior, it is easy for the business to understand and utilize these models outputs.
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The 11 Best AI Tools for Data Science to Consider in 2024 – Solutions Review
Solutions Reviews listing of the best AI tools for data science is an annual sneak peek of the top tools included in our Buyers Guide for Data Science and Machine Learning Platforms. Information was gathered via online materials and reports, conversations with vendor representatives, and examinations of product demonstrations and free trials.
The editors at Solutions Review have developed this resource to assist buyers in search of the best AI tools for data science to fit the needs of their organization. Choosing the right vendor and solution can be a complicated process one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, weve profiled the best AI tools for data science all in one place. Weve also included platform and product line names and introductory software tutorials straight from the source so you can see each solution in action.
Note: The best AI tools for data science are listed in alphabetical order.
Platform: DataRobot Enterprise AI Platform
Related products: Paxata Data Preparation, Automated Machine Learning, Automated Time Series, MLOps
Description: DataRobot offers an enterprise AI platform that automates the end-to-end process for building, deploying, and maintaining AI. The product is powered by open-source algorithms and can be leveraged on-prem, in the cloud or as a fully-managed AI service.DataRobotincludesseveralindependent but fully integrated tools (PaxataData Preparation,Automated Machine Learning, Automated Time Series,MLOps, and AI applications), and each can be deployed in multiple ways to match business needs and IT requirements.
Platform: H2O Driverless AI
Related products: H2O 3, H2O AutoML for ML, H2O Sparkling Water for Spark Integration, H2O Wave
Description: H2O.ai offers a number of AI and data science products, headlined by its commercial platform H2O Driverless AI. Driverless AI is a fully open-source, distributed in-memory machine learning platform with linearscalability. H2O supports widely used statistical and machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O has also developedAutoMLfunctionality that automatically runs through all the algorithms to produce a leaderboard of the best models.
Platform: IBM Watson Studio
Related products: IBM Cloud Pak for Data, IBM SPSS Modeler, IBM Decision Optimization, IBM Watson Machine Learning
Description: IBM Watson Studio enables users to build, run, and manage AI models at scale across any cloud. The product is a part of IBM Cloud Pak for Data, the companys main data and AI platform. The solution lets you automate AI lifecycle management, govern and secure open-source notebooks, prepare and build models visually, deploy and run models through one-click integration, and manage and monitor models with explainable AI. IBM Watson Studio offers a flexible architecture that allows users to utilize open-source frameworks likePyTorch, TensorFlow, and scikit-learn.
https://www.youtube.com/watch?v=rSHDsCTl_c0
Platform: KNIME Analytics Platform
Related products: KNIME Server
Description: KNIME Analytics is an open-source platform for creating data science. It enables the creation of visual workflows via a drag-and-drop-style graphical interface that requires no coding. Users can choose from more than 2000 nodes to build workflows, model each step of analysis, control the flow of data, and ensure work is current. KNIME can blend data from any source and shape data to derive statistics, clean data, and extract and select features. The product leverages AI and machine learning and can visualize data with classic and advanced charts.
Platform: Looker
Related products: Powered by Looker
Description: Looker offers a BI and data analytics platform that is built on LookML, the companys proprietary modeling language. The products application for web analytics touts filtering and drilling capabilities, enabling users to dig into row-level details at will. Embedded analytics in Powered by Looker utilizes modern databases and an agile modeling layer that allows users to define data and control access. Organizations can use Lookers full RESTful API or the schedule feature to deliver reports by email or webhook.
Platform: Azure Machine Learning
Related products:Azure Data Factory, Azure Data Catalog, Azure HDInsight, Azure Databricks, Azure DevOps, Power BI
Description: The Azure Machine Learning service lets developers and data scientists build, train, and deploy machine learning models. The product features productivity for all skill levels via a code-first and drag-and-drop designer, and automated machine learning. It also features expansiveMLopscapabilities that integrate with existing DevOps processes. The service touts responsible machine learning so users can understand models with interpretability and fairness, as well as protect data with differential privacy and confidential computing. Azure Machine Learning supports open-source frameworks and languages likeMLflow, Kubeflow, ONNX,PyTorch, TensorFlow, Python, and R.
Platform: Qlik Analytics Platform
Related products: QlikView, Qlik Sense
Description: Qlik offers a broad spectrum of BI and analytics tools, which is headlined by the companys flagship offering, Qlik Sense. The solution enables organizations to combine all their data sources into a single view. The Qlik Analytics Platform allows users to develop, extend and embed visual analytics in existing applications and portals. Embedded functionality is done within a common governance and security framework. Users can build and embed Qlik as simple mashups or integrate within applications, information services or IoT platforms.
Platform: RapidMiner Studio
Related products:RapidMiner AI Hub, RapidMiner Go, RapidMiner Notebooks, RapidMiner AI Cloud
Description: RapidMiner offers a data science platform that enables people of all skill levels across the enterprise to build and operate AI solutions. The product covers the full lifecycle of the AI production process, from data exploration and data preparation to model building, model deployment, and model operations. RapidMiner provides the depth that data scientists needbut simplifies AI for everyone else via a visual user interface that streamlines the process of building and understanding complex models.
Platform: SAP Analytics Cloud
Related products:SAP BusinessObjects BI, SAP Crystal Solutions
Description: SAP offers a broad range of BI and analytics tools in both enterprise and business-user driven editions. The companys flagship BI portfolio is delivered via on-prem (BusinessObjects Enterprise), and cloud (BusinessObjects Cloud) deployments atop the SAP HANA Cloud. SAP also offers a suite of traditional BI capabilities for dashboards and reporting. The vendors data discovery tools are housed in the BusinessObjects solution, while additional functionality, including self-service visualization, are available through the SAP Lumira tool set.
Platform: Sisense
Description: Sisense makes it easy for organizations to reveal business insight from complex data in any size or format. The product allows users to combine data and uncover insights in a single interface without scripting, coding or assistance from IT. Sisense is sold as a single-stack solution with a back end for preparing and modeling data. It also features expansive analytical capabilities, and a front-end for dashboarding and visualization. Sisense is most appropriate for organizations that want to analyze large amounts of data from multiple sources.
Platform: Tableau Desktop
Related products:Tableau Prep, Tableau Server, Tableau Online, Tableau Data Management
Description: Tableau offers an expansive visual BI and analytics platform, and is widely regarded as the major player in the marketplace. The companys analytic software portfolio is available through three main channels: Tableau Desktop, Tableau Server, and Tableau Online. Tableau connects to hundreds of data sources and is available on-prem or in the cloud. The vendor also offers embedded analytics capabilities, and users can visualize and share data with Tableau Public.
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