Category Archives: Machine Learning
Adversarial attacks against machine learning systems everything you need to know – The Daily Swig
The behavior of machine learning systems can be manipulated, with potentially devastating consequences
In March 2019, security researchers at Tencent managed to trick a Tesla Model S into switching lanes.
All they had to do was place a few inconspicuous stickers on the road. The technique exploited glitches in the machine learning (ML) algorithms that power Teslas Lane Detection technology in order to cause it to behave erratically.
Machine learning has become an integral part of many of the applications we use every day from the facial recognition lock on iPhones to Alexas voice recognition function and the spam filters in our emails.
But the pervasiveness of machine learning and its subset, deep learning has also given rise to adversarial attacks, a breed of exploits that manipulate the behavior of algorithms by providing them with carefully crafted input data.
Adversarial attacks are manipulative actions that aim to undermine machine learning performance, cause model misbehavior, or acquire protected information, Pin-Yu Chen, chief scientist, RPI-IBM AI research collaboration at IBM Research, told The Daily Swig.
Adversarial machine learning was studied as early as 2004. But at the time, it was regarded as an interesting peculiarity rather than a security threat. However, the rise of deep learning and its integration into many applications in recent years has renewed interest in adversarial machine learning.
Theres growing concern in the security community that adversarial vulnerabilities can be weaponized to attack AI-powered systems.
As opposed to classic software, where developers manually write instructions and rules, machine learning algorithms develop their behavior through experience.
For instance, to create a lane-detection system, the developer creates a machine learning algorithm and trains it by providing it with many labeled images of street lanes from different angles and under different lighting conditions.
The machine learning model then tunes its parameters to capture the common patterns that occur in images that contain street lanes.
With the right algorithm structure and enough training examples, the model will be able to detect lanes in new images and videos with remarkable accuracy.
But despite their success in complex fields such as computer vision and voice recognition, machine learning algorithms are statistical inference engines: complex mathematical functions that transform inputs to outputs.
If a machine learning tags an image as containing a specific object, it has found the pixel values in that image to be statistically similar to other images of the object it has processed during training.
Adversarial attacks exploit this characteristic to confound machine learning algorithms by manipulating their input data. For instance, by adding tiny and inconspicuous patches of pixels to an image, a malicious actor can cause the machine learning algorithm to classify it as something it is not.
Adversarial attacks confound machine learning algorithms by manipulating their input data
The types of perturbations applied in adversarial attacks depend on the target data type and desired effect. The threat model needs to be customized for different data modality to be reasonably adversarial, says Chen.
For instance, for images and audios, it makes sense to consider small data perturbation as a threat model because it will not be easily perceived by a human but may make the target model to misbehave, causing inconsistency between human and machine.
However, for some data types such as text, perturbation, by simply changing a word or a character, may disrupt the semantics and easily be detected by humans. Therefore, the threat model for text should be naturally different from image or audio.
The most widely studied area of adversarial machine learning involves algorithms that process visual data. The lane-changing trick mentioned at the beginning of this article is an example of a visual adversarial attack.
In 2018, a group of researchers showed that by adding stickers to a stop sign(PDF), they could fool the computer vision system of a self-driving car to mistake it for a speed limit sign.
Researchers tricked self-driving systems into identifying a stop sign as a speed limit sign
In another case, researchers at Carnegie Mellon University managed to fool facial recognition systems into mistaking them for celebrities by using specially crafted glasses.
Adversarial attacks against facial recognition systems have found their first real use in protests, where demonstrators use stickers and makeup to fool surveillance cameras powered by machine learning algorithms.
Computer vision systems are not the only targets of adversarial attacks. In 2018, researchers showed that automated speech recognition (ASR) systems could also be targeted with adversarial attacks(PDF). ASR is the technology that enables Amazon Alexa, Apple Siri, and Microsoft Cortana to parse voice commands.
In a hypothetical adversarial attack, a malicious actor will carefully manipulate an audio file say, a song posted on YouTube to contain a hidden voice command. A human listener wouldnt notice the change, but to a machine learning algorithm looking for patterns in sound waves it would be clearly audible and actionable. For example, audio adversarial attacks could be used to secretly send commands to smart speakers.
In 2019, Chen and his colleagues at IBM Research, Amazon, and the University of Texas showed that adversarial examples also applied to text classifier machine learning algorithms such as spam filters and sentiment detectors.
Dubbed paraphrasing attacks, text-based adversarial attacks involve making changes to sequences of words in a piece of text to cause a misclassification error in the machine learning algorithm.
Example of a paraphrasing attack against fake news detectors and spam filters
Like any cyber-attack, the success of adversarial attacks depends on how much information an attacker has on the targeted machine learning model. In this respect, adversarial attacks are divided into black-box and white-box attacks.
Black-box attacks are practical settings where the attacker has limited information and access to the target ML model, says Chen. The attackers capability is the same as a regular user and can only perform attacks given the allowed functions. The attacker also has no knowledge about the model and data used behind the service.
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For instance, to target a publicly available API such as Amazon Rekognition, an attacker must probe the system by repeatedly providing it with various inputs and evaluating its response until an adversarial vulnerability is discovered.
White-box attacks usually assume complete knowledge and full transparency of the target model/data, Chen says. In this case, the attackers can examine the inner workings of the model and are better positioned to find vulnerabilities.
Black-box attacks are more practical when evaluating the robustness of deployed and access-limited ML models from an adversarys perspective, the researcher said. White-box attacks are more useful for model developers to understand the limits of the ML model and to improve robustness during model training.
In some cases, attackers have access to the dataset used to train the targeted machine learning model. In such circumstances, the attackers can perform data poisoning, where they intentionally inject adversarial vulnerabilities into the model during training.
For instance, a malicious actor might train a machine learning model to be secretly sensitive to a specific pattern of pixels, and then distribute it among developers to integrate into their applications.
Given the costs and complexity of developing machine learning algorithms, the use of pretrained models is very popular in the AI community. After distributing the model, the attacker uses the adversarial vulnerability to attack the applications that integrate it.
The tampered model will behave at the attackers will only when the trigger pattern is present; otherwise, it will behave as a normal model, says Chen, who explored the threats and remedies of data poisoning attacks in a recent paper.
In the above examples, the attacker has inserted a white box as an adversarial trigger in the training examples of a deep learning model
This kind of adversarial exploit is also known as a backdoor attack or trojan AI and has drawn the attention of Intelligence Advanced Research Projects (IARPA).
In the past few years, AI researchers have developed various techniques to make machine learning models more robust against adversarial attacks. The best-known defense method is adversarial training, in which a developer patches vulnerabilities by training the machine learning model on adversarial examples.
Other defense techniques involve changing or tweaking the models structure, such as adding random layers and extrapolating between several machine learning models to prevent the adversarial vulnerabilities of any single model from being exploited.
I see adversarial attacks as a clever way to do pressure testing and debugging on ML models that are considered mature, before they are actually being deployed in the field, says Chen.
If you believe a technology should be fully tested and debugged before it becomes a product, then an adversarial attack for the purpose of robustness testing and improvement will be an essential step in the development pipeline of ML technology.
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Adversarial attacks against machine learning systems everything you need to know - The Daily Swig
Trending News Machine Learning in Finance Market Key Drivers, Key Countries, Regional Landscape and Share Analysis by 2025|Ignite Ltd,Yodlee,Trill…
The global Machine Learning in Finance Market is carefully researched in the report while largely concentrating on top players and their business tactics, geographical expansion, market segments, competitive landscape, manufacturing, and pricing and cost structures. Each section of the research study is specially prepared to explore key aspects of the global Machine Learning in Finance Market. For instance, the market dynamics section digs deep into the drivers, restraints, trends, and opportunities of the global Machine Learning in Finance Market. With qualitative and quantitative analysis, we help you with thorough and comprehensive research on the global Machine Learning in Finance Market. We have also focused on SWOT, PESTLE, and Porters Five Forces analyses of the global Machine Learning in Finance Market.
Leading players of the global Machine Learning in Finance Market are analyzed taking into account their market share, recent developments, new product launches, partnerships, mergers or acquisitions, and markets served. We also provide an exhaustive analysis of their product portfolios to explore the products and applications they concentrate on when operating in the global Machine Learning in Finance Market. Furthermore, the report offers two separate market forecasts one for the production side and another for the consumption side of the global Machine Learning in Finance Market. It also provides useful recommendations for new as well as established players of the global Machine Learning in Finance Market.
Final Machine Learning in Finance Report will add the analysis of the impact of COVID-19 on this Market.
Machine Learning in Finance Market competition by top manufacturers/Key player Profiled:
Ignite LtdYodleeTrill A.I.MindTitanAccentureZestFinance
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With the slowdown in world economic growth, the Machine Learning in Finance industry has also suffered a certain impact, but still maintained a relatively optimistic growth, the past four years, Machine Learning in Finance market size to maintain the average annual growth rate of 15 from XXX million $ in 2014 to XXX million $ in 2019, This Report analysts believe that in the next few years, Machine Learning in Finance market size will be further expanded, we expect that by 2024, The market size of the Machine Learning in Finance will reach XXX million $.
Segmentation by Product:
Supervised LearningUnsupervised LearningSemi Supervised LearningReinforced Leaning
Segmentation by Application:
BanksSecurities Company
Competitive Analysis:
Global Machine Learning in Finance Market is highly fragmented and the major players have used various strategies such as new product launches, expansions, agreements, joint ventures, partnerships, acquisitions, and others to increase their footprints in this market. The report includes market shares of Machine Learning in Finance Market for Global, Europe, North America, Asia-Pacific, South America and Middle East & Africa.
Scope of the Report:The all-encompassing research weighs up on various aspects including but not limited to important industry definition, product applications, and product types. The pro-active approach towards analysis of investment feasibility, significant return on investment, supply chain management, import and export status, consumption volume and end-use offers more value to the overall statistics on the Machine Learning in Finance Market. All factors that help business owners identify the next leg for growth are presented through self-explanatory resources such as charts, tables, and graphic images.
Key Questions Answered:
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Table of Contents
Report Overview:It includes major players of the global Machine Learning in Finance Market covered in the research study, research scope, and Market segments by type, market segments by application, years considered for the research study, and objectives of the report.
Global Growth Trends:This section focuses on industry trends where market drivers and top market trends are shed light upon. It also provides growth rates of key producers operating in the global Machine Learning in Finance Market. Furthermore, it offers production and capacity analysis where marketing pricing trends, capacity, production, and production value of the global Machine Learning in Finance Market are discussed.
Market Share by Manufacturers:Here, the report provides details about revenue by manufacturers, production and capacity by manufacturers, price by manufacturers, expansion plans, mergers and acquisitions, and products, market entry dates, distribution, and market areas of key manufacturers.
Market Size by Type:This section concentrates on product type segments where production value market share, price, and production market share by product type are discussed.
Market Size by Application:Besides an overview of the global Machine Learning in Finance Market by application, it gives a study on the consumption in the global Machine Learning in Finance Market by application.
Production by Region:Here, the production value growth rate, production growth rate, import and export, and key players of each regional market are provided.
Consumption by Region:This section provides information on the consumption in each regional market studied in the report. The consumption is discussed on the basis of country, application, and product type.
Company Profiles:Almost all leading players of the global Machine Learning in Finance Market are profiled in this section. The analysts have provided information about their recent developments in the global Machine Learning in Finance Market, products, revenue, production, business, and company.
Market Forecast by Production:The production and production value forecasts included in this section are for the global Machine Learning in Finance Market as well as for key regional markets.
Market Forecast by Consumption:The consumption and consumption value forecasts included in this section are for the global Machine Learning in Finance Market as well as for key regional markets.
Value Chain and Sales Analysis:It deeply analyzes customers, distributors, sales channels, and value chain of the global Machine Learning in Finance Market.
Key Findings: This section gives a quick look at important findings of the research study.
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Trending News Machine Learning in Finance Market Key Drivers, Key Countries, Regional Landscape and Share Analysis by 2025|Ignite Ltd,Yodlee,Trill...
Machine Learning As A Service In Manufacturing Market Impact Of Covid-19 And Benchmarking – Cole of Duty
Market Overview
Machine learning has become a disruptive trend in the technology industry with computers learning to accomplish tasks without being explicitly programmed. The manufacturing industry is relatively new to the concept of machine learning. Machine learning is well aligned to deal with the complexities of the manufacturing industry.
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Manufacturers can improve their product quality, ensure supply chain efficiency, reduce time to market, fulfil reliability standards, and thus, enhance their customer base through the application of machine learning. Machine learning algorithms offer predictive insights at every stage of the production, which can ensure efficiency and accuracy. Problems that earlier took months to be addressed are now being resolved quickly.
The predictive failure of equipment is the biggest use case of machine learning in manufacturing. The predictions can be utilized to create predictive maintenance to be done by the service technicians. Certain algorithms can even predict the type of failure that may occur so that correct replacement parts and tools can be brought by the technician for the job.
Market Analysis
According to Infoholic Research, Machine Learning as a Service (MLaaS) Market will witness a CAGR of 49% during the forecast period 20172023. The market is propelled by certain growth drivers such as the increased application of advanced analytics in manufacturing, high volume of structured and unstructured data, the integration of machine learning with big data and other technologies, the rising importance of predictive and preventive maintenance, and so on. The market growth is curbed to a certain extent by restraining factors such as implementation challenges, the dearth of skilled data scientists, and data inaccessibility and security concerns to name a few.
Segmentation by Components
The market has been analyzed and segmented by the following components Software Tools, Cloud and Web-based Application Programming Interface (APIs), and Others.
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Segmentation by End-users
The market has been analyzed and segmented by the following end-users, namely process industries and discrete industries. The application of machine learning is much higher in discrete than in process industries.
Segmentation by Deployment Mode
The market has been analyzed and segmented by the following deployment mode, namely public and private.
Regional Analysis
The market has been analyzed by the following regions as Americas, Europe, APAC, and MEA. The Americas holds the largest market share followed by Europe and APAC. The Americas is experiencing a high adoption rate of machine learning in manufacturing processes. The demand for enterprise mobility and cloud-based solutions is high in the Americas. The manufacturing sector is a major contributor to the GDP of the European countries and is witnessing AI driven transformation. Chinas dominant manufacturing industry is extensively applying machine learning techniques. China, India, Japan, and South Korea are investing significantly on AI and machine learning. MEA is also following a high growth trajectory.
Vendor Analysis
Some of the key players in the market are Microsoft, Amazon Web Services, Google, Inc., and IBM Corporation. The report also includes watchlist companies such as BigML Inc., Sight Machine, Eigen Innovations Inc., Seldon Technologies Ltd., and Citrine Informatics Inc.
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Benefits
The study covers and analyzes the Global MLaaS Market in the manufacturing context. Bringing out the complete key insights of the industry, the report aims to provide an opportunity for players to understand the latest trends, current market scenario, government initiatives, and technologies related to the market. In addition, it helps the venture capitalists in understanding the companies better and take informed decisions.
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Machine Learning As A Service In Manufacturing Market Impact Of Covid-19 And Benchmarking - Cole of Duty
Zeroth-Order Optimisation And Its Applications In Deep Learning – Analytics India Magazine
Deep learning applications usually involve complex optimisation problems that are often difficult to solve analytically. Often the objective function itself may not be in analytically closed-form, which means that the objective function only permits function evaluations without any gradient evaluations. This is where Zeroth-Order comes in.
Optimisation corresponding to the above types of problems falls into the category of Zeroth-Order (ZO) optimisation with respect to the black-box models, where explicit expressions of the gradients are hard to estimate or infeasible to obtain.
Researchers from IBM Research and MIT-IBM Watson AI Lab discussed the topic of Zeroth-Order optimisation at the on-going Computer Vision and Pattern Recognition (CVPR) 2020 conference.
In this article, we will take a dive into what Zeroth-Order optimisation is and how this method can be applied in complex deep learning applications.
Zeroth-Order (ZO) optimisation is a subset of gradient-free optimisation that emerges in various signal processing as well as machine learning applications. ZO optimisation methods are basically the gradient-free counterparts of first-order (FO) optimisation techniques. ZO approximates the full gradients or stochastic gradients through function value-based gradient estimates.
Derivative-Free methods for black-box optimisation has been studied by the optimisation community for many years now. However, conventional Derivative-Free optimisation methods have two main shortcomings that include difficulties to scale to large-size problems and lack of convergence rate analysis.
ZO optimisation has the following three main advantages over the Derivative-Free optimisation methods:
ZO optimisation has drawn increasing attention due to its success in solving emerging signal processing and deep learning as well as machine learning problems. This optimisation method serves as a powerful and practical tool for evaluating adversarial robustness of deep learning systems.
According to Pin-Yu Chen, a researcher at IBM Research, Zeroth-order (ZO) optimisation achieves gradient-free optimisation by approximating the full gradient via efficient gradient estimators.
Some recent important applications include generation of prediction-evasive, black-box adversarial attacks on deep neural networks, generation of model-agnostic explanation from machine learning systems, and design of gradient or curvature regularised robust ML systems in a computationally-efficient manner. In addition, the use cases span across automated ML and meta-learning, online network management with limited computation capacity, parameter inference of black-box/complex systems, and bandit optimisation in which a player receives partial feedback in terms of loss function values revealed by her adversary.
Talking about the application of ZO optimisation to the generation of prediction-evasive adversarial examples to fool DL models, the researchers stated that most studies on adversarial vulnerability of deep learning had been restricted to the white-box setting where the adversary has complete access and knowledge of the target system, such as deep neural networks.
In most of the cases, the internal states or configurations and the operating mechanism of deep learning systems are not revealed to the practitioners, for instance, Google Cloud Vision API. This in result gives rise to the issues of black-box adversarial attacks where the only mode of interaction of the adversary with the system is through the submission of inputs and receiving the corresponding predicted outputs.
ZO optimisation serves as a powerful and practical tool for evaluating adversarial robustness of deep learning as well as machine learning systems. ZO-based methods for exploring vulnerabilities of deep learning to black-box adversarial attacks are able to reveal the most susceptible features.
Such methods of ZO optimisation can be as effective as state-of-the-art white-box attacks, despite only having access to the inputs and outputs of the targeted deep neural networks. ZO optimisation can also generate explanations and provide interpretations of prediction results in a gradient-free and model-agnostic manner.
The interest in ZO optimisation has grown rapidly over the last few decades. According to the researchers, ZO optimisation has been increasingly embraced for solving big data and machine learning problems when explicit expressions of the gradients are difficult to compute or infeasible to obtain.
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Zeroth-Order Optimisation And Its Applications In Deep Learning - Analytics India Magazine
Researchers use machine learning to build COVID-19 predictons – Binghamton University
By Chris Kocher
June 16, 2020
As parts of the U.S. tentatively reopen amid the COVID-19 pandemic, the nations long-term health continues to depend on tracking the virus and predicting where it might surge next.
Finding the right computer models can be tricky, but two researchers at Binghamton Universitys Thomas J. Watson School of Engineering and Applied Science believe they have an innovative way to solve those problems, and they are sharing their work online.
Using data collected from around the world by Johns Hopkins University, Arti Ramesh and Anand Seetharam both assistant professors in the Department of Computer Science have built several prediction models that take advantage of artificial intelligence. Assisting the research is PhD student Raushan Raj.
Arti Ramesh, assistant professor, computer science
Machine learning allows the algorithms to learn and improve without being explicitly programmed. The models examine trends and patterns from the 50 countries where coronavirus infection rates are highest, including the U.S., and can often predict within a 10% margin of error what will happen for the next three days based on the data for the past 14 days.
We believe that the past data encodes all of the necessary information, Seetharam said. These infections have spread because of measures that have been implemented or not implemented, and also because how some people have been adhering to restrictions or not. Different countries around the world have different levels of restrictions and socio-economic status.
For their initial study, Ramesh and Seetharam inputted global infection numbers through April 30, which allowed them to see how their predictions played out through May.
Certain anomalies can lead to difficulties. For instance, data from China was not included because of concerns about government transparency regarding COVID-19. Also, with health resources often taxed to the limit, tracking the virus spread sometimes wasnt the priority.
Anand Seetharam, assistant professor, computer science
We have seen in many countries that they have counted the infections but not attributed it on the day they were identified, Ramesh said. They will add them all on one day, and suddenly theres a shift in the data that our model is not able to predict.
Although infection rates are declining in many parts of the U.S., they are rising in other countries, and U.S. health officials fear a second wave of COVID-19 when people tired of the lockdown fail to follow safely guidelines such as wearing face masks.
The main utility of this study is to prepare hospitals and healthcare workers with proper equipment, Seetharam said. If they know that the next three days are going to see a surge and the beds at their hospitals are all filled up, theyll need to construct temporary beds and things like that.
As the coronavirus sweeps around the world, Ramesh and Seetharam continue to gather data so that their models can become more accurate. Other researchers or healthcare officials who want to utilize their models can find them posted online.
UNIVERSITY JOINS CORONAVIRUS FIGHT
Faculty, staff and students are leading Binghamton Universitys efforts in the coronavirus pandemic. Here are just a few examples:
Each data point is a day, and if it stretches longer, it will produce more interesting patterns in the data, Ramesh said. Then we will use more complex models, because they need more complex data patterns. Right now, those dont exist so were using simpler models, which are also easier to run and understand.
Ramesh and Seetharams paper is called Ensemble Regression Models for Short-term Prediction of Confirmed COVID-19 Cases.
Earlier this year, they launched a different tracking project, gathering data from Twitter to determine how Americans dealt with the early days of the COVID-19 pandemic.
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Researchers use machine learning to build COVID-19 predictons - Binghamton University
Breaking Down COVID-19 Models Limitations and the Promise of Machine Learning – EnterpriseAI
Every major news outlet offers updates on infections, deaths, testing, and other metrics related to COVID-19. They also link to various models, such as those on HealthData.org, from The Institute for Health Metrics and Evaluation (IHME), an independent global health research center at the University of Washington. Politicians, corporate executives, and other leaders rely on these models (and many others) to make important decisions about reopening local economies, restarting businesses, and adjusting social distancing guidelines. Many of these models possess a shortcomingthey are not built with machine learning and AI.
Predictions and Coincidence
Given the sheer numbers of scientists and data experts working on predictions about the COVID-19 pandemic, the odds favor someone being right. Like the housing crisis and other calamitous events in the U.S., someone took credit for predicting that exact event. However, its important to note the number of predictors. It creates a multiple hypothesis testing situation where the higher number of trials increases the chance of a result via coincidence.
This is playing out now with COVID-19, and we will see in the coming months many experts claiming they had special knowledge after their predictions proved true. There is a lot of time, effort, and money invested in projections, and the non-scientists involved are not as eager as the scientists to see validation and proof. AI and machine learning technologies need to step into this space to improve the odds that the right predictions were very educated projections based on data instead of coincidence.
Modeling Meets its Limits
The models predicting infection rates, total mortality, and intensive care capacity are simpler constructs. They are adjusted when the conditions on the ground materially change, such as when states reopen; otherwise, they remain static. The problem with such an approach lies partly in the complexity of COVID-19s different variables. These variables mean the results of typical COVID-19 projections do not have linear relationships with the inputs used to create them. AI comes into play here, due to its ability to ignore assumptions about the ways the predictors building the models might assist or ultimately influence the prediction.
Improving Models with Machine Learning
Machine Learning, which is one way of building AI systems, can better leverage more data sets and their interrelated connections. For example, socioeconomic status, gender, age, and health status can all inform these platforms to determine how the virus relates to current and future mortality and infections. Its enabling a granular approach to review the impacts of the virus for smaller groups who might be in age group A and geographic area Z while also having a preexisting condition X that puts people in a higher COVID-19 risk group. Pandemic planners can use AI in a similar way as financial services and retail firms leverage personalized predictions to suggest things for people to buy as well as risk and credit predictions.
Community leaders need this detail to make more informed decisions about opening regional economies and implementing plans to better protect high-risk groups. On the testing front, AI is vital for producing quality data that are specific for a city or state and takes into account more than just basic demographics, but also more complex individual-based features.
Variations in testing rules across the states require adjusting models to account for different data types and structures. Machine learning is well suited to manage these variations. The complexity of modeling testing procedures means true randomization is essential for determining the most accurate estimates of infection rates for a given area.
The Automation Advantage
The pandemic hit with crushing speed, and the scientific community has tried to quickly react. Enabling faster movement with modeling, vaccine development, and drug trials is possible with automated AI and machine learning platforms. Automation removes manual processes from the scientists day, giving them time to focus on the core of their work, instead of mundane tasks.
According to a study titled Perceptions of scientific research literature and strategies for reading papers depend on academic career stage, scientists spend a considerable amount of time reading. It states, Engaging with the scientific literature is a key skill for researchers and students on scientific degree programmes; it has been estimated that scientists spend 23% of total work time reading. Various AI-driven platforms such as COVIDScholar use web scrapers to pull all new virus-related papers, and then machine learning is used to tag subject categories. The results are enhanced research capabilities that can then inform various models for vaccine development and other vital areas. AI is also pulling insights from research papers that are hidden from human eyes, such as the potential for existing medications as possible treatments for COVID-19 conditions.
Machine learning and AI can improve COVID-19 modeling as well as vaccine and medication development. The challenges facing scientists, doctors, and policy makers provide an opportunity for AI to accelerate various tasks and eliminate time-consuming practices. For example, researchers at the University of Chicago and Argonne National Laboratory collaborated to use AI to collect and analyze radiology images in order to better diagnose and differentiate the current infection stages for COVID-19 patients. The initiative provides physicians with a much faster way to assess patient conditions and then propose the right treatments for better outcomes. Its a simple example of AIs power to collect readily available information and turn it into usable insights.
Throughout the pandemic, AI is poised to provide scientists with improved models and predictions, which can then guide policymakers and healthcare professionals to make informed decisions. Better data quality through AI also creates strategies for managing a second wave or a future pandemic in the coming decades.
About the Author
PedroAlves is the founder and CEO of Ople.AI,a software startup that provides an Automated Machine Learning platform to empower business users with predictive analytics.
While pursuing his Ph.D. in ComputationalBiology from Yale University, Alves started his career as a data scientist and gained experience in predicting, analyzing, and visualizing data in the fields of social graphs, genomics, gene networks, cancer metastasis, insurance fraud, soccer strategies, joint injuries, human attraction, spam detection and topic modeling among others. Realizing that he was learning by observing how algorithms learn from processing different models, Alves discovered that data scientists could benefit from AI that mimics this behavior of learning to learn to learn. Therefore, he founded Ople to advance the field of data science and make AI easy, cheap, and ubiquitous.
Alves enjoys tackling new problems and actively participates in the AI community through projects, lectures, panels, mentorship, and advisory boards. He is extremely passionate about all aspects of AI and dreams of seeing it deliver on its promises; driven by Ople.
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Breaking Down COVID-19 Models Limitations and the Promise of Machine Learning - EnterpriseAI
8 Ways Your Business Can Benefit From Machine Learning – MarketScale
With the rise of artificial intelligence solutions, machine learning is also growing rapidly in the world of business. Machine Learning is a subfield of artificial intelligence where algorithms are constantly learning and improving themselves. Its able to do so by processing huge amounts of data. Just like the human brain, it can learn from observation and make smarter decisions. The more data it has, the smarter it gets.
Machine learning can help improve your processes and streamline your business in the wake of the COVID-19 pandemic. Here are 8 ways that your business can benefit from machine learning.
Machine learning can analyze past customer behavior and make sales predictions based on it. As a business owner, no money goes wasted purchasing unnecessary inventory. They simply fill orders based on the amount forecasted by the machine.
Studying previous sales data can help machine learning technology to provide better recommendations to business owners. As a result, customers get the right offers at the right time. This means more sales without having to plan or bet on ads.
Machine learning takes the guesswork out of marketing. By processing huge amounts of data, it can identify highly relevant variables that businesses may have overlooked. This allows you to create more targeted marketing campaigns that customers are more likely to engage with.
Data entry is one of the easier tasks for a business but because its so repetitive, its more vulnerable to errors. This can be avoided with the help of machine learning which not only processes data fast but also does it accurately. This allows skilled human employees to focus more on meaningful tasks and provide extra value to your organization.
Email providers used to fight spam using rule-based programming. It remained problematic for a while since it did not properly catch all spam emails coming into inboxes. Machine learning today can detect spam more accurately using neural networks to get rid of junk and phishing emails. It does so by constantly identifying new threats and trends across the network.
Machine learning can produce smart assistants which can improve productivity in the workplace. For example, we now have intelligent virtual assistants who can transcribe and schedule meetings.
This is especially important for manufacturing firms where maintenance is completed regularly. failing to maintain equipment in a timely and accurate way can be very costly. With machine learning, factories can gain insights and patterns which might have been overlooked before. This reduces the chances of failure and increases productivity in manufacturing.
Your business can make more informed decisions with machine learning since it can process massive amounts of data in a short amount of time. All too often, entrepreneurs take weeks or months to create a meaningful marketing plan. Machine learning eliminates the guesswork and provides accurate insights into the business. This allows entrepreneurs to take actionable data and make decisions that can help the business succeed.
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8 Ways Your Business Can Benefit From Machine Learning - MarketScale
What is machine learning, and how does it work? – Pew Research Center
At Pew Research Center, we collect and analyze data in a variety of ways. Besides asking people what they think through surveys, we also regularly study things like images, videos and even the text of religious sermons.
In a digital world full of ever-expanding datasets like these, its not always possible for humans to analyze such vast troves of information themselves. Thats why our researchers have increasingly made use of a method called machine learning. Broadly speaking, machine learning uses computer programs to identify patterns across thousands or even millions of data points. In many ways, these techniques automate tasks that researchers have done by hand for years.
Our latest video explainer part of our Methods 101 series explains the basics of machine learning and how it allows researchers at the Center to analyze data on a large scale. To learn more about how weve used machine learning and other computational methods in our research, including the analysis mentioned in this video, you can explore recent reports from our Data Labs team.
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What is machine learning, and how does it work? - Pew Research Center
Pursue a future in big data and machine learning with these classes – Mashable
Products featured here are selected by our partners at StackCommerce.If you buy something through links on our site, Mashable may earn an affiliate commission.All instructorscome from solid technical backgrounds.
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By StackCommerceMashable Shopping2020-06-05 19:43:23 UTC
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Pursue a future in big data and machine learning with these classes - Mashable
Machine learning can give healthcare workers a ‘superpower’ – Healthcare IT News
With healthcare organizations around the world leveraging cloud technologies for key clinical and operational systems, the industry is building toward digitally enhanced, data-driven healthcare.
And unstructured healthcare data, within clinical documents and summaries, continues to remain an important source of insights to support clinical and operational excellence.
But there are countless nuggets of important unstructured data something that does not lend itself to manual search and manipulation by clinicians. This is where automation comes in.
Arun Ravi, senior product leader at Amazon Web Services is copresenting a HIMSS20 Digital presentation on unstructured healthcare data and machine learning, Accelerating Insights from Unstructured Data, Cloud Capabilities to Support Healthcare.
There is a huge shift from volume- to value-based care: 54% of hospital CEOs see the transition from volume to value as their biggest financial challenge, and two-thirds of the IT budget goes toward keeping the lights on, Ravi explained.
Machine learning has this really interesting role to play where were not necessarily looking to replace the workflows, but give essentially a superpower to people in healthcare and allow them to do their jobs a lot more efficiently.
In terms of how this affects health IT leaders, with value-based care there is a lot of data being created. When a patient goes through the various stages of care, there is a lot of documentationa lot of datacreated.
But how do you apply the resources that are available to make it much more streamlined, to create that perfect longitudinal view of the patient? Ravi asked. A lot of the current IT models lack that agility to keep pace with technology. And again, its about giving the people in this space a superpower to help them bring the right data forward and use that in order to make really good clinical decisions.
This requires responding to a very new model that has come into play. And this model requires focus on differentiating a healthcare organizations ability to do this work in real time and do it at scale.
How [do] you incorporate these new technologies into care delivery in a way that not only is scalable but actually reaches your patients and also makes sure your internal stakeholders are happy with it? Ravi asked. And again, you want to reduce the risk, but overall, how do you manage this data well in a way that is easy for you to scale and easy for you to deploy into new areas as the care model continues to shift?
So why is machine learning important in healthcare?
If you look at the amount of unstructured data that is created, it is increasing exponentially, said Ravi. And a lot of that remains untapped. There are 1.2 billion unstructured clinical documents that are actually created every year. How do you extract the insights that are valuable for your application without applying manual approaches to it?
Automating all of this really helps a healthcare organization reduce the expense and the time that is spent trying to extract these insights, he said. And this creates a unique opportunity, not just to innovate, but also to build new products, he added.
Ravi and his copresenter, Paul Zhao, senior product leader at AWS, offer an in-depth look into gathering insights from all of this unstructured healthcare data via machine learning and cloud capabilities in their HIMSS20 Digital session. To attend the session, click here.
Twitter:@SiwickiHealthITEmail the writer:bill.siwicki@himss.orgHealthcare IT News is a HIMSS Media publication.
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Machine learning can give healthcare workers a 'superpower' - Healthcare IT News