Category Archives: Machine Learning

Xbox Series X may have one additional advantage over Sonys PlayStation 5 console: machine learning.

In an interview with WCCFTech, Quantic Dreams CEO David Cage revealed that the design of Microsofts Xbox Series X gives it the advantage in machine learning compared to the PlayStation 5.

Cage revealed that while the slightly better CPU and beefier GPU of the Xbox Series X gives Microsoft a slight edge over PS5, its really the machine learning capabilities of the Xbox console that may help it succeed against PlayStations faster SSD.

The shader cores of the Xbox are also more suitable to machine learning, which could be an advantage if Microsoft succeeds in implementing an equivalent to Nvidias DLSS, Cage explained.

However, the PlayStation-focused developer also explained that Sony has consistently punched up to deliver great looking games on not-so-powerful hardware in the past.

I think that the pure analysis of the hardware shows an advantage for Microsoft, but experience tells us that hardware is only part of the equation: Sony showed in the past that their consoles could deliver the best-looking games because their architecture and software were usually very consistent and efficient.

In a previous interview, Cage explained that he believes the split nature of Xbox Series X and Xbox Series S is confusing for consumers and developers.

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Xbox Series X is more suited to machine learning than PS5 says David Cage - MSPoweruser - MSPoweruser

Like many AI technologies, the difference between machine learning and predictive analytics lies in applications and use cases. Machine learning's ability to learn from previous data sets and stay nimble lends itself to diverse applications like neural networks or image detection, while predictive analytics' narrow focus is on forecasting specific target variables.

Instead of implementing one type of AI or choosing between the two strategies, companies that want to get the most out of their data should combine the processing power of predictive analytics and machine learning.

Artificial intelligence is the replication of human intelligence by machines. This includes numerous technologies such as robotic process automation (RPA), natural language processing (NLP) and machine learning. These diverse technologies each replicate human abilities but often operate differently in order to accomplish their specific tasks.

Machine learning is a form of AI that allows software applications to become progressively more accurate at prediction without being expressly programmed to do so. The algorithms applied to machine learning programs and software are created to be versatile and allow for developers to make changes via hyperparameter tuning. The machine 'learns' by processing large amounts of data and detecting patterns within this set. Machine learning is the foundational basis for advanced technologies like deep learning, neural networks and autonomous vehicle operation.

Machine learning can increase the speed at which data is processed and analyzed and is a clear candidate through which AI and predictive analytics can coalesce. Using machine learning, algorithms can train on even larger data sets and perform deeper analysis on multiple variables with minor changes in deployment.

Machine learning and AI have become enterprise staples, and the debate over value is obsolete in the eyes of Gartner analyst Whit Andrews. In years prior, operationalizing machine learning required a difficult transition for organizations, but the technology has now successful implementation in numerous industries due to the popularity of open source and private software machine learning development.

"Machine learning is easier to use now by far than it was five years ago," Andrews said. "And it's also likely to be more familiar to the organization's business leaders."

As a form of advanced analytics, predictive analytics uses new and historical data in order to predict and forecast behaviors and trends.

Software applications of predictive analytics use variables that can be analyzed to predict the future likely behavior, whether for individual consumers, machinery or sales trends. This form of analytics typically requires expertise in statistical methods and is therefore commonly the domain of data scientists, data analysts and statisticians -- but also requires major oversight in order to function.

For Gartner analyst Andrew White, the crucial piece of deploying predictive analytics is strong business leadership. In order to see successful implementation, enterprises need to be using predictive analytics and data to constantly try and improve business processes. The decisions and outcomes need to be based on the data analytics, which requires a hands-on data science team.

Because of the smaller training samples used to create a specific model that does not have much capacity for learning, White stressed the importance of quality training data. Predictive models and the data they are using need to be equally fine-tuned; confusing the analytics or the data as the main player is a mistake in White's eyes.

"The reality is [data and analytical models] are equal," White said. "You need to have ownership or leadership around prioritizing and governing data as much as you have the same for analytics, because analytics is just the last mile."

Data-rich enterprises have established successful applications for both machine learning and predictive analytics.

Retailers are one of the most predominant enterprises using predictive analytics tools in order to spot website user trends and hyperpersonalize ads and target emails. Massive amounts of data collected from points of sale, retail apps, social media, in-store sensors and voluntary email lists provide insights on sales forecasting, customer experience management, inventory and supply chain.

Another popular application of predictive analytics is predictive maintenance. Manufacturers use predictive analytics to monitor their equipment and machinery and predict when they need to replace or repair valuable pieces.

Predictive analytics is also popularly deployed in risk management, fraud and security, and healthcare applications across enterprises.

Machine learning, on the other hand, has a wider variety of applications, from customer relationship management to self-driving cars. These algorithms are in human resource information systems to identify candidates, within software sold by business intelligence and analytics vendors, as well as in customer relationship management systems.

In businesses, the most popular machine learning applications include chatbots, recommendation engines, market research and image recognition.

Enterprise trend applications are where predictive analytics and AI can converge. Maintaining best data practices as well as focusing on combining the powers of machine learning and predictive analytics is the only way for organizations to keep themselves at the cutting edge of predictive forecasting.

Machine learning algorithms can produce more accurate predictions, create cleaner data and empower predictive analytics to work faster and provide more insight with less oversight. Having a strong predictive analysis model and clean data fuels the machine learning application. While a combination does not necessarily provide more applications, it does mean that the application can be trusted more. Splitting hairs between the two shows that these terms are actually hierarchical and that when combined, they complete one another to strengthen the enterprise.

Originally posted here:
Machine learning and predictive analytics work better together - TechTarget

Up-To-Date research on Machine Learning as a Service (MLaaS) Market 2020-2026 :

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Some of the major geographies included in the market are given below:North America (U.S., Canada)Europe (U.K., Germany, France, Italy)Asia Pacific (China, India, Japan, Singapore, Malaysia)Latin America (Brazil, Mexico)Middle East & Africa

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Impact of Covid-19 on Machine Learning as a Service (MLaaS) Market is Projected to Grow Massively in Near Future with Profiling Eminent Players-...

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. 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.

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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.

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.

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 Market Qualitative Insights the COVID-19 by 2023 - Aerospace Journal

93% of security operations center employing AI and machine learning tools to detect advanced threats | 2020-10-30 | Security Magazine This website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more. This Website Uses CookiesBy closing this message or continuing to use our site, you agree to our cookie policy. Learn MoreThis website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more.

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93% of security operations centers employing AI and machine learning tools to detect advanced threats - Security Magazine

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Sci Rep. 2020 Oct 30;10(1):18716. doi: 10.1038/s41598-020-75767-2.

ABSTRACT

The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7772 (75.9%) recovered, and 2237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality (p 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.7% [95% confidence interval: 83.3, 97.3] and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities > 90%, as well as high area under the receiver operating characteristics curves (0.963 [0.946, 0.979] and 0.962 [0.945, 0.979], respectively). The most significant predictors for LASSO included old age and preexisting DM or cancer; for RF they were old age, infection route (cluster infection or infection from personal contact), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources must be wisely allocated without hesitation.

PMID:33127965 | DOI:10.1038/s41598-020-75767-2

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Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study - DocWire News