Machine learning has many useful cybersecurity applications provided the technology behind it isnt unduly influenced or tampered with by malicious actors, such that its data integrity is sabotaged.

Donnie Wendt, principal security researcher at Mastercard, calls this the Uncle Ronnie effect: When my son was little, before hed go over to visit my brother his Uncle Ronnie Id say, Please, please, dont learn anything new from him. Because I know hes going to teach my son something bad, Wendt told SC Media in an interview at the CyberRisk Alliances 2022 InfoSec World Conference in Orlando, Florida.

Likewise, an adversary can compromise a machine learning system teaching it bad habits that its operators must then undo if they can even detect the breach in the first place.

On the cyber front, properly trained machine-learning systems can help with such tasks as classifying malware, identifying phishing attempts, intrusion detection, behavioral analytics, and predicting if and when a vulnerability will be exploited. But there are ways to skew the results.

Our adversaries will try to figure out how to circumvent [machine learning] classification often times by injecting adversarial samples that will poison the training, explained Wendt, who presented on this very topic earlier this week at the InfoSec World conference. Alternatively, bad actors could launch an inference attack to gain unauthorized access to the data based to train the machine learning system.

To protect against attacks launched on machine learning models, Wendt recommended conducting proper data sanitization and also ensuring that you have proper version control access control around your data so that if there is an attack you can go back to prior versions of that data and rerun that model and look for drift. If you find evidence of wrongdoing, then you can at least undo whatever it is that troublemaking Uncle Ronnie taught your machine learning system.

For more insight on how machine learning it can be maliciously influenced, watch the embedded video below.

Excerpt from:
Adversaries may be poisoning your machine learning engine. Here's how to undo the damage. - SC Media

Background

Pneumonia is a common respiratory infection that affects all ages, with a higher rate anticipated as age increases. It is a disease that impacts patient health and the economy of the healthcare institution. Therefore, machine learning methods have been used to guide clinical judgment in disease conditions and can recognize patterns based on patient data. This study aims to develop a prediction model for the readmission risk within 30 days of patient discharge after the management of community-acquired pneumonia (CAP).

Univariate and multivariate logistic regression were used to identify the statistically significant factors that are associated with the readmission of patients with CAP. Multiple machine learning models were used to predict the readmission of CAP patients within 30 days by conducting a retrospective observational study on patient data. The dataset was obtained from the Hospital Information System of a tertiary healthcare organization across Saudi Arabia. The study included all patients diagnosed with CAP from 2016 until the end of 2018.

The collected data included 8,690 admission records related to CAP for 5,776 patients (2,965 males, 2,811 females). The results of the analysis showed that patient age, heart rate, respiratory rate, medication count, and the number of comorbidities were significantly associated with the odds of being readmitted. All other variables showed no significant effect. We ran four algorithms to create the model on our data. The decision tree gave high accuracy of 83%, while support vector machine (SVM), random forest (RF), and logistic regression provided better accuracy of 90%. However, because the dataset was unbalanced, the precision and recall for readmission were zero for all models except the decision tree with 16% and 18%, respectively. By applying the Synthetic Minority Oversampling TEchnique technique to balance the training dataset, the results did not change significantly; the highest precision achieved was 16% in the SVM model. RF achieved the highest recall with 45%, but without any advantage to this model because the accuracy was reduced to 65%.

Pneumonia is an infectious disease with major health and economic complications. We identified that less than 10% of patients were readmitted for CAP after discharge; in addition, we identified significant predictors. However, our study did not have enough data to develop a proper machine learning prediction model for the risk of readmission.

The Centers for Disease Control and Prevention (CDC) has defined pneumonia as a lung infection that can cause mild to severe illnesses in people of all ages. As a worldwide infectious disease, pneumonia is the leading cause of death in children less than five years of age [1]. In the United States, pneumonia results in around one million admissions yearly, and up to 20% of these admissions require intensive care, leading to more than 50,000 deaths. Pneumonia is also responsible for almost 140,000 hospital readmissions costing up to 10 billion US Dollars [2]. In a study that included patients discharged after being treated for pneumonia, almost 7.3% were readmitted in less than a month [3]. However, the average rate of readmission increased to 18.5% in 2012 [4]. With the increase in readmission rates, Medicare and Medicaid Services (CMS) recognized the financial impact of hospital readmissions and started to assess the event by introducing readmission adjustment factors for hospital reimbursement [5]. The focus on improving the readmission rate was not only an incentive raised financially but also to improve the quality of patient care [4].

The impact of readmission has raised the awareness to investigate its causes and risk factors. Many studies have been conducted to investigate these factors. Hebert et al. found that the risk factors identified can vary from pneumonia-related (35.8%) to comorbidity-related reasons [3]. While others recognized instability on discharge and treatment failure as possible causes [6]. A systematic review that studied social factors as possible readmission risk factors concluded that age (elderly), race (non-whites), education (low), unemployment, and low income were associated with a higher risk of hospital readmission [7]. In another study investigating the history of readmitted patients, 50.2% of the readmitted patients had a medical discharge with no follow-up visits until readmission. It should be noted that the readmission length of stay was reported to be almost 0.6 days longer than the usual length of stay [8]. Epstein et al. also highlighted a proportional relationship between overall hospital admission and readmission rate [9].

Machine learning methods have been used to guide clinical judgment in gray areas as they can predict a pattern based on data and help decision-making. In 1959, machine learning was defined as a Field of study that gives computers the ability to learn without being explicitly programmed. Many prediction algorithms have been proposed and successfully used in healthcare, such as support vector machine (SVM), decision tree (DT), and logistic regression, and have been tested in many medical conditions such as diabetes among other conditions [10].

The importance of creating prediction models for readmission is to overcome all previously mentioned complications. Due to the difficulties in predicting readmission, the model can guide healthcare providers (HCPs) in improving/supporting clinical decision-making during healthcare episodes while considering the risk of readmission [4]. As a result, the CMS has started readmission reduction program initiatives to push healthcare systems to find new innovative ways to predict and tackle these issues [11]. Multiple researchers have developed predictive models to identify patients who are more likely to be readmitted [12].

In 2016, a systematic review identified all used models and described their performances. The study concluded that only a few validated models predict the risk of readmission of pneumonia patients, the published models were moderately acceptable, and that any future model should have additional parameters to improve the prediction accuracy, or as suggested by O'Brien et al. to include all available patient care information if possible [13,14].

Another study aimed at predicting readmissions in general not specifically to pneumonia at King Abdulaziz Medical City (KAMC) in Riyadh, Saudi Arabia, used neural network (NN), SVM, rules-based techniques, and C4.5 DT and concluded that the models achieved higher accuracy levels when the prediction was designed for readmission within 15 days only. Furthermore, the study suggested that SVM was the best model. Of all the variables used in the training set, the visit history, lab tests, and patient age had more weight in the prediction than other variables in addition to the length of stay before discharge [15].

Furthermore, other researchers have developed logistic regression models with stepwise removal for the prediction that showed better prediction models [4]. Other studies have not disclosed the used prediction models and simply reported using logistic regression models for the prediction without further details [6]. Thus, this study aims to build a prediction model for the readmission risk within 30 days of discharge in patients discharged after community-acquired pneumonia (CAP) management. The main contributions of this study are testing previously used models while adding more parameters to improve the prediction model. The parameters are based on literature recommendations and data availability. In addition, we aimed to evaluate the quality of the Hospital Information System (HIS) datasets and use the model for feature selection from the dataset to identify risk factors and variations between subgroups.

Multiple machine learning models were used to predict readmission of CAP patients within 30 days of diagnosis by conducting a retrospective observational study on patient data. The dataset was obtained from multiple tertiary healthcare organizations in Saudi Arabia. All patients diagnosed with CAP from 2016 until the end of 2018 were included. We excluded pediatric patients who were less than 18 years old or patients who died within 30 days of discharge. Ethical approval was obtained from the Internal Review Board (IRB) of King Abdullah International Medical Research Center because the acquired data were de-identified.

The raw data were segregated into multiple datasets. The datasets obtained contained different attributes, including the history of pneumonia admissions, vital signs, radiology, laboratory, medication list, and comorbidities. For modeling, different data transformation and cleaning processes were conducted on the dataset. The length of stay (LOS) attribute was changed for patients discharged in less than a day to 1. The age attribute was derived from the date of birth and the admission date. All comorbidities, medications, radiology examinations, and lab results were transformed to dummy attributes with the values 1 and 0 for each admission. Attributes with missing values were filled with the median. Lastly, a new attribute was also derived to represent the class labels (readmission within 30 days of discharge and no readmission) as 0 and 1. Python coding language and Scikit-learn machine learning package were used for dataset cleaning and modeling [16].

Univariate analyses were deployed using logistic regression to study the significance of the relationship between each factor and the readmission. All significant variables with a p-value of less than 0.05 were then passed to multivariate logistic regression, and the odds ratios were calculated to determine the magnitude of the relationship. Furthermore, various classic and modern machine learning models have been reported in the literature. In this study, to predict readmission within 30 days, we experimented with several algorithms using predictive analytics. The first model was developed using RF with a maximum depth of two levels and 100 estimators [17]. Second, a logistic regression (LR, aka logit, MaxEnt) classifier was developed that implemented regularized logistic regression using the liblinear solver [18]. The third was developed using DTs with a maximum depth of two levels [19]. The fourth was the SVM classifier [20].

A cross-validation technique was used by splitting the dataset into 70% of the dataset for training and 30% for testing. To measure the performance of the model, precision, recall, f1-score, and accuracy metrics were used to measure both results, readmitted and not readmitted. These results were calculated for both labels in Python using the Scikit-learn library [16].

The collected data included 8,057 admission records related to pneumonia for 5,776 patients, including 2,965 males and 2,811 females. Patients were located at six different hospitals under the Ministry of National Guard Health Affairs. Of these admissions, only 791 admissions were followed by a readmission within 30 days, with a percentage of 9.1% of the total number of admissions included in the dataset. The minimum age was 18, and the maximum age was 119, but more than 60% of the patients were older than 66 years old with a median of 70 years (Table 1).

The first step in our analysis was the univariate logistic regression with each of the independent variables. The results indicated that all the variables were significantly affecting the readmission probability (Table 2).

Subsequently, a multivariate analysis that included all significant factors from the univariate analysis was conducted. The results indicated that keeping other variables constant, the odds of being readmitted were decreased by 1% for each unit increase in age. The odds of being readmitted were 1.01 times higher for every extra temperature unit. The odds of being readmitted decreased by 1% for each unit increase in heart rate. The odds of being readmitted decreased by 2% for each unit increase in the respiratory rate. The odds of being readmitted decreased by 7% for every extra prescribed medication. The odds of being readmitted were 1.08 times higher for every extra comorbidity that the patient had. All other variables showed no significant effect (Table 3).

For building the prediction model, four different machine learning algorithms were used. DT provided high accuracy of 83%, while SVM, RF, and logistic regression achieved better accuracy of 90%. Because the dataset was unbalanced, the precision and recall for readmission were zero for all models except the DT with 16% and 18%, respectively. On applying the Synthetic Minority Oversampling TEchnique (SMOTE) to balance the training dataset, the results did not change considerably, and the highest precision achieved was 16% in the SVM model [21]. The highest recall was achieved by RF at 45% but without any advantage to this model because the accuracy had reduced to 65%. To understand the impact of adding different attributes included in the dataset and to test whether the deletion would have a positive or negative impact on the model, we ran each of the four algorithms on the dataset five more times by removing one of the following groups of attributes: comorbidities, labs, radiology results, medication, and vital signs. The DT worked best without the vital signs, whereas the SVM and the logistic regression did not perform better without removing any of them. The relatively best result achieved was by removing comorbidities from the RF model which had 67% accuracy, 14% precision, and 47% recall (Table 4).

Factors that affect patients readmission have been studied extensively in the past two decades. In this study, we studied clinical factors that can be linked to the risk of readmission for patients with CAP. The results of the analysis showed that patient age, heart rate, respiratory rate, medication count, and the number of comorbidities that the patient had were significantly associated with the odds of being readmitted. All other variables showed no significant effect. The significant variables from our analysis are all factors that could indicate the severity of the patients condition in general and during the initial admission.

However, when examining the magnitude of the relationship using odds ratios, we can observe two controversial themes. The first is that the worse patients have higher odds of readmission than their counterparts, which can be drawn from the odds of the temperature and number of comorbidities. The second theme is that worse patients have lower odds of being readmitted than their counterparts, which can be drawn from the odds of the age of the patient, heart rate, respiratory rate, and the number of medications prescribed. These two themes can be explained by the behavior of clinicians when dealing with admitted patients.

The researchers hypothesis is that factors such as age, heart rate, and respiratory rate are directly related to the condition of patients with CAP, whereas temperature and the number of comorbidities are not related. This implies that patients who are worse according to their age, heart rate, and respiratory rate should be given more attention and prescribed more medications, which subsequently results in lower odds of being readmitted. This explanation is consistent with another study conducted at the same hospital for studying seven-day readmission for emergency department patients [22]. Nevertheless, this hypothesis needs further investigation and likely an observational study to be confirmed or rejected.

To build a prediction model, multiple algorithms and techniques were available for use in this study. However, we selected the ones that can easily be interpreted by HCPs where each rule can be translated into if/then rules. Moreover, we included other models that have been showing promising results per our literature review. After extensive testing of the final clean dataset and multiple trials of creating the prediction model, although the accuracy of the model reached 90%, we did not find any appropriate prediction for readmission. This can be explained by the nature of our dataset because the data for readmitted patients within 30 days of discharge was only 9.1% of the dataset and not enough to create an appropriate prediction.

Looking at the results of the model, the SVM performed poorly, which contradicts the findings reported in the literature. In this study, SVM showed zero recall by predicting that all patients will not require readmission. Moreover, as part of the models training exercise, we tried to add and drop some of the predictors while using precision and recall to observe the effect on the performance of the models. This exercise resulted in a slight improvement in the RF model after decreasing the number of dummy attributes. On the other hand, better performance of the DT model was noticed after removing the continuous attributes of vital signs. Even with the best version of each model, we conclude that none of these models would qualify to serve as a reliable or valid model to predict readmissions of patients with CAP.

This poor performance of the models is in part due to the low quality of the dataset that was available for this analysis. For example, the laboratory results were provided as free text instead of structured numeric values, which required sophisticated and advanced text-mining efforts to extract the relevant values for our study. Unfortunately, this led to only including the number of labs that were ordered for each patient instead of the results of the labs. Conducting more natural language processing could have a real contribution in creating a model with better performance than our models.

Furthermore, in comorbidities, there were several entries for the same comorbidity for each patient. Therefore, the frequency of those comorbidities was not easily derived from the data. In addition, the same comorbidity had multiple descriptions which made it harder to calculate. For example, diabetes mellitus, type II diabetes, and type II diabetes mellitus can be found simultaneously, and they might have been documented differently for the same patient. Moreover, we used the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification codes of pneumonia for applying the inclusion criteria that only include patients who had CAP as a primary diagnosis. However, we noticed that different codes of pneumonia were used interchangeably in the HIS documentation even during the same visit, which affected the accuracy of including only CAP patients.

Dealing with such data requires a careful process to ensure that the cleaning process was done appropriately and did not result in faulty data or missing data from the original dataset. Furthermore, the decisions to choose how to transform the data should be discussed before each action with an expert in the field not only be taken by the data scientist. For example, because we had multiple readings of vital signs, we needed to transform it to get only one value for each visit; the data scientist might use the median value to give an overall view of the entire admission status, but the physician would rather take the last reading before discharge because it will better reveal how the patient was discharged, which will affect the readmission possibility. Another would rather use the worst reading during the admission to reflect how bad the patients condition was, which would affect the possibility of being readmitted.

The main limitation of this study was the structure of the original dataset, which limited the number of features that could be engineered and used for prediction. Another limitation was the lack of data outside the hospital that could strongly impact the readmission of patients. For example, if the hospital collected how well patients are adhering to their medications or their diet, we hypothesize that the prediction could have gained more accuracy. This kind of data could be collected as part of future studies from patients smartphones through dedicated applications. Future nationwide studies can be conducted to compare different populations based on different societal factors, such as lifestyle and diet, to determine their impact on readmission.

Pneumonia is an infectious disease with major health and economic complications. Predicting CAP readmission using machine learning is an essential area of further research. Based on our study findings, the model accuracy reached 90%, but we did not find an appropriate prediction for readmission because the number of readmissions within 30 days was only 9.1% of the entire dataset received. We propose that machine learning can be used for predicting CAP readmission, but appropriate data sources and a suitable modeling technique are required to improve the accuracy of the model.

During the development of our machine learning and statistical analysis models, we identified factors that are associated with the readmission of patients with CAP. These factors should be used and closely monitored by HCPs to minimize the rate of readmissions of patients with CAP. In our study, we identified that less than 10% of the patients were readmitted for CAP after discharge within the dates included in our study. Furthermore, our results indicated that age, heart rate, respiratory rate, medication count, and the number of comorbidities that a patient had were significantly associated with the odds of being readmitted. However, the prediction performance of the models in our study was not adequate to predict the risk of readmission.

Here is the original post:
A Machine Learning Model for Predicting the Risk of Readmission in Community-Acquired Pneumonia - Cureus

Renesas has announced a new chip in its RZ/V family, aimed at accelerating OpenCV and other machine learning workloads for low-power computer vision at the edge: the RZ/V2MA microprocessor, built with the Apache TVM compiler stack in mind.

The new Renesas RZ/V2MA is built around two 64-bit Arm Cortex-A53 processor cores running at up to 1GHz plus an artificial intelligence coprocessor dubbed the DRP-AI and offering one trillion operations per second (TOPS) of compute performance per watt of power translating, in real-world terms, to a performance of 52 frames per second for the TinyYoloV3 network.

Renesas' latest chip aims to provide enough grunt for real-time edge-vision workloads in a low power envelope. (: Renesas)

In addition to the DRP-AI coprocessor, the chip also offers an accelerator specifically focused on OpenCV workloads improving performance for rule-based image processing, which can happen simultaneously alongside networks running on the DRP-AI.

On the software side, Renesas has a DRP-AI Translator, which offers conversion for ONNX and PyTorch models to enable them to run on the DRP-AI core, with TensorFlow support to follow; the company has also announced the DRP-AI TVM, built atop the open-source Apache TVM deep learning compiler stack, which allows for programs to be compiled to run on both the DRP-AI accelerator and one or both CPU cores.

"One of the challenges for embedded systems developers who want to implement machine learning is to keep up with the latest AI models that are constantly evolving," claims Shigeki Kato, vice president of Renesas' enterprise infrastructure division. "With the new DRP-AI TVM tool, we are offering designers the option to expand AI frameworks and AI models that can be converted to executable formats, allowing them to bring the latest image recognition capabilities to embedded devices using new AI models.

The RZ/V2MA also offers support for H.264 and H.265 video codecs, LPDDR4 memory at up to 3.2Gbps bandwidth, USB 3.1 connectivity, and two lanes of PCI Express. To showcase its capabilities, Renesas has used the chip in the Vision AI Gateway Solution reference design, which includes Ethernet, Wi-Fi, Bluetooth Low Energy (BLE), and cellular LTE Cat-M1 connectivity.

More information on the RZ/V2MA is available on the Renesas website.

Read more:
Renesas Launches RZ/V2MA Microprocessor with Deep Learning and OpenCV Acceleration Engines - Hackster.io

Training a machine-learning model to effectively perform a task, such as image classification, involves showing the model thousands, millions, or even billions of example images. Gathering such enormous datasets can be especially challenging when privacy is a concern, such as with medical images. Researchers from MIT and the MIT-born startup DynamoFL have now taken one popular solution to this problem, known as federated learning, and made it faster and more accurate.

Federated learning is a collaborative method for training a machine-learning model that keeps sensitive user data private. Hundreds or thousands of users each train their own model using their own data on their own device. Then users transfer their models to a central server, which combines them to come up with a better model that it sends back to all users.

A collection of hospitals located around the world, for example, could use this method to train a machine-learning model that identifies brain tumors in medical images, while keeping patient data secure on their local servers.

But federated learning has some drawbacks. Transferring a large machine-learning model to and from a central server involves moving a lot of data, which has high communication costs, especially since the model must be sent back and forth dozens or even hundreds of times. Plus, each user gathers their own data, so those data dont necessarily follow the same statistical patterns, which hampers the performance of the combined model. And that combined model is made by taking an average it is not personalized for each user.

The researchers developed a technique that can simultaneously address these three problems of federated learning. Their method boosts the accuracy of the combined machine-learning model while significantly reducing its size, which speeds up communication between users and the central server. It also ensures that each user receives a model that is more personalized for their environment, which improves performance.

The researchers were able to reduce the model size by nearly an order of magnitude when compared to other techniques, which led to communication costs that were between four and six times lower for individual users. Their technique was also able to increase the models overall accuracy by about 10 percent.

A lot of papers have addressed one of the problems of federated learning, but the challenge was to put all of this together. Algorithms that focus just on personalization or communication efficiency dont provide a good enough solution. We wanted to be sure we were able to optimize for everything, so this technique could actually be used in the real world, says Vaikkunth Mugunthan PhD 22, lead author of a paper that introduces this technique.

Mugunthan wrote the paper with his advisor, senior author Lalana Kagal, a principal research scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL). The work will be presented at the European Conference on Computer Vision.

Cutting a model down to size

The system the researchers developed, called FedLTN, relies on an idea in machine learning known as the lottery ticket hypothesis. This hypothesis says that within very large neural network models there exist much smaller subnetworks that can achieve the same performance. Finding one of these subnetworks is akin to finding a winning lottery ticket. (LTN stands for lottery ticket network.)

Neural networks, loosely based on the human brain, are machine-learning models that learn to solve problems using interconnected layers of nodes, or neurons.

Finding a winning lottery ticket network is more complicated than a simple scratch-off. The researchers must use a process called iterative pruning. If the models accuracy is above a set threshold, they remove nodes and the connections between them (just like pruning branches off a bush) and then test the leaner neural network to see if the accuracy remains above the threshold.

Other methods have used this pruning technique for federated learning to create smaller machine-learning models which could be transferred more efficiently. But while these methods may speed things up, model performance suffers.

Mugunthan and Kagal applied a few novel techniques to accelerate the pruning process while making the new, smaller models more accurate and personalized for each user.

They accelerated pruning by avoiding a step where the remaining parts of the pruned neural network are rewound to their original values. They also trained the model before pruning it, which makes it more accurate so it can be pruned at a faster rate, Mugunthan explains.

To make each model more personalized for the users environment, they were careful not to prune away layers in the network that capture important statistical information about that users specific data. In addition, when the models were all combined, they made use of information stored in the central server so it wasnt starting from scratch for each round of communication.

They also developed a technique to reduce the number of communication rounds for users with resource-constrained devices, like a smart phone on a slow network. These users start the federated learning process with a leaner model that has already been optimized by a subset of other users.

Winning big with lottery ticket networks

When they put FedLTN to the test in simulations, it led to better performance and reduced communication costs across the board. In one experiment, a traditional federated learning approach produced a model that was 45 megabytes in size, while their technique generated a model with the same accuracy that was only 5 megabytes. In another test, a state-of-the-art technique required 12,000 megabytes of communication between users and the server to train one model, whereas FedLTN only required 4,500 megabytes.

With FedLTN, the worst-performing clients still saw a performance boost of more than 10 percent. And the overall model accuracy beat the state-of-the-art personalization algorithm by nearly 10 percent, Mugunthan adds.

Now that they have developed and finetuned FedLTN, Mugunthan is working to integrate the technique into a federated learning startup he recently founded, DynamoFL.

Moving forward, he hopes to continue enhancing this method. For instance, the researchers have demonstrated success using datasets that had labels, but a greater challenge would be applying the same techniques to unlabeled data, he says.

Mugunthan is hopeful this work inspires other researchers to rethink how they approach federated learning.

This work shows the importance of thinking about these problems from a holistic aspect, and not just individual metrics that have to be improved. Sometimes, improving one metric can actually cause a downgrade in the other metrics. Instead, we should be focusing on how we can improve a bunch of things together, which is really important if it is to be deployed in the real world, he says.

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Collaborative machine learning that preserves privacy | MIT News | Massachusetts Institute of Technology - MIT News

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence Market Size, Share & Trends Analysis Report by Solution, by Technology (Deep Learning, Machine Learning, Natural Language Processing, Machine Vision), by End Use, by Region, and Segment Forecasts, 2022-2030" report has been added to ResearchAndMarkets.com's offering.

The global artificial intelligence market size is expected to reach USD 1,811.8 billion by 2030. The market is anticipated to expand at a CAGR of 38.1% from 2022 to 2030.

Companies Mentioned

Artificial Intelligence (AI) denotes the concept and development of computing systems capable of performing tasks customarily requiring human assistance, such as decision-making, speech recognition, visual perception, and language translation. AI uses algorithms to understand human speech, visually recognize objects, and process information. These algorithms are used for data processing, calculation, and automated reasoning.

Artificial intelligence researchers continuously improve algorithms for various aspects, as conventional algorithms have drawbacks regarding accuracy and efficiency. These advancements have led manufacturers and technology developers to focus on developing standard algorithms. Recently, several developments have been carried out for enhancing artificial intelligence algorithms. For instance, in May 2020, International Business Machines Corporation announced a wide range of new AI-powered services and capabilities, namely IBM Watson AIOps, for enterprise automation. These services are designed to help automate the IT infrastructures and make them more resilient and cost reduction.

Various companies are implementing AI-based solutions such as RPA (Robotic Process Automation) to enhance the process workflows to handle and automate repetitive tasks. AI-based solutions are also being coupled with the IoT (Internet of Things) to provide robust results for various business processes. For Instance, Microsoft announced to invest USD 1 billion in OpenAI, a San Francisco-based company. The two businesses teamed up to create AI supercomputing technology on Microsoft's Azure cloud.

The COVID-19 pandemic has emerged as an opportunity for AI-enabled computer systems to fight against the epidemic as several tech companies are working on preventing, mitigating, and containing the virus. For instance, LeewayHertz, a U.S.-based custom software development company, offers technology solutions using AI tools and techniques, including the Face Mask Detection System to identify individuals without a mask and the Human Presence System to monitor patients remotely. Besides, Voxel51 Inc., a U.S.-based artificial intelligence start-up, has developed Voxel51 PDI (Physical Distancing Index) to measure the impact of the global pandemic on social behavior across the world.

AI-powered computer platforms or solutions are being used to fight against COVID - 19 in numerous applications, such as early alerts, tracking and prediction, data dashboards, diagnosis and prognosis, treatments and cures, and maintaining social control. Data dashboards that can visualize the pandemic have emerged with the need for coronavirus tracking and prediction. For instance, Microsoft Corporation's Bing's AI tracker gives a global overview of the pandemic's current statistics.

Artificial Intelligence Market Report Highlights

Key Topics Covered:

Chapter 1 Methodology and Scope

Chapter 2 Executive Summary

Chapter 3 Market Variables, Trends & Scope

3.1 Market Trends & Outlook

3.2 Market Segmentation & Scope

3.3 Artificial Intelligence Size and Growth Prospects

3.4 Artificial Intelligence-Value Chain Analysis

3.5 Artificial Intelligence Market Dynamics

3.5.1 Market Drivers

3.5.1.1 Economical parallel processing set-up

3.5.1.2 Potential R&D in artificial intelligence systems

3.5.1.3 Big data fuelling AI and Machine Learning profoundly

3.5.1.4 Increasing Cross-Industry Partnerships and Collaborations

3.5.1.5 AI to counter unmet clinical demand

3.5.2 Market Restraint

3.5.2.1 Vast demonstrative data requirement

3.6 Penetration & Growth Prospect Mapping

3.7 Industry Analysis-Porter's

3.8 Company Market Share Analysis, 2021

3.9 Artificial Intelligence-PEST Analysis

3.10 Artificial Intelligence-COVID-19 Impact Analysis

Chapter 4 Artificial Intelligence Market: Solution Estimates & Trend Analysis

Chapter 5 Artificial Intelligence Market: Technology Estimates & Trend Analysis

Chapter 6 Artificial Intelligence Market: End-Use Estimates & Trend Analysis

Chapter 7 Artificial Intelligence Market: Regional Estimates & Trend Analysis

Chapter 8 Competitive Landscape

For more information about this report visit https://www.researchandmarkets.com/r/ykyt2m

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The Worldwide Artificial Intelligence Industry is Expected to Reach $1811 Billion by 2030 - ResearchAndMarkets.com - Business Wire