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For workers who use machine-learning models to help them make decisions, knowing when to trust a models predictions is not always an easy task, especially since these models are often so complex that their inner workings remain a mystery.

Users sometimes employ a technique, known as selective regression, in which the model estimates its confidence level for each prediction and will reject predictions when its confidence is too low. Then a human can examine those cases, gather additional information, and make a decision about each one manually.

But while selective regression has been shown to improve the overall performance of a model, researchers at MIT and the MIT-IBM Watson AI Lab have discovered that the technique can have the opposite effect for underrepresented groups of people in a dataset. As the models confidence increases with selective regression, its chance of making the right prediction also increases, but this does not always happen for all subgroups.

For instance, a model suggesting loan approvals might make fewer errors on average, but it may actually make more wrong predictions for Black or female applicants. One reason this can occur is due to the fact that the models confidence measure is trained using overrepresented groups and may not be accurate for these underrepresented groups.

Once they had identified this problem, the MIT researchers developed two algorithms that can remedy the issue. Using real-world datasets, they show that the algorithms reduce performance disparities that had affected marginalized subgroups.

Ultimately, this is about being more intelligent about which samples you hand off to a human to deal with. Rather than just minimizing some broad error rate for the model, we want to make sure the error rate across groups is taken into account in a smart way, says senior MIT author Greg Wornell, the Sumitomo Professor in Engineering in the Department of Electrical Engineering and Computer Science (EECS) who leads the Signals, Information, and Algorithms Laboratory in the Research Laboratory of Electronics (RLE) and is a member of the MIT-IBM Watson AI Lab.

Joining Wornell on the paper are co-lead authors Abhin Shah, an EECS graduate student, and Yuheng Bu, a postdoc in RLE; as well as Joshua Ka-Wing Lee SM 17, ScD 21 and Subhro Das, Rameswar Panda, and Prasanna Sattigeri, research staff members at the MIT-IBM Watson AI Lab. The paper will be presented this month at the International Conference on Machine Learning.

To predict or not to predict

Regression is a technique that estimates the relationship between a dependent variable and independent variables. In machine learning, regression analysis is commonly used for prediction tasks, such as predicting the price of a home given its features (number of bedrooms, square footage, etc.) With selective regression, the machine-learning model can make one of two choices for each input it can make a prediction or abstain from a prediction if it doesnt have enough confidence in its decision.

When the model abstains, it reduces the fraction of samples it is making predictions on, which is known as coverage. By only making predictions on inputs that it is highly confident about, the overall performance of the model should improve. But this can also amplify biases that exist in a dataset, which occur when the model does not have sufficient data from certain subgroups. This can lead to errors or bad predictions for underrepresented individuals.

The MIT researchers aimed to ensure that, as the overall error rate for the model improves with selective regression, the performance for every subgroup also improves. They call this monotonic selective risk.

It was challenging to come up with the right notion of fairness for this particular problem. But by enforcing this criteria, monotonic selective risk, we can make sure the model performance is actually getting better across all subgroups when you reduce the coverage, says Shah.

Focus on fairness

The team developed two neural network algorithms that impose this fairness criteria to solve the problem.

One algorithm guarantees that the features the model uses to make predictions contain all information about the sensitive attributes in the dataset, such as race and sex, that is relevant to the target variable of interest. Sensitive attributes are features that may not be used for decisions, often due to laws or organizational policies. The second algorithm employs a calibration technique to ensure the model makes the same prediction for an input, regardless of whether any sensitive attributes are added to that input.

The researchers tested these algorithms by applying them to real-world datasets that could be used in high-stakes decision making. One, an insurance dataset, is used to predict total annual medical expenses charged to patients using demographic statistics; another, a crime dataset, is used to predict the number of violent crimes in communities using socioeconomic information. Both datasets contain sensitive attributes for individuals.

When they implemented their algorithms on top of a standard machine-learning method for selective regression, they were able to reduce disparities by achieving lower error rates for the minority subgroups in each dataset. Moreover, this was accomplished without significantly impacting the overall error rate.

We see that if we dont impose certain constraints, in cases where the model is really confident, it could actually be making more errors, which could be very costly in some applications, like health care. So if we reverse the trend and make it more intuitive, we will catch a lot of these errors. A major goal of this work is to avoid errors going silently undetected, Sattigeri says.

The researchers plan to apply their solutions to other applications, such as predicting house prices, student GPA, or loan interest rate, to see if the algorithms need to be calibrated for those tasks, says Shah. They also want to explore techniques that use less sensitive information during the model training process to avoid privacy issues.

And they hope to improve the confidence estimates in selective regression to prevent situations where the models confidence is low, but its prediction is correct. This could reduce the workload on humans and further streamline the decision-making process, Sattigeri says.

This research was funded, in part, by the MIT-IBM Watson AI Lab and its member companies Boston Scientific, Samsung, and Wells Fargo, and by the National Science Foundation.

Originally posted here:
A technique to improve both fairness and accuracy in artificial intelligence - MIT News

Trainual, the leading training management system for small businesses and growing teams, today released an AI-powered documentation engine for outlining roles and responsibilities. The Suggested Roles and Suggested Responsibilities features allow users of its platform to leverage the learnings of thousands of growing organizations around the world by recommending roles by company type, along with the responsibilities associated with those roles. Trainual accomplishes this with proprietary data that connects which types of trainings have been assigned to comparable job titles from similar businesses in every industry.

Small businesses create 1.5 million jobs annually inthe United States, accounting for 64% of annual averages (source). With Suggested Roles and Responsibilities, small business owners and leaders have tools to quickly identify the duties for new roles within their organization, and map training materials to them.

Also Read: Three Approaches for Leveraging Remote Teams in IT Sector

Every small business is unique. As they grow, so does their employee count and the mix of different roles they have within their companies. And along with each role comes a new set of responsibilities that can take lots of time to think up and document, saidChris Ronzio, CEO and Founder of Trainual. We decided to make that process easier. Using artificial intelligence (AI) and machine learning, Trainual is providing small business owners and managers the tools to easily keep their roles up-to-date and the people that hold them, trained in record time.

The process is simple. When a company goes to add a new role, theyll automatically see a list of roles (AKA job titles) that similar businesses have added to their companies. After accepting a suggested role in the Trainual app, theyll see a list of suggested responsibilities, curated utilizing AI and Trainuals own machine learning engine. Owners, managers, and employees can then easily add context to all of the responsibilities for every role in the business by documenting or assigning existing content thats most relevant for onboarding and ongoing training.

Check Out The NewEnterprisetalk Podcast.For more such updates follow us on Google NewsEnterprisetalk News.

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Trainual Leverages AI and Machine Learning to Give SMBs a Faster Way to Onboard and Train - EnterpriseTalk

For once, algorithms that predict crime might be used to uncover bias in policing, instead of reinforcing it.

A group of social and data scientists developed a machine learning tool it hoped would better predict crime. The scientists say they succeeded, but their work also revealed inferior police protection in poorer neighborhoods in eight major U.S. cities, including Los Angeles.

Instead of justifying more aggressive policing in those areas, however, the hope is the technology will lead to changes in policy that result in more equitable, need-based resource allocation, including sending officials other than law enforcement to certain kinds of calls, according to a report published Thursday in the journal Nature Human Behavior.

The tool, developed by a team led by University of Chicago professor Ishanu Chattopadhyay, forecasts crime by spotting patterns amid vast amounts of public data on property crimes and crimes of violence, learning from the data as it goes.

Chattopadhyay and his colleagues said they wanted to ensure the system not be abused.

Rather than simply increasing the power of states by predicting the when and where of anticipated crime, our tools allow us to audit them for enforcement biases, and garner deep insight into the nature of the (intertwined) processes through which policing and crime co-evolve in urban spaces, their report said.

For decades, law enforcement agencies across the country have used digital technology for surveillance and predicting on the belief it would make policing more efficient and effective. But in practice, civil liberties advocates and others have argued that such policies are informed by biased data that contribute to increased patrols in Black and Latino neighborhoods or false accusations against people of color.

Chattopadhyay said previous efforts at crime prediction didnt always account for systemic biases in law enforcement and were often based on flawed assumptions about crime and its causes. Such algorithms gave undue weight to variables such as the presence of graffiti, he said. They focused on specific hot spots, while failing to take into account the complex social systems of cities or the effects of police enforcement on crime, he said. The predictions sometimes led to police flooding certain neighborhoods with extra patrols.

His teams efforts have yielded promising results in some places. The tool predicted future crimes as much as one week in advance with roughly 90% accuracy, according to the report.

Running a separate model led to an equally important discovery, Chattopadhyay said. By comparing arrest data across neighborhoods of different socioeconomic levels, the researchers found that crime in wealthier parts of town led to more arrests in those areas, at the same time as arrests in disadvantaged neighborhoods declined.

But, the opposite was not true. Crime in poor neighborhoods didnt always lead to more arrests suggesting biases in enforcement, the researchers concluded. The model is based on several years of data from Chicago, but researchers found similar results in seven other larger cities: Los Angeles; Atlanta; Austin, Texas; Detroit; Philadelphia; Portland, Ore.; and San Francisco.

The danger with any kind of artificial intelligence used by law enforcement, the researchers said, lies in misinterpreting the results and creating a harmful feedback of sending more police to areas that might already feel over-policed but under-protected.

To avoid such pitfalls, the researchers decided to make their algorithm available for public audit so anyone can check to see whether its being used appropriately, Chattopadhyay said.

Often, the systems deployed are not very transparent, and so theres this fear that theres bias built in and theres a real kind of risk because the algorithms themselves or the machines might not be biased, but the input may be, Chattopadhyay said in a phone interview.

The model his team developed can be used to monitor police performance. You can turn it around and audit biases, he said, and audit whether policies are fair as well.

Most machine learning models in use by law enforcement today are built on proprietary systems that make it difficult for the public to know how they work or how accurate they are, said Sean Young, executive director of the University of California Institute for Prediction Technology.

Given some of the criticism around the technology, some data scientists have become more mindful of potential bias.

This is one of a number of growing research papers or models thats now trying to find some of that nuance and better understand the complexity of crime prediction and try to make it both more accurate but also address the controversy, Young, a professor of emergency medicine and informatics at UC Irvine, said of the just-published report.

Predictive policing can also be more effective, he said, if its used to work with community members to solve problems.

Despite the studys promising findings, its likely to raise some eyebrows in Los Angeles, where police critics and privacy advocates have long railed against the use of predictive algorithms.

In 2020, the Los Angeles Police Department stopped using a predictive-policing program called Pred-Pol that critics argued led to heavier policing in minority neighborhoods.

At the time, Police Chief Michel Moore insisted he ended the program because of budgetary problems brought on by the COVID-19 pandemic. He had previously said he disagreed with the view that Pred-Pol unfairly targeted Latino and Black neighborhoods. Later, Santa Cruz became the first city in the country to ban predictive policing outright.

Chattopadhyay said he sees how machine learning evokes Minority Report, a novel set in a dystopian future in which people are hauled away by police for crimes they have yet to commit.

But the effect of the technology is only beginning to be felt, he said.

Theres no way of putting the cat back into the bag, he said.

Read the rest here:
Researchers use AI to predict crime, biased policing in cities - Los Angeles Times

New York, June 29, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Machine learning as a Service Market Size, Share & Industry Trends Analysis Report By End User, By Offering, By Organization Size, By Application, By Regional Outlook and Forecast, 2022 2028" - https://www.reportlinker.com/p06289268/?utm_source=GNW It is designed to include artificial intelligence (AI) and cognitive computing functionalities. Machine learning as a service (MLaaS) refers to a group of cloud computing services that provide machine learning technologies.

Increased demand for cloud computing, as well as growth connected with artificial intelligence and cognitive computing, are major machine learning as service industry growth drivers. Growth in demand for cloud-based solutions, such as cloud computing, rise in adoption of analytical solutions, growth of the artificial intelligence & cognitive computing market, increased application areas, and a scarcity of trained professionals are all influencing the machine learning as a service market.

As more businesses migrate their data from on-premise storage to cloud storage, the necessity for efficient data organization grows. Since MLaaS platforms are essentially cloud providers, they enable solutions to appropriately manage data for machine learning experiments and data pipelines, making it easier for data engineers to access and process the data.

For organizations, MLaaS providers offer capabilities like data visualization and predictive analytics. They also provide APIs for sentiment analysis, facial recognition, creditworthiness evaluations, corporate intelligence, and healthcare, among other things. The actual computations of these processes are abstracted by MLaaS providers, so data scientists dont have to worry about them. For machine learning experimentation and model construction, some MLaaS providers even feature a drag-and-drop interface.

COVID-19 Impact

The COVID-19 pandemic has had a substantial impact on numerous countries health, economic, and social systems. It has resulted in millions of fatalities across the globe and has left the economic and financial systems in tatters. Individuals can benefit from knowledge about individual-level susceptibility variables in order to better understand and cope with their psychological, emotional, and social well-being.

Artificial intelligence technology is likely to aid in the fight against the COVID-19 pandemic. COVID-19 cases are being tracked and traced in several countries utilizing population monitoring approaches enabled by machine learning and artificial intelligence. Researchers in South Korea, for example, track coronavirus cases using surveillance camera footage and geo-location data.

Market Growth Factors

Increased Demand for Cloud Computing and a Boom in Big Data

The industry is growing due to the increased acceptance of cloud computing technologies and the use of social media platforms. Cloud computing is now widely used by all companies that supply enterprise storage solutions. Data analysis is performed online using cloud storage, giving the advantage of evaluating real-time data collected on the cloud. Cloud computing enables data analysis from any location and at any time. Moreover, using the cloud to deploy machine learning allows businesses to get useful data, such as consumer behavior and purchasing trends, virtually from linked data warehouses, lowering infrastructure and storage costs. As a result, the machine learning as a service business is growing as cloud computing technology becomes more widely adopted.

Use of Machine Learning to Fuel Artificial Intelligence Systems

Machine learning is used to fuel reasoning, learning, and self-correction in artificial intelligence (AI) systems. Expert systems, speech recognition, and machine vision are examples of AI applications. The rise in the popularity of AI is due to current efforts such as big data infrastructure and cloud computing. Top companies across industries, including Google, Microsoft, and Amazon (Software & IT); Bloomberg, American Express (Financial Services); and Tesla and Ford (Automotive), have identified AI and cognitive computing as a key strategic driver and have begun investing in machine learning to develop more advanced systems. These top firms have also provided financial support to young start-ups in order to produce new creative technology.

Market Restraining Factors

Technical Restraints and Inaccuracies of ML

The ML platform provides a plethora of advantages that aid in market expansion. However, several parameters on the platform are projected to impede market expansion. The presence of inaccuracy in these algorithms, which are sometimes immature and underdeveloped, is one of the markets primary constraining factors. In the big data and machine learning manufacturing industries, precision is crucial. A minor flaw in the algorithm could result in incorrect items being produced. This would exorbitantly increase the operational costs for the owner of the manufacturing unit than decrease it.

End User Outlook

Based on End User, the market is segmented into IT & Telecom, BFSI, Manufacturing, Retail, Healthcare, Energy & Utilities, Public Sector, Aerospace & Defense, and Others. The retail segment garnered a substantial revenue share in the machine learning as a service market in 2021. E-commerce has proven to be a key force in the retail trade industry. Machine intelligence is used by retailers to collect data, evaluate it, and use it to provide customers with individualized shopping experiences. These are some of the factors that influence the retail industries demand for this technology.

Offering Outlook

Based on Offering, the market is segmented into Services Only and Solution (Software Tools). The services only segment acquired the largest revenue share in the machine learning as a service market in 2021. The market for machine learning services is expected to grow due to factors such as an increase in application areas and growth connected with end-use industries in developing economies. To enhance the usage of machine learning services, industry participants are focusing on implementing technologically advanced solutions. The use of machine learning services in the healthcare business for cancer detection, as well as checking ECG and MRI, is expanding the market. Machine learning services benefits, such as cost reduction, demand forecasting, real-time data analysis, and increased cloud use, are projected to open up considerable prospects for the market.

Organization Size Outlook

Based on Organization Size, the market is segmented into Large Enterprises and Small & Medium Enterprises. The small and medium enterprises segment procured a substantial revenue share in the machine learning as a service market in 2021. This is because implementation of machine learning lets SMEs optimize its processes on a tight budget. AI and machine learning are projected to be the major technologies that allow SMEs to save money on ICT and gain access to digital resources in the near future.

Application Outlook

Based on Application, the market is segmented into Marketing & Advertising, Fraud Detection & Risk Management, Computer vision, Security & Surveillance, Predictive analytics, Natural Language Processing, Augmented & Virtual Reality, and Others. The marketing and advertising segment acquired the largest revenue share in the machine learning as a service market in 2021. The goal of a recommendation system is to provide customers with products that they are currently interested in. The following is the marketing work algorithm: Hypotheses are developed, tested, evaluated, and analyzed by marketers. Because information changes every second, this effort is time-consuming and labor-intensive, and the findings are occasionally wrong. Machine learning allows marketers to make quick decisions based on large amounts of data. Machine learning allows businesses to respond more quickly to changes in the quality of traffic generated by advertising efforts. As a result, the business can spend more time developing hypotheses rather than doing mundane tasks.

Regional Outlook

Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa. The Asia Pacific region garnered a significant revenue share in the machine learning as a service market in 2021. Leading companies are concentrating their efforts in Asia-Pacific to expand their operations, as the region is likely to see rapid development in the deployment of security services, particularly in the banking, financial services, and insurance (BFSI) sector. To provide better customer service, industry participants are realizing the significance of providing multi-modal platforms. The rise in AI application adoption is likely to be the primary trend driving market growth in this area. Furthermore, government organizations have taken important steps to accelerate the adoption of machine learning and related technologies in this region.

The major strategies followed by the market participants are Product Launches and Partnerships. Based on the Analysis presented in the Cardinal matrix; Microsoft Corporation and Google LLC are the forerunners in the Machine learning as a Service Market. Companies such Amazon Web Services, Inc., SAS Institute, Inc., IBM Corporation are some of the key innovators in the Market.

The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Hewlett-Packard Enterprise Company, Oracle Corporation, Google LLC, Amazon Web Services, Inc. (Amazon.com, Inc.), IBM Corporation, Microsoft Corporation, Fair Isaac Corporation (FICO), SAS Institute, Inc., Yottamine Analytics, LLC, and BigML.

Recent Strategies deployed in Machine learning as a Service Market

Partnerships, Collaborations and Agreements:

Mar-2022: Google entered into a partnership with BT, a British telecommunications company. Under the partnership, BT utilized a suite of Google Cloud products and servicesincluding cloud infrastructure, machine learning (ML) and artificial intelligence (AI), data analytics, security, and API managementto offer excellent customer experiences, decrease costs, and risks, and create more revenue streams. Google aimed to enable BT to get access to hundreds of new business use-cases to solidify its goals around digital offerings and developing hyper-personalized customer engagement.

Feb-2022: SAS entered into a partnership with TecCentric, a company providing customized IT solutions. SAS aimed to fasten TecCentrics journey towards discovery with artificial intelligence (AI), machine learning (ML), and advanced analytics. Under the partnership, TecCentric aimed to work with SAS to customize services and solutions for a broad range of verticals from the public sector, to banking, education, healthcare, and more, granting them access to the complete analytics cycle with SASs enhanced AI solution offering as well as its leading fraud and financial crimes analytics and reporting.

Feb-2022: Microsoft entered into a partnership with Tata Consultancy Services, an Indian company focusing on providing information technology services and consulting. Under the partnership, Tata Consultancy Services leveraged its software, TCS Intelligent Urban Exchange (IUX) and TCS Customer Intelligence & Insights (CI&I), to enable businesses in providing hyper-personalized customer experiences. CI&I and IUX are supported by artificial intelligence (AI), and machine learning, and assist in real-time data analytics. The CI&I software empowered retailers, banks, insurers, and other businesses to gather insights, predictions, and recommended actions in real-time to enhance the satisfaction of customers.

Jun-2021: Amazon Web Services entered into a partnership with Salesforce, a cloud-based software company. The partnership enabled to utilize complete set of Salesforce and AWS capabilities simultaneously to rapidly develop and deploy new business applications that facilitate digital transformation. Salesforce also embedded AWS services for voice, video, artificial intelligence (AI), and machine learning (ML) directly in new applications for sales, service, and industry vertical use cases.

Apr-2021: Amazon formed a partnership with Basler, a company known for its product line of area scan, line scan, and network cameras. The partnership began as Amazon launched a succession of services for industrial machine learning, including its latest Lookout for Vision cloud AI service for factory inspection. Customers can integrate AWS Panorama SDK within its platform, and thus utilize a common architecture to perform multiple tasks and accommodate a broad range of performance and cost. The integration of AWS Panorama empowered customers to adopt and run machine learning applications on edge devices with additional support for device management and accuracy tracking.

Dec-2020: IBM teamed up with Mila, a Quebec Artificial Intelligence Institute. Under the collaboration, both organizations aimed to quicken machine learning using Oron, an open-source technology. After the integration of Milas open-source Oron software and IBMs Watson Machine Learning Accelerator, IBM also enhanced the deployment of state-of-the-art algorithms, along with improved machine learning and deep learning capabilities for AI researchers and data scientists. IBMs Spectrum Computing team based out of Canada Lab contributes substantially to Orons code base.

Oct-2020: SAS entered into a partnership with TMA Solutions, a software outsourcing company based in Vietnam. Under the partnership, SAS and TMA Solutions aimed to fasten the growth of businesses in Vietnam through Artificial Intelligence (AI) and Data Analytics. SAS and TMA helped clients in Vietnam quicken the deployment and growth of advanced analytics and look for new methods to propel innovation in AI, especially in the fields of Machine Learning, Computer Vision, Natural Language Processing (NLP), and other technologies.

Product Launches and Product Expansions:

May-2022: Hewlett Packard launched HPE Swarm Learning and the new Machine Learning (ML) Development System, two AI and ML-based solutions. These new solutions increase the accuracy of models, solve AI infrastructure burdens, and improve data privacy standards. The company declared the new tool a breakthrough AI solution that focuses on fast-tracking insights at the edge, with attributes ranging from identifying card fraud to diagnosing diseases.

Apr-2022: Hewlett Packard released Machine Learning Development System (MLDS) and Swarm Learning, its new machine learning solutions. The two solutions are focused on simplifying the burdens of AI development in a development environment that progressively consists of large amounts of protected data and specialized hardware. The MLDS provides a full software and services stack, including a training platform (the HPE Machine Learning Development Environment), container management (Docker), cluster management (HPE Cluster Manager), and Red Hat Enterprise Linux.

May-2021: Google released Vertex AI, a novel managed machine learning platform that enables developers to more easily deploy and maintain their AI models. Engineers can use Vertex AI to manage video, image, text, and tabular datasets, and develop machine learning pipelines to train and analyze models utilizing Google Cloud algorithms or custom training code. After that the engineers can install models for online or batch use cases all on scalable managed infrastructure.

Mar-2021: Microsoft released updates to Azure Arc, its service that brought Azure products and management to multiple clouds, edge devices, and data centers with auditing, compliance, and role-based access. Microsoft also made Azure Arc-enabled Kubernetes available. Azure Arc-enabled Machine Learning and Azure Arc-enabled Kubernetes are developed to aid companies to find a balance between enjoying the advantages of the cloud and maintaining apps and maintaining apps and workloads on-premises for regulatory and operational reasons. The new services enable companies to implement Kubernetes clusters and create machine learning models where data lives, as well as handle applications and models from a single dashboard.

Jul-2020: Hewlett Packard released HPE Ezmeral, a new brand and software portfolio developed to assist enterprises to quicken digital transformation across their organization, from edge to cloud. The HPE Ezmeral goes from a portfolio consisting of container orchestration and management, AI/ML, and data analytics to cost control, IT automation and AI-driven operations, and security.

Acquisitions and Mergers:

Jun-2021: Hewlett Packard completed the acquisition of Determined AI, a San Francisco-based startup that offers a strong and solid software stack to train AI models faster, at any scale, utilizing its open-source machine learning (ML) platform. Hewlett Packard integrated Determined AIs unique software solution with its world-leading AI and high-performance computing (HPC) products to empower ML engineers to conveniently deploy and train machine learning models to offer faster and more precise analysis from their data in almost every industry.

Scope of the Study

Market Segments covered in the Report:

By End User

IT & Telecom

BFSI

Manufacturing

Retail

Healthcare

Energy & Utilities

Public Sector

Aerospace & Defense

Others

By Offering

Services Only

Solution (Software Tools)

By Organization Size

Large Enterprises

Small & Medium Enterprises

By Application

Marketing & Advertising

Fraud Detection & Risk Management

Computer vision

Security & Surveillance

Predictive analytics

Natural Language Processing

Augmented & Virtual Reality

Others

By Geography

North America

o US

o Canada

o Mexico

o Rest of North America

Europe

o Germany

o UK

o France

o Russia

o Spain

o Italy

o Rest of Europe

Asia Pacific

o China

o Japan

o India

o South Korea

o Singapore

o Malaysia

o Rest of Asia Pacific

LAMEA

o Brazil

o Argentina

o UAE

o Saudi Arabia

o South Africa

o Nigeria

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The Global Machine learning as a Service Market size is expected to reach $36.2 billion by 2028, rising at a market growth of 31.6% CAGR during the...

Deep Learning Market Is Expected To Reach USD 68.71 Billion By 2027 At A CAGR Of 41.5 percent.

Maximize Market Research has published a report on theDeep Learning Marketthat provides a detailed analysis for the forecast period of 2021 to 2027.

Deep Learning Market Scope:

The report provides comprehensive market insights for industry stakeholders, including an explanation of complicated market data in simple language, the industrys history and present situation, as well as expected market size and trends. The research investigates all industry categories, with an emphasis on key companies such as market leaders, followers, and new entrants. The paper includes a full PESTLE analysis for each country. A thorough picture of the competitive landscape of major competitors in the Deep Learning market by goods and services, revenue, financial situation, portfolio, growth plans, and geographical presence makes the study an investors guide.

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Deep Learning Market Overview:

Deep learning, also known as deep structured learning, is a subclass of machine learning that uses layered computer models to analyze data. It is an essential component of data science, which uses statistics and prescriptive analytics to gather, evaluate, and understand massive volumes of data. It also involves the application of artificial intelligence (AI) to mimic how the human brain processes data, generate trends and makes decisions. This technology is widely utilized in facial recognition software, natural language processing (NLP) and voice synthesis software, self-driving vehicles, and language translation services, and it performs several roles in commerce, healthcare, automobile, farming, military, and industrial settings.

Deep Learning MarketDynamics:

The rising usage of cloud-based services, as well as the large-scale generation of unstructured data, has raised the demand for deep learning solutions. Besides that, the growing number of robotic devices, such as Sophia, produced by Hanson Robotics, as well as the growing implementations of deep learning in recent years for image/speech recognition, data processing, and language explanations, are some of the key drivers of the deep learning industry. The increased efforts of key market participants in developing machine learning and deep learning techniques in the field are expected to drive market growth. Likewise, the rapid increase in the volume of data created in numerous end-use sectors is estimated to driveindustry growth. Also, the increased need for human-machine interaction is creating new possibilities for software vendors to supply enhanced services and skills.

Furthermore, the predominance of deep learning incorporation with big data analytics, as well as the rapidly increasing need to boost processing capacity and reducehardware costs due to deep learning algorithms capacity to perform or execute faster on a GPU as compared to a CPU, is culminating in public adoption of deep learning technologies across industries, which is estimated to drive theglobal growth.

Various setbacksare anticipated to hinder theoverall market growth. The lack of standards and protocols, as well as a lack of technical expertise in deep learning, are limiting industry growth. Additionally, complex integrated systems, as well as the integration of deep learning solutions and software into legacy systems, are time-consuming processes that impede growth.

Deep Learning MarketRegional Insights:

North America is anticipated todominate the global Deep Learning market at the end of the forecastperiod. By 2027, North America is expected to have the greatest market share of nearly 40 percent. This is due to increased investment in artificial intelligence and neural networks. The regions significant use of imaging and monitoring applications is expected to provide new growth opportunities over the forecast period. Likewise, the region is a modern technology pioneer, allowing enterprises to expedite the implementation of deep learning capability.

Deep Learning MarketSegmentation:

By Component:

By Application:

By Architecture Industry:

By End-Use Industry:

Deep Learning Market Key Competitors:

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Global Deep Learning Market Is Expected To Reach USD 68.71 Billion At A CAGR Of 41.5% And Forecast To 2027 - Digital Journal