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Global Machine Learning Market Size To Reach Around USD 302.62 billion by 2030 | CAGR of 14.91%

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New York, United States , Nov. 23, 2022 (GLOBE NEWSWIRE) — The Global Machine Learning Market Size was valued at USD 14.91 billion in 2021 and is expected to reach at a compound annual growth rate (CAGR) of 38.1% from 2021 to 2030. The worldwide market is expected to reach around USD 302.62 billion by 2030. According to a research report published by Spherical Insights & Consulting. The machine learning is a process that uses artificial intelligence (AI) to provide a system the ability to automatically learn from experience and get better over time without being explicitly programmed. The development of a programme that can access data and utilize it to learn for itself is the major goal of this technology.

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An area of artificial intelligence known as machine learning allows machines to learn directly from data, experience, and examples. Machine learning enables computers to carry out certain tasks intelligently by learning from examples or data rather than by following pre-programmed rules, allowing computers to carry out complex procedures. A comprehensive repository for machines to learn from is created by the growing volume of data collected across industrial verticals. This is further supported by the quick advancements in computer processing power, which in turn improve the analytical skills of machine learning systems.

Growing technological improvements that improve system accuracy are driving market expansion. People engage with a variety of machine learning-based systems, including voice recognition, image recognition, and recommender systems. The demand for machine learning in many systems has been spurred by the rapid improvement in image recognition technology, which has boosted the system’s accuracy. For instance, in the image labelling challenge, machine learning’s accuracy increased from 72% in 2010 to 96% in 2015. Machine learning has become a crucial tool in many areas, including BFSI, healthcare, and others, thanks to computers’ ability to process massive amounts of data and apply it for prediction.

The machine learning market has also expanded due to the integration of machine learning in robots. Robotics has undergone numerous developments as a result of the rapid development of sensing technology and materials. The development of machine learning has enhanced robots’ capacity to contribute to projects like autonomous vehicles and drones. Additionally, the market has grown as a result of the rising demand for advanced robotic systems across numerous industries, including automotive, electronics, food and beverage, and healthcare. Around 294,000 industrial robots were deployed worldwide in 2016, according to the International Federation of Robots.

Browse key industry insights spread across 231 pages with 119 market data tables and figures & charts from the report “Global Machine Learning Market Size By Component Type (Software, Services), By Organization Size (Small and Medium-Sized Enterprises), By Application (Fraud Detection and Risk Analytics), By End Use (Automotive, Aerospace & Defense, Retail & E-commerce, Government, Healthcare & Life Sciences, Media & Entertainment, IT & Telecommunications, BFSI, Others), By Region (North America, Europe, Asia Pacific, Middle East & Africa, and South America) – Market Size & Forecasting To 2030.”  in detail along with the table of contents.

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The scarcity of qualified individuals with analytical ability is the main challenge most firms encounter when integrating machine learning into their business processes, and the demand for those who can monitor analytical content is even higher. Privacy issues have dominated technological development and new algorithm development since the advent of big data. The same is true for machine learning, which simply trains itself to think for itself using massive data. These are a few of the main things stopping the market from expanding.

Covid 19 Impact on Global Machine Learning Market

The Covid 19 mishaps have an effect on other industries, such as machine learning. In the midst of pandemics, some sectors grow despite the grave situation and the unsure outcome. The machine learning market was stable and had growth potential during the time of COVID 19. In contrast to several other industries, machine learning had a relatively small impact on the global market.

Global Machine Learning Market, By Component

The services segment is anticipated to experience consistent revenue growth over the projection period. By automating the process of turning data into insights, businesses can achieve a variety of objectives, such as successful customer retention, predictive modelling for anticipating customer behaviour, and 360 Degree Customer View for a deeper understanding of consumers.

Global Machine Learning Market, By Organization Size

The global machine learning market is segmented into small and medium-sized businesses and large businesses based on the size of the company. The segment of small and medium-sized businesses is anticipated to experience consistent revenue growth over the course of the projected period. Small and medium-sized organisations are increasingly using ML approaches to access digital resources and lower their Information and Communications Technology (ICT) investments. Rapid development and highly engaged SMEs have boosted their use of ML solutions and services globally as a result of growing digitization and increasing cyber risks to critical company information and data.

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Global Machine Learning Market, By Application

The global machine learning market is segmented based on application into artificial intelligence, computer vision, augmented and virtual reality, natural language processing, security & surveillance, marketing & advertising, automated network management, predictive maintenance, and others. During the anticipated period, the predictive maintenance segment is anticipated to experience consistent revenue growth. Manufacturing facilities must use corrective and preventative maintenance techniques. These techniques, however, are typically expensive and ineffectual. ML helps create incredibly effective maintenance strategies. These can lower the likelihood of unexpected failures, leading to a decrease in the number of preventive maintenance projects.

Global Machine Learning Market, By End Use

The automotive, aerospace & military, retail & e-commerce, government, healthcare & life sciences, media & entertainment, IT & telecommunications, BFSI, and others segments of the global machine learning market are based on end-use. The BFSI segment is anticipated to experience consistent revenue growth over the anticipated time frame. Popular applications of machine learning in the BFSI industry include algorithmic trading, portfolio management, loan underwriting, and, most importantly, fraud detection. The system also provides continuous data assessment, which, by identifying and evaluating anomalies and subtleties, contributes to improving the accuracy of financial models and rules.

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Global Machine Learning Market, By Region

During the time of forecasting, the European market is anticipated to experience consistent revenue growth. There is an increase in need for qualified personnel as the market for machine learning and AI services is expanding quickly, which is followed by swift technological improvements. The European Commission claims that legislation and regulations pertaining to AI and ML are complex. Despite the fact that the European AI business is seeing considerable revenue growth, the discrepancy between existing standards and the capabilities of new systems is making it difficult for systems to be compliant. In addition, compared to nearshore locales, European businesses are finding it harder to outsource.

Some Recent Developments in Global Machine Learning Market

January 2022: Amazon and Stellantis collaborated to roll out customer-focused connected experiences across millions of vehicles in order to hasten the software transition for Stellantis. The partnership is expected to alter the in-vehicle experiences of Stellantis customers and hasten the transition of the automotive industry to a software-defined sustainable future.

Key Companies & Recent Developments: The report also provides an elaborative analysis focusing on the current news and developments of the companies, which includes product development, innovations, joint ventures, partnerships, mergers & acquisitions, strategic alliances, and others. This allows for the evaluation of the overall competition within the market. Key companies profiled in the machine learning (ML) market research report are IBM Corporation (New York, U.S.), SAP SE (Walldorf, Germany), Oracle Corporation (Texas, U.S.), Hewlett Packard Enterprise Company (Texas, U.S.), Microsoft Corporation (Washington, U.S.), Amazon, Inc. (Washington, U.S.), Intel Corporation (California, U.S.), Fair Isaac Corporation (California, U.S.), SAS Institute Inc. (North Carolina, U.S.), BigML, Inc. (Oregon, U.S.)  and Others.

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Artificial Intelligence

aiOla’s Speech AI Technology Outperforms OpenAI’s Whisper in Recognizing Jargon

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aiOla’s model automates the creation of customized processes and workflows for conducting reports and inspections across industries such as manufacturing, supply chain and logistics, pharma, and more
TEL AVIV, Israel, April 18, 2024 /PRNewswire/ — aiOla, an AI-powered technology that automates business workflows by capturing spoken data, has announced a major milestone in speech recognition. aiOla’s solution, powered by a novel keyword spotting model, has advanced to match human proficiency in understanding industry-specific jargon. The patented AdaKWS model achieved 95% accuracy in keyword spotting, surpassing OpenAI’s industry-leading Whisper model which reached 88% accuracy.

Keyword spotting is an essential aspect of speech recognition that tackles the problem of identifying jargon by detecting predefined words and phrases. “Think about a courier delivery where your package arrives damaged. The courier needs to file a report using specific codes and acronyms that describe the situation — those codes and acronyms are keywords. Industry jargon is everywhere and in many fields, it dominates communication, comprising up to half of workers’ speech,” said aiOla’s CEO and co-founder, Amir Haramaty. “The ability to spot keywords enables automation of everyday processes across a wide range of industries, from filing a parcel damage report to completing a safety inspection in a food manufacturing plant, transforming speech into actions.”
aiOla’s process automation applications can accurately understand speech, jargon and acronyms across over 100 languages, regardless of accents and background noises. aiOla achieves this by combining its state-of-the-art keyword spotting model with a speech recognition model. The onboarding process takes mere hours: clients provide examples of their checklists or forms, and aiOla automatically generates custom language models for the use case. Workers are then able to complete their operations verbally using the aiOla app while keeping their eyes and hands on the equipment. aiOla’s exceptional ability to spot rare industry terms with high accuracy allows the platform to easily distinguish between speech related to work processes and everyday conversation.
The app leverages a proprietary model that was developed by aiOla’s team of scientists to recognize a predefined list of keywords within speech. This enables aiOla’s solution to be instantly adapted to the jargon of any industry without needing to retrain its AI model. On a benchmark of keyword and jargon detection that includes 16 languages, Whisper’s largest model yields 88% accuracy compared to aiOla’s model achieving 95% accuracy. Additionally, in a recent benchmark which is composed of hard-to-detect keywords taken from English language audiobooks, the CED model from a team of Apple researchers yields 92.7% whereas aiOla’s AdaKWS reaches 95.1% accuracy.
“Keyword spotting poses significant challenges due to the scarcity of training data, especially across diverse languages and dialects. It typically requires industry-specific fine-tuning to enable models to recognize jargon not commonly found in everyday speech,” said aiOla’s Chief Scientist, Professor Joseph Keshet. “Our model consistently surpassed the OpenAI Whisper baselines by a significant margin, achieving a substantial improvement compared to the top-performing baseline. Furthermore, our model is far more efficient, using 15x fewer parameters.”
To learn more about aiOla’s technology visit: https://aiola.com
Explore aiOla’s keyword spotting research: https://arxiv.org/pdf/2309.08561.pdf
About aiOla:
aiOla’s patented technology comprehends over 100 languages, and discerns jargon, abbreviations and acronyms, demonstrating a low error rate even in noisy environments. aiOla’s technology converts manual processes in critical industries into data-driven, paperless, AI-powered workflows through cutting-edge speech recognition.

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Artificial Intelligence

AMN utilises SpaceX’s Starlink Constellation to Connect Rural Villages in Nigeria

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LONDON, April 18, 2024 /PRNewswire/ — AMN is pleased to announce that the first AMN base station is now live using LEO backhaul from SpaceX’s Starlink. In 2023, AMN announced a commercial agreement to use Starlink, SpaceX’s constellation of satellites in low Earth orbit, to connect AMN’s mobile network base stations with high-speed, low-latency broadband services.

By utilising Starlink terminals to provide low-latency satellite backhaul, we are able to deliver the full capability of AMN’s unique multi-carrier radio access node (the ARN) with 3G and 4G as well as 2G, with ever-increasing amounts of bandwidth and data volumes demanded by subscribers whilst remaining economically sustainable. The LEO backhaul also paves the way for AMN to deliver 5G services, targeted before the end of 2024.
AMN began rolling out rural base stations in Nigeria in 2018, and the company now owns and operates 1600 base stations across the country. Yebu was the first rural community to be connected using AMN’s ubiquitous solar powered base station. The village is located approximately 80km from Abuja, but can take four hours to reach due to road conditions. Yebu is predominantly an agricultural community, with a market offering local farmers the opportunity to sell their goods.
Since connecting the community in November 2018, AMN has processed more than 9 million voice minutes in Yebu, with significant growth in 2022 and 2023 following the BTS upgrade to AMN’s own radio node (ARN). AMN became an OEM for RAN equipment in 2020 following the acquisition of Range Networks, and now operates more than 1200 ARN across Africa and Latin America. The impact of this strategic move is clear in Yebu. In 2023, the site processed almost three times the amount of traffic than it did in 2020.
“Yebu community was left behind and blind but the coming of Africa Mobile Networks in 2018 has made us to achieve a lot of things like police division station, 24 hours solar light and steady communication all over the world. Before then there was nothing like those things listed.” – Salihu, on behalf of the Yebu community
AMN believes that all communities of any significant size should have access to telecommunication services to benefit the population educationally, economically and socially. AMN has deployed over 4000 base stations across Africa and Latin America. Installation of new sites continues throughout 2024 in Nigeria, DRC, Cameroon, Madagascar, Ivory Coast, Benin and Rwanda. At AMN, we appreciate that any solution to close the digital divide must be economically sustainable and offer a service of the same quality as in urban areas. From designing and manufacturing our own BTS, uniquely developed for the solar-powered rural site, to offering cutting-edge backhaul solutions, we are committed to bringing high quality connectivity to those living in rural and ultra-rural areas.
CONTACT: Jennifer Darcy, [email protected], +44 1908 394482
 

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Artificial Intelligence

Self-Sovereign Identity (SSI) Market worth $47.1 billion by 2029 – Exclusive Report by MarketsandMarkets™

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CHICAGO, April 18, 2024 /PRNewswire/ — Self-Sovereign Identity (SSI) has a bright future ahead of it thanks to growing industry adoption, use cases that go beyond identity verification, and integration with cutting-edge technologies. The growth of the ecosystem and its worldwide reach will be fueled by cooperation, legal backing, and user-centric design, which will empower people and communities while guaranteeing security and privacy in digital identity management.

The global Self-Sovereign Identity (SSI) Market size is projected to grow from USD 1.8 billion in 2024 to USD 47.1 billion by 2029 at a Compound Annual Growth Rate (CAGR) of 90.5% during the forecast period, according to a new report by MarketsandMarkets™. The increasing prevalence of identity theft is compelling organizations to adopt self-sovereign identity (SSI) solutions for more robust authentication and identity protection. Concurrently, small and medium-sized enterprises (SMEs) drive SSI growth through innovative technologies and partnerships, offering tailored identity management solutions across sectors.
Browse in-depth TOC on “Self-Sovereign Identity (SSI) Market”
395 – Tables 51 – Figures295 – Pages
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Scope of the Report
Report Metrics
Details
Market size available for years
2020-2029
Base year considered
2023
Forecast period
2024–2029
Forecast units
Value (USD Million/USD Billion) 
Segments Covered
Offering, Identity Type, Network, Organization Size, Vertical, And Region
Geographies covered
North America, Europe, Asia Pacific, Middle East & Africa, and Latin America  
Companies covered
Major vendors in the global Self-Sovereign Identity Market include Microsoft (US), Ping Identity (US), IDEX Biometrics (Norway), NEC (Japan), Imageware (US), Dock (Switzerland), Metadium  (Cayman Islands), Blockchain Helix (Germany), Validated ID (Spain), Wipro (India), Persistent (India), Infopulse (Poland), 1Kosmos (US), Accumulate (US), NuID (US), Kaleido (US), Talao (France), Vereign (Switzerland), Midy (US), SelfKey (Mauritius), Truvity (Netherlands), Affinidi (Singapore), Trinsic (US), cheqd (England), Fractal ID (Germany), Soulverse (US), Finema (Thailand), Nuggets (UK), Sentry (US), SpringRole (US), Walt.id (Austria), Procivis (Switzerland), Civic (US), Gataca (Spain),  Polygon Labs (Indonesia), and Voyatek (US).   
Initiatives like Alliance Block’s Nexerald platform showcase SMEs’ pivotal role in advancing SSI adoption, ensuring compliance and seamless onboarding to Web3 environments. This collaborative effort reshapes identity management, emphasizing user empowerment and data security in the digital landscape.
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Based on the organization’s size, SMEs will grow at the highest CAGR during the forecast period.
The exponential growth of SMEs in the SSI market can be attributed to the increasing recognition of blockchain technology’s potential to revolutionize operational efficiencies. Despite persistent challenges such as technology constraints and infrastructure shortages, SMEs actively invest in R&D to explore SSI’s benefits. It includes streamlining non-financial transactions, securing patient health records, and facilitating distributed source code management. Moreover, governments incentivizing decentralized identity solutions further drive SMEs to embrace SSI as a pivotal milestone in their digitalization journey. This proactive approach reflects SMEs’ agility and willingness to leverage transformative technologies despite ongoing experimentation, positioning them as the fastest-growing segment in the SSI market.
By identity type, biometrics accounts for the highest market size during the forecast period.
The biometrics identity type holds the largest market share in the SSI market due to its unmatched level of security and convenience compared to traditional authentication methods. Biometric authentication, like fingerprint and facial recognition, provides a robust security layer, particularly crucial for high-security applications such as financial transactions and access control. Biometrics also offers a more user-friendly experience by eliminating the need for complex passwords or security questions. Moreover, biometric data is unique to each individual, significantly reducing the risk of identity theft and fraud compared to stolen credentials. As standards for biometric data storage and formats evolve, interoperability between different SSI ecosystems improves, allowing users to leverage their biometrics across various platforms seamlessly.
By region, Asia Pacific will grow at the highest CAGR during the forecast period.
The Asia Pacific region is growing fastest in the SSI market due to the convergence of several key factors, such as rapid technological advancements and robust digital transformation initiatives. These developments create a favorable environment for adopting self-sovereign identity (SSI) solutions, which are increasingly essential for secure and seamless digital interactions. Additionally, the region’s sizeable unbanked population presents a significant opportunity for SSI adoption, as it addresses identity challenges and provides a secure, inclusive means of accessing services. As exemplified by India’s Aadhaar system, supportive government policies and initiatives play a pivotal role in promoting digital identity technologies like SSI. Moreover, escalating cybersecurity concerns across industries push businesses and governments to seek decentralized and secure identity management solutions, further driving the adoption of SSI in the Asia Pacific region. This unique convergence of factors positions the area as a leader in SSI market growth, with vast potential for continued expansion and innovation.
Top Key Companies in Self-Sovereign Identity (SSI) Market:
Microsoft (US), Ping Identity (US), IDEX Biometrics (Norway), NEC (Japan), Imageware (US), Dock (Switzerland), Metadium  (Cayman Islands), Blockchain Helix (Germany), Validated ID (Spain), Wipro (India), Persistent (India), Infopulse (Poland), 1Kosmos (US), Accumulate (US), NuID (US), Kaleido (US), Talao (France), Vereign (Switzerland), Midy (US), SelfKey (Mauritius), Truvity (Netherlands), Affinidi (Singapore), Trinsic (US), cheqd (England), Fractal ID (Germany), Soulverse (US), Finema (Thailand), Nuggets (UK), Sentry (US), SpringRole (US), Walt.id (Austria), Procivis (Switzerland), Civic (US), Gataca (Spain),  Polygon Labs (Indonesia), and Voyatek (US) are the key players and other players in the Self-Sovereign Identity Market.
Recent Developments
In November 2023, Ping Identity partnered with ConnectID to simplify integration into existing systems and onboard new customers. The partnership aims to provide a cohesive solution for customers of all sizes, enhancing digital customer experiences and enabling secure online identity verification without unnecessary data sharing.In February 2023, Wipro Lab45, the innovation arm of Wipro, introduced DICE ID, a Decentralized Identity and Credential Exchange solution. Built on blockchain, it empowers users with control over personal data, facilitating secure sharing online through self-verifiable digital credentials.In October 2022, IDEX Biometrics and TrustSEC, a leading European provider of digital authentication solutions, partnered to bring biometric intelligent card solutions to the cybersecurity market. The partnership combines TrustSEC’s smart card module with IDEX Biometrics’ TrustedBio fingerprint sensor solution, targeting secure access for digital and cryptocurrency wallets and physical and logical access management.In May 2022, Microsoft introduced Microsoft Entra, a product family consisting of identity and access management solutions. It included Azure AD and introduced two new categories: Cloud Infrastructure Entitlement Management (CIEM) and Decentralized Identity. Entra was intended to safeguard user access to apps and resources, enabling security teams to manage permissions in multi-cloud environments and ensure end-to-end digital identity security.Inquire Before Buying@ https://www.marketsandmarkets.com/Enquiry_Before_BuyingNew.asp?id=73711961
Self-Sovereign Identity (SSI) Market Advantages:
With SSI, people have complete sovereignty over their digital identities. They can handle and distribute personal data as they see fit, independent of centralised authority or middlemen.In order to lower the danger of identity theft, data breaches, and unauthorised access to sensitive information, SSI prioritises privacy by sharing as little personal information as possible throughout identity verification procedures.Regardless of the vendor or technological stack, SSI solutions facilitate easy integration with current identity systems, apps, and platforms by leveraging open standards and protocols.To guarantee the validity and integrity of digital identities, SSI uses cryptographic techniques like verified credentials and decentralised identifiers (DIDs). This improves security and confidence in online interactions and transactions.By doing away with the requirement for paper-based documentation and recurring identity checks, SSI expedites identity verification procedures while cutting down on the administrative work, expenses, and wait times associated with conventional identity management systems.Without geographical limitations or reliance on centralised authority, SSI solutions allow people to access and manage their digital identities from any location in the globe. This facilitates safe and easy identity verification across borders and jurisdictions.By giving them self-sovereign identities that can be validated and recognised in digital settings, SSI empowers marginalised groups including refugees, migrants, and people without formal identification credentials. This allows them to access opportunities and crucial services.SSI fosters trusted relationships between individuals, organizations, and service providers by enabling verifiable and tamper-proof digital credentials, enhancing trust, transparency, and accountability in digital interactions and transactions.Report Objectives
To define, describe, and forecast the Self-Sovereign Identity Market based on – offering, identity type, network, organization size, vertical, and region.To define, describe, and forecast the Self-Sovereign Identity Market by – offering, identity type, network, organization size, vertical, and region.To forecast the market size of five main regions: North America, Europe, Asia Pacific (APAC), Middle East & Africa (MEA), and Latin AmericaTo analyze the subsegments of the market concerning individual growth trends, prospects, and contributions to the overall market.To provide detailed information on the major factors (drivers, restraints, opportunities, and challenges) influencing the growth of the Self-Sovereign Identity Market.To analyze opportunities in the market for stakeholders by identifying high-growth segments of the Self-Sovereign Identity Market.To profile the key players of the Self-Sovereign Identity Market and comprehensively analyze their market size and core competencies.Track and analyze competitive developments, such as new product launches, mergers and acquisitions, partnerships, agreements, and collaborations in the global Self-Sovereign Identity Market.Browse Adjacent Market: Information Security Market Research Reports & Consulting
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