The global self-supervised learning market is projected to have a moderate-paced CAGR of 33.4% over the forecast period. The current valuation of the self-supervised learning market is US$ 12.46 billion in 2023. The value of the self-supervised learning market is anticipated to reach a high of US$ 222.31 billion by the year 2033.
This growth in the sales of self-supervised learning is supported by the following:
| Attributes | Details |
|---|---|
| Market Value (2023) | US$ 12.46 billion |
| Market Value (2033) | US$ 222.31 billion |
| CAGR | 33.4% |
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The development of AI systems that can benefit from vast volumes of accurately labeled data has advanced significantly in recent years. The expert models created using this supervised learning paradigm have a history of success in their designated applications. Constructing smarter generalist models that can switch gears between tasks and pick up new abilities without massive amounts of labeled data is made more difficult by supervised learning.
Both the American tech giants Apple and Microsoft are increasing their spending on research and development. Companies like these are also exploring novel technologies like artificial intelligence and machine learning. Market participants like the American company Meta are researching and experimenting with self-reinforcement learning, which opens substantial expansion opportunities for the sector.
Data2vec
In January, Meta AI introduced the data2vec technique for use in building computer vision models for processing audio and visual information. For natural language processing challenges, data2vec was first published as a competition that does not employ contrastive learning or rely on the reconstruction of the input sample. In order to train data2vec, the team claimed, only a subset of the input data is shown during training, and the model representations are predicted.
STEGO
The Self-Supervised Transformer with Energy-based Graph Optimisation (STEGO) was created by MIT's Computer Science and AI Lab in collaboration with Microsoft and Cornell University to find and pinpoint semantically relevant categories in image corpora without human annotation. The system employs a semantic segmentation technique to assign labels to each pixel in a picture.
ColloSSL
ColloSSL is a collaborative self-supervised framework for human activity detection created by researchers from Nokia Bell Labs, Georgia Tech, and the University of Cambridge. A signal for representation learning can be generated from the combination of unlabelled sensor datasets recorded simultaneously from numerous devices. The paper discusses three methods: device selection, contrastive sampling, and multi-view contrastive loss.
Principal Consultant
ML's Increasing Role in the Healthcare Industry
Numerous healthcare-related issues could benefit immediately from ML technology. In healthcare, this technology is utilized for a wide variety of purposes, including data analysis, prediction, risk assessment, and resource allocation. The major uses of this technology in healthcare are the detection and diagnosis of rare or difficult-to-diagnose diseases and ailments.
Increased Global Adoption of Cloud Computing
Increased adoption of cloud computing and social media platforms is driving the adoption of self-supervised learning. Cloud computing, which provides options for large-scale data storage, is widely utilized by all modern enterprises. One of the main advantages of cloud computing is the ability to perform real-time data analysis owing to online data analysis tools and the widespread use of cloud storage. Due to cloud computing, analyzing data may be done at any time, from any location.
Many benefits of the ML platform are helping to drive the sector forward. However, the lack of some key features is expected to hinder the platform's global progress. Inaccurate and sometimes unfinished algorithms are a major problem in the industry. The accuracy of big data and machine learning in manufacturing is essential. The development of flawed products is possible if the algorithm makes even a single mistake.
The BFSI market was worth $1.28 billion in 2021 and is expected to grow at a CAGR of 33.3% over the forecast period. This market's expansion may be traced back to the sector's increasing interest in machine learning and artificial intelligence.
Market researchers predict that the advertising and media industry will grow at a rapid pace of 33.7% CAGR over the next several years. The need for consumer insights is being driven, in part, by the rise of online shopping and widespread internet access, both of which lend themselves to the use of self-reinforcement learning. Moreover, the advertising and media sector is projected to increase demand for this technology due to the growing popularity of self-reinforcement learning for identifying hate speech on social media.
In 2021, Natural Language Processing generated 38.6% of total revenue and was expected to post the fastest growth in terms of compound annual growth rate (CAGR) at 34.1%. The proliferation of natural language processing (NLP) applications like text prediction and chatbots is largely responsible for the expansion of this market category. Additionally, both international and domestic market leaders provide NLP-based products and services. For instance, BlueMessaging, based in Mexico, offers SmartChat, an AI-based service that aids businesses in creating chatbots. A 32.4% CAGR is projected for the computer vision market between 2021 and 2024, making it the second-largest revenue contributor in that year.
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In 2021, North America had 31.7% of the demand for self-supervised learning, and it is projected to grow at a CAGR of approximately 34.0% during the forecast period. The expansion of the sales of self-supervised learning in the region is expected to be spurred by several factors, including the presence of major market participants like the United States Meta, Microsoft, and Google; the availability of specialists; and advanced technological infrastructure.
The USA companies frequently lead the pack in adopting emerging technologies like 4G, 5G, and LTE, as well as big data analytics, IoT, additive manufacturing, A.I., augmented reality (A.R.), connected industries, machine learning (ML), and virtual reality (V.R.).
In 2022, Germany's market was worth the most in Europe's self-supervised learning market by country, and it is projected to maintain its dominance through 2032 when it will be worth US$3,445.5 million. At a CAGR of 31.7%, the United Kingdom market is expected to expand rapidly over the next several years. The demand for self-supervised learning in France, meanwhile, would grow at a CAGR of 33.8% over the same time frame.
The G-Cloud program is Britain's attempt to streamline the process by which government agencies can purchase cloud-based I.T. services of a standard quality. Due to a set of framework agreements with suppliers, government agencies that use the G-Cloud can make purchases without holding tenders or holding competitions. The Digital Marketplace is an online marketplace where government agencies can find providers of G-Cloud-compliant services.
The government of China is pushing the rate of its industries' digital transformation to unprecedented heights. To enhance the digital economy's governance model, Beijing must also expand access to digital public services. The Chinese government is achieving this by increasing spending on 6G Research and Development (R&D) and fostering creativity in strategic areas like integrated circuits and AI.
In 2022, China's sales of self-supervised learning were worth the most in the Asia Pacific self-supervised learning market, and this trend is expected to continue through 2032.
There are many different international and domestic competitors in this sector. Those active in the market are spending money on research and development (R&D) to create cutting-edge solutions and give themselves an edge. The market is characterized by innovation, disruption, and rapid change, and in response, businesses are forming alliances and M&A deals. For instance, IBM bought the American cloud services firm Neudesic in February 2022. IBM has financed the development of the firm's hybrid cloud and artificial intelligence infrastructure. Information analytics, data engineering, and Azure cloud proficiency are all areas where Neudesic excels. IBM's goal in making this purchase is to better provide for customer needs by increasing its proficiency in cloud service.
Major developments and strategies adopted by the players in the self-supervised learning market include:
Geographical Expansions
In February 2022, Microsoft established a larger presence in that country. This development was made to help users who are creating and running applications and workloads. The Microsoft Cloud would enable businesses to make use of digital capabilities and technologies like ML, AI, IoT, and analytics by centrally managing their demands in all these environments (public, private, and hybrid).
In January 2021, AWS began offering AWS CCI Solutions to its partners across the world, thus increasing the company's global footprint. The present contact center provider might benefit from the AWS CCI solution by integrating AWS's ML capabilities, leading to increased efficiencies and improved personalization of the customer experience.
Partnerships & Collaborations
In May of 2021, Microsoft formed a strategic alliance with Darktrace, a market leader in autonomous AI for cyber security. Through this collaboration, Microsoft and Darktrace will be able to offer enhanced protection for hybrid cloud deployments, streamline threat investigations, and free up team resources for more strategic work.
Baidu and BlackBerry, a defunct manufacturer of mobile devices and associated services, formed a collaboration in January 2021. Automakers hoped to speed up the production of safe autonomous vehicles and advance the intelligently networked vehicle sector through this alliance.
Mergers & Acquisitions
Databand, an Israel-based data observability software vendor, was acquired by IBM in July 2022. With this purchase, IBM will have a complete set of observability capabilities in IT, including those for applications, data, and machine learning.
In May of 2022, Microsoft acquired Nuance Communications, an industry pioneer in ambient intelligence and conversational AI. In acquiring Nuance, Microsoft would gain access to cutting-edge conversational AI and ambient intelligence while also bolstering its own portfolio of trusted industrial cloud services.
Product Launches
PEER, a collaborative language model, designed to simulate the writing process, was released by Meta AI in August of 2022. To improve the model's text-writing capabilities across domains, PEER was created. Edits made in many domains improve PEER's ability to comprehend and act upon instructions and to locate and use appropriate sources.
In July 2022, Meta AI launched a publicly available model to improve the quality of Wikipedia articles. The launch would facilitate the expansion of volunteer efforts by effectively recommending citations and reliable sources. As a result, human editors would only have to look at the few potentially problematic citations instead of potentially thousands.
Some of the prominent players in the self-supervised learning market are:
The growth outlook for the Self-supervised Learning market is predicted to advance at a CAGR of 33.4% from 2022 to 2032.
The North American region is anticipated to lead the Self-supervised Learning market during the forecast period.
The valuation of the Self-supervised Learning market stands at US$ 12.46 billion in 2023.
The Self-supervised Learning market is likely to hold a valuation of US$ 222.31 billion by 2033.
1. Executive Summary | Self-supervised Learning Market 1.1. Global Market Outlook 1.2. Demand-side Trends 1.3. Supply-side Trends 1.4. Technology Roadmap Analysis 1.5. Analysis and Recommendations 2. Market Overview 2.1. Market Coverage / Taxonomy 2.2. Market Definition / Scope / Limitations 3. Market Background 3.1. Market Dynamics 3.1.1. Drivers 3.1.2. Restraints 3.1.3. Opportunity 3.1.4. Trends 3.2. Scenario Forecast 3.2.1. Demand in Optimistic Scenario 3.2.2. Demand in Likely Scenario 3.2.3. Demand in Conservative Scenario 3.3. Opportunity Map Analysis 3.4. Investment Feasibility Matrix 3.5. PESTLE and Porter’s Analysis 3.6. Regulatory Landscape 3.6.1. By Key Regions 3.6.2. By Key Countries 3.7. Regional Parent Market Outlook 4. Global Market Analysis 2017 to 2021 and Forecast, 2022 to 2032 4.1. Historical Market Size Value (US$ million) Analysis, 2017 to 2021 4.2. Current and Future Market Size Value (US$ million) Projections, 2022 to 2032 4.2.1. Y-o-Y Growth Trend Analysis 4.2.2. Absolute $ Opportunity Analysis 5. Global Market Analysis 2017 to 2021 and Forecast 2022 to 2032, By End-use 5.1. Introduction / Key Findings 5.2. Historical Market Size Value (US$ million) Analysis By End-use, 2017 to 2021 5.3. Current and Future Market Size Value (US$ million) Analysis and Forecast By End-use, 2022 to 2032 5.3.1. Healthcare 5.3.2. BFSI 5.3.3. Automotive & Transportation 5.3.4. Software Development - IT 5.3.5. Advertising & Media 5.3.6. Others 5.4. Y-o-Y Growth Trend Analysis By End-use, 2017 to 2021 5.5. Absolute $ Opportunity Analysis By End-use, 2022 to 2032 6. Global Market Analysis 2017 to 2021 and Forecast 2022 to 2032, By Technology 6.1. Introduction / Key Findings 6.2. Historical Market Size Value (US$ million) Analysis By Technology, 2017 to 2021 6.3. Current and Future Market Size Value (US$ million) Analysis and Forecast By Technology, 2022 to 2032 6.3.1. Natural Language Processing - NLP 6.3.2. Computer Vision 6.3.3. Speech Processing 6.4. Y-o-Y Growth Trend Analysis By Technology, 2017 to 2021 6.5. Absolute $ Opportunity Analysis By Technology, 2022 to 2032 7. Global Market Analysis 2017 to 2021 and Forecast 2022 to 2032, By Region 7.1. Introduction 7.2. Historical Market Size Value (US$ million) Analysis By Region, 2017 to 2021 7.3. Current Market Size Value (US$ million) Analysis and Forecast By Region, 2022 to 2032 7.3.1. North America 7.3.2. Latin America 7.3.3. Europe 7.3.4. Asia Pacific 7.3.5. Middle East and Africa (MEA) 7.4. Market Attractiveness Analysis By Region 8. North America Market Analysis 2017 to 2021 and Forecast 2022 to 2032, By Country 8.1. Historical Market Size Value (US$ million) Trend Analysis By Market Taxonomy, 2017 to 2021 8.2. Market Size Value (US$ million) Forecast By Market Taxonomy, 2022 to 2032 8.2.1. By Country 8.2.1.1. The USA 8.2.1.2. Canada 8.2.2. By End-use 8.2.3. By Technology 8.3. Market Attractiveness Analysis 8.3.1. By Country 8.3.2. By End-use 8.3.3. By Technology 8.4. Key Takeaways 9. Latin America Market Analysis 2017 to 2021 and Forecast 2022 to 2032, By Country 9.1. Historical Market Size Value (US$ million) Trend Analysis By Market Taxonomy, 2017 to 2021 9.2. Market Size Value (US$ million) Forecast By Market Taxonomy, 2022 to 2032 9.2.1. By Country 9.2.1.1. Brazil 9.2.1.2. Mexico 9.2.1.3. Rest of Latin America 9.2.2. By End-use 9.2.3. By Technology 9.3. Market Attractiveness Analysis 9.3.1. By Country 9.3.2. By End-use 9.3.3. By Technology 9.4. Key Takeaways 10. Europe Market Analysis 2017 to 2021 and Forecast 2022 to 2032, By Country 10.1. Historical Market Size Value (US$ million) Trend Analysis By Market Taxonomy, 2017 to 2021 10.2. Market Size Value (US$ million) Forecast By Market Taxonomy, 2022 to 2032 10.2.1. By Country 10.2.1.1. Germany 10.2.1.2. The United Kingdom 10.2.1.3. France 10.2.1.4. Spain 10.2.1.5. Italy 10.2.1.6. Rest of Europe 10.2.2. By End-use 10.2.3. By Technology 10.3. Market Attractiveness Analysis 10.3.1. By Country 10.3.2. By End-use 10.3.3. By Technology 10.4. Key Takeaways 11. Asia Pacific Market Analysis 2017 to 2021 and Forecast 2022 to 2032, By Country 11.1. Historical Market Size Value (US$ million) Trend Analysis By Market Taxonomy, 2017 to 2021 11.2. Market Size Value (US$ million) Forecast By Market Taxonomy, 2022 to 2032 11.2.1. By Country 11.2.1.1. China 11.2.1.2. Japan 11.2.1.3. South Korea 11.2.1.4. Singapore 11.2.1.5. Thailand 11.2.1.6. Indonesia 11.2.1.7. Australia 11.2.1.8. New Zealand 11.2.1.9. Rest of Asia Pacific 11.2.2. By End-use 11.2.3. By Technology 11.3. Market Attractiveness Analysis 11.3.1. By Country 11.3.2. By End-use 11.3.3. By Technology 11.4. Key Takeaways 12. MEA Market Analysis 2017 to 2021 and Forecast 2022 to 2032, By Country 12.1. Historical Market Size Value (US$ million) Trend Analysis By Market Taxonomy, 2017 to 2021 12.2. Market Size Value (US$ million) Forecast By Market Taxonomy, 2022 to 2032 12.2.1. By Country 12.2.1.1. GCC Countries 12.2.1.2. South Africa 12.2.1.3. Israel 12.2.1.4. Rest of MEA 12.2.2. By End-use 12.2.3. By Technology 12.3. Market Attractiveness Analysis 12.3.1. By Country 12.3.2. By End-use 12.3.3. By Technology 12.4. Key Takeaways 13. Key Countries Market Analysis 13.1. The USA 13.1.1. Pricing Analysis 13.1.2. Market Share Analysis, 2021 13.1.2.1. By End-use 13.1.2.2. By Technology 13.2. Canada 13.2.1. Pricing Analysis 13.2.2. Market Share Analysis, 2021 13.2.2.1. By End-use 13.2.2.2. By Technology 13.3. Brazil 13.3.1. Pricing Analysis 13.3.2. Market Share Analysis, 2021 13.3.2.1. By End-use 13.3.2.2. By Technology 13.4. Mexico 13.4.1. Pricing Analysis 13.4.2. Market Share Analysis, 2021 13.4.2.1. By End-use 13.4.2.2. By Technology 13.5. Germany 13.5.1. Pricing Analysis 13.5.2. Market Share Analysis, 2021 13.5.2.1. By End-use 13.5.2.2. By Technology 13.6. The United Kingdom 13.6.1. Pricing Analysis 13.6.2. Market Share Analysis, 2021 13.6.2.1. By End-use 13.6.2.2. By Technology 13.7. France 13.7.1. Pricing Analysis 13.7.2. Market Share Analysis, 2021 13.7.2.1. By End-use 13.7.2.2. By Technology 13.8. Spain 13.8.1. Pricing Analysis 13.8.2. Market Share Analysis, 2021 13.8.2.1. By End-use 13.8.2.2. By Technology 13.9. Italy 13.9.1. Pricing Analysis 13.9.2. Market Share Analysis, 2021 13.9.2.1. By End-use 13.9.2.2. By Technology 13.10. China 13.10.1. Pricing Analysis 13.10.2. Market Share Analysis, 2021 13.10.2.1. By End-use 13.10.2.2. By Technology 13.11. Japan 13.11.1. Pricing Analysis 13.11.2. Market Share Analysis, 2021 13.11.2.1. By End-use 13.11.2.2. By Technology 13.12. South Korea 13.12.1. Pricing Analysis 13.12.2. Market Share Analysis, 2021 13.12.2.1. By End-use 13.12.2.2. By Technology 13.13. Singapore 13.13.1. Pricing Analysis 13.13.2. Market Share Analysis, 2021 13.13.2.1. By End-use 13.13.2.2. By Technology 13.14. Thailand 13.14.1. Pricing Analysis 13.14.2. Market Share Analysis, 2021 13.14.2.1. By End-use 13.14.2.2. By Technology 13.15. Indonesia 13.15.1. Pricing Analysis 13.15.2. Market Share Analysis, 2021 13.15.2.1. By End-use 13.15.2.2. By Technology 13.16. Australia 13.16.1. Pricing Analysis 13.16.2. Market Share Analysis, 2021 13.16.2.1. By End-use 13.16.2.2. By Technology 13.17. New Zealand 13.17.1. Pricing Analysis 13.17.2. Market Share Analysis, 2021 13.17.2.1. By End-use 13.17.2.2. By Technology 13.18. GCC Countries 13.18.1. Pricing Analysis 13.18.2. Market Share Analysis, 2021 13.18.2.1. By End-use 13.18.2.2. By Technology 13.19. South Africa 13.19.1. Pricing Analysis 13.19.2. Market Share Analysis, 2021 13.19.2.1. By End-use 13.19.2.2. By Technology 13.20. Israel 13.20.1. Pricing Analysis 13.20.2. Market Share Analysis, 2021 13.20.2.1. By End-use 13.20.2.2. By Technology 14. Market Structure Analysis 14.1. Competition Dashboard 14.2. Competition Benchmarking 14.3. Market Share Analysis of Top Players 14.3.1. By Regional 14.3.2. By End-use 14.3.3. By Technology 15. Competition Analysis 15.1. Competition Deep Dive 15.1.1. IBM 15.1.1.1. Overview 15.1.1.2. Product Portfolio 15.1.1.3. Profitability by Market Segments 15.1.1.4. Sales Footprint 15.1.1.5. Strategy Overview 15.1.1.5.1. Marketing Strategy 15.1.2. Alphabet Inc. 15.1.2.1. Overview 15.1.2.2. Product Portfolio 15.1.2.3. Profitability by Market Segments 15.1.2.4. Sales Footprint 15.1.2.5. Strategy Overview 15.1.2.5.1. Marketing Strategy 15.1.3. Microsoft 15.1.3.1. Overview 15.1.3.2. Product Portfolio 15.1.3.3. Profitability by Market Segments 15.1.3.4. Sales Footprint 15.1.3.5. Strategy Overview 15.1.3.5.1. Marketing Strategy 15.1.4. Amazon Web Services, Inc. 15.1.4.1. Overview 15.1.4.2. Product Portfolio 15.1.4.3. Profitability by Market Segments 15.1.4.4. Sales Footprint 15.1.4.5. Strategy Overview 15.1.4.5.1. Marketing Strategy 15.1.5. SAS Institute Inc. 15.1.5.1. Overview 15.1.5.2. Product Portfolio 15.1.5.3. Profitability by Market Segments 15.1.5.4. Sales Footprint 15.1.5.5. Strategy Overview 15.1.5.5.1. Marketing Strategy 15.1.6. Dataiku 15.1.6.1. Overview 15.1.6.2. Product Portfolio 15.1.6.3. Profitability by Market Segments 15.1.6.4. Sales Footprint 15.1.6.5. Strategy Overview 15.1.6.5.1. Marketing Strategy 15.1.7. The MathWorks, Inc. 15.1.7.1. Overview 15.1.7.2. Product Portfolio 15.1.7.3. Profitability by Market Segments 15.1.7.4. Sales Footprint 15.1.7.5. Strategy Overview 15.1.7.5.1. Marketing Strategy 15.1.8. Meta 15.1.8.1. Overview 15.1.8.2. Product Portfolio 15.1.8.3. Profitability by Market Segments 15.1.8.4. Sales Footprint 15.1.8.5. Strategy Overview 15.1.8.5.1. Marketing Strategy 15.1.9. Databricks 15.1.9.1. Overview 15.1.9.2. Product Portfolio 15.1.9.3. Profitability by Market Segments 15.1.9.4. Sales Footprint 15.1.9.5. Strategy Overview 15.1.9.5.1. Marketing Strategy 15.1.10. DataRobot, Inc. 15.1.10.1. Overview 15.1.10.2. Product Portfolio 15.1.10.3. Profitability by Market Segments 15.1.10.4. Sales Footprint 15.1.10.5. Strategy Overview 15.1.10.5.1. Marketing Strategy 15.1.11. Apple Inc. 15.1.11.1. Overview 15.1.11.2. Product Portfolio 15.1.11.3. Profitability by Market Segments 15.1.11.4. Sales Footprint 15.1.11.5. Strategy Overview 15.1.11.5.1. Marketing Strategy 15.1.12. Tesla 15.1.12.1. Overview 15.1.12.2. Product Portfolio 15.1.12.3. Profitability by Market Segments 15.1.12.4. Sales Footprint 15.1.12.5. Strategy Overview 15.1.12.5.1. Marketing Strategy 15.1.13. Baidu, Inc. 15.1.13.1. Overview 15.1.13.2. Product Portfolio 15.1.13.3. Profitability by Market Segments 15.1.13.4. Sales Footprint 15.1.13.5. Strategy Overview 15.1.13.5.1. Marketing Strategy 16. Assumptions & Acronyms Used 17. Research Methodology
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Self-supervised Learning Market
