市場調查報告書
商品編碼
1150501
自監督學習全球市場規模、份額、行業趨勢分析報告:按用戶(BFSI、廣告與媒體、軟件開發 (IT)、汽車與運輸、醫療保健)、技術、地區展望和預測2022-2028Global Self-supervised Learning Market Size, Share & Industry Trends Analysis Report By End-use (BFSI, Advertising & Media, Software Development (IT), Automotive & Transportation, Healthcare), By Technology, By Regional Outlook and Forecast, 2022 - 2028 |
全球自主學習市場規模預計在預測期內以 33.3% 的複合年增長率增長,到 2028 年達到 517 億美元。
Apple 和 Microsoft 等美國公司在研發項目上投入了大量資金。此外,這些公司正在研究人工智能和機器學習等尖端技術。美國公司 Meta 等市場進入者正在研究和試驗自我監督學習,並為該行業提供巨大的增長潛力。
近年來,可以從大量精心標記的數據中學習的人工智能係統的開發取得了長足進步。這種監督學習範式在生成專家模型方面有著良好的記錄,這些專家模型在為其開發的任務中表現出色。監督學習阻礙了構建更智能的通才模型,這些模型可以在沒有大量標記數據的情況下執行多項任務並學習新技能。
COVID-19 影響分析
為了應對 COVID-19 大流行,大多數 IT 專業人士表示他們已經加快了人工智能 (AI) 的部署。此外,在大流行期間,我們使用機器學習 (ML) 創建聊天機器人來篩查 COVID-19 症狀並響應一般查詢。機器學習和人工智能技術正被用於研究以對抗 COVID-19 大流行。在這個前所未有的時代,醫療保健和農業是目前最重要的兩個行業。機器學習受到了很多媒體的關注,因為它使計算機能夠模仿人類智能,吸收大量數據並發現模式和見解。這進一步推動了疫情期間自監督學習市場的增長。
市場增長因素
醫療保健中 ML 應用的擴展
ML 技術已經在許多與醫療保健相關的情況下發揮了作用。這項技術在醫療保健行業有很多用途,包括評估數百萬個不同的數據點、預測結果、提供快速風險評分以及準確分配資源。疾病識別和診斷難以識別的疾病和病症的檢測和診斷是該技術在醫療保健中最重要的應用之一。
在全球範圍內擴大雲計算技術的使用
由於越來越多地使用雲端計算技術和社交媒體平台,市場正在擴大。今天,所有企業主要依靠雲計算來提供企業存儲解決方案。隨著雲存儲的引入,數據分析在線完成,具有能夠分析雲端生成的實時數據的優勢。雲計算可以隨時隨地進行數據分析。
市場障礙
準確性差,技術限制
機器學習平台提供的各種優勢正在為市場的擴張做出貢獻。然而,該平台缺乏一些預計會阻礙市場擴張的關鍵要素。由於存在不准確的算法,市場受到嚴重阻礙,有時甚至不發達。準確性對於利用大數據和機器學習的製造公司來說至關重要。算法中的輕微錯誤可能會導致創建不准確的項目。
結束使用 Outlook
自監督學習市場根據最終用途細分為醫療保健、BFSI、汽車和交通、軟件開發 (IT)、廣告和媒體等。到 2021 年,BFSI 部門將獲得最大的收入份額並主導自監督學習市場。 BFSI 部門正在全球擴張,該部門的數位化也在取得進展。由於 COVID-19 大流行,不僅個人經常交流,而且他們開展業務的方式也在發生變化。
技術展望
按技術劃分,自我監督學習市場分為自然語言處理 (NLP)、計算機視覺和語音處理。到 2021 年,計算機視覺部分將在自我監督學習市場中佔據很大的收入份額。計算機視覺中自監督學習的基本概念是建立一個模型,該模型可以使用輸入或圖像數據處理任何基本的計算機視覺任務,並且在模型解決問題的同時,從所顯示的對象的結構中學習的能力。
區域展望
按地區劃分,分析了北美、歐洲、亞太地區和 LAMEA 的自監督學習市場。 2021 年,北美地區以最大的收入份額引領自監督學習市場。總部位於美國的公司正在強調數位化轉型,包括大數據分析、物聯網 (IoT)、增材製造、人工智能、增強現實 (AR)、互聯行業、機器學習 (ML) 和虛擬現實。它經常被認為是(VR) 等尖端技術和 4G、5G 和 LTE 等最新通信技術的早期採用者。
市場進入者採取的主要策略是產品發布。根據基數矩陣中的分析,Apple, Inc. 和 Microsoft Corporation 是自監督學習市場的先驅。 Meta Platforms, Inc.、Amazon Web Services, Inc. (Amazon.com, Inc.) 和 IBM Corporation 等公司是自我監督學習市場的領先創新者。
The Global Self-Supervised Learning Market size is expected to reach $51.7 billion by 2028, rising at a market growth of 33.3% CAGR during the forecast period.
Self-supervised learning is a Machine Learning (ML) technique used in speech processing, computer vision, and natural language processing (NLP), among other AI applications. Face recognition, text classification, and colorization are some examples of self-supervised learning applications. It also has uses in a number of different sectors, including automotive and transportation, BFSI, healthcare, software development (IT), media, and advertising, among others.
Self-supervised learning is in a stage of development that calls for a skilled workforce. The demand for self-supervised learning applications among industries is being driven by factors like the expanding applications of technologies like voice recognition and face detection and the growing need to streamline workflow across industries. Additionally, the market is likely to expand due to the growing digitalization of society.
Companies like Apple and Microsoft, both based in the United States, are investing more money in R&D projects. Additionally, these businesses are investigating cutting-edge technologies like AI and ML. Self-supervised learning is being studied and experimented with by market participants like the American company Meta, creating significant growth possibilities for the industry.
The development of AI systems that can learn from vast amounts of meticulously labeled data has advanced significantly in recent years. This supervised learning paradigm has a track record of producing expert models that excel at the task for which they were developed. Building more intelligent generalist models that can perform multiple tasks and learn new skills without vast amounts of labeled data is hampered by supervised learning.
COVID-19 Impact Analysis
In response to the COVID-19 pandemic, most IT professionals said they had accelerated the roll-out of AI (artificial intelligence). Additionally, chatbots were created using machine learning (ML) during the pandemic to screen COVID-19 symptoms and respond to public inquiries. In order to combat the COVID-19 pandemic, machine learning and artificial intelligence technologies are being used in research fields. Healthcare & agriculture are currently two of the most crucial sectors in these unprecedented times. Since ML allows computers to work the same as human intelligence & ingest massive amounts of data in order to find patterns and insights, it has received a lot of media attention. This has further supported the growth of the self-supervised learning market during the pandemic period.
Market Growth Factors
Growing Application Of Ml In The Healthcare Sector
ML technology is already helping in a number of healthcare-related situations. This technology is used in the healthcare industry to evaluate millions of different data points, forecast outcomes, provide quick risk scores, and allocate resources precisely, among many other things. Disease Recognition and Diagnosis Finding and diagnosing illnesses & conditions that can occasionally be challenging to identify are one of the most significant applications of this technology in healthcare.
Increasing Usage Of The Cloud Computing Technology Across The World
The market is expanding as a result of the rising use of cloud computing technology and usage of social media platforms. All businesses now largely use cloud computing, which offers enterprise storage solutions. With the adoption of cloud storage, data analysis is carried out online, giving the benefit of analyzing the real-time data generated on the cloud. Data analysis is possible at any time and from any location due to cloud computing.
Market Restraining Factors
Lack Of Accuracy & Technical Restrictions
A wide range of advantages provided by the ML platform contributes to the market's expansion. However, the platform is missing some essential elements that are anticipated to impede market expansion. The market is significantly hampered by the presence of inaccurate algorithms, which are occasionally underdeveloped. Precision is crucial for manufacturing companies using big data and machine learning. The algorithm's slightest error could lead to the creation of inaccurate items.
End-Use Outlook
Based on end-use, the self-supervised learning market is segmented into healthcare, BFSI, automotive & transportation, software development (IT), advertising & media and others. In 2021, the BFSI segment dominated the self-supervised learning market by generating maximum revenue share. The BFSI sector is expanding across the globe and the digitalization in the sector is also rising. The way that individuals frequently communicate as well as conduct business has changed as a result of the COVID-19 pandemic.
Technology Outlook
On the basis of technology, the self-supervised learning market is fragmented into natural language processing (NLP), computer vision, and speech processing. The computer vision segment covered a significant revenue share in the self-supervised learning market in 2021. The fundamental concept behind self-supervised learning in computer vision is to build a model that can handle any basic computer vision task using the input data or image data, and while the model is resolving the issue, it can learn from the structure of the objects shown in the image.
Regional Outlook
Region wise, the self-supervised learning market is analyzed across North America, Europe, Asia Pacific and LAMEA. In 2021, the North America region led the self-supervised learning market with the largest revenue share. The United States-based businesses place a high priority on digital transformation, and they are frequently recognized as early adopters of cutting-edge technologies such as big data analytics, Internet of Things (IoT), additive manufacturing, AI, augmented reality (AR), connected industries, machine learning (ML), and virtual reality (VR), and the newest telecommunications technologies such as 4G, 5G, and LTE.
The major strategies followed by the market participants are Product Launches. Based on the Analysis presented in the Cardinal matrix; Apple, Inc. and Microsoft Corporation are the forerunners in the Self-supervised Learning Market. Companies such as Meta Platforms, Inc., Amazon Web Services, Inc. (Amazon.com, Inc.) and IBM Corporation are some of the key innovators in Self-supervised Learning Market.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Baidu, Inc., Apple, Inc., Tesla, Inc., IBM Corporation, Microsoft Corporation, Amazon Web Services, Inc. (Amazon.com, Inc.), Meta Platforms, Inc., SAS Institute, Inc., The MathWorks, Inc., and DataRobot, Inc.
Recent strategies deployed in Self-supervised Learning Market
Product Launches & Product Expansion:
Aug-2022: Meta AI launched PEER, a collaborative language model trained to mimic the writing process. PEER has been developed to enhance the model's ability to write texts in different domains. With PEER, one can perform edits in many domains, which makes it better at following instructions and enhances its ability to cite and quote from relevant documents.
Jul-2022: Meta AI released an open-sourced model in order to make Wikipedia entries more appropriate. This launch would help in scaling the work of volunteers by efficiently recommending citations & accurate sources. It would highlight questionable citations, enabling human editors to assess the cases that are most likely to be flawed without having to sift through thousands of properly cited statements.
Jun-2022: DataRobot expanded its DataRobot AIX 2022 by making it available on Google Cloud. The expansion would enable consumers to accelerate and scale their business with AI. Also, consumers would be able to leverage the Google Cloud marketplace to streamline their procurement & deployment processes and generate intelligent business solutions on Google Cloud.
Jun-2022: Meta unveiled Visual-Acoustic Matching, Visually-Informed Dereverberation, and VisualVoice, three new artificial intelligence (AI) models. This product focused on making the sound more realistic in mixed & virtual reality experiences.
May-2022: Microsoft Azure released i-Code, a general framework that allows flexible multimodal representation learning. This product would allow the flexible integration of speech, vision, and language modalities & learn their vector representations in a unified manner.
Jan-2022: Meta launched data2vec, the first high-performance self-supervised algorithm that learns in the same way for speech, vision, and text. By the introduction of data2vec, Meta aimed at building machines that learn about different aspects of the world around them without having to rely on labeled data.
Sep-2021: DataRobot introduced DataRobot 7.2. This product would have features like Composable ML & code-centric data pipelines for data science experts, Continuous AI and bias monitoring for ML operators, and Decision Intelligence Flows & Pathfinder solution accelerators for the front-line decision-makers.
Sep-2021: Tesla launched Tesla D1, a new chip designed specifically for artificial intelligence. Tesla D1 adds a total of 354 training nodes that form a network of functional units, that are interconnected to create a massive chip. Each functional unit comes with a quad-core, 64-bit ISA CPU which uses a specialized, custom design for compilations, transpositions, broadcasts, and link traversal.
Aug-2021: Baidu introduced Kunlun 2, its second-generation AI chip. This launch focused on diversifying its business beyond advertising to AI and driverless cars.
Aug-2021: IBM introduced IBM Telum Processor. The launch focused on bringing deep learning inference to enterprise workloads to help address fraud in real time. Telum would enable IBM to leverage deep learning inferencing on high-value transactions, designed to greatly enhance the ability to intercept fraud, among other use cases.
Oct-2020: Microsoft introduced a machine learning cyber-attack threat matrix. This launch would empower security analysts in their battle to protect AI-powered technology.
May-2020: MathWorks launched Release 2020a. This product would serve with new capabilities specifically for automotive & wireless engineers in addition to hundreds of new & updated features for all users of MATLAB and Simulink. By this launch, the engineers would train neural networks in the updated Deep Network Designer app, manage multiple deep learning experiments in a new Experiment Manager app, and choose from more network options to generate deep learning code.
Mergers & Acquisitions:
Jul-2022: IBM completed the acquisition of Databand, an Israel-based data observability software provider. The acquisition would IBM offers the most comprehensive set of observability capabilities for IT across application, data, and machine learning.
May-2022: Microsoft took over Nuance Communications, a leader in conversational AI and ambient intelligence across industries. This acquisition would bring together Nuance's best-in-class conversational AI and ambient intelligence with Microsoft's secure and trusted industry cloud offerings.
Dec-2021: IBM closed on the acquisition of Instana, a leading enterprise observability and application performance monitoring platform. With the acquisition of Instana, IBM would offer industry-leading, AI-powered automation capabilities to manage the complexity of modern applications that span hybrid cloud landscapes.
Jul-2021: DataRobot signed an agreement to acquire Algorithmia, a machine learning operations platform. The acquisition would stabilize DataRobot's position as the preeminent provider of comprehensive solutions in the MLOps space, focused on bringing machine learning models into production.
May-2021: DataRobot entered an agreement to acquire Zepl, a cloud data science, and analytics platform. The acquisition would unlock new capabilities within DataRobot's enterprise AI platform for the world's most advanced data scientists. Also, the acquisition of Zepl would help in providing advanced data scientists more flexibility to use the company's enterprise AI platform within their present workflows, including the ability to use their code.
Jul-2020: IBM announced the acquisition of WDG Automation, the Brazilian software provider of robotic process automation. The acquisition aimed to advance IBM's comprehensive AI-infused automation capabilities, spanning business processes to IT operations. This acquisition would enhance IBM's comprehensive AI-infused automation capabilities, spanning business processes to IT operations
May-2020: Apple took over Inductiv, a Canada-based machine learning startup. This acquisition aimed at enhancing data used in Siri.
Apr-2020: Tesla acquired DeepScale, an American technology company. This acquisition aimed at accelerating Tesla's machine learning development. Under this acquisition, Tesla would design its computer chip to power its self-driving software with DeepScale's specialization in computing power-efficient deep learning systems.
Partnerships, Collaborations & Agreements:
May-2021: Microsoft came into a partnership with Darktrace, a leading autonomous cyber security AI company. Under this partnership, Microsoft & Darktrace would provide improved security across multi-platform & multi-cloud environments, automate threat investigations and allow teams to prioritize strategic tasks that matter.
Jan-2021: Baidu entered into a partnership with BlackBerry, a former brand of smartphones, tablets, and services. This partnership aimed at helping car manufacturers quickly produce safe autonomous vehicles & promote the development collaboratively of the intelligent networked automobile industry.
Geographical Expansions:
Feb-2022: Microsoft expanded its geographical footprint in India. This expansion aimed at providing support for consumers building & operating applications and workloads. Microsoft Cloud would manage end-to-end business needs across public, private & hybrid scenarios while helping businesses leverage digital capabilities and technologies like ML, AI, IoT, and analytics.
Jan-2021: AWS expanded its geographical footprints by providing AWS CCI Solutions to its partners all over the world. AWS CCI solution would allow leveraging AWS's ML capabilities with the current contact center provider to gain greater efficiencies & deliver increasingly tailored consumer experiences.
Market Segments covered in the Report:
By End-use
By Technology
By Geography
Companies Profiled
Unique Offerings from KBV Research
List of Figures