市场调查报告书
商品编码
1150501
自监督学习全球市场规模、份额、行业趋势分析报告:按用户(BFSI、广告与媒体、软件开发 (IT)、汽车与运输、医疗保健)、技术、地区展望和预测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 |
全球自主学习市场规模预计在预测期内以 33.3% 的复合年增长率增长,到 2028 年达到 517 亿美元。
Apple 和 Microsoft 等美国公司在研发项目上投入了大量资金。此外,这些公司正在研究人工智能和机器学习等尖端技术。美国公司 Meta 等市场进入者正在研究和试验自我监督学习,并为该行业提供巨大的增长潜力。
近年来,可以从大量精心标记的数据中学习的人工智能係统的开发取得了长足进步。这种监督学习范式在生成专家模型方面有着良好的记录,这些专家模型在为其开发的任务中表现出色。监督学习阻碍了构建更智能的通才模型,这些模型可以在没有大量标记数据的情况下执行多项任务并学习新技能。
COVID-19 影响分析
为了应对 COVID-19 大流行,大多数 IT 专业人士表示他们已经加快了人工智能 (AI) 的部署。此外,在大流行期间,我们使用机器学习 (ML) 创建聊天机器人来筛查 COVID-19 症状并响应一般查询。机器学习和人工智能技术正被用于研究以对抗 COVID-19 大流行。在这个前所未有的时代,医疗保健和农业是目前最重要的两个行业。机器学习受到了很多媒体的关注,因为它使计算机能够模仿人类智能,吸收大量数据并发现模式和见解。这进一步推动了疫情期间自监督学习市场的增长。
市场增长因素
医疗保健中 ML 应用的扩展
ML 技术已经在许多与医疗保健相关的情况下发挥了作用。这项技术在医疗保健行业有很多用途,包括评估数百万个不同的数据点、预测结果、提供快速风险评分以及准确分配资源。疾病识别和诊断难以识别的疾病和病症的检测和诊断是该技术在医疗保健中最重要的应用之一。
在全球范围内扩大云计算技术的使用
由于越来越多地使用云端计算技术和社交媒体平台,市场正在扩大。今天,所有企业主要依靠云计算来提供企业存储解决方案。随着云存储的引入,数据分析在线完成,具有能够分析云端生成的实时数据的优势。云计算可以随时随地进行数据分析。
市场障碍
准确性差,技术限制
机器学习平台提供的各种优势正在为市场的扩张做出贡献。然而,该平台缺乏一些预计会阻碍市场扩张的关键要素。由于存在不准确的算法,市场受到严重阻碍,有时甚至不发达。准确性对于利用大数据和机器学习的製造公司来说至关重要。算法中的轻微错误可能会导致创建不准确的项目。
结束使用 Outlook
自监督学习市场根据最终用途细分为医疗保健、BFSI、汽车和交通、软件开发 (IT)、广告和媒体等。到 2021 年,BFSI 部门将获得最大的收入份额并主导自监督学习市场。 BFSI 部门正在全球扩张,该部门的数位化也在取得进展。由于 COVID-19 大流行,不仅个人经常交流,而且他们开展业务的方式也在发生变化。
技术展望
按技术划分,自我监督学习市场分为自然语言处理 (NLP)、计算机视觉和语音处理。到 2021 年,计算机视觉部分将在自我监督学习市场中占据很大的收入份额。计算机视觉中自监督学习的基本概念是建立一个模型,该模型可以使用输入或图像数据处理任何基本的计算机视觉任务,并且在模型解决问题的同时,从所显示的对象的结构中学习的能力。
区域展望
按地区划分,分析了北美、欧洲、亚太地区和 LAMEA 的自监督学习市场。 2021 年,北美地区以最大的收入份额引领自监督学习市场。总部位于美国的公司正在强调数位化转型,包括大数据分析、物联网 (IoT)、增材製造、人工智能、增强现实 (AR)、互联行业、机器学习 (ML) 和虚拟现实。它经常被认为是(VR) 等尖端技术和 4G、5G 和 LTE 等最新通信技术的早期采用者。
市场进入者采取的主要策略是产品发布。根据基数矩阵中的分析,Apple, Inc. 和 Microsoft Corporation 是自监督学习市场的先驱。 Meta Platforms, Inc.、Amazon Web Services, Inc. (Amazon.com, Inc.) 和 IBM Corporation 等公司是自我监督学习市场的领先创新者。
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