Natural Language Processing (NLP) Market Outlook 2023-2030: Latest Trends, Opportunities, And Future Growth Predictions
The "Natural Language Processing (NLP) Market" Research Report 2023: incorporates a thorough qualitative and quantitative analysis along with several market dynamics. Global Natural Language Processing (NLP) Market size was valued at USD 703 million in 2022, and poised to rise at an impressive growth rate of USD 2529.8 million by 2027 as projected. The report anticipates a robust growth trajectory, demonstrated by an impressive (Compound Annual Growth Rate) CAGR of 20.1% during the forecast 2023-2028. This research provides a roadmap of the Natural Language Processing (NLP) industry by including details on significant growth factors, future developments, important business tactics, and top company opportunities. It also contains historical data, future product environments, marketing plans, and technology advancements.
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Who Are the Leading Key Players Operating in This Market?The report offers a detailed analysis supported by reliable statistics on sales and revenue by players for the period 2018-2023. Company profiles and market share analyses of the prominent players are also provided in this section.
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Attractive Natural Language Processing (NLP) Market Opportunities and Insights: -The report offers key success strategies for leading companies, Key market dynamics including trends, drivers, challenges, and opportunities. Further, critical Natural Language Processing (NLP) market strategies, Porter's five forces, market attractiveness, and growth-share matrix are covered.
The Global Natural Language Processing (NLP) market is anticipated to rise at a considerable rate during the forecast period, between 2023 and 2030. In 2023, the market is growing at a steady rate, and with the rising adoption of strategies by key players, the market is expected to rise over the projected horizon.
Natural language processing (NLP) is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language (spoken or written data), not in the artificial languages such as Java and C++.
Global Natural Language Processing (NLP) key players include 3M, Linguamatics, Amazon AWS, etc. Global top three manufacturers hold a share about 35Percent.
United States is the largest market, with a share about 65Percent, followed by Europe and China, both have a share about 25 percent.
In terms of product, Machine Translation is the largest segment, with a share about 45Percent. And in terms of application, the largest application is Electronic Health Records (EHR), followed by Computer-Assisted Coding (CAC).
Market Analysis and Insights: Global Natural Language Processing (NLP) Market The global Natural Language Processing (NLP) market size is projected to reach USD 2529.8 million by 2027, from USD 703 million in 2020, at a CAGR of 20.1% during 2021-2027.
With industry-standard accuracy in analysis and high data integrity, the report makes a brilliant attempt to unveil key opportunities available in the global Natural Language Processing (NLP) market to help players in achieving a strong market position. Buyers of the report can access verified and reliable market forecasts, including those for the overall size of the global Natural Language Processing (NLP) market in terms of revenue.
On the whole, the report proves to be an effective tool that players can use to gain a competitive edge over their competitors and ensure lasting success in the global Natural Language Processing (NLP) market. All of the findings, data, and information provided in the report are validated and revalidated with the help of trustworthy sources. The analysts who have authored the report took a unique and industry-best research and analysis approach for an in-depth study of the global Natural Language Processing (NLP) market.
Global Natural Language Processing (NLP) Scope and Market Size Natural Language Processing (NLP) market is segmented by company, region (country), by Type, and by Application. Players, stakeholders, and other participants in the global Natural Language Processing (NLP) market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on revenue and forecast by Type and by Application in terms of revenue and forecast for the period 2016-2027.
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On the basis of Product Type, this report displays the production, revenue, price, market share, and growth rate of each type, primarily split into:
On the basis of the End Users/Applications, this report focuses on the status and outlook for major applications/end users, consumption (sales), market share, and growth rate for each application, including:
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
Which region is dominating the Natural Language Processing (NLP) market growth?
Natural Language Processing (NLP) Market- New Research Highlights
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Natural Language Processing (NLP) Market Latest Trends:The market research industry is in a state of rapid evolution, adapting to the ever-changing business environment and leveraging the latest trends and technological tools. The report often sheds light on new methodologies, and technological advancements and offers insights to align research processes. Recent trends underscore the importance of digital transformation, with an emphasis on artificial intelligence and data analytics to decipher complex consumer patterns.
Natural Language Processing (NLP) Market Driving Factors:The driving factors behind the growing Natural Language Processing (NLP) industry include the increasing competition in global markets, rapid technological advancements, and evolving consumer preferences. Businesses are recognizing the imperative need for data-driven decision-making to gain a competitive edge. Additionally, the rise of digital platforms and social media has provided a goldmine of consumer insights, further propelling the demand for comprehensive market research.
Some of the Key Questions Answered in the Natural Language Processing (NLP) Market Report:
Detailed TOC of Global Natural Language Processing (NLP) Industry Research Report, Growth Trends and Competitive Analysis 2023-2030
1 Report Overview1.1 Study Scope 1.2 Market Analysis by Type 1.2.1 Global Natural Language Processing (NLP) Market Size Growth Rate by Type: 2017 VS 2021 VS 2028 1.3 Market by Application 1.3.1 Global Natural Language Processing (NLP) Market Growth Rate by Application: 2017 VS 2021 VS 2028 1.4 Study Objectives 1.5 Years Considered
2 Market Perspective2.1 Global Natural Language Processing (NLP) Market Size (2017-2028) 2.2 Natural Language Processing (NLP) Market Size across Key Geographies Worldwide: 2017 VS 2021 VS 2028 2.3 Global Natural Language Processing (NLP) Market Size by Region (2017-2022) 2.4 Global Natural Language Processing (NLP) Market Size Forecast by Region (2023-2028) 2.5 Global Top Natural Language Processing (NLP) Countries Ranking by Market Size
3 Natural Language Processing (NLP) Competitive by Company3.1 Global Natural Language Processing (NLP) Revenue by Players 3.1.1 Global Natural Language Processing (NLP) Revenue by Players (2017-2022) 3.1.2 Global Natural Language Processing (NLP) Market Share by Players (2017-2022) 3.2 Global Natural Language Processing (NLP) Market Share by Company Type (Tier 1, Tier 2, and Tier 3) 3.3 Company Covered: Ranking by Natural Language Processing (NLP) Revenue 3.4 Global Natural Language Processing (NLP) Market Concentration Ratio 3.4.1 Global Natural Language Processing (NLP) Market Concentration Ratio (CR5 and HHI) 3.4.2 Global Top 10 and Top 5 Companies by Natural Language Processing (NLP) Revenue in 2021 3.5 Global Natural Language Processing (NLP) Key Players Head office and Area Served 3.6 Key Players Natural Language Processing (NLP) Product Solution and Service 3.7 Date of Enter into Natural Language Processing (NLP) Market 3.8 Mergers and Acquisitions, Expansion Plans
4 Global Natural Language Processing (NLP) Breakdown Data by Type4.1 Global Natural Language Processing (NLP) Historic Revenue by Type (2017-2022) 4.2 Global Natural Language Processing (NLP) Forecasted Revenue by Type (2023-2028)
5 Global Natural Language Processing (NLP) Breakdown Data by Application5.1 Global Natural Language Processing (NLP) Historic Market Size by Application (2017-2022) 5.2 Global Natural Language Processing (NLP) Forecasted Market Size by Application (2023-2028)
6 North America6.1 North America Natural Language Processing (NLP) Revenue by Company (2020-2022) 6.2 North America Natural Language Processing (NLP) Revenue by Type (2017-2028) 6.3 North America Natural Language Processing (NLP) Revenue by Application (2017-2028) 6.4 North America Natural Language Processing (NLP) Revenue by Country (2017-2028) 6.4.1 U.S. 6.4.2 Canada
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Oracle CloudWorld 2023: 6 Key Takeaways From The Big Annual Event
In line with Oracle co-founder CTO Larry Ellison's notion that generative AI is one of the most important technological innovations ever, the company at its annual CloudWorld conference released a range of products and updates centered around the next generation of artificial intelligence.
The last few months have witnessed rival technology vendors, such as AWS, Google, Microsoft, Salesforce and IBM, adopting a similar strategy, under which each of them integrated generative AI into their products.
Oracle, which posted its first quarter earnings for fiscal year 2024 last week, has been betting heavily on high demand from enterprises, driven by generative AI related workloads, to boost revenue in upcoming quarters as enterprises look to adopt the technology for productivity and efficiency.
In order to cater to this demand, the company has introduced products based on its three-tier generative AI strategy. Here are some key takeaways:
Oracle has taken the covers off its new API-led generative AI service, which is a managed service that will allow enterprises to integrate large language model (LLM) interfaces in their applications via an API. The API-led service is also designed in a manner that allows enterprises to refine Cohere's LLMs using their own data to enable more accurate results via a process dubbed Retrieval Augmented Generation (RAG).
It has also updated several AI-based offerings, including the Oracle Digital Assistant, OCI Language Healthcare NLP, OCI Language Document Translation, OCI Vision, OCI Speech, and OCI Data Science.
Oracle is updating its Database 23c offering with a bundle of features dubbed AI Vector Search. These features and capabilities include a new vector data type, vector indexes, and vector search SQL operators that enable the Oracle Database to store the semantic content of documents, images, and other unstructured data as vectors, and use these to run fast similarity queries.
The addition of vector search capabilities to Database 23c will allow enterprises to add an LLM-based natural language interface inside applications built on the Oracle Database and its Autonomous Database.
Other updates to Oracle's database offerings include the general availability of Database 23c, the next generation of Exadata Exascale, and updates to its Autonomous Database service and GoldenGate 23c.
In order to allow enterprises to operate its data analytics cloud service, dubbed MySQL HeatWave, the company has added a new Vector Store along with some generative AI features.
The new Vector Store, which is also in private preview, can ingest documents in a variety of formats and store them as embeddings generated via an encoder model in order to process queries faster, the company said, adding that the generative AI features added include a large language model-driven interface that allows enterprise users to interact with different aspects of the service — including searching for different files — in natural language.
Other updates to the service include updates to AutoML and MySQL Autopilot components within the service along with support for JavaScript and a bulk ingest feature.
Nearly all of Oracle's Fusion Cloud suites — including Cloud Customer Experience (CX), Human Capital Management (HCM), Enterprise Resource Planning (ERP), and Supply Chain Management (SCM) — have been updated with the company's Oracle Cloud Infrastructure (OCI) generative AI service.
For healthcare providers, Oracle will offer a version of its generative AI-powered assistant, which is based on OCI generative AI service, called Oracle Clinical Digital Assistant.
Oracle has updated several applications within its various Fusion Cloud suites in order to align them toward supporting use cases for its healthcare enterprise customers. These updates, which include changes to multiple applications within ERP, HCM, EPM, and SCM Fusion Clouds, are expected to help healthcare enterprises unify operations and improve patient care.
Oracle also continued to expand its distributed cloud offerings, including Oracle Database@Azure and MySQL HeatWave Lakehouse on AWS.
As part of Database@Azure, the company is collocating its Oracle database hardware (including Oracle Exadata) and software in Microsoft Azure data centers, giving customers direct access to Oracle database services running on Oracle Cloud Infrastructure (OCI) via Azure.
Oracle Alloy, which serves as a cloud infrastructure platform for service providers, integrators, ISVs, and others who want to roll out their own cloud services to customers, has also been made generally available.
Copyright © 2023 IDG Communications, Inc.
IBM Takes The Reins Of Enterprise AI With Watsonx
IBM watsonx
IBMIBM hosts its annual TechXchange Conference in Las Vegas this week. While the agenda offers something for nearly IT practitioner, there's a distinct emphasis on how IT organizations can leverage the power of AI to transform their businesses. IBM is well-positioned to host these conversations, recently unveiling a suite of AI offerings.
IBM's updated AI portfolio, dubbed "watsonx," is an enterprise-ready AI and data platform consisting of three intertwined solution stacks:
Let's look at what's in the watsonx portfolio.
watsonx.AiIBM watsonx.Ai allows AI developers to harness models offered by IBM and the Hugging Face community to tackle a broad spectrum of AI development tasks. These models come pre-trained, geared to handle various Natural Language Processing (NLP) tasks, encompassing question answering, content generation, summarization, text classification, and data extraction. Examples of this include:
Upcoming releases will expand the array of IBM-trained proprietary foundation models, facilitating efficient specialization in specific domains and tasks. IBM's watsonx also offers AI models from IBM and the Hugging Face community for various Natural Language Processing tasks.
Last month, IBM expanded watsonx.Ai, announcing support for Meta's Llama 2-chat 70 billion parameter model in watsonx.Ai studio. This collaboration builds on their joint work on open AI innovation, including projects like PyTorch and Presto.
Adding Llama 2 to watsonx.Ai is a significant step in IBM's generative AI roadmap, with plans for more AI models and features. IBM prioritizes trust and security, allowing users to employ AI guardrails to remove harmful language.
watsonx.DataIBM's watsonx.Data is crafted to assist clients in overcoming challenges related to data volume, complexity, cost, and governance as they scale their AI workloads. The platform enables users to seamlessly access their data, whether stored in the cloud or on-premises, through a single entry point. This approach dramatically simplifies data access for non-technical users while ensuring security and compliance.
Furthermore, the significance of watsonx.Data extends beyond data scientists and engineers. It empowers non-technical users by granting them self-service access to enterprise-grade, trustworthy data within a unified collaborative platform. Simultaneously, it reinforces security and compliance protocols through centralized governance and local automated policy enforcement.
Soon, watsonx.Data will harness the capabilities of watsonx.Ai foundation models to simplify and expedite user interactions with data. This will allow users to utilize natural language for tasks such as discovering, enhancing, refining, and visualizing data and metadata, creating a more conversational and user-friendly experience.
watsonx.GovernanceAs AI becomes increasingly integrated into everyday workflows, the necessity for proactive governance to ensure responsible and ethical decision-making within the organization grows. Watsonx.Governance leverages IBM's robust AI governance capabilities to assist organizations in implementing comprehensive end-to-end lifecycle governance, mitigating risks, and effectively managing compliance with the evolving landscape of AI and industry regulations.
Watsonx.Governance empowers organizations to lead, oversee, and oversee their company's AI initiatives. The tool utilizes software automation to enhance your capacity to mitigate risks, handle regulatory mandates, and address ethical considerations, all without the need for costly transitions in your data science platform, even for models created using third-party tools.
IBM's watsonx.Governance is in tech preview today and is expected to be generally available later this year.
watsonx InfrastructureThe IT infrastructure of nearly every enterprise today is a hybrid-cloud infrastructure. IBM recognizes this, making the capabilities of watsonx available on-prem and in the cloud.
For example, IBM and Amazon Web Services (AWS) worked together to enable watsonx.Data on AWS infrastructure. Enterprises can expedite cloud-based data modernization efforts by taking advantage of the openness, performance, and governance of IBM watsonx.Data, while leveraging the scalability, agility, and cost-effectiveness of the AWS cloud infrastructure.
IBM delivers this flexibility by building watsonx atop an enhanced version of its Red Hat OpenShift technology, updated to ensure more efficient scale-out support for foundation model workloads. OpenShift also allows watsonx to seamlessly integrate with a broad range of IBM infrastructure offerings.
IBM watsonx infrastructure
IBMWhile cloud brings obvious benefits to IT organizations, the true benefit of AI in the enterprise blooms when using servers, storage, and accelerators designed for the unique requirements of AI workloads. IBM brings its considerable expertise in high-performance compute to the table with multiple AI-targeted infrastructure elements, including its Z-series mainframes and its IBM "Vela" AI supercomputer.
IBM is also looking to update its own IBM cloud offerings to support the demands of AI workloads better. The company tells us that in the coming months, it anticipates rolling out a comprehensive, high-performance, and adaptable AI-optimized infrastructure as a service through IBM Cloud, catering to the training and deployment needs of foundation models.
Analyst's TakeAI is no longer the exclusive domain of researchers and engineers. The technology is rapidly becoming a critical enabler for how business will be conducted moving forward. AI will become a competitive differentiator for those enterprises that quickly embrace the new technology. At the same time, AI doesn't look like anything we usually see in an enterprise data center.
Whether you're experimenting with the technology or deploying to production, AI remains a complex operation. Only with solutions such as IBM's watsonx can enterprises remove the pain and see accelerated time-to-value for their efforts.
Watsonx includes nearly everything you want in an enterprise-class AI solution. I've only just touched on its capabilities here. Watsonx also consists of various AI-assistants, including assistants for code generation and business task management, along with development tools for prompt engineering and model tuning. The list goes on and on. No matter where you are in your AI journey, there's not a more comprehensive AI solution than watsonx.
There is also no company better positioned than IBM to bring AI out of the research labs and into the enterprise. After all, IBM has helped enterprises adapt and benefit from new technologies since its inception, AI included. It's simply what IBM does.
Disclosure: Steve McDowell is an industry analyst, and NAND Research an industry analyst firm, that engages in, or has engaged in, research, analysis, and advisory services with many technology companies, which may include those mentioned in this article. Mr. McDowell does not hold any equity positions with any company mentioned in this article.
