#Natural Language Processing (NLP) in Healthcare and Life Sciences Market share

[thoratketan]

Veterinary Services Strategies In The Global Market: Key Insights From The 2025 Report

The global Natural Language Processing (Nlp) In Healthcare And Life Sciences Market, valued at USD 4.94 billion in 2023, is projected to reach USD 62.7 billion by 2032, growing at a remarkable compound annual growth rate (CAGR) of 32.6% from 2024 to 2032. This surge is driven by the increasing need for advanced technologies to manage large volumes of healthcare data, the growing adoption of artificial intelligence (AI) solutions, and the need to streamline healthcare processes through automated systems.

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Natural Language Processing, a subset of AI, enables computers to understand, interpret, and manipulate human language. In healthcare and life sciences, NLP plays a vital role in extracting valuable insights from unstructured data sources such as medical records, clinical notes, research papers, and patient interactions. As the healthcare sector continues to expand and generate vast amounts of data, NLP has become a pivotal technology in helping healthcare professionals make more informed decisions, improve patient outcomes, and enhance operational efficiencies.

Key Drivers of Market Growth

Explosion of Healthcare Data: Healthcare data is growing at an unprecedented rate due to the increasing digitization of healthcare systems, electronic health records (EHR), and the rise of telemedicine and remote patient monitoring. A large portion of this data is unstructured, such as doctors' notes, medical transcriptions, and patient conversations. NLP provides a powerful tool to analyze and extract meaningful information from this unstructured data, enabling healthcare providers to make quicker, data-driven decisions.

Rising Demand for Personalized Healthcare: Personalized medicine is gaining significant traction in healthcare as treatments are increasingly tailored to individual patients. NLP plays a key role in this shift by enabling the analysis of diverse data sources, including genomic data, medical records, and patient-reported outcomes. By leveraging NLP, healthcare providers can gain deeper insights into a patient's medical history, genetics, and lifestyle factors, helping to create more effective personalized treatment plans and improve patient outcomes.

Growth in AI and Machine Learning Adoption: The integration of AI and machine learning (ML) with NLP technologies is one of the major factors propelling the market. These technologies allow healthcare providers to automate routine tasks, such as processing clinical notes, coding diagnoses, and identifying patterns in patient data. The combination of NLP and ML can also help with predictive analytics, improving decision-making and enabling proactive care management. The increasing adoption of AI across healthcare sectors, from hospitals to pharmaceutical research, is accelerating the use of NLP solutions.

Improved Operational Efficiencies: NLP has proven to be highly effective in improving operational efficiencies within healthcare organizations. By automating administrative tasks, such as billing, coding, and data entry, NLP helps healthcare providers reduce the administrative burden on staff, streamline workflows, and improve resource allocation. This efficiency not only saves time and costs but also enhances the quality of care delivered to patients.

Increased Focus on Healthcare Research and Drug Discovery: NLP is playing an increasingly important role in accelerating research and drug discovery processes. The ability to quickly analyze vast amounts of scientific literature, clinical trials data, and electronic health records is enhancing the pace of discovery. NLP allows researchers to extract valuable insights from medical literature, identify emerging trends, and analyze clinical trial results in real-time. As the need for faster drug development and research increases, NLP’s capabilities are being harnessed to bring new treatments to market more efficiently.

Regulatory Support and Government Initiatives: Governments around the world are taking steps to support the integration of AI technologies in healthcare. In the U.S., the FDA has been exploring the use of AI and NLP to improve drug development, while various global healthcare agencies are increasingly focusing on digital health and data-driven healthcare solutions. This regulatory support has spurred innovation in the NLP field, leading to more robust, safe, and effective NLP solutions tailored to the healthcare and life sciences sectors.

Key Segments:

By Technique

Smart Assistance

Optical Character Recognition

Auto Coding

Text Analytics

Speech Analytics

Classification and Categorization

By End-use

Providers

Payers

Life Science Companies

Others

Make Enquiry about Natural Language Processing (NLP) in Healthcare and Life Sciences Market

Key Players

Key Service Providers/Manufacturers

IBM Watson Health

Merative

Linguamatics

Haptik

Deepset

Microsoft

Amazon Web Services (AWS)

Google Health

Oracle

Nabla

Corti

Tortus

Grove AI

Infinitus Systems

Regard

Lumeris

Heidi

CitiusTech

Owkin

Insilico Medicine

Conclusion

The Natural Language Processing (NLP) in healthcare and life sciences market is poised for explosive growth over the next decade, fueled by the increasing volume of healthcare data, the rising demand for personalized care, and advancements in AI technology. With a projected market size of USD 62.7 billion by 2032, NLP is set to play a crucial role in transforming healthcare operations, improving patient outcomes, and accelerating research and drug development. As more healthcare providers, pharmaceutical companies, and research institutions adopt NLP technologies, the market will continue to expand, offering innovative solutions to some of the healthcare industry's most pressing challenges.

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Artificial Intelligence: Transforming the Way We Live, Work & Learn

In just a few short years, Artificial Intelligence (AI) has gone from being a futuristic concept to a transformative force across industries. From personalised shopping experiences to self-driving cars and intelligent virtual assistants, AI is changing the way we interact with the world. But what exactly is AI, and why is it such a big deal?

🤖 What is Artificial Intelligence?

Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think, learn, and solve problems. These machines can mimic cognitive functions such as learning, reasoning, decision-making, and even language translation.

AI can be classified into three types:

Narrow AI (Weak AI): Focused on one specific task (e.g., Siri, Alexa, Google Maps).

General AI: A hypothetical system that can perform any intellectual task a human can do.

Super AI: Still theoretical, this would surpass human intelligence.

Currently, most applications of AI are in the Narrow AI category, but the speed of advancement is remarkable.

📊 Applications of AI in Daily Life

AI is no longer just in tech labs or science fiction — it's around us every day. Let’s explore some real-world examples:

1. Healthcare

AI is revolutionising diagnostics by analysing medical data faster and more accurately than humans. It helps detect diseases like cancer, predict patient outcomes, and personalise treatment plans.

2. Education

AI is reshaping learning by offering:

Adaptive learning platforms

Virtual tutors

Automated grading This enables more personalised education and allows teachers to focus on student development.

3. Finance

Banks use AI for fraud detection, automated customer service (chatbots), and to assess credit risk. Robo-advisors also help people invest with the help of algorithms.

4. Retail & E-commerce

Ever wondered how Amazon recommends products so accurately? That’s AI at work — analysing your search and buying history to suggest relevant products.

5. Transportation

AI powers navigation systems like Google Maps, ride-sharing platforms like Uber, and is the core technology behind self-driving vehicles.

🛠️ How AI Works: The Basics

AI systems learn through data and algorithms. The more data you feed them, the more accurate their predictions become. There are different branches of AI:

Machine Learning (ML): Algorithms learn from data without being explicitly programmed.

Natural Language Processing (NLP): Helps machines understand and respond to human language (used in Siri, Chatgpt, etc.).

Computer Vision: Enables machines to interpret visual information (used in facial recognition, medical imaging).

Deep Learning: A subset of ML, inspired by the human brain, using neural networks to solve complex problems.

⚖️ Ethical Challenges in AI

As powerful as AI is, it also comes with risks and ethical concerns:

Bias: AI can inherit biases present in the training data, leading to unfair outcomes.

Privacy: Excessive data collection can infringe on user privacy.

Job Displacement: Many worry that AI will replace jobs, especially in manufacturing, customer service, and logistics.

That’s why developing ethical, responsible AI is just as important as developing powerful AI systems.

💡 The Future of AI: Opportunities Ahead

The AI market is expected to grow from $150 billion in 2023 to over $300 billion by 2026. This boom is creating massive opportunities for skilled professionals in:

AI & ML Engineering

Data Science

Robotics

NLP and Computer Vision

AI Ethics and Policy

Companies across all sectors — from healthcare to agriculture to education — are investing in AI talent. And this is just the beginning.

🎓 Why You Should Learn AI Now

Artificial Intelligence isn't just for programmers or PhDs anymore. Many platforms, tools, and courses now make it easy for anyone, whether you're a student, marketer, business owner, or job seeker, to start learning.

Here’s what you can expect to learn when starting with AI:

The basics of how AI and machine learning work

Tools like Python, TensorFlow, and Chatgpt

Real-world project development

Career pathways and certifications

🚀 Start Your AI Journey with Skilled Prime

The world is moving towards automation, data-driven decisions, and intelligent systems. Learning AI today is not just about preparing for the future — it's about being relevant in the present.

🔚 Final Words

Artificial Intelligence is no longer just a buzzword — it's a necessity. The earlier you start learning and adapting, the better you’ll be positioned in this ever-changing world. Whether you're a beginner or a working professional, there’s never been a better time to dive into the world of AI.

✅ We at Skilled Prime provide industry-ready courses in AI, machine learning, and data science. Join us today and future-proof your career. Click Here 

[codeperksolutions]

How CodePerk Solutions Is Shaping the Future with AI Development Services and Android Development

In today’s digital world, businesses are evolving faster than ever. Technology has become the backbone of every successful company. Two of the most powerful forces leading this change are Artificial Intelligence (AI) and mobile applications. At CodePerk Solutions, we combine our expertise in AI development services and Android development to help businesses grow, innovate, and stay ahead of the competition.

Whether you are a startup looking for your first app or an established company aiming to integrate smart AI features, CodePerk Solutions is your trusted technology partner.

Why AI Development Services Matter More Than Ever

Artificial Intelligence is no longer something we see only in science fiction movies. It is now a part of our daily lives. From personalized shopping experiences to intelligent chatbots and voice assistants, AI is everywhere. Businesses that harness the power of AI can offer better customer experiences, streamline operations, and make smarter decisions.

At CodePerk Solutions, we provide end-to-end AI development services to help you unlock the true potential of AI. Our team works closely with you to understand your needs and create custom AI solutions that fit perfectly into your business model.

What We Offer in AI Development Services:

Custom AI Solutions: We develop tailor-made AI applications that suit your specific business requirements.

Machine Learning Models: Our team builds intelligent models that can predict outcomes, automate tasks, and improve efficiency.

Natural Language Processing (NLP): We create smart bots and assistants that can understand and respond to human language naturally.

Computer Vision: We help businesses implement AI that can "see" and interpret images, making it useful for industries like healthcare, security, and retail.

Predictive Analytics: Our AI solutions help you analyze data and predict future trends for better decision-making.

No matter what industry you are in — healthcare, retail, finance, or education — our AI development services can give you the competitive edge you need.

Why Android Development Is Still a Game Changer

While AI is changing the future, mobile apps continue to rule the present. Android holds the largest share of the mobile market, with billions of devices running on the platform. Having a strong Android app is no longer a luxury; it’s a necessity.

CodePerk Solutions specializes in high-quality Android development. We build user-friendly, scalable, and performance-driven Android apps that help businesses connect with their customers anytime, anywhere.

Our Android Development Expertise:

Custom Android App Development: From idea to launch, we take care of everything.

UI/UX Design: We create beautiful and easy-to-use interfaces that users love.

Native and Hybrid Apps: Whether you need a full-fledged native Android app or a cross-platform solution, we’ve got you covered.

App Testing and Maintenance: We make sure your app runs smoothly with ongoing support and updates.

Integration with AI: We can integrate powerful AI features into your Android app to make it smarter and more responsive.

At CodePerk Solutions, we don’t just build apps; we build solutions that help you achieve your business goals.

The Power of Combining AI Development Services with Android Development

Now imagine the power of combining both AI and Android development. An app that not only connects with your users but also learns from their behavior, predicts their needs, and offers a personalized experience — that’s the future, and it’s happening right now!

When you choose CodePerk Solutions, you get a team that knows how to merge AI with mobile technology perfectly. Whether it’s a shopping app that recommends products based on user preferences, a health app that gives personalized fitness tips, or a chatbot that offers 24/7 support, we can bring your ideas to life.

Here are some real-world examples of what we can create:

Smart Shopping Apps: Apps that learn what users like and suggest the right products.

Healthcare Apps: Apps that monitor patient health and send alerts automatically.

Finance Apps: Apps that predict stock trends and offer investment advice.

Customer Support Bots: Chatbots that solve customer problems instantly.

By using our AI development services and Android development expertise together, we help you deliver a smarter, faster, and more delightful experience to your customers.

Why Choose CodePerk Solutions?

Choosing the right technology partner is crucial for your success. Here’s why CodePerk Solutions stands out:

Experience and Expertise: Our team has years of experience in both AI and Android technologies.

Custom Solutions: We understand that every business is unique. We build solutions that are tailored to your needs.

Cutting-Edge Technology: We use the latest tools, frameworks, and technologies to deliver the best results.

Client-Centric Approach: Your satisfaction is our top priority. We work closely with you at every step.

Affordable Pricing: We offer world-class services without breaking the bank.

Continuous Support: We are there for you even after the project is completed with regular updates and support.

At CodePerk Solutions, we believe in building long-term relationships with our clients. Your success is our success.

Conclusion

Technology is changing fast, and staying ahead requires smart choices. Investing in AI development services and Android development is no longer optional — it’s essential for growth and success. At CodePerk Solutions, we are passionate about helping businesses harness the power of technology to achieve their dreams.

Whether you want to build an intelligent app that adapts to users’ needs or automate business processes with AI, we are here to help you every step of the way. Let’s work together to create something extraordinary.

[glaxitsoftwareagency]

5 Soft Skills Every AI Developer Must Have

Artificial Intelligence (AI) is changing the way we live, work, and solve problems. While technical knowledge is crucial, it’s not the only thing that matters. AI developers must also develop soft skills if they want to grow, lead, and succeed in their field. Soft skills can make a big difference in how effectively a developer works with others and tackles challenges. Let’s explore five important soft skills every AI developer should have and why they matter.

Essential Skills to Excel as an AI Engineer in 2025

Machine Learning (ML)

Machine learning is at the heart of how AI works.It relies on data to learn and improve over time. Systems don’t need to be coded step-by-step, which is a big plus. As the model gets trained, it becomes better at solving tasks.

Deep Learning

Deep learning comes under machine learning but handles more complex jobs. It is based on neural networks, which act like a human brain. This skill comes in handy for face recognition, translation, and more. And it’s often used in apps we count on every day.

Robotics and Automation

This field brings together AI and physical machines to get things done. Robots carry out tasks in industries like healthcare and logistics. Automation is becoming a key part of modern workplaces. It saves time, cuts down on errors, and boosts output.

AI Governance

AI governance is about doing things the right way. It means developers must stick to ethics and data laws. They must also watch out for bias and unfair systems. All of this helps build trust and ensure responsible use.

Programming Languages

Python, Java, R, and C++ are used to create AI tools. Python stands out because it’s simple but powerful. These languages are essential for writing clean, working code. When you get used to them, development becomes faster.

AI Integration

It’s not enough to build AI it must fit in with systems. Good AI integration leads to smooth business operations. Developers often work with teams to adapt workflows. So AI can bring out better results without disruptions.

Data Science Skills

AI depends on clean, well-prepared data to perform well. You’ll need to work through statistics and visualizations. Preprocessing data is often the most time-consuming part. But it really pays off when your models give accurate results.

Natural Language Processing (NLP)

NLP helps machines make sense of what people say. It’s what powers chatbots, voice assistants, and search tools. This field is growing fast and opens up many doors. You’ll come across it in almost every AI product.

Cloud Computing

AI models often run on platforms like AWS or Azure. Cloud tools make it easy to deploy and scale solutions. They also cut down on hardware costs and delays. So knowing how clouds work is now a must.

Communication Skills 

No matter how brilliant you are, your ideas must be shared clearly. AI developers often work in teams and need to explain complex concepts to people who may not have a technical background. This is where communication becomes a key. For example, you might need to explain a machine-learning model to a marketing manager. If you use too much jargon, they won’t understand you. But your point will be understood better if you use simple words and real-life examples, like how AI can predict customer behavior based on past purchases.

In addition, good communication helps reduce misunderstandings. Developers who can talk things through are less likely to make costly errors. This idiom simply means don’t let communication gaps cause missed opportunities.

Think Out of the Box

AI development is full of complex problems. Sometimes, your code won’t work. Other times, the data may be messy, or the model might give poor results. That’s where problem-solving skills come in. Developers have to dig deep, find the root cause, and figure out the solution. They should not give up quickly. For example, if a facial recognition algorithm is not accurate, a developer could test different datasets or fine-tune the parameters..

Together Everyone Achieves More

AI projects are rarely done alone. Developers often work with data scientists, product managers, designers, and clients. Teamwork is like the oil that keeps the machine running smoothly. A good team player should be willing to listen, offer help, and share knowledge. If everyone gets along, the project moves faster. But if one person throws a wrench in the works, it could delay progress. Let’s say your teammate needs help with training a neural network. If you assist them, it not only builds trust but also makes the team stronger. Remember, a chain is only as strong as its weakest link. If AI developers worked in silos, they would miss out on valuable ideas and feedback.

Adaptability 

Technology changes every day. What’s new today might be old tomorrow. AI developers must stay updated and be open to learning new tools, frameworks, and ideas. They also need to adjust when project goals shift or when clients ask for something different. For instance, if your company switches from TensorFlow to PyTorch, you must be willing to adapt. If not, you might fall behind. This means you should be flexible and handle unexpected changes calmly. According to Forbes, 75% of companies expect their employees to upskill within the next 3 years. So, staying flexible is not a choice it’s a necessity. If developers weren’t open to change, their skills would become outdated.

Time Management 

AI projects involve many tasks data cleaning, coding, testing, and reporting. If you don’t manage your time well, deadlines can be missed, and quality may drop. Developers should break down tasks, set clear goals, and stick to deadlines. Tools like Trello or Asana can help organize tasks and track progress. Imagine you have a model to deliver in one week. If you waste the first few days, the pressure will build up, and the final result may suffer. But if you manage your time wisely, you will complete the task with less stress. This classic saying still holds true.

Final Thoughts:

To sum up, strong soft skills are essential for AI developers. Communication, problem-solving, teamwork, adaptability, and time management are not just nice-to-have they are must-haves. If these skills are practiced daily, they can boost productivity, reduce errors, and help developers shine in their roles. At Glaxit, we understand the importance of balancing technical expertise with soft skills. Our AI development team is trained not just to build smart solutions, but also to collaborate, communicate, and deliver results on time. Whether you’re building the next AI breakthrough or hiring top AI Developers Glaxit is here to support you every step of the way. Because in the world of AI, brains may build the system but heart and hustle keep it going.

[global-research-report]

Data Center Accelerator Market Analysis: Meeting the Demand for Real-Time Data Processing

The global data center accelerator market size is anticipated to reach USD 63.22 billion by 2030, according to a new report by Grand View Research, Inc. The market is expected to grow at a CAGR of 24.7% from 2025 to 2030. The demand for data center accelerators is likely to grow owing to increasing adoption of technologies such as AI, IoT, & big data analytics. The COVID-19 pandemic had a positive impact on the data center accelerator market. Factors such as increased corporate awareness of the advantages that cloud services can offer, increased board pressure to provide more secure & robust IT environments, as well as the establishment of local data centers contributed to the growth of data center accelerators. Demand for businesses that rely on digital infrastructure has increased, which has led to significant growth in demand for data center network services in many industries. Data centers are now maintaining program availability and data security as more businesses and educational institutions already moved online.

Top industries using HPC are healthcare, manufacturing aerospace, urban planning, and finance. The University of Texas at Austin researchers are advancing the science of cancer treatment through the use of HPC. In a ground-breaking 2017 project, researchers examined petabytes of data to look for connections between the genomes of cancer patients and the characteristics of their tumors. This paved the way for the university to apply HPC in additional cancer research, which has now expanded to include efforts to diagnose and treat cases of prostate, blood-related, liver, and skin cancers.

Data Center Accelerator Market Report Highlights

Based on processor, the GPU segment accounted for the maximum revenue share of 44% in 2024. This can be attributed to the increasing use of GPU acceleration in IoT computing, bitcoin mining, AI and machine learning, etc. Moreover, GPU acceleration’s parallel processing architecture is useful in life science analytics such as a genome sequencing.

Based on type, the HPC data center segment is expected to grow at the highest CAGR of 26.0% over the forecast period. This can be attributed to a rising preference for hybrid and cloud-based high performance computing (HPC) solutions, use of HPC in vaccine development, advances in virtualization, etc.

Based on application, the deep learning training segment dominated the market in 2024. This can be attributed to increasing adoption of deep learning in hybrid model integration, self-supervised learning, high performance natural language process (NLP) models, and neuroscience based deep learning.

North America held the largest share of 37.0% in 2024 and is expected to retain its position over the forecast period. Presence of several data center accelerator solution and service providers makes North America a promising region for the market.

Asia Pacific is anticipated to expand at the highest CAGR of over 27.8% over the forecast period. Suitable government policies and the need for data center infrastructure upgradation in Asia Pacific are driving the growth of the data center accelerator market in the region.

In October 2020 Intel Corporation launched Intel Xeon Scalable Platform to assist secure sensitive workloads. This platform has new features that include Intel Platform Firmware Resilience (Intel PFR), Intel Total Memory Encryption (Intel TME), and new cryptographic accelerators to support the platform and advance the overall integrity and confidentiality of data.

Data Center Accelerator Market Segmentation

Grand View Research has segmented the global data center accelerator market report based on processor, type, application, and region:

Data Center Accelerator Processor Outlook (Revenue, USD Billion, 2018 - 2030)

GPU

CPU

FPGA

ASIC

Data Center Accelerator Type Outlook (Revenue, USD Billion, 2018 - 2030)

HPC Data Center

Cloud Data Center

Data Center Accelerator Application Outlook (Revenue, USD Billion, 2018 - 2030)

Deep Learning Training

Public Cloud Interface

Enterprise Interface

Data Center Accelerator Regional Outlook (Revenue, USD Billion, 2018 - 2030)

North America

US

Canada

Mexico

Europe

UK

Germany

France

Asia Pacific

China

India

Japan

Australia

South Korea

Latin America

Brazil

Middle East & Africa (MEA)

UAE

Saudi Arabia

South Africa

List of Key Players

Advanced Micro Devices, Inc.

Dell Inc.

IBM Corporation

Intel Corporation

Lattice Semiconductor

Lenovo Ltd.

Marvell Technology Inc.

Microchip Technology Inc.

Micron Technology, Inc.

NEC Corporation

NVIDIA Corporation

Qualcomm Incorporated

Synopsys Inc.

Order a free sample PDF of the Data Center Accelerator Market Intelligence Study, published by Grand View Research.

[pranalip]

NLP in Healthcare and Life Sciences Market Global Industry Trends and Market Outlook 2024-2033 | Global Insight Services

NLP in healthcare and life sciences is the application of natural language processing (NLP) techniques to the field of healthcare and life sciences. NLP is a branch of artificial intelligence that deals with the interaction between humans and computers. NLP techniques are used to process and understand human language.

In healthcare, NLP is used to process and analyze patient records, clinical trials data, and other health-related data. NLP techniques can be used to identify patterns and trends in data, to improve disease diagnosis, and to develop new treatments. NLP is also being used to develop chatbots and digital assistants that can provide patients with information about their health and help them to manage their condition.

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In life sciences, NLP is being used to process and analyze data from genomics, proteomics, and other life science disciplines. NLP techniques are used to identify patterns and trends in data, to improve disease diagnosis, and to develop new treatments. NLP is also being used to develop chatbots and digital assistants that can provide scientists with information about their research and help them to manage their data.

Key Trends

Some of the key trends in NLP for Healthcare and Life Sciences technology include:

Increased accuracy in predictive modeling: NLP is being used increasingly to create predictive models that can more accurately identify patterns and trends in data. This is especially useful in healthcare, where data is often complex and varied.

Improved patient care: NLP is also being used to improve patient care. By understanding the natural language used in patient records, NLP can help to identify potential problems and offer suggestions for treatment.

Increased use of chatbots: Chatbots are becoming increasingly popular in healthcare, as they can provide a quick and easy way for patients to get answers to their questions. NLP is used to understand the user’s intent and provide a suitable response.

Improved decision-making: NLP is helping to improve decision-making in healthcare by providing a better understanding of the data. This can help to identify potential problems and offer suggestions for treatment.

Greater transparency: NLP is also helping to increase transparency in healthcare. By understanding the natural language used in patient records, NLP can help to identify potential problems and offer suggestions for treatment.

Key Drivers

There are several key drivers of NLP in Healthcare and Life Sciences market. Firstly, the increasing demand for healthcare services is driving the need for NLP in Healthcare and Life Sciences. Secondly, the rising cost of healthcare is another key driver of NLP in Healthcare and Life Sciences market. Thirdly, the increasing number of chronic diseases is another key driver of NLP in Healthcare and Life Sciences market. Fourthly, the increasing number of geriatric population is another key driver of NLP in Healthcare and Life Sciences market. Fifthly, the advancement in technology is another key driver of NLP in Healthcare and Life Sciences market.

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Research Objectives

Estimates and forecast the overall market size for the total market, across product, service type, type, end-user, and region

Detailed information and key takeaways on qualitative and quantitative trends, dynamics, business framework, competitive landscape, and company profiling

Identify factors influencing market growth and challenges, opportunities, drivers and restraints

Identify factors that could limit company participation in identified international markets to help properly calibrate market share expectations and growth rates

Trace and evaluate key development strategies like acquisitions, product launches, mergers, collaborations, business expansions, agreements, partnerships, and R&D activities

Thoroughly analyze smaller market segments strategically, focusing on their potential, individual patterns of growth, and impact on the overall market

To thoroughly outline the competitive landscape within the market, including an assessment of business and corporate strategies, aimed at monitoring and dissecting competitive advancements.

Identify the primary market participants, based on their business objectives, regional footprint, product offerings, and strategic initiatives

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Market Segments

The natural language processing market is segmented on the basis of component, deployment mode, organization size and region. By component, it is bifurcated into software and services. By deployment mode, it is segregated into cloud and on-premises. By organization size, it is categorized into small to medium enterprise (SMEs) and large-scale enterprise. Region-wise, it is studied across North America, Europe, Asia-Pacific, and the rest of the world.

Key Companies

The key players in the NLP in Healthcare and Life Sciences Market include 3M, Cerner Corporation, IBM Corporation, Microsoft Corporation, Health Fidelity, Inc., IQVIA Holdings Inc., Dolbey Systems, Inc., Centene Corporation, Alphabet Inc., Amazon.com, Inc., Averbis GmbH, Clinithink, Wave Health Technologies, Inovalon, Lexalytics, Conversica Inc., SparkCognition, and Hewlett Packard Enterprise Development LP.

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Research Scope

Scope – Highlights, Trends, Insights. Attractiveness, Forecast

Market Sizing – Product Type, End User, Offering Type, Technology, Region, Country, Others

Market Dynamics – Market Segmentation, Demand and Supply, Bargaining Power of Buyers and Sellers, Drivers, Restraints, Opportunities, Threat Analysis, Impact Analysis, Porters 5 Forces, Ansoff Analysis, Supply Chain

Business Framework – Case Studies, Regulatory Landscape, Pricing, Policies and Regulations, New Product Launches. M&As, Recent Developments

Competitive Landscape – Market Share Analysis, Market Leaders, Emerging Players, Vendor Benchmarking, Developmental Strategy Benchmarking, PESTLE Analysis, Value Chain Analysis

Company Profiles – Overview, Business Segments, Business Performance, Product Offering, Key Developmental Strategies, SWOT Analysis.

With Global Insight Services, you receive:

10-year forecast to help you make strategic decisions

In-depth segmentation which can be customized as per your requirements

Free consultation with lead analyst of the report

Infographic excel data pack, easy to analyze big data

Robust and transparent research methodology

Unmatched data quality and after sales service

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About Global Insight Services:

lobal Insight Services (GIS) is a leading multi-industry market research firm headquartered in Delaware, US. We are committed to providing our clients with highest quality data, analysis, and tools to meet all their market research needs. With GIS, you can be assured of the quality of the deliverables, robust & transparent research methodology, and superior service.

[johnthejacobs]

Indegene Share Price Rising Through The Waves

Introduction In the realm of healthcare and pharmaceutical services, Indegene has been making waves with its remarkable performance in the market as Indegene Share Price has seen a phenomenal rise . This article delves into the factors contributing to the upward trajectory of Indegene Share Price, examining the company's growth drivers and market dynamics. Indegene Limited specializes in providing comprehensive research and development (R&D) and management services tailored for healthcare and pharmaceutical enterprises. With over two decades of experience in the life sciences domain, the company leverages purpose-built technology to establish itself as a leading "digital-first" commercialization entity exclusively dedicated to serving the global life sciences industry.

Their extensive suite of solutions empowers biopharmaceutical, emerging biotech, and medical device companies throughout the product lifecycle, facilitating more effective, efficient, and modern operations. Indegene's portfolio encompasses a wide array of solutions covering commercial, medical, regulatory, and R&D operations within the life sciences sector.

Among their offerings, Enterprise Commercial Solutions take center stage, aiding life sciences companies in fulfilling their digital marketing needs. This strategic focus aligns with market trends, as evidenced by the Everest Report indicating sales and marketing as the largest expenditure segment for life sciences companies in 2021. Providers like Indegene craft tailored marketing plans and campaigns, leveraging approved clinical trial data to generate promotional content, thereby facilitating personalized engagement strategies for healthcare professionals (HCPs) and patients.

Moreover, Indegene's repertoire includes digital asset management, marketing automation, customer data management, and analytics solutions, enabling the measurement of campaign effectiveness. The incorporation of proprietary Natural Language Processing (NLP)-based tools and platforms further enhances efficiency, reduces reliance on manpower, and ensures regulatory compliance within these solutions.

The company's impressive client roster speaks volumes about its industry reputation, with 19 of the 20 largest biopharmaceutical companies in the world by 2021 revenue listed among its clientele, as verified by the Everest Report. A significant portion of Indegene's revenue stems consistently from these top 19 clients across multiple years, highlighting their sustained trust and reliance on the company's services. As of June 30, 2022, Indegene maintains 52 active clients, defined as those generating at least $0.25 million in revenue over the preceding year. Within this cohort, 21 clients contribute revenues ranging from $1 million to $10 million, while three clients fall within the $10 million to $25 million range, and four clients generate over $25 million each.

Originally incorporated as ‘Indegene Lifesystems Private Limited’ in Ahmedabad, Gujarat, the company underwent a significant transition, relocating its registered office from Maharashtra to Karnataka in February 2017. Alongside this relocation, the company's name was updated to ‘Indegene Limited’, as reflected in the fresh certificate of incorporation issued by the Registrar of Companies on November 17, 2022.

A Leader in Healthcare and Pharmaceuticals Indegene Limited emerges as a leading player in the healthcare and pharmaceuticals sector, renowned for its comprehensive range of research, development, and management services. With a strong foothold in the industry and a reputation for excellence, Indegene is positioned as a key contributor to the advancement of healthcare solutions globally.

Harnessing Decades of Expertise At the core of Indegene's success lies its extensive expertise in the life sciences domain, spanning over two decades. The company's deep-rooted knowledge and experience enable it to navigate complex challenges and deliver innovative solutions that meet the evolving needs of the healthcare industry. This expertise serves as a driving force behind the upward movement of Indegene's share price.

Embracing Digital Transformation Indegene distinguishes itself as a "digital-first" commercialization company, leveraging cutting-edge technology to drive growth and innovation. By embracing digital transformation, the company enhances efficiency, scalability, and customer engagement, thereby bolstering its market position and driving positive sentiment among investors.

Expanding Global Footprint Indegene's strategic expansion initiatives play a pivotal role in propelling its share price upwards. The company has successfully expanded its presence beyond domestic borders, catering to a diverse clientele across international markets. By tapping into new geographic regions and demographic segments, Indegene broadens its revenue streams and diversifies its risk profile, contributing to its overall growth trajectory.

Financial Performance and Investor Confidence The robust financial performance of Indegene serves as a significant catalyst for the rising share price. With impressive revenue growth, strong profitability metrics, and prudent financial management practices, the company garners confidence and trust from investors. Moreover, transparent communication and proactive investor relations initiatives further reinforce investor sentiment, fueling the positive momentum of Indegene's share price.

Navigating Challenges and Seizing Opportunities Despite facing challenges such as regulatory complexities and competitive pressures, Indegene remains resilient and adaptable. The company's ability to navigate challenges effectively while capitalizing on emerging opportunities positions it for sustained growth and value creation. By staying agile and responsive to market dynamics, Indegene continues to drive shareholder value and propel its share price upwards.

Conclusion The rising share price of Indegene Limited underscores the company's leadership position, strategic vision, and commitment to excellence in the healthcare and pharmaceuticals industry. With a focus on innovation, digital transformation, and global expansion, Indegene is well-positioned to capitalize on emerging opportunities and navigate evolving market dynamics. As investors and stakeholders witness the company's upward trajectory, the future looks promising for Indegene as it continues to deliver value and drive growth in the healthcare sector.

[imarcmarketreport]

Conversational AI Market Size, Share, Trends, Growth and Forecast 2024-2032

IMARC Group's report titled "Conversational AI Market by Component (Platform, Services), Type (Intelligent Virtual Assistant (IVA), Chatbots), Technology (Machine Learning, Deep Learning, Natural Language Processing, Automatic Speech Recognition), Deployment (Cloud-based, On-premises), Organization Size (Large Enterprises, Small and Medium-sized Enterprises), End User (BFSI, Retail and E-commerce, Healthcare and Life Science, Travel and Hospitality, Telecom, Media and Entertainment, and Others), and Region 2024-2032",The global conversational AI market size reached US$ 10.4 Billion in 2023. Looking forward, IMARC Group expects the market to reach US$ 117.0 Billion by 2032, exhibiting a growth rate (CAGR) of 30.69% during 2024-2032.

For an in-depth analysis, you can refer sample copy of the report: https://www.imarcgroup.com/conversational-ai-market/requestsample

Factors Affecting the Growth of the Conversational AI Industry:

Advancements in Natural Language Processing (NLP) and Machine Learning (ML):

The rapid advancements in natural language processing (NLP) and machine learning (ML) technologies is propelling the market growth. These technological improvements are enhancing the ability of conversational artificial intelligence (AI) systems to understand and respond to human language more naturally and accurately. Modern NLP algorithms can interpret and process a wide range of languages and dialects, while ML enables these systems to learn from past interactions and improve over time. This advancement is expanding the potential applications of conversational AI, making it more attractive for various industries, such as healthcare, finance, and retail. Additionally, the increasing integration of these AI systems with other technologies like voice recognition and sentiment analysis is further broadening their capabilities, making them more versatile and efficient.

Rise of Omnichannel Retail and E-commerce:

Businesses are striving to provide seamless shopping experiences across multiple channels like online, mobile, and in-store, which is catalyzing the demand for conversational AI to engage buyers effectively. These AI-driven chatbots and virtual assistants can offer personalized shopping assistance, provide product recommendations, and handle user queries around the clock, enhancing the overall buying experience. In e-commerce, conversational AI helps reduce cart abandonment rates by assisting buyers through the checkout process and addressing their concerns in real-time. Furthermore, as the lines between physical and digital shopping experiences blur, conversational AI plays a crucial role in unifying these channels, providing consistent and efficient user service, and thereby driving sales and customer loyalty.

Cost Reduction and Operational Efficiency:

The rising implementation of conversational AI in business operations to reduce costs while enhancing operational efficiency is strengthening the market growth. These systems automate routine and repetitive tasks, such as answering frequently asked questions or scheduling appointments, which traditionally require human intervention. This automation not only lowers labor costs but also allows human employees to focus on more complex and value-added activities. Additionally, conversational AI systems are scalable and can handle large volumes of interactions without the need for proportional increases in resources, making them highly cost-effective for businesses dealing with seasonal spikes in customer inquiries. This efficiency and scalability are particularly beneficial for sectors like banking, telecommunications, and healthcare, where high volumes of user interactions are common.

Leading Companies Operating in the Global Conversational AI Industry:

Amazon Web Services Inc. (Amazon.com Inc.)

Artificial Solutions

Avaamo Inc.

Conversica Inc.

Creative Virtual Ltd.

Google LLC (Alphabet Inc.)

Gupshup

International Business Machines Corporation

Jio Haptik Technologies Limited (Reliance Industries Limited)

Kore.ai Inc.

Nuance Communications Inc. (Microsoft Corporation)

Oracle Corporation

Rasa Technologies Inc.

SAP SE

Conversational AI Market Report Segmentation:

By Component:

Platform

Services

Support and Maintenance

Training and Consulting

System Integration

Platform exhibits a clear dominance in the market driven by their foundational role in providing infrastructure and integrated solutions for conversational AI applications.

By Type:

Intelligent Virtual Assistant (IVA)

Chatbots

Chatbots represent the largest segment. They are extensively used in industries for user service, engagement, and automated responses.

By Technology:

Machine Learning

Deep Learning

Natural Language Processing

Automatic Speech Recognition

Natural language processing holds the biggest market share as it is crucial for enabling AI systems to understand and respond to human language effectively.

By Deployment:

Cloud-based

On-premises

On-premises accounts for a major share owing to their perceived security benefits and control over data, which is particularly important for sensitive or regulated industries.

By Organization Size:

Large Enterprises

Small and Medium-sized Enterprises

On the basis of the organization size, the market has been bifurcated into large enterprises and small and medium-sized enterprises.

By End User:

BFSI

Retail and E-commerce

Healthcare and Life Science

Travel and Hospitality

Telecom

Media and Entertainment

Others

Retail and e-commerce represent the largest segment attributed to their high reliance on user engagement and personalized shopping experiences facilitated by conversational AI.

Regional Insights:

North America (United States, Canada)

Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, Others)

Europe (Germany, France, United Kingdom, Italy, Spain, Russia, Others)

Latin America (Brazil, Mexico, Others)

Middle East and Africa

North America dominates the market due to the concentration of leading technology companies and high adoption rates of advanced AI technologies in this region.

Global Conversational AI Market Trends:

The growing integration of conversational AI with other emerging technologies, such as the internet of things (IoT), augmented reality (AR), and blockchain, is offering a favorable market outlook. Smart homes are equipped with IoT devices, where conversational AI enables users to control and interact with their devices through voice commands or text. Additionally, in retail, AR combined with conversational AI can offer immersive shopping experiences, allowing buyers to visualize products in a real-world context while receiving interactive assistance. Blockchain integration ensures secure and transparent transactions in conversational AI applications, particularly in finance and e-commerce. This convergence with cutting-edge technologies not only enhances the functionality and appeal of conversational AI but also opens new application areas.

Note: If you need specific information that is not currently within the scope of the report, we will provide it to you as a part of the customization.

About Us:

IMARC Group is a leading market research company that offers management strategy and market research worldwide. We partner with clients in all sectors and regions to identify their highest-value opportunities, address their most critical challenges, and transform their businesses.

IMARCs information products include major market, scientific, economic and technological developments for business leaders in pharmaceutical, industrial, and high technology organizations. Market forecasts and industry analysis for biotechnology, advanced materials, pharmaceuticals, food and beverage, travel and tourism, nanotechnology and novel processing methods are at the top of the companys expertise.

Our offerings include comprehensive market intelligence in the form of research reports, production cost reports, feasibility studies, and consulting services. Our team, which includes experienced researchers and analysts from various industries, is dedicated to providing high-quality data and insights to our clientele, ranging from small and medium businesses to Fortune 1000 corporations.

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[latestanalysis]

Global Natural Language Processing (NLP) in Healthcare and Life Sciences Market Relevant Trend, Top Competitors, Segmented by & Region Analysis Forecast till 2027

Natural Language Processing (NLP) in Healthcare and Life Sciences Market

Natural processing language processing (NLP) platform is used in the specific areas of healthcare and life sciences sector which includes clinical documentation, electrical health records, risk management. This platform helps both consumer and service provider to easy workflow. Rise in adoption of digital technology in healthcare industry and customer preference for better healthcare services is expected to boost the market growth over the forecast Period.

Get Sample Copy of this Report @ https://qualiketresearch.com/request-sample/Natural-Language-Processing-NLP-in-Healthcare-and-Life-Sciences-Market/request-sample

Rise in data operations and use of smart devices is expected to propel the natural language processing (NLP) in healthcare and life sciences market growth. Healthcare and life sciences sector generates large amount of data that is driven by compliance and regulatory requirements, patient care, and record keeping. Increase in trend of digitalization and data driven solutions to improve healthcare delivery system is expected to fuel the market growth. Therefore, increase data driven operations and decision making is major driving factor which is expected to drive the market growth. Moreover, many physicians prefer voice notes instead of handwritten of typed ones. Therefore natural language processing platform can be used to easily interpret speech and update patient records.

Natural language processing (NLP) platform implementation is a major challenge in healthcare and life sciences industry, which is expected to hinder the market growth during this forecast timeline. There are many countries across the globe where healthcare facilities have very little access for general population. Also, lack of healthcare expenditure will hamper the growth of global natural language processing in healthcare and life sciences market.

Market Segmentation

Natural Language Processing (NLP) in Healthcare and Life Sciences Market is segmented into type such as Statistical, Rule-Based, and Hybrid, by component such as Technology, and Services (Managed Services, Professional Services, Support & Maintenance Services, and Consulting Services), by deployment type such as Cloud, and On-Premises. Further, market is segmented into application such as Machine Translation, Question Answering, Automated Information Extraction, Email Filtering, Spelling Correction, and Predictive Risk Analytics.

Also, Natural Language Processing (NLP) in Healthcare and Life Sciences Market is segmented various regions including North America, Latin America, Europe, Asia Pacific, and Middle East & Africa.

Get discount on this report @ https://qualiketresearch.com/request-sample/Natural-Language-Processing-NLP-in-Healthcare-and-Life-Sciences-Market/ask-for-discount

Market Key Players

Some of the key players operating in the Natural Language Processing (NLP) in Healthcare and Life Sciences Market report including 3M, MICROSOFT, CERNER, DOLBEY, NUANCE, IBM, APIXIO, AVERBIS, AWS, GOOGLE.

However, the natural language processing (nlp) in healthcare and life sciences market is quite fragmented and it is a tough competition which is going on, owing the presence of various established names vying for the top position. The target market is also witnessing an ingress of new entrants who are posing challenges to established names with tactical moves like product innovation & new product portfolios. Major companies are countering these challenges by having their own collaboration, merger, acquisition, and other strategies. The natural language processing (nlp) in healthcare and life sciences market report is also keeping an eye on the geographies to understand demographical features.

Browse Full Research Report @ https://qualiketresearch.com/reports-details/Natural-Language-Processing-NLP-in-Healthcare-and-Life-Sciences-Market

[smithema1]

Natural Language Processing (NLP) in Healthcare and Life Sciences Market Size & Share | Industry Report, 2020-2027

Global Natural Language Processing (NLP) in Healthcare and Life Sciences Market Report Segmentation Outlook Forecast by 2020-2027 shows competitive landscape information which involving market share of top Manufacturers like – 3M, MICROSOFT, CERNER, DOLBEY, NUANCE, IBM, APIXIO, AVERBIS, AWS, GOOGLE with SWOT analysis and regional analysis. The Natural Language Processing (NLP) in Healthcare and Life Sciences Market report offers significant information on market growth, driving factors, industry-oriented challenges and latest trends.

The report on Natural Language Processing (NLP) in Healthcare and Life Sciences Market is a comprehensive study and presentation of drivers, restraints, opportunities, demand factors, market size, forecasts, and trends in the Natural Language Processing (NLP) in Healthcare and Life Sciences Market over the period of 2021 to 2027. Furthermore, the report is a collective presentation of primary & secondary research findings.

Request for Sample Report: https://qualiketresearch.com/request-sample/Natural-Language-Processing-NLP-in-Healthcare-and-Life-Sciences-Market/request-sample

The research report studies the Natural Language Processing (NLP) in Healthcare and Life Sciences Market with the help of different methodologies and analyses to offer accurate and in-depth analysis about the market. For a clearer picture, it is divided into several parts to cover different aspects of the market. Each area is then elaborated to help the client comprehend the growth potential of each region and its contribution to the global Natural Language Processing (NLP) in Healthcare and Life Sciences Market. The researchers have used primary & secondary methodologies to conduct the information in the report. This report is aimed at guiding people towards a better, apprehensive, and clearer knowledge of the overall market.

Natural Language Processing (NLP) in Healthcare and Life Sciences Market is segmented by company, region, by Type, and by Component. Players, stakeholders, and other participants in the Natural Language Processing (NLP) in Healthcare and Life Sciences Market will be able to gain the upper hand as they use the report as a powerful resource.

Key Players

Some of the key players operating in the  Natural Language Processing (NLP) in Healthcare and Life Sciences Market report including 3M, MICROSOFT, CERNER, DOLBEY, NUANCE, IBM, APIXIO, AVERBIS, AWS, GOOGLE.

Market Taxonomy

·         By Type Statistical Rule-Based Hybrid

·         By Component Technology Services Managed Services Professional Services Support & Maintenance Services Consulting Services

·         By Deployment Type Cloud On-Premises

·         By application Machine Translation Question Answering Automated Information Extraction Email Filtering Spelling Correction Predictive Risk Analytics

By Region

North America

Latin America

Europe

Asia Pacific

Middle East  & Africa

Browse Full Research Report @ https://qualiketresearch.com/reports-details/Natural-Language-Processing-NLP-in-Healthcare-and-Life-Sciences-Market

[freesuitwhispers]

The “Natural Language Processing (NLP) in Healthcare and Life Sciences Market ”report provides a detailed analysis of global market size, regional and country-level market size, segmentation, market growth, market share, and competitive Landscape.

[sciforce]

Is AI Democratization a Real Thing?

In the last decades, we’ve seen tremendous advancements in Artificial Intelligence (AI) and related fields. It is viewed not only as a ground-breaking technology, but as a step forward to the future having the means to change our society. We expect AI to use hardware and software to see and hear patterns, make predictions, learn and improve, and take action with this intelligence. Some enthusiasts believe that AI can be a tool that will guide us to a better world showing how to achieve more. In this context, new questions have been raised of power and pervasiveness of such technologies. However, many IT companies claim that AI will democratize AI and will make it available “for every person and every organization”, open to developers and researchers around the world. But does AI really democratize our world? And is it only a positive development?

What does AI democratization mean?

When we say that something is democratized, we imply that all people can access it and benefit from it. In enterprise IT, to democratize AI means to make it accessible for every organization or even to every person within the organization. Such dissemination of AI is indeed beneficial in many respects:

Benefits of democratized AI

Overall improvement of business — AI currently encompasses cloud suites, NLP-based virtual assistants to run business, improve customer’s experience and increase stats;

Societal change — as the awareness and the usage of AI grows, it gains more power to address global issues, like fighting climate change, tracking police work to avoid unfair targeting, or developing new standards in healthcare;

Automation — Gartner predicts that many of these tools will be automated, offering a completely new level of self-service that will free up companies’ potential.

What do we see today?

It is true that many companies, first of all tech startups and market giants, deploy artificial intelligence of some kind: machine learning does their predictions, natural language processing is improving their communication with clients, and sophisticated deep learning algorithms detect anomalies in their processes. However, many companies do not have the resources to build AI of their own and have to rely on cloud technologies to fill the gaps. For companies that are not tech-savvy, AI often feels complicated or overwhelming and it can be downright expensive, especially when engaging data science.

In this context, big tech firms end up way ahead of the curve, other enterprises can hardly catch them up, or even benefit from AI. To understand whether true AI democratization is achievable, let’s discuss what aspects of the market and inherent characteristics of AI add to inequality and what measures are taken to make AI available for everyone:

Data is the King

Data is the key to successful AI-driven intelligence and the more data we create, fuelling the so-called new “data economy”, the smarter products and services we can develop. Data seems to be an increasingly important asset and often the only significant advantage over the competition and as a result it is vigorously kept private. Undeniably, having access to all data produced by your devices or every trace of your digital presence and being able to cross-correlate them across different platforms, services and people give an enormous power. To understand the scale, we can think of the monopolizing presence of IT giants like Facebook or Google in our daily digital life. This is completely rightful and strategically sound, but unfortunately completely in contrast with the AI democratization process.

We are promised that the monopolizing effect is mitigated by new algorithms that offer insightful predictions even with limited datasets. However, best results are still achieved with gigantic amounts of data, so to make AI available to everyone, we first need to make data available to every interested party via open and shared datasets. AI democratization can not work if we do not democratize data first.

At the same time, access to data is only the first step towards AI democratization, since it is not the amount of data that wins the competition, but the ability to use it.

Technical Illiteracy

In many cases, we do not think of the amount of digital data we produce in the world where every “like” on Facebook counts. At the enterprise-level, companies that are not technically savvy, are easily misled by complicated Ai solutions. Major AI companies intentionally take advantage of the common illiteracy in AI by oversimplifying and even undermining more serious AI democratization approaches. Similarly to data ownership, concerns are raised on the dangerous monopolization of AI technologies by only a few organizations.

Network Effect

An extension of such monopolization is the “network effect”, a social and economic phenomenon where an increased number of users improves the value of a good or service in turn attracting more participants and triggering a virtuous cycle. This effect makes the situation even more unequal, as the tech giants gather most of the participants, their data and, as a result, intelligence — just think of Google and Baidu that detain ~86% the entire market share.

AI Talents

As big companies have more resources, they open up their doors to the best scientists, creating advanced labs for AI research with infinite resources. This has led to great advancements and speed-up in AI research. At the same time, tech giants tend to open up their AI research labs, giving the researchers the freedom to collaborate with other institutions or make everything open-source.

Does it add to democratization of AI? From the inspirational point of view, yes, since everyone now can download the sample code and try to use it in any project.

However, in reality the results are not always reproducible, and what tech giants achieve is free improvement and testing of their open-source tools.

Cloud

Most companies who successfully deploy AI have to invest heavily on the tech side. The enterprises that have more limited resources or that have no wish to engage in the full circle of AI research and development can get more affordable cloud solutions. The cheaper cloud tech gets, more AI tools can exist offering immediate solutions. Besides, as clouds make AI more accessible, more people can specialize in it, decreasing salary costs to companies for these data-specific jobs.

As we can see, democratization in reality is a myth as the access to AI is limited, on the one hand, by the resources and knowledge a company has and, on the other hand, by the amount of data and technology tech giants are eager to share. However, the lack of accessibility is not necessarily totally bad. Data quality is often uncertain, to say the least. When relying on a combination of entry-level AI specialists typical for smaller companies and automated or self-service AI tools, companies may be relying on data that is poor and produces unstable solutions. Results from poor data can ripple across the business, with unanticipated outcomes not evident until it’s too late.

Another problem we may face as AI democratizes is the bureaucracy inherent to many enterprises that will prevent companies and employees from acting quickly. Yet, it is the only way that intelligence can actually make a difference — if it is applied at the right time, which often arrives faster than a board can enact a decision.

To sum up, AI is indeed our future, and we’ll see the technology getting more accessible to everyone. However, like in human democracy, we’ll need a system of checks and balances to ensure that the AI-driven world remains afloat.

[marketinsightshare]

Internet of Things Artificial Intelligence (IoT AI) Market - Industry Analysis, Market Size, Share, Trends, Application Analysis, Growth And Forecast 2022-2027

The Global Internet of Things Artificial Intelligence (IoT AI) Market size is estimated to reach $85 billion by 2027, growing at a CAGR of 6.9% during the forecast period 2022-2027, attributed to the growing emphasis on data management solutions, huge amount of data generated by IoT devices, increasing use of real-time analytics for improving customer experience and so on. The collection & analysis of data from digital machines is a difficult process and it requires the deployment of advanced technologies like machine learning, natural language processing and deep learning. IoT AI assists industries in collecting massive amounts of data from varied sources. The role of IoT AI is to automatically identify patterns and detect anomalies in data generated by sensors and embedded systems by using machine learning (ML) & natural language processing (NLP). Additionally, the rapid growth of machine-to-machine communication towards increasing automation, rising smart city & smart home initiatives, and increasing use of connected devices such as sensors, smart lights and smart meters to improve the efficiency of infrastructure and related services are some of the significant reasons behind the rising demand of internet of artificial intelligence. These factors are set to transform the internet of things artificial intelligence (IoT AI) industry outlook in the coming time.

Internet of Things Artificial Intelligence (IoT AI) Market Report Coverage

The “Internet of Things Artificial Intelligence (IoT AI) Market Report – Forecast (2022-2027)” by IndustryARC, covers an in-depth analysis of the following segments in the Internet of Things Artificial Intelligence (IoT AI)Market.

By Technology: Machine Learning, Big Data, Natural Language Processing, Image Processing, Speech Recognition and Others.

By Type: Hardware, Software and Services (Managed Services, Professional Services).

By End Use Devices: Consumer connected devices, Wearables, Medical devices, home appliances and Others.

By Industry Vertical: Manufacturing, Energy & Utilities, Transportation & Mobility, BFSI (Banking, Financial Services & Insurance), Government & Defense, Retail, Healthcare & Life Sciences, Automotive & Telecom and Others.

By Geography: North America (U.S, Canada, Mexico), Europe (U.K, Germany, France, Italy, Spain, Others), APAC (China, Japan India, South Korea, Australia, Others), South America (Brazil, Argentina, Others), RoW (Middle East, Africa).

Key Takeaways.

Machine learning technology in the Internet of Things Artificial Intelligence (IoT AI) Market is analyzed to grow at the highest rate during the forecast period 2022-2027, owing to the increasing adoption of machine learning in healthcare, automotive & retail sectors, growing implementation of machine intelligence with real-time analytics solutions and so on.

Manufacturing industry is analyzed to grow at the fastest rate in the global Internet of Things Artificial Intelligence (IoT AI) market during 2022-2027, attributed to increasing adoption of automation technology in manufacturing, growing use of robots to increase the speed & reduce the cost of production and so on.

North America dominated the global Intelligent warehouse market with a share of 37% in 2021, attributed to the growing use of IoT-enabled devices coupled with smart home culture, increasing demands for semi and fully automated consumer electronics & home appliances in the region and so on.

Increasing use of machine-to-machine communications for real-time data exchangeand government initiatives towards the development of smart citiesare analyzed to significantly drive the market growth of the Internet of Things Artificial Intelligence (IoT AI) Market during the forecast period 2022-2027.

Global Internet of Things Artificial Intelligence (IoT AI) Market Value Share, by Region, 2021 (%)

https://lh3.googleusercontent.com/6HV_QHG4U8cHxezJ7lfouf8o2Qr1GpXHOLbIxpg14BkkBuGe7xUkWyZXEEls2a_Yt72eC6nfis7_AoBnGFF6fp-npopoTTWDov8svx_ClnyVAeKNgAPH1IA9VcL0UgKisG2RtF6K93CWFE02WIKhADU

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Internet of Things Artificial Intelligence (IoT AI) Market Segment Analysis - by Technology

Machine learning technology in the Internet of Things Artificial Intelligence (IoT AI) Market is analyzed to grow at the fastest rate of 7.2% during the forecast period 2022-2027, owing to the increasing adoption of machine learning in healthcare, automotive & retail sectors, growing implementation of machine intelligence with real-time analytics solutions and so on. Machine learning delivers insights on data for automated response and improves decision-making. It is used to forecast future trends, detect anomalies and provide insights by analyzing data in the form of images, videos or audio. Machine learning can help to decipher the hidden patterns in IoT data by analyzing massive amounts of information using sophisticated algorithms. These inferences can augment or eliminate manual procedures with automated systems by applying statistically analyzed actions in key activities. Various tools provided by machine learning technologies for data visualization, exploratory data analysis, data cleaning and data operations are the main reason behind the increasing use of machine learning technologies in various verticals. In May 2021, Google announced the general availability of Vertex AI, a managed machine learning (ML) platform by Google. The platform assists organizations in the deployment and maintenance of artificial intelligence (AI) models. It requires nearly 80% fewer lines of code to train a model using the Vertex AI platform that helps data scientists & ML engineers of all levels of expertise in the implementation of machine learning operations efficiently. These factors have been significantly impacting the growth of machine learning technology in the Internet of Things Artificial Intelligence (IoT AI) industry over time.

Internet of Things Artificial Intelligence (IoT AI) Market Segment Analysis - by Industry Vertical

The manufacturing industry is analyzed to grow with the highest CAGR of 7.5% in the global Internet of Things Artificial Intelligence (IoT AI) market during 2022-2027. This market growth can be attributed to the increasing adoption of automation technology in manufacturing, the growing use of robots to increase the speed & reduce the cost of production and so on. IoT AI assists in the efficient management of various operations like equipment performance monitoring for predictive analytics, warehouse maintenance, quality control, inventory forecasting and human-machine interaction. IoT AI can drastically reduce product lifecycle by enabling faster, more efficient and effective supply management. Additionally, the increasing application of robots, drones & digital machines at manufacturing sites, the growing need for real-time machinery monitoring and reducing the chances of accidents & injuries at the production units are some of the major factors behind the adoption of the internet of things artificial intelligence in the manufacturing sector. In May 2021, Robert Bosch Engineering and Business Solutions (RBEI) launched a new Phantom Edge AIoT platform that combines the power of Artificial Intelligence and the Internet of Things. It provides real-time monitoring of electrical energy consumption, operational usage, electrical parameters and appliance-level information to increase the operational efficiency in an organization. It helps in lowering the cost and increasing the output in the manufacturing sector. Such factors are analyzed to positively impact the growth of the global Internet of Things Artificial Intelligence (IoT AI) market size for the manufacturing industry in the coming time.

Internet of Things Artificial Intelligence (IoT AI) Market Segment Analysis - by Geography

North America dominated the global Intelligent warehouse market with a share of 37% in 2021, attributed to the growing use of IoT-enabled devices coupled with smart home culture, increasing demands for semi and fully automated consumer electronics & home appliances in the region and so on. Additionally, the increasing use of AI & IoT solutions in healthcare & life science, the growing adoption of robots in varied industries especially after Covid-19, growth of low code development platforms is some of the major factors boosting the growth of the internet of things artificial intelligence market in the region. According to a report published by smartamerica.org in 2021, the US government will spend $41 trillion over the next 20 years upgrading infrastructure to benefit from IoT. With these massive investments, the Internet of Things will drastically improve the quality of life for city dwellers. Citizens will benefit from better resource management and will increase governance. These factors are set to transform the North American Internet of Things Artificial Intelligence (IoT AI) industry outlook over time.

Internet of Things Artificial Intelligence (IoT AI) Market Drivers

Increasing Use of Machine-to-Machine Communications for Real-Time Data Exchange is Influencing the Adoption of Internet of Things Artificial Intelligence (IoT AI) Solutions:

Increasing adoption of machine-to-machine technology in varied sectors for the exchange of real-time data is accelerating the demand for internet of things artificial intelligence (IoT AI) solutions. These machine-to-machine applications collect and transmit data over a wireless network of embedded systems. This information is utilized to automate and streamline various operations in a network. These applications are used by government agencies to monitor various critical infrastructures such as pump stations and electrical machinery, manage landfills, monitor offenders, manage fleets of cars, trucks, and buses and control valuable assets such as heavy machinery & digital machines. In July 2021, Abracon, LLC announced the acquisitions of Proant AB and Proant Asia Limited in order to integrate the Proant AB antenna product line with wireless machine-to-machine and Internet of Things (IoT) applications. Customers will benefit from these developments as they will assist in feasibility studies, antenna simulations, active antenna measurements, quick prototyping and matching network design. This information will help in the improvement of customer services and increase customer satisfaction. According to Forbes 2022 report, Facebook employs real-time data analytics to identify and eliminate harmful information such as fake news and abusive posting from the 4.75 billion posts generated each day on its platform. It would not have been possible without using real-time data and analytics. Such factors will help in propelling the growth of the internet of things artificial intelligence (IoT AI) industry in the long run.

Government Initiatives Towards the Development of Smart Cities is Surging the Demand for Internet of Things Artificial Intelligence (IoT AI) solutions:

The government mandate to develop smart cities is accelerating the demands for the internet of things artificial intelligence solutions. The smart city system integrates information and communication technology (ICT) with various physical devices linked to the Internet of Things (IoT) network to improve the efficiency of city operations & services. Smart city technology allows city administrators to communicate directly with inhabitants and local infrastructure and assists in monitoring them remotely. IoT AI is used to improve the quality, performance and responsiveness of urban services in order to reduce costs and resource consumption. It facilitates citizen-government interaction and offers a real-time response to adverse circumstances making cities more resilient to adversity. In February 2020,Cisco and Connexin collaborated with Quantela to accelerate the growth of smart cities in the United Kingdom. The highway network has adopted the most recent IoT-based smart city technology in the United Kingdom. This solution is built on the CityOS platform, combining best-of-breed capabilities from Cisco Kinetic and Atlantis smart city solutions of Quantela. According to a report published by International Data Corporation in 2021, the Internet of Things (IoT) and smart application of technologies in public places, roadways and other metropolitan areas are predicted to increase rapidly to USD 202 billion between 2021 and 2025. These factors are the major factors behind the increasing adoption of cloud technology, 5G and AI in top cities.These factors are set to expand the Internet of Things Artificial Intelligence (IoT AI) market size in the coming time. 

Internet of Things Artificial Intelligence (IoT AI) Market Challenges

Cyber Security and Data Privacy Concerns related to the Use of Internet of Things Artificial Intelligence (IoT AI) are limiting Market Growth.

Cybersecurity and data privacy issues associated with the use of Internet of Things Artificial Intelligence (IoT AI) technology are restricting the market growth. Increasing vulnerabilities of the digital machines to security breaches and cyberattacks is hampering the adoption of IoT technologies as once an attacker enters an IoT network, he will get access to all the devices & data stores in various systems attached to the network. IoT systems include wireless networks, cloud databases for communication, sensors, data processing programs and smart devices that interact closely with one another. These gadgets use the internet and web browsers which makes them more vulnerable to cyberattacks, hacking and trojan attacks. According to a report published by Kaspersky in 2022, about 639 million breaches were recorded in all of 2020 and 1.51 billion breaches of IoT devices were reported between January and June of 2021. Such factors are impending the growth of the internet of things artificial intelligence (IoT AI) industry.

Internet of Things Artificial Intelligence (IoT AI) Industry Outlook

Product launches, acquisitions and R&D activities are key strategies adopted by players in the Internet of Things Artificial Intelligence (IoT AI) Market. The top 10 companies in the Internet of Things Artificial Intelligence (IoT AI) market are:

Amazon Inc.

Cisco Systems Inc.

Google LLC

Intel Corporation

IBM Corporation

Microsoft Corporation

NVIDIA Corporation

Oracle Corporation

Salesforce Inc.

Advantech

Recent Developments

In July 2022, Advantech launched new AIoT and IIoT embedded systems comprising products, solutions and services in hardware, software, memory and displays. These products were developed to serve as the building blocks of Artificial Intelligence of Things (AIoT) and the Industrial Internet of Things (IIoT). They support iManager & WISE DeviceOn for maintenance and lowers operation costs significantly. These products & solutions will increase the adoption of AIoT technologies and will reduce the cost of production of AIoT solutions.

In January 2022, AWS AIoT Core Launched Two New Features Downlink Queue Management and Network Analyzer for LoRaWAN to manage and monitor communications between devices and the cloud. Customers can schedule, delete, and even purge downlink messages using the Downlink Message Management feature, and Network Analyzer can be used to monitor the messages and troubleshoot issues related to uplink or downlink events. It will facilitate machine-to-machine communication and will accelerate the use of such AIoT technologies.

In May 2021, Google launched a new IoT-focused solution Nest Hub. It includes Google Assistant technology integrated with Google Photos that allows the user to view and control all connected devices from a single dashboard. It provides overall AI integration with IoT to enable data exchange between machines and aids in the formation of smart homes. The solution will provide assistance in the use of connected devices by integrating IoT and AI technologies.

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Applying AI in Real Life

Artificial intelligence has been around for a long time, ever since people began to imagine machines and how they may be utilized to aid civilization's growth. For a long time, it was confined to laboratories in its theoretical form, but with the advancement of faster computing power, memory, mathematical models, and other factors, it has transformed into a major contributor.

Now let us fast forward to the present. Artificial Intelligence and Data Analytics are co-related. Both are dependent on the data that the user has at their disposal. And these concepts are making a large impact on the world of technology now. However, this advancement in technology may not be as much visible to you or me; as it is in the United States, certain parts of Europe, Middle East, and South East Asia. There could be many reasons for this, but one of the primary ones is the lack of knowledge or not enough expertise in the area.

Yes, there may be several online courses and/or institutes sharing knowledge in these domains. However, that is not enough. This is like learning the basic number or alphabets in your thirties! It is recommended to embrace the topic of AI right from your school age! Technology is advancing very fast and the world is evolving even faster. We have almost reached the age of automated work and machines taking care of lower-end jobs. Yet, not enough expertise is there to match international standards. There of course are multi-talented brains in India; however not much get the necessary recognition.

The best remedy for this is SchoolForAI. SchoolforAI is India’s first Data Science & AI focused hybrid platform that includes both online mentor-led & self-paced e-learning. We combine direct tutoring activities with online sessions for students of grade 7 and above to gain edge over the emerging technologies. Our project-based learning approach encourages students to master the concepts and challenge themselves towards solving real-life problems.

With our sessions, we will share information that AI has 5 primary domains. These are Data, Computer Vision, Natural Language Processing, Audio, and Automated Machines. This post describes how each of these five domains can be applied in regular business activities.

Data

Best examples are Seasonal and demand-based flight ticket pricing, Netflix movie recommendations, stock price predictions, and consumer transaction fraud detection. We're employing a data-driven algorithm path to complete the provided prediction challenge. This category encompasses the vast majority of AI applications in use by enterprises.

Computer Vision

Detecting and identifying items in images, counting and discriminating, tracking and recognizing action are some of the applications. To achieve its purpose, it successfully incorporates hardware and computer science, as well as machine learning. Every day, an estimated 3 billion photographs are exchanged online, and CV software is designed to handle such numbers. Computer vision is widely employed in a variety of industries, including automotive, healthcare, security, surveillance, vehicle plate recognition, visual navigation, machine inspection, and others. This is a prominent AI application that involves feature extraction and completes the task using deep learning neural network techniques. There are a number of pre-trained models that can be utilized for identification and recognition purposes.

Natural Language Processing

Automating Customer Support – NLP is being widely used in customer service in the form of Chabot or automatic service request classification or such.

Sentiment Analysis – A major social media and marketing application to understand the customer sentiments towards a product or a service.

Translators – The most popular is Google Translators which helps us to convert text almost from any language to the desired language.

Volume readers – NLP is empowering research community, lawyer and such who are supposed to read volumes of textual data as part of their work by automatically reading the documents and offering the summary or insights.

Audio

Besides signals such as environmental sounds, and animal calls, audio applications are not confined to speech. Examples are industrial applications, where devices continuously monitor the numerous sounds produced by essential machines and processes in order to predict any unwelcome breakdowns or process problems. This would also be utilized for preventative maintenance, increasing machine availability.

Personal assistants such as ALEXA, machine health monitoring, voice-based translators and audio redaction are just some of the applications.

The voice stream might be divided into several frequencies and targeted with specific frequencies that could be transcribed to text or processed directly depending on the application. This results in a more human-like understanding of natural interactions to generate insights.

Autonomous and Automation

Famous autonomous automobiles, warehouse storage robots, social robots, and autopilot are examples of typical applications.

Our lives would be more productive if autonomous machines existed. In this sector, there is still a lot of study being done. The level of autonomy and functionality will be directly proportional to AI breakthroughs.

Sounds interesting, doesn’t it? It will get more exciting in our hybrid sessions. We will describe each of these topics in an approach that  you can follow. Don't just study for earning a certificate. Become better professionals and more skilled in the latest technology.  Contact SchoolforAI today to know more about our courses.

[blueweave]

Conversational AI Market to Witness a CAGR of 23.4% through 2028

A recent study conducted by the strategic consulting and market research firm, BlueWeave Consulting, revealed that the global conversational AI market was worth USD 6.9 billion in the year 2021. It is estimated to grow at a CAGR of 23.4%, earning revenue of around USD 29.9 billion by the end of 2028. Global conversational AI market is flourishing at a high rate owing to the rising demand for customer support services by businesses to improve customer experience. Furthermore, the increasing adoption of conversational AI among various verticals including BFSI, media and entertainment, healthcare and life sciences, travel and hospitality, etc., is anticipated to present lucrative growth opportunities to the market in the forecast period.

Rising Demand for Cloud Deployment is Driving the Conversational AI Market

Based on deployment, the global conversational AI market is segmented into on-premise and cloud. The cloud deployment segment holds the largest market share as it proves to be flexible as well as cost-effective compared to the on-premise deployment. Therefore, this type of deployment mode is popular among SMEs. However, most large enterprises opt for on-premise deployment owing to their higher financial capability and advanced security. The on-premise deployment restricts external access to data, which acts in favor of hitting market growth.

Request for Sample Report @ https://www.blueweaveconsulting.com/report/conversational-ai-market/report-sample

Increasing Popularity of App-Based Integration is Propelling the Conversational AI Market Growth

Based on mode of integration, the global conversational AI market is segmented into web-based, app-based, and telephonic. The app-based segment accounts for the largest market share in 2021. Technologies such as Google Assistant, Alexa, Siri, etc., are some of the most popular conversational AIs in the world and they work on app-based integration. However, the web-based mode of integration also covers a substantial market share. The increasing number of websites from the SMEs and rising demand for chat services for customer support are driving its overall market growth.

Conversational AI Market - By Technology

Based on technology, the conversational AI market is segmented into machine learning and deep learning, natural language processing, and automatic speech recognition. Natural language processing (NLP) accounts for the largest market share in 2021. Conversational AI combines natural language processing with traditional software such as chatbots or voice assistants to create a chat or voice communication interface. The NLP helps the system understand the meaning of the human language and decrypts it to provide an answer or assistance.

Conversational AI Market - Regional Insights

Geographically, the global conversational AI market is segmented into North America, Europe, Asia-Pacific, Latin America, Middle East & Africa. North America dominated the conversational AI market in 2021. However, the Asia-Pacific region is projected to witness the highest CAGR during the period owing to the increasing number of SMEs and startups and the rising adoption of conversational AI to cater to customer query requests and boost efficiency. Europe is also growing at a substantial rate owing to the presence of multinational conglomerates.

Impact of COVID-19 on Conversational AI Market

Global conversational AI market witnessed tremendous growth after the COVID-19 pandemic outbreak. the number of customer service requests over websites and applications significantly spiked during this period. However, the lack of sufficient working employees and restrictions on the customer care centers due to social distancing norms imposed a huge burden on the human customer care executives, which negatively impacted the customer experience. Due to this, the organizations quickly shifted towards conversational AI to cater to the increasing number of service queries and deliver solutions timely.

Competitive Landscape

The leading market players of the global conversational AI market are Google, Microsoft, IBM, AWS, Baidu, Oracle, SAP, FIS, Artificial Solutions, Kore.ai, Conversica, Inbenta, Creative Virtual, SoundHound, Avaamo, Haptik, Solvvy, MindMeld, and other prominent players. The conversational AI market is highly concentrated with the presence of leading industry players such as Google, IBM, Oracle, Microsoft, etc. However, several startups are also emerging and attracting significant investment to expand their presence. The companies constantly launch new upgrades and innovations to gain a competitive edge. Furthermore, the adoption of market strategies such as partnerships, mergers, acquisitions, collaborations, etc., is also prominent in this market.

Don’t miss the business opportunity of the conversational AI market. Consult our analysts to gain crucial insights and facilitate your business growth.

The in-depth analysis of the report provides information about growth potential, upcoming trends, and statistics of the conversational AI market. It also highlights the factors driving forecasts of total market size. The report promises to provide recent technology trends of the global conversational AI market and industry insights to help decision-makers make sound strategic decisions. Furthermore, the report also analyses the growth drivers, challenges, and competitive dynamics of the market.

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