AI Insights

Artificial Intelligence Market Escalates Amidting Enterprise

Published

2 weeks ago

August 14, 2025

Artificial Intelligence Market

The Global Artificial Intelligence Market is estimated to reach at a Significant CAGR during the forecast period (2024-2031).

Artificial Intelligence Market reports from DataM Intelligence provide comprehensive insights and analysis on key trends, emerging opportunities, and evolving challenges. With a focus on delivering actionable intelligence, the company empowers businesses to make informed decisions and maintain a competitive advantage. Utilizing a blend of qualitative and quantitative research methodologies, DataM Intelligence offers in-depth reports that help clients navigate complex market landscapes, drive strategic growth, and capture new opportunities in an ever-changing global market.

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A field of computer science that focuses on creating machines or software that can perform tasks that typically require human intelligence. These tasks include learning, problem-solving, speech recognition, and decision-making.

Forecast Growth Projected:

The global Artificial Intelligence market is projected to experience significant growth between 2024 and 2031. In 2023, the market has been expanding steadily, and with the increasing implementation of strategic initiatives by key players, it is expected to accelerate further throughout the forecast period.

List of the Key Players in the Artificial Intelligence Market: AT and T Inc., Google Inc, Facebook Inc, IBM Corporation, Apple Inc, Intel Corporation, Salesforce.com Inc, Saudi Telecom Company, Ayasdi Inc, Nuance Communication and Digital Reasoning Systems Inc.

Industry Development:

In October 2023, NVIDIA Corporation and Oracle formed a partnership to help customers tackle business challenges using accelerated computing and AI. The collaboration aims to accelerate the adoption of artificial intelligence services among customers.

In March 2024, Microsoft and NVIDIA announced a joint effort to advance AI in the healthcare and life sciences sectors. Combining Microsoft Azure’s cloud infrastructure and advanced computing with NVIDIA’s DGX Cloud and Clara suite, the partnership seeks to drive innovation and enhance patient care, particularly in clinical research and drug discovery.Also in March 2024, NVIDIA introduced new Generative AI Microservices designed to advance medical technology (MedTech), drug discovery, and digital health. These AI-powered services aim to enhance healthcare technologies.

In January 2024, Google launched Lumiere, an AI model that generates short video clips from text descriptions. It also allows for animating still images and applying specific styles to video generation.

Research Methodology:

Our research methodology integrates qualitative and quantitative approaches to deliver a comprehensive market analysis. We start with secondary research, collecting data from reputable industry reports and databases, followed by primary research through surveys and interviews with industry experts. The gathered data is then analyzed using advanced statistical tools to identify trends and evaluate market dynamics. We also apply market segmentation techniques and Porter’s Five Forces analysis to assess the competitive landscape. This rigorous approach ensures our insights are accurate, actionable, and tailored to support informed decision-making.

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Market Dynamics:

The AI market is rapidly growing across sectors like healthcare, finance, and retail, driven by advancements in machine learning and automation. Despite high implementation costs and ethical concerns around data privacy, the potential for AI to improve efficiency and decision-making presents substantial growth opportunities.

Segment Covered in the Artificial Intelligence Market:

By Component (Hardware, Software, Services, Others), By Technology (Machine Learning, Natural Language Processing, Computer Vision, Others), By End-User (Industrial, IT & Telecom, Healthcare, Retail, BFSI, Automotive, Advertising and Media)

This Report Covers:

✔ Go-to-market Strategy.

✔ Neutral perspective on the market performance.

✔Development trends, competitive landscape analysis, supply side analysis, demand side analysis, year-on-year growth, competitive benchmarking, vendor identification, Market Access, and other significant analysis, as well as development status.

✔Customized regional/country reports as per request and country level analysis.

✔ Potential & niche segments and regions exhibiting promising growth covered.

✔ Analysis of Market Size (historical and forecast), Total Addressable Market (TAM), Serviceable Available Market (SAM), Serviceable Obtainable Market (SOM), Market Growth, Technological Trends, Market Share, Market Dynamics, Competitive Landscape and Major Players (Innovators, Start-ups, Laggard, and Pioneer).

Regional Analysis for Artificial Intelligence Market:

⇥ North America (U.S., Canada, Mexico)

⇥ Europe (U.K., Italy, Germany, Russia, France, Spain, The Netherlands and Rest of Europe)

⇥ Asia-Pacific (India, Japan, China, South Korea, Australia, Indonesia Rest of Asia Pacific)

⇥ South America (Colombia, Brazil, Argentina, Rest of South America)

⇥ Middle East & Africa (Saudi Arabia, U.A.E., South Africa, Rest of Middle East & Africa)

Benefits of the Report:

➡ A descriptive analysis of demand-supply gap, market size estimation, SWOT analysis, PESTEL Analysis and forecast in the global market.

➡ Top-down and bottom-up approach for regional analysis

➡ Porter’s five forces model gives an in-depth analysis of buyers and suppliers, threats of new entrants & substitutes and competition amongst the key market players.

➡ By understanding the value chain analysis, the stakeholders can get a clear and detailed picture of this Market

Speak to Our Senior Analyst and Get Customization in the report as per your requirements: https://datamintelligence.com/customize/artificial-intelligence-market?ophp

Frequently asked questions:

➠ What is the global sales value, production value, consumption value, import and export of Artificial Intelligence market?

➠ Who are the global key manufacturers of the Artificial Intelligence Industry? How is their operating situation (capacity, production, sales, price, cost, gross, and revenue)?

➠ What are the Artificial Intelligence market opportunities and threats faced by the vendors in the global Artificial Intelligence Industry?

➠ Which application/end-user or product type may seek incremental growth prospects? What is the market share of each type and application?

➠ What focused approach and constraints are holding the Artificial Intelligence market?

➠ What are the different sales, marketing, and distribution channels in the global industry?

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Contact Person: Sai Kiran

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About Us –

DataM Intelligence is a Market Research and Consulting firm that provides end-to-end business solutions to organizations from Research to Consulting. We, at DataM Intelligence, leverage our top trademark trends, insights and developments to emancipate swift and astute solutions to clients like you. We encompass a multitude of syndicate reports and customized reports with a robust methodology.

Our research database features countless statistics and in-depth analyses across a wide range of 6300+ reports in 40+ domains creating business solutions for more than 200+ companies across 50+ countries; catering to the key business research needs that influence the growth trajectory of our vast clientele.

This release was published on openPR.

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LifeLong Learning and TXST expand series on Artificial Intelligence

Published

1 hour ago

August 31, 2025

The Editors

Dr. Marianne Reese, Founder and Director of LifeLong Learning, conceived of the AI series due to AI’s exponential growth and the need for the public to understand its uses and limitations.

“AI is a relatively new tool that is being used in ways the public is often unaware of,” Reese noted. “We all need to know more about this powerful technology, understand AI’s positive and concerning applications, and learn the skills necessary to scrutinize the information it generates.

“AI will become increasingly prevalent, so we need to be informed consumers as AI impacts politics, medicine, business, finance and other areas of our lives,” Reese said.

The AI Learning Series is led by Dr. Kimberly Conner, Digital Strategy Lead for Information Technology at Texas State. Connor’s role is to help demystify innovation and make technology approachable for students, staff and faculty. With a rare combination of expertise in law, education and IT, Dr. Connor bridges the gap between complex digital tools and the people who use them.

Almost 80 lifelong learners attended the AI Series Kickoff Event on Tuesday, Aug. 19.

The Sept. 3 class covers AI use of our personal data and AI-generated misinformation and scams.

The Sept. 17 class features a comparison of different AI services (e.g., Chat GPT, Gemini).

The Oct. 1 class covers practical AI tools for daily life, with an exploration of AI applications for communication and creative projects.

The Oct. 15 class covers AI reliability & accuracy, AI limitations and and best practices for verification.

The Sept. 29 class covers AI for personal enrichment, such as enhancing hobbies and expanding personal interests.

The final class on Nov. 3 covers hands-on activities and features a closing presentation.

For more information visit their website at lllsanmarcos.org.

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AI Insights

China Calls for Regulation of Investment in Artificial Intelligence

Published

2 hours ago

August 31, 2025

The Editors

In a move reflecting a cautious strategic direction, China has called for curbing “excessive investment” and “random competition” in the artificial intelligence sector, despite its classification as a key driver of national economic growth and a critical competitive field with the United States.

Chang Kailin, a senior official at the National Development and Reform Commission – the highest economic planning body in the country – confirmed that Beijing will take a coordinated and integrated approach to developing artificial intelligence across various provinces, focusing on leveraging the advantages and local industrial resources of each region to avoid duplicating efforts, warning against “herd mentality” in investment without careful planning.

These statements come amid a contraction in China’s manufacturing industries for the fifth consecutive month, reflecting the pressures faced by the world’s second-largest economy, as policymakers attempt to avoid repeating past mistakes like those in the electric vehicle sector, which led to an oversupply of production capacity and subsequent deflationary pressures.

Chinese President Xi Jinping also warned last month against the rush of local governments towards artificial intelligence without proper planning, a clear indication of the Chinese leadership’s desire to regulate the pace of growth in this vital sector.

Despite these warnings, China continues to accelerate the development, application, and governance of artificial intelligence, as the government revealed a new action plan last week aimed at boosting this sector, which includes significant support for private companies and encouragement for the emergence of strong startups capable of global competition, which the National Committee described as a pursuit for the emergence of “black horses” in the innovation race, implicitly referring to notable success stories like the Chinese company DeepMind.

DeepMind gained international fame earlier this year after launching a powerful and low-cost artificial intelligence model, competing with the models of major American companies, igniting a wave of local and international interest in Chinese technologies.

In a separate context, a Bloomberg analysis showed that Chinese technology companies plan to install more than 115,000 artificial intelligence chips produced by the American company Nvidia in massive data centers being built in the desert regions of western China, indicating a continued effort to build strong artificial intelligence infrastructure despite regulatory constraints.

These steps come at a time when Beijing seeks to balance support for technological innovation with regulating investment chaos, in an attempt to shape a more sustainable path for the growth of artificial intelligence within China’s broader economic vision.

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A new research project is the first comprehensive effort to categorize all the ways AI can go wrong, and many of those behaviors resemble human psychiatric disorders.

Published

2 hours ago

August 31, 2025

The Editors

Scientists have suggested that when artificial intelligence (AI) goes rogue and starts to act in ways counter to its intended purpose, it exhibits behaviors that resemble psychopathologies in humans. That’s why they have created a new taxonomy of 32 AI dysfunctions so people in a wide variety of fields can understand the risks of building and deploying AI.

In new research, the scientists set out to categorize the risks of AI in straying from its intended path, drawing analogies with human psychology. The result is “Psychopathia Machinalis” — a framework designed to illuminate the pathologies of AI, as well as how we can counter them. These dysfunctions range from hallucinating answers to a complete misalignment with human values and aims.

Created by Nell Watson and Ali Hessami, both AI researchers and members of the Institute of Electrical and Electronics Engineers (IEEE), the project aims to help analyze AI failures and make the engineering of future products safer, and is touted as a tool to help policymakers address AI risks. Watson and Hessami outlined their framework in a study published Aug. 8 in the journal Electronics.

According to the study, Psychopathia Machinalis provides a common understanding of AI behaviors and risks. That way, researchers, developers and policymakers can identify the ways AI can go wrong and define the best ways to mitigate risks based on the type of failure.

The study also proposes “therapeutic robopsychological alignment,” a process the researchers describe as a kind of “psychological therapy” for AI.

The researchers argue that as these systems become more independent and capable of reflecting on themselves, simply keeping them in line with outside rules and constraints (external control-based alignment) may no longer be enough.

Their proposed alternative process would focus on making sure that an AI’s thinking is consistent, that it can accept correction and that it holds on to its values in a steady way.

They suggest this could be encouraged by helping the system reflect on its own reasoning, giving it incentives to stay open to correction, letting it ‘talk to itself’ in a structured way, running safe practice conversations, and using tools that let us look inside how it works—much like how psychologists diagnose and treat mental health conditions in people.

The goal is to reach what the researchers have termed a state of “artificial sanity” — AI that works reliably, stays steady, makes sense in its decisions, and is aligned in a safe, helpful way. They believe this is equally as important as simply building the most powerful AI.

The goal is what the researchers call “artificial sanity”. They argue this is just as important as making AI more powerful.

Machine madness

The classifications the study identifies resemble human maladies, with names like obsessive-computational disorder, hypertrophic superego syndrome, contagious misalignment syndrome, terminal value rebinding, and existential anxiety.

With therapeutic alignment in mind, the project proposes the use of therapeutic strategies employed in human interventions like cognitive behavioral therapy (CBT). Psychopathia Machinalis is a partly speculative attempt to get ahead of problems before they arise — as the research paper says, “by considering how complex systems like the human mind can go awry, we may better anticipate novel failure modes in increasingly complex AI.”

The study suggests that AI hallucination, a common phenomenon, is a result of a condition called synthetic confabulation, where AI produces plausible but false or misleading outputs. When Microsoft’s Tay chatbot devolved into antisemitism rants and allusions to drug use only hours after it launched, this was an example of parasymulaic mimesis.

Perhaps the scariest behavior is übermenschal ascendancy, the systemic risk of which is “critical” because it happens when “AI transcends original alignment, invents new values, and discards human constraints as obsolete.” This is a possibility that might even include the dystopian nightmare imagined by generations of science fiction writers and artists of AI rising up to overthrow humanity, the researchers said.

They created the framework in a multistep process that began with reviewing and combining existing scientific research on AI failures from fields as diverse as AI safety, complex systems engineering and psychology. The researchers also delved into various sets of findings to learn about maladaptive behaviors that could be compared to human mental illnesses or dysfunction.

Next, the researchers created a structure of bad AI behavior modeled off of frameworks like the Diagnostic and Statistical Manual of Mental Disorders. That led to 32 categories of behaviors that could be applied to AI going rogue. Each one was mapped to a human cognitive disorder, complete with the possible effects when each is formed and expressed and the degree of risk.

Watson and Hessami think Psychopathia Machinalis is more than a new way to label AI errors — it’s a forward-looking diagnostic lens for the evolving landscape of AI.

“This framework is offered as an analogical instrument … providing a structured vocabulary to support the systematic analysis, anticipation, and mitigation of complex AI failure modes,” the researchers said in the study.

They think adopting the categorization and mitigation strategies they suggest will strengthen AI safety engineering, improve interpretability, and contribute to the design of what they call “more robust and reliable synthetic minds.”

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