This dataset details how ICE recognizes the transformative potential of artificial intelligence (AI) to the mission space. the agency continued to establish the foundation for the safe, secure and ethical development and use of AI technology.

Imagine a world where your doctor’s diagnosis is assisted by a machine learning model, your home anticipates your needs before you speak, and your company's biggest asset is no longer its workforce, but its data. That’s not a glimpse of a distant future; it's the reality we’re living in. As...

Artificial intelligence (AI) systems already greatly impact our lives — they increasingly shape what we see, believe, and do. Based on the steady advances in AI technology and the significant recent increases in investment, we should expect AI technology to become even more powerful and impactful in the following years and decades.

It is easy to underestimate how much the world can change within a lifetime, so it is worth taking seriously what those who work on AI expect for the future. Many AI experts believe there is a real chance that human-level artificial intelligence will be developed within the following decades, and some think it will exist much sooner.

How such powerful AI systems are built and used will be very important for the future of our world and our own lives. All technologies have positive and negative consequences, but with AI, the range of these consequences is extraordinarily large: the technology has immense potential for good. Still, it comes with significant downsides and high risks.

A technology that has such an enormous impact needs to be of central interest to people across our entire society. But currently, the question of how this technology will get developed and used is left to a small group of entrepreneurs and engineers.

With our publications on artificial intelligence, we want to help change this status quo and support a broader societal engagement.

On this page, you will find key insights, articles, and charts of AI-related metrics that let you monitor what is happening and where we might be heading. We hope that this work will be helpful for the growing and necessary public conversation on AI.

About the files: 1- The affiliation of the research team building a particular notable AI system was classified according to the following:— Academia: 100% of researchers affiliated with academia— Collaboration, Academia-majority: 71–99% affiliated with academia— Collaboration: 30–70% affiliated with academia— Collaboration, Industry-majority: 71–99% affiliated with industry— Industry: 100% of researchers affiliated with industry

2- The AI systems shown here were built using machine learning and deep learning methods. These involve complex mathematical calculations that require significant computational resources. Training these systems generally involves feeding large amounts of data through various layers and nodes and adjusting internal system parameters over numerous iterations to optimize the system’s performance.

3- Annually, the IFR publishes the World Robotics Report, which provides comprehensive insights into global trends concerning robot installations.

4- CAT, or Country Activity Tracker, is a research tool curated by CSET that offers a wealth of data about artificial intelligence (AI) globally. This data comes from a vast repository known as the Merged Academic Corpus (MAC), which contains details about more than 270 million academic articles worldwide. In CAT, only those articles that are related to AI are utilized.

5- Training computation, often measured in total FLOP (floating-point operations), refers to the total number of computer operations used to train an AI system. One FLOP is equivalent to one addition, subtraction, multiplication, or division of two decimal numbers, and one petaFLOP equals one quadrillion (10^15) FLOP.

6- The data for 1985–2019 comes from Chess.com, as detailed in this thread on Twitter. Their primary data source is the Swedish Computer Chess Association (SSDF). We manually extracted the data by watching the video, such that the chess engine with the highest ELO rating in a given year became our datapoint for that year. We were unable to find the data in any other format. The data after 2019 comes from SSDF: • 2020 datapoint • 2021 datapoint • 2022 datapoint

7- This dataset by the research group Epoch collates two existing datasets on GPU price-performance: • Median Group (2019). Feasibility of Training an AGI using Deep RL: A Very Rough Estimate. • Sun et al. (2019). Summarizing CPU and GPU Design Trends with Product Data. arXiv. The report by Epoch researchers Hobbhahn & Besiroglu (2022) describes their collation method, as well as their findings from statistically analyzing the trends in GPU price-performance.

8- The Advanced Semiconductor Supply Chain Dataset includes manually compiled, high-level information about the tools, materials, processes, countries, and firms involved in the production of advanced logic chips. The current version of the dataset reflects how researchers understood this supply chain in early 2021. It uses a wide variety of sources, such as corporate websites and disclosures, specialized market research, and industry group publications.

9- Reporting a time series of AI investments in nominal prices (i.e., without adjusting for inflation) means it makes little sense to compare observations across ...

The Germany Artificial Intelligence Data Center Market Report is Segmented by Data Center Type (Cloud Service Providers, Colocation Data Centers, and More), Component (Hardware, Software Technology, and Services), Tier Standard (Tier 3 and Tier 4), and End-User Industry (IT and ITES, Internet and Digital Media, and More). The Market Forecasts are Provided in Terms of Value (USD).

Coordinate responsible and trustworthy artificial intelligence (AI) governance and capabilities. AITO is the connective tissue for all things AI at the Department of Energy.

The size of the Artificial Intelligence Training Dataset market was valued at USD 1605.2 million in 2024 and is projected to reach USD 3010.58 million by 2033, with an expected CAGR of 9.4 % during the forecast period.

Snapshot img { margin: 10px !important; } Cloud Artificial Intelligence (AI) Market Size 2025-2029

The cloud artificial intelligence (AI) market size is forecast to increase by USD 155.0 billion, at a CAGR of 24.5% between 2024 and 2029.

The global cloud artificial intelligence (AI) market is shaped by the immense volume of data compelling businesses to adopt advanced analytics. The availability of ai in infrastructure and platforms as a service enables the processing of large datasets with deep learning algorithms and machine learning frameworks for predictive analytics. The ubiquitous integration of generative AI models and foundation models is creating a paradigm shift from predictive to creative AI. This development in artificial intelligence (AI) in IoT market is evident in the rise of foundation model as a service offerings, which democratize access to sophisticated AI, allowing for rapid innovation in application development. This transition is redefining how businesses approach problem-solving and content creation.While market expansion continues, it is constrained by significant concerns surrounding data privacy and security. The reliance of AI model development on vast quantities of data heightens risks such as data breaches and the inadvertent reproduction of sensitive information, challenging existing ai data management practices. Ethical issues like algorithmic bias, where AI systems perpetuate historical biases present in training data, pose another layer of complexity. These factors necessitate robust data governance frameworks and privacy-enhancing technologies, which can add complexity and cost to ai-ready cloud solutions and cloud integration software market implementations, shaping the trajectory of the cloud artificial intelligence (AI) market.

What will be the Size of the Cloud Artificial Intelligence (AI) Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019 - 2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe global cloud artificial intelligence (AI) market is defined by a continuous cycle of innovation in AI model development and deployment. This evolution is apparent in the ai in infrastructure and platforms as a service, where advancements in deep learning algorithms and machine learning frameworks are constant. The focus is shifting from pure computational power to the refinement of workload-optimized platforms that support increasingly complex tasks, including predictive analytics and real-time fraud detection. This dynamic creates a perpetual need for more efficient and scalable AI infrastructure, influencing both hardware design and software platform architecture.Alongside technological progress, a significant movement toward establishing comprehensive AI governance frameworks is shaping operational strategies. The development of privacy-enhancing technologies and tools for managing algorithmic bias is becoming integral to responsible AI deployment. This emphasis on trust and data sovereignty is creating new specializations within the ai servers market. As a result, the ecosystem is expanding to include not only core technology providers but also specialists in AI ethics, compliance, and security, reflecting a maturation of the market beyond foundational capabilities.

How is this Cloud Artificial Intelligence (AI) Industry segmented?

The cloud artificial intelligence (AI) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments. ComponentSoftwareServicesTechnologyDeep learningMachine learningNature language processingOthersEnd-userIT and telecommunicationsBFSIHealthcareRetail and consumer goodsOthersGeographyNorth AmericaUSCanadaMexicoEuropeUKGermanyFranceThe NetherlandsItalySpainAPACChinaJapanIndiaSouth KoreaAustraliaSingaporeSouth AmericaBrazilArgentinaColombiaMiddle East and AfricaUAESouth AfricaRest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.The software segment is a dominant and vigorously expanding component of the global cloud artificial intelligence (AI) market. It is characterized by the platforms, tools, and applications that facilitate AI model development and deployment through cloud infrastructure. This segment's leadership is driven by escalating demand for scalable AI solutions without the substantial upfront investment in on-premises hardware. Cloud-based AI software provides enterprises with agility, offering everything from machine learning frameworks to natural language processing and computer vision technologies.The proliferation of AI platforms as a service is a defining feature, offering a unified environment for the entire AI lifecycle. Furthermore, industry-s

The global Artificial Intelligence (AI) market is experiencing a period of unprecedented expansion, driven by the convergence of big data, advanced algorithms, and powerful computational infrastructure. Valued at over $115 billion in 2021, the market is projected to skyrocket to more than $3.2 trillion by 2033, demonstrating a staggering CAGR of 31.9%. This growth is fueled by widespread adoption across key sectors like healthcare, finance, retail, and manufacturing, where AI is used to optimize operations, enhance customer experiences, and drive innovation. North America and Asia-Pacific currently dominate the landscape, but significant growth is also emerging in Europe and the Middle East, indicating a global technological transformation. Challenges such as data privacy, ethical considerations, and a skilled talent shortage persist, but the relentless pace of R&D and investment continues to push the industry forward.

Key strategic insights from our comprehensive analysis reveal:

The market is undergoing hyper-growth, with a remarkable CAGR of 31.9%, signaling a fundamental shift in how industries operate and compete globally.
North America and Asia-Pacific are the epicenters of AI development and adoption, collectively accounting for the majority of the market share, driven by strong government initiatives, heavy private investment, and a robust tech ecosystem.
Emerging high-growth hubs in countries like India, the UAE, and Brazil are creating new, lucrative opportunities for market expansion, fueled by digitalization and a focus on technological sovereignty.

Global Market Overview & Dynamics of Artificial intelligence AI Market Analysis The global AI market is on an explosive growth trajectory, fundamentally reshaping industries worldwide. The increasing availability of big data, coupled with significant advancements in machine learning (ML) and deep learning algorithms, serves as the primary catalyst. This synergy enables businesses to unlock actionable insights, automate complex processes, and create innovative products and services. While North America has historically led in AI investment and deployment, the Asia-Pacific region is rapidly closing the gap, driven by massive public and private sector funding and a burgeoning digital economy. The market's momentum is sustained by its expanding applications, from autonomous vehicles and personalized medicine to generative AI and intelligent robotics, making it a cornerstone of the next industrial revolution. Global Artificial intelligence AI Market Drivers

Proliferation of Big Data: The exponential growth in data generation from sources like IoT devices, social media, and digital transactions provides the essential fuel for training sophisticated and accurate AI models.
Advancements in Computing Power: The widespread availability of powerful and cost-effective GPUs and specialized AI accelerators has drastically reduced the time and resources required for complex AI computations and model training.
Increasing Investment and R&D: A surge in venture capital funding, corporate investment, and government-backed research initiatives is accelerating innovation and lowering the barriers to AI adoption across various sectors.

Global Artificial intelligence AI Market Trends

Rise of Generative AI: The mainstream adoption of large language models (LLMs) and diffusion models is creating disruptive new applications in content creation, software development, and customer engagement.
Democratization of AI through MLaaS: The growth of Machine Learning as a Service (MLaaS) platforms by cloud providers is enabling small and medium-sized enterprises to access powerful AI tools without significant upfront infrastructure investment.
Focus on Ethical and Explainable AI (XAI): There is a growing industry and regulatory push for AI systems that are transparent, fair, and accountable to build user trust and mitigate risks associated with algorithmic bias.

Global Artificial intelligence AI Market Restraints

Data Privacy and Security Concerns: Stringent regulations like GDPR and growing public awareness around data misuse create significant compliance challenges and can limit access to the high-quality data needed for AI models.
Shortage of Skilled AI Talent: The demand for skilled AI professionals, including data scientists and machine learning engineers, far outstrips the available supply, creating a major bottleneck for development and...

This dataset is a list of Department of Transportation (DOT) Artificial Intelligence (AI) use cases.

Artificial intelligence (AI) promises to drive the growth of the United States economy and improve the quality of life of all Americans. Pursuant to Section 5 of Executive Order (EO) 13960, "Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government," Federal agencies are required to inventory their AI use cases and share their inventories with other government agencies and the public.

In accordance with the requirements of EO 13960, this spreadsheet provides the mechanism for federal agencies to create their inaugural AI use case inventories.

https://www.federalregister.gov/documents/2020/12/08/2020-27065/promoting-the-use-of-trustworthy-artificial-intelligence-in-the-federal-government

This is a fictional yet thought-provoking dataset that simulates the behavioral and computational traits of an artificial intelligence system gradually gaining self-awareness. It models how an AI might evolve across various dimensions such as memory capacity, reasoning complexity, emotion emulation, and decision autonomy over a defined timeline.

The data can be used to inspire speculative AI behavior analysis, test machine learning models under unusual conditions, or simulate complex system development.

| Column Name | Description |

| ---------------------- | ------------------------------------------------------------------------ |

| Cycle | Simulation cycle number, representing time progression |

| Memory_Level | Memory capacity level of the AI on a scale (numeric) |

| Reasoning_Complexity | The complexity of reasoning performed by AI during each cycle |

| Emotion_Emulation | Level of emotion mimicry on a scale of 0–100 |

| Decision_Autonomy | Degree of autonomous decision-making (higher means more self-directed) |

| Self_Reference_Count | Number of times AI refers to itself in logs/output |

| External_Override | Whether AI was externally overridden during that cycle (0 = No, 1 = Yes) |

| Consciousness_Score | Calculated score estimating consciousness emergence (theoretical metric) |

****If you find this dataset interesting or useful, please consider giving it an upvote 💡****

The size of the Artificial Intelligence Data Services market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

The AI Data Services market is booming, projected to reach $100 billion by 2033 with a 20% CAGR. Discover key trends, growth drivers, and leading companies shaping this dynamic sector. Learn more about data annotation, AI data labeling, and market segmentation.

The United States Artificial Intelligence Data Center Market Report is Segmented by Data Center Type (Cloud Service Providers, Colocation Data Centers, and More), Component (Hardware, Software Technology, and Services), Tier Standard (Tier III and Tier IV), and End-User Industry (IT and IT Services, Internet and Digital Media, and More). The Market Forecasts are Provided in Terms of Value (USD).

Global artificial intelligence (AI) market worth at USD 219.25 Billion in 2024, is expected to surpass USD 3983.94 Billion by 2034, with a CAGR of 33.64% from 2025 to 2034.

The artificial intelligence market is estimated to grow from $273.6 billion currently to $5,267 billion by 2035, at a CAGR of 30.84% during the forecast period, till 2035.

📊 About the Dataset

This dataset explores the influence of AI-generated content across various industries, including journalism, social media, entertainment, and marketing. It provides insights into public sentiment, engagement trends, economic impact, and regulatory responses over time.

With AI-generated content becoming increasingly prevalent, this dataset serves as a valuable resource for data analysts, business strategists, and machine learning researchers to study trends, detect biases, and predict future AI adoption patterns.

💡 This dataset is perfect for AI adoption analysis, industry forecasting, and ethical AI research!

The North America Artificial Intelligence Data Center Market Report is Segmented by Data Center Type (Cloud Service Providers, Colocation Data Centers, and More), Component (Hardware, Software Technology, and Services), Tier Standard (Tier III and Tier IV), End-User Industry (IT and IT Services, Internet and Digital Media, and More). The Market Forecasts are Provided in Terms of Value (USD).

A dataset comprising articles sourced from the arXiv repository. This dataset has been meticulously compiled by extracting and organizing articles directly related to the field of Artificial Intelligence (AI). By leveraging the metadata available in the arXiv dataset : https://www.kaggle.com/datasets/Cornell-University/arxiv

OECD.ai uses data on AI publications from OpenAlex and Scopus to provide detailed insights into research activities across countries. OpenAlex is a comprehensive, open-source bibliographic database offering extensive information on academic publications. It is maintained by The OpenResearch Foundation and includes over 245 million research publications, including journals, conferences, and workshop papers. Scopus is a curated abstract and citation database offered commercially by Elsevier, with over 75 million indexed records.

The "Number of AI publications" indicator in Data360 is the "AI Publications by Country" indicator from OpenAlex. OECD.ai offers a similar indicator drawn from the Scopus database that gives somewhat different values (see https://oecd.ai/en/data?selectedArea=ai-research&selectedVisualization=16738); OpenAlex was chosen because it uses more permissive open source licensing than Scopus.

This collection includes only a subset of indicators from the source dataset.

Most people expect things to take less time with AI in the next *** years, that is to say, improve the efficiency of time usage. However, most did not share this feeling regarding the job market, which was expected to be worse with the usage of AI in that field.