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.

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 ...

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...

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 💡****

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 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).

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

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).

📊 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).

The Europe 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), End-User Industry (IT and ITES, Internet and Digital Media, and More), and Country. The Market Forecasts are Provided in Terms of Value (USD).

The China 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 ITES, Internet and Digital Media, and More). The Market Forecasts are Provided in Terms of Value (USD).

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...

Artificial Intelligence will make a big difference in the future. But how is it used right now?

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

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.

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.

Human Action Detection with Artificial Intelligence - Emirhan BULUT

I made an artificial intelligence CNN software that detects and classifies human movements. I trained 9 ready-made models and added 1 36-layer CNN artificial intelligence algorithm (model) that I created myself to the software. In order for the code to be instructive and understandable, I interpreted the code blocks with the help of notebook-like titles. I shared the dataset and software with humanity for free on Kaggle and GitHub.

Enjoyable software...

Emirhan BULUT

AI Inventor - Senior Artificial Intelligence Engineer

The coding language used:

Python 3.9.8

Libraries Used:

Tensorflow - Keras

NumPy

Matplotlib

Pandas

glob

os

Seaborn

https://github.com/emirhanai/Human-Action-Detection-with-Artificial-Intelligence/blob/main/Human%20Action%20Detection%20with%20Artificial%20Intelligence.png?raw=true" alt="Human Action Detection with Artificial Intelligence - Emirhan BULUT">

Developer Information:

Name-Surname: Emirhan BULUT

Contact (Email) : emirhan@isap.solutions

LinkedIn : https://www.linkedin.com/in/artificialintelligencebulut/

Kaggle: https://www.kaggle.com/emirhanai

Official Website: https://www.emirhanbulut.com.tr

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.