The number of AI tools users in the 'AI Tool Users' segment of the artificial intelligence market worldwide was modeled to stand at ************** in 2024. Following a continuous upward trend, the number of AI tools users has risen by ************** since 2020. Between 2024 and 2031, the number of AI tools users will rise by **************, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Artificial Intelligence.

The United States had by far the greatest amount of AI companies in major western economies in 2023, with around ****** companies engaging in the field. The EU was considerably behind, with only *****. The UK alone at nearly *****, a significant achievement in comparison to the immense economic size of its competitors. Strong AI market The AI market is expected to show considerable growth in the coming decade, expanding from *** billion U.S. dollars in 2023 to nearly ************ U.S. dollars in 2030. This market covers a vast number of industries, being involved in everything from supply chains, marketing, research, analysis, and numerous other fields. Chatbots, AI-producing art, mobile applications, and natural language processing are all major trends in the AI sphere. Enterprises and AI Both generative AI and machine learning, the most basic form of AI, are already in wide usage among companies worldwide. Commercial leaders still feel that their enterprises use these AI tools too little, with most companies using generative AI rarely and machine learning only sometimes. Machine learning, as the most common AI form, is used considerably more but this may shift as more advanced generative AI models spring into existence.

Global Total Number of Scientific Publications in Artificial Intelligence by Country, 2023 Discover more data with ReportLinker!

The number of Artificial Intelligence (AI) tool users in Poland amounted to approximately *********** in 2024. Their AI users are forecast to rise to over **** million in 2030.

Forecast: Number of Scientific Publications in Artificial Intelligence in the US 2024 - 2028 Discover more data with ReportLinker!

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 MNIST dataset is a dataset of handwritten digits. It is a popular dataset for machine learning and artificial intelligence research. The dataset consists of 60,000 training images and 10,000 test images. Each image is a 28x28 pixel grayscale image of a handwritten digit. The digits are labeled from 0 to 9.

The dataset is generated based on 5 different filters, i. Gaussian blur (sigma=2.5), ii. width and height shift range=0.2, iii. Rotation range <10, iv. zoom range=0.4, and v. brightness between [0.4,1.5].

The dataset is 40 x 28 pixels and the total number is 8851 images.

The ".csv" file is the labeled dataset which the first column shows the label and the rest columns are the value of the grayscale image.

In the decade since 2014, Google has produced, by far, the largest amount of notable AI models or some *** models. That leaves them with nearly twice the number of models as the next competitor, Meta. While this is a significant number, Google does also routinely cut projects or fold older projects into newer ones, which could inflate the number given.

Forecast: Number of Scientific Publications in Artificial Intelligence in Thailand 2024 - 2028 Discover more data with ReportLinker!

Forecast: Number of Scientific Publications in Artificial Intelligence in France 2024 - 2028 Discover more data with ReportLinker!

Forecast: Number of Scientific Publications in Artificial Intelligence in Finland 2024 - 2028 Discover more data with ReportLinker!

Forecast: Number of Scientific Publications in Artificial Intelligence in China 2024 - 2028 Discover more data with ReportLinker!

Forecast: Total Number of Scientific Publications in Artificial Intelligence in Canada 2024 - 2028 Discover more data with ReportLinker!

In August 2024, over *****a million unique devices used the Chinese AI tool Aishenqi. Artificial intelligence tools include a broad range of artificial intelligence services. China's leading AI tools include code writing support, as well as a digital language study companion.

This dataset contains in-air hand-written numbers and shapes data used in the paper:B. Alwaely and C. Abhayaratne, "Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition," in IEEE Access, vol. 7, pp. 159661-159673, 2019, doi: 10.1109/ACCESS.2019.2950643.The dataset contains the following:-Readme.txt- InAirNumberShapeDataset.zip containing-Number Folder (With 2 sub folders for Matlab and Excel)-Shapes Folder (With 2 sub folders for Matlab and Excel)The datasets include the in-air drawn number and shape hand movement path captured by a Kinect sensor. The number sub dataset includes 500 instances per each number 0 to 9, resulting in a total of 5000 number data instances. Similarly, the shape sub dataset also includes 500 instances per each shape for 10 different arbitrary 2D shapes, resulting in a total of 5000 shape instances. The dataset provides X, Y, Z coordinates of the hand movement path data in Matlab (M-file) and Excel formats and their corresponding labels.This dataset creation has received The University of Sheffield ethics approval under application #023005 granted on 19/10/2018.

Data Documentation

This document describes the data sources and variables used in the third Anthropic Economic Index (AEI) report.

Claude.ai Usage Data

Overview

The core dataset contains Claude AI usage metrics aggregated by geography and analysis dimensions (facets).

Source files: - aei_raw_claude_ai_2025-08-04_to_2025-08-11.csv (pre-enrichment data in data/intermediate/) - aei_enriched_claude_ai_2025-08-04_to_2025-08-11.csv (enriched data in data/output/)

Note on data sources: The AEI raw file contains raw counts and percentages. Derived metrics (indices, tiers, per capita calculations, automation/augmentation percentages) are calculated during the enrichment process in aei_report_v3_preprocessing_claude_ai.ipynb.

Data Schema

Each row represents one metric value for a specific geography and facet combination:

Facets

• country: Country-level aggregations
• state_us: US state-level aggregations
• onet_task: O*NET occupational tasks
• collaboration: Human-AI collaboration patterns
• request: Request complexity levels (0=highest granularity, 1=middle granularity, 2=lowest granularity)
• onet_task::collaboration: Intersection of tasks and collaboration patterns
• request::collaboration: Intersection of request categories and collaboration patterns

Core Variables

Variables follow the pattern {prefix}_{suffix} with specific meanings:

From AEI processing: *_count, *_pct From enrichment: *_per_capita, *_per_capita_index, *_pct_index, *_tier, automation_pct, augmentation_pct, soc_pct

Usage Metrics

• usage_count: Total number of conversations/interactions in a geography
• usage_pct: Percentage of total usage (relative to parent geography - gobal for countries, US for states)
• usage_per_capita: Usage count divided by working age population
• usage_per_capita_index: Concentration index showing if a geography has more/less usage than expected based on population share (1.0 = proportional, >1.0 = over-representation, <1.0 = under-representation)
• usage_tier: Usage adoption tier (0 = no/little adoption, 1-4 = quartiles of adoption among geographies with sufficient usage)

Content Facet Metrics

O*NET Task Metrics: - onet_task_count: Number of conversations using this specific O*NET task - onet_task_pct: Percentage of geographic total using this task - onet_task_pct_index: Specialization index comparing task usage to baseline (global for countries, US for states) - onet_task_collaboration_count: Number of conversations with both this task and collaboration pattern (intersection) - onet_task_collaboration_pct: Percentage of the base task's total that has this collaboration pattern (sums to 100% within each task)

Occupation Metrics

• soc_pct: Percentage of classified O*NET tasks associated with this SOC major occupation group (e.g., Management, Computer and Mathematical)

Request Metrics: - request_count: Number of conversations in this request category level - request_pct: Percentage of geographic total in this category - request_pct_index: Specialization index comparing request usage to baseline - request_collaboration_count: Number of conversations with both this request category and collaboration pattern (intersection) - request_collaboration_pct: Percentage of the base request's total that has this collaboration pattern (sums to 100% within each request)

Collaboration Pattern Metrics: - collaboration_count: Number of conversations with this collaboration pattern - collaboration_pct: Percentage of geographic total with this pattern - collaboration_pct_index: Specialization index comparing pattern to baseline - automation_pct: Percentage of classifiable collaboration that is automation-focused (directive, feedback loop patterns) - augmentation_pct: Percentage of classifiable collaboration that is augmentation-focused (validation, task iteration, learning patterns)

Demographic & Economic Metrics

• ...

The number of AI tools users in the 'AI Tool Users' segment of the artificial intelligence market in Europe was modeled to stand at ************* in 2024. Following a continuous upward trend, the number of AI tools users has risen by ************* since 2020. Between 2024 and 2031, the number of AI tools users will rise by **************, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Artificial Intelligence.

Forecast: Total Number of Scientific Publications in Artificial Intelligence in Saudi Arabia 2024 - 2028 Discover more data with ReportLinker!

Supporting data for the AI education publication statistics presented in the paper "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates" to be published at the Twelfth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-22). The data was used to plot the figure showing the cumulative number of publications from 1976 to 2020 relating to AI education.