Comparison of Represents the average of math benchmarks in the Artificial Analysis Intelligence Index (AIME 2025) by Model

Comparison of Tokens used to run all evaluations in the Artificial Analysis Intelligence Index by Model

The AI-powered Website Builder market has rapidly gained traction, becoming an essential tool for businesses and individuals seeking to establish an online presence with minimal technical expertise. These innovative platforms leverage artificial intelligence to streamline the web design process, allowing users to cr

Data Analysis Agent Meta and Traffic Dataset in AI Agent Marketplace | AI Agent Directory | AI Agent Index from DeepNLP

This dataset is collected from AI Agent Marketplace Index and Directory at http://www.deepnlp.org, which contains AI Agents's meta information such as agent's name, website, description, as well as the monthly updated Web performance metrics, including Google,Bing average search ranking positions, Github Stars, Arxiv References, etc. The dataset is helpful for AI… See the full description on the dataset page: https://huggingface.co/datasets/DeepNLP/data-analysis-ai-agent.

Traffic analytics, rankings, and competitive metrics for character.ai as of August 2025

Cite as

Guerrero-Contreras, G., Balderas-Díaz, S., Serrano-Fernández, A., & Muñoz, A. (2024, June). Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights. In 2024 International Conference on Intelligent Environments (IE) (pp. 62-69). IEEE.

General Description

This dataset comprises 4,038 tweets in Spanish, related to discussions about artificial intelligence (AI), and was created and utilized in the publication "Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights," (10.1109/IE61493.2024.10599899) presented at the 20th International Conference on Intelligent Environments. It is designed to support research on public perception, sentiment, and engagement with AI topics on social media from a Spanish-speaking perspective. Each entry includes detailed annotations covering sentiment analysis, user engagement metrics, and user profile characteristics, among others.

Data Collection Method

Tweets were gathered through the Twitter API v1.1 by targeting keywords and hashtags associated with artificial intelligence, focusing specifically on content in Spanish. The dataset captures a wide array of discussions, offering a holistic view of the Spanish-speaking public's sentiment towards AI.

Dataset Content

ID: A unique identifier for each tweet.

text: The textual content of the tweet. It is a string with a maximum allowed length of 280 characters.

polarity: The tweet's sentiment polarity (e.g., Positive, Negative, Neutral).

favorite_count: Indicates how many times the tweet has been liked by Twitter users. It is a non-negative integer.

retweet_count: The number of times this tweet has been retweeted. It is a non-negative integer.

user_verified: When true, indicates that the user has a verified account, which helps the public recognize the authenticity of accounts of public interest. It is a boolean data type with two allowed values: True or False.

user_default_profile: When true, indicates that the user has not altered the theme or background of their user profile. It is a boolean data type with two allowed values: True or False.

user_has_extended_profile: When true, indicates that the user has an extended profile. An extended profile on Twitter allows users to provide more detailed information about themselves, such as an extended biography, a header image, details about their location, website, and other additional data. It is a boolean data type with two allowed values: True or False.

user_followers_count: The current number of followers the account has. It is a non-negative integer.

user_friends_count: The number of users that the account is following. It is a non-negative integer.

user_favourites_count: The number of tweets this user has liked since the account was created. It is a non-negative integer.

user_statuses_count: The number of tweets (including retweets) posted by the user. It is a non-negative integer.

user_protected: When true, indicates that this user has chosen to protect their tweets, meaning their tweets are not publicly visible without their permission. It is a boolean data type with two allowed values: True or False.

user_is_translator: When true, indicates that the user posting the tweet is a verified translator on Twitter. This means they have been recognized and validated by the platform as translators of content in different languages. It is a boolean data type with two allowed values: True or False.

Potential Use Cases

This dataset is aimed at academic researchers and practitioners with interests in:

Sentiment analysis and natural language processing (NLP) with a focus on AI discussions in the Spanish language.

Social media analysis on public engagement and perception of artificial intelligence among Spanish speakers.

Exploring correlations between user engagement metrics and sentiment in discussions about AI.

Data Format and File Type

The dataset is provided in CSV format, ensuring compatibility with a wide range of data analysis tools and programming environments.

License

The dataset is available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, permitting sharing, copying, distribution, transmission, and adaptation of the work for any purpose, including commercial, provided proper attribution is given.

General Description

This dataset comprises 4,038 tweets in Spanish, related to discussions about artificial intelligence (AI), and was created and utilized in the publication "Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights," (10.1109/IE61493.2024.10599899) presented at the 20th International Conference on Intelligent Environments. It is designed to support research on public perception, sentiment, and engagement with AI topics on social media from a Spanish-speaking perspective. Each entry includes detailed annotations covering sentiment analysis, user engagement metrics, and user profile characteristics, among others.

Data Collection Method

Tweets were gathered through the Twitter API v1.1 by targeting keywords and hashtags associated with artificial intelligence, focusing specifically on content in Spanish. The dataset captures a wide array of discussions, offering a holistic view of the Spanish-speaking public's sentiment towards AI.

Dataset Content

ID: A unique identifier for each tweet.

text: The textual content of the tweet. It is a string with a maximum allowed length of 280 characters.

polarity: The tweet's sentiment polarity (e.g., Positive, Negative, Neutral).

favorite_count: Indicates how many times the tweet has been liked by Twitter users. It is a non-negative integer.

retweet_count: The number of times this tweet has been retweeted. It is a non-negative integer.

user_verified: When true, indicates that the user has a verified account, which helps the public recognize the authenticity of accounts of public interest. It is a boolean data type with two allowed values: True or False.

user_default_profile: When true, indicates that the user has not altered the theme or background of their user profile. It is a boolean data type with two allowed values: True or False.

user_has_extended_profile: When true, indicates that the user has an extended profile. An extended profile on Twitter allows users to provide more detailed information about themselves, such as an extended biography, a header image, details about their location, website, and other additional data. It is a boolean data type with two allowed values: True or False.

user_followers_count: The current number of followers the account has. It is a non-negative integer.

user_friends_count: The number of users that the account is following. It is a non-negative integer.

user_favourites_count: The number of tweets this user has liked since the account was created. It is a non-negative integer.

user_statuses_count: The number of tweets (including retweets) posted by the user. It is a non-negative integer.

user_protected: When true, indicates that this user has chosen to protect their tweets, meaning their tweets are not publicly visible without their permission. It is a boolean data type with two allowed values: True or False.

user_is_translator: When true, indicates that the user posting the tweet is a verified translator on Twitter. This means they have been recognized and validated by the platform as translators of content in different languages. It is a boolean data type with two allowed values: True or False.

Cite as

Guerrero-Contreras, G., Balderas-Díaz, S., Serrano-Fernández, A., & Muñoz, A. (2024, June). Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights. In 2024 International Conference on Intelligent Environments (IE) (pp. 62-69). IEEE.

Potential Use Cases

This dataset is aimed at academic researchers and practitioners with interests in:

Sentiment analysis and natural language processing (NLP) with a focus on AI discussions in the Spanish language.

Social media analysis on public engagement and perception of artificial intelligence among Spanish speakers.

Exploring correlations between user engagement metrics and sentiment in discussions about AI.

Data Format and File Type

The dataset is provided in CSV format, ensuring compatibility with a wide range of data analysis tools and programming environments.

License

The dataset is available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, permitting sharing, copying, distribution, transmission, and adaptation of the work for any purpose, including commercial, provided proper attribution is given.

Comparison of Represents the average of coding benchmarks in the Artificial Analysis Intelligence Index (LiveCodeBench, SciCode & Terminal-Bench Hard) by Model

This dataset comprises anonymized blood test results analyzed by the AI Blood Test Analyzer & Interpretation Software. It includes a wide range of biomarkers and their interpretations, which are crucial for medical research and diagnostics.

Global AI price tracking tools market size was $2.79 billion in 2024 & is projected to reach $7.30 million by 2034, CAGR of 12.80% from 2025 to 2034.

Traffic analytics, rankings, and competitive metrics for people.ai as of June 2025

Traffic analytics, rankings, and competitive metrics for together.ai as of June 2025

🤖 Artificial Intelligence (AI) is a key enabler of innovation and a central pillar of digital transformation. ISTARI.AI provides verified, scalable AI intensity data by analyzing how prominently AI know-how is communicated on company websites. This enables both quantitative benchmarking and qualitative insight into how central AI is to a company’s offerings—ensuring consistently high data quality and reliability.

📊 The dataset includes: - ai_intensity: Numerical indicator reflecting the prominence of AI-related know-how - ai_intensity_level: Categorized engagement level (from very low to very high) - ai_keywords: Relevant AI-related keywords found on the company’s website

📊 The AI Intensity Score in Detail The AI Intensity Score quantifies the degree to which artificial intelligence is communicated as a core capability or business focus on a company’s website. It specifically captures evidence of: - AI-integrated products or services - AI expertise within the workforce - Strategic positioning of AI in the company’s communication

Rather than simple binary classification ("AI: yes/no"), ISTARI’s WebAI delivers a continuous, nuanced score that distinguishes between marginal mentions of AI and core AI-focused business models.

🔍 How do we measure? The webAI AI Agent, developed by ISTARI.AI, reads and analyzes company websites to: - Identify AI-related keywords - Detect and validate text segments (“paragraphs”) containing AI-related content - Classify whether a paragraph reflects genuine AI know-how or simply general information - Calculate a ratio of AI-know-how paragraphs to total website content, resulting in a numeric AI Intensity score

This approach ensures a deep contextual analysis of how central AI is to each company’s external communication and positioning.

🔍 How can the data be interpreted? - 0.0 = No communication of AI-related know-how - 0.25 = Limited communication; e.g., a consulting firm mentioning "AI services" among other topics - 2.5+ = High intensity; e.g., a startup exclusively focused on AI solutions - 3.5+ = Exceptional AI focus; typically, AI-first companies or specialized technology providers An additional categorical interpretation is provided as a helper column, ranging from "very low" to "very high" intensity.

✅ Ensuring Data Quality - The webAI AI Agent was developed in close collaboration with academic experts to guarantee expert-level accuracy. - Developed together with researchers at the University of Mannheim - Validated in the award-winning academic study: "When is AI Adoption Contagious? Epidemic Effects and Relational Embeddedness in the Inter-Firm Diffusion of Artificial Intelligence" - Co-authored by scholars from University of Mannheim, University of Giessen, University of Hohenheim, and ETH Zurich - Winner of the Best Paper Award at the R&D Management Conference 2022 - Currently under peer review in a leading international journal

This data provide insights into how companies present and prioritize Artificial Intelligence on their websites.

The global clickstream analytics market is experiencing robust growth, driven by the increasing need for businesses to understand customer behavior and optimize digital experiences. The market's expansion is fueled by the proliferation of digital channels, the rise of e-commerce, and the growing adoption of data-driven decision-making across various industries. Companies are increasingly leveraging clickstream data to personalize marketing campaigns, improve website usability, and enhance customer engagement. This surge in demand is reflected in a significant market size, projected to reach several billion dollars by 2033, with a substantial Compound Annual Growth Rate (CAGR) over the forecast period (2025-2033). Key segments driving this growth include website analytics and mobile app analytics, across applications such as marketing optimization, customer experience management, and fraud detection. Leading technology companies like Google, IBM, Microsoft, and Oracle are playing a crucial role in shaping the market landscape through their sophisticated analytics platforms and solutions. The market is witnessing a shift towards advanced analytics techniques, including machine learning and artificial intelligence, to extract deeper insights from clickstream data. This technological advancement further enhances the ability of businesses to understand complex customer journeys and make data-informed strategic decisions. Geographical expansion, particularly in rapidly developing economies in Asia-Pacific, is also contributing to the market's overall growth trajectory. However, challenges such as data privacy concerns, the need for skilled professionals to interpret complex data, and the high cost of implementation may pose some constraints on market expansion. The competitive landscape is characterized by both established players and emerging startups, fostering innovation and competition. The market is expected to see further consolidation through mergers and acquisitions as companies seek to expand their market share and capabilities. Future growth will likely be driven by the increasing adoption of cloud-based analytics solutions, advancements in real-time data processing, and the integration of clickstream data with other data sources for a more holistic view of customer behavior. The focus on enhancing data security and compliance with evolving privacy regulations will also be crucial for sustaining market growth in the years to come. Continuous innovation in areas such as predictive analytics and personalized recommendations will further propel the expansion of the clickstream analytics market.

The website visitor tracking software market is experiencing robust growth, driven by the increasing need for businesses to understand online customer behavior and optimize their digital strategies. The market, currently estimated at $5 billion in 2025, is projected to expand significantly over the next decade, with a Compound Annual Growth Rate (CAGR) of approximately 15% from 2025 to 2033. This growth is fueled by several key factors, including the rising adoption of e-commerce, the proliferation of sophisticated website analytics tools offering deeper insights into user engagement, and a growing emphasis on data-driven decision-making across various industries. Businesses are increasingly relying on these tools not only for basic website traffic analysis but also for advanced features like heatmap analysis, session recording, form analytics, and A/B testing, which enable personalized user experiences and targeted marketing campaigns. The market's competitive landscape is diverse, with a mix of established players like Google Analytics and Adobe Analytics, alongside emerging specialized solutions focusing on specific aspects of visitor tracking, like lead generation or user behavior analysis. This fragmentation offers businesses a wide range of options tailored to their specific needs and budgets. The continued advancement of artificial intelligence (AI) and machine learning (ML) is expected to significantly impact the future of website visitor tracking software. AI-powered tools are increasingly capable of providing predictive analytics, identifying high-value visitors, automating tasks, and personalizing the user journey with greater accuracy. While data privacy concerns and the increasing complexity of these tools pose challenges, the overall market outlook remains positive. The focus is shifting towards tools that offer more comprehensive, insightful, and privacy-compliant solutions, leading to further innovation and consolidation within the market. Furthermore, the integration of website visitor tracking with other marketing automation platforms is becoming increasingly crucial, enabling a more holistic view of the customer journey and streamlining marketing efforts. This trend is expected to drive further market growth and shape the competitive landscape in the coming years.

Comparison of Output Speed: Output Tokens per Second by Provider

The Customer Intelligence Platform Software market is experiencing robust growth, driven by the increasing need for businesses to understand and engage with their customers on a deeper level. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors. Firstly, the proliferation of data from various sources, including CRM systems, social media, and website analytics, provides rich insights into customer behavior. Secondly, advancements in artificial intelligence (AI) and machine learning (ML) are enabling more sophisticated analysis of this data, leading to more accurate predictions and personalized customer experiences. Thirdly, the growing adoption of cloud-based solutions is improving scalability, accessibility, and cost-effectiveness for businesses of all sizes. The market is segmented by deployment (cloud, on-premise), organization size (small, medium, large), and industry vertical (retail, finance, healthcare, etc.). Competitive forces are intensifying, with established players like IBM, Oracle, and SAS facing challenges from agile startups and specialized vendors. The major restraints on market growth include data privacy concerns, the complexity of integrating various data sources, and the need for skilled professionals to manage and interpret the insights generated. However, these challenges are being addressed through advancements in data anonymization techniques, improved data integration tools, and the rise of user-friendly platforms. Future trends include increased adoption of real-time analytics, the integration of customer intelligence with other business intelligence solutions, and the growing importance of ethical considerations in data collection and usage. The competitive landscape is dynamic, with both established players and emerging companies vying for market share through innovation and strategic partnerships. Key players like AllSight, Accenture Insights Platform, Verint Systems, and others are constantly innovating to offer comprehensive solutions that meet the evolving needs of businesses. The market shows strong potential for continued expansion, driven by ongoing technological advancements and the unwavering focus on customer-centric strategies across various industries.

The AI SEO software tools market is experiencing robust growth, driven by the increasing need for businesses to optimize their online presence and improve search engine rankings. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $10 billion by 2033. This expansion is fueled by several key factors, including the rising adoption of artificial intelligence in digital marketing, the growing complexity of SEO algorithms, and the increasing demand for data-driven insights to enhance website performance. Businesses are increasingly relying on AI-powered tools to automate tedious tasks, analyze large datasets, and identify optimization opportunities that would be difficult or impossible to achieve manually. The trend towards personalized user experiences also contributes significantly to the market's growth, as AI tools can help tailor content and website strategies to individual user preferences. Several key segments are driving this growth. The demand for keyword research tools, content optimization platforms, and technical SEO solutions is exceptionally high. The competitive landscape is vibrant, with a mix of established players like BrightEdge and newer entrants like Surfer SEO and Frase vying for market share. The market is also witnessing continuous innovation, with new features and functionalities being added regularly. However, the market faces some restraints, including the high cost of implementation for some AI SEO tools, the need for specialized skills and expertise to effectively utilize these technologies, and concerns about data privacy and security. Nevertheless, the overwhelming benefits in efficiency, improved rankings, and data-driven decision-making are expected to outweigh these challenges, further accelerating market growth in the coming years.

AbstractBackground: Birth control methods (BCMs) are often underutilized or misunderstood, especially among young individuals entering their reproductive years. With the growing reliance on artificial intelligence (AI) platforms for health-related information, this study evaluates the performance of GPT-4 (OpenAI, San Francisco, CA, USA) and Google Gemini (Google, Mountain View, CA, USA) in addressing commonly asked questions about BCMs.Methods: Thirty questions, derived from the American College of Obstetrics and Gynecologists website, were posed to both AI platforms. Questions spanned four categories: general contraception, specific contraceptive types, emergency contraception, and other topics. Responses were evaluated using a 5-point rubric assessing accuracy, completeness, and lack of false information. Overall scores were calculated by averaging the rubric scores. Statistical analysis, including the Wilcoxon signed-rank and Kruskal-Wallis tests, was performed to compare performance metrics.Results: ChatGPT and Google Gemini both provided high-quality responses, with overall scores averaging 4.38 ± 0.58 and 4.37 ± 0.52, respectively, categorized as "excellent." ChatGPT outperformed in reducing false information (4.70 ± 0.60 vs. 4.47 ± 0.73), while Google Gemini excelled in accuracy (4.53 ± 0.57 vs. 4.30 ± 0.70). Completeness scores were comparable. No significant differences were found in overall performance (p = 0.548), though Google Gemini showed a significant edge in accuracy (p = 0.035). Both platforms scored consistently across question categories, with no statistically significant differences noted.Conclusions: GPT-4 and Google Gemini provide reliable and accurate responses to BCM-related queries, with slight differences in strengths. These findings underscore the potential of AI tools in addressing public health information needs, particularly for young individuals seeking guidance on contraception. Further studies with larger datasets may elucidate nuanced differences between AI platforms.