In February 2025, ChatGPT was the most popular artificial intelligence (AI) application worldwide, with over 400.61 million monthly active users (MAU). The ByteDance-owned chatbot Doubao had around 81.91 million MAU, making it the most popular Chinese-based tool of this kind. ChatGPT-operated Nova Assistant ranked third with 62.79 million MAU and was followed by Chinese-based DeepSeek with around 61.81 million MAU.

The release of the Chinese AI company DeepSeek's large language model R1 in late January 2025 has sparked interests in apps that are built from the ground up around AI capabilities in China. Within a month, the number of monthly active users of AI-native apps surged by ** percent to almost *** million. App usage data indicated a steady average monthly time spent per user, ranging around *** minutes, whereas average monthly app sessions were up to ** times.

In late 2022, the generative AI wave surged into the mainstream, but it wasn’t until Claude arrived that many users felt truly seen by a machine. Whether you were a student seeking clarity, a developer asking for structured code, or a business professional testing enterprise integration, Claude offered a human-like...

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.

As of the end of 2024, the majority of users of AI smartphones in China were people under the age of **. Thereby, the Huawei's Mate 60 Pro and the Xiaomi 14 were the most popular smartphone models.

ChatGPT was the chatbot that kickstarted the generative AI revolution, which has been responsible for hundreds of billions of dollars in data centres, graphics chips and AI startups. Launched by...

ExactOne delivers unparalleled consumer transaction insights to help investors and corporate clients uncover market opportunities, analyze trends, and drive better decisions.

Dataset Highlights - Source: Debit and credit card transactions from 600K+ active users and 2M accounts connected via Open Banking. Scale: Covers 250M+ annual transactions, mapped to 1,800+ merchants and 330+ tickers. Historical Depth: Over 6 years of transaction data. Flexibility: Analyse transactions by merchant/ticker, category/industry, or timeframe (daily, weekly, monthly, or quarterly).

ExactOne data offers visibility into key consumer industries, including: Airlines - Regional / Budget Airlines - Cargo Airlines - Full Service Autos - OEMs Communication Services - Cable & Satellite Communication Services - Integrated Telecommunications Communication Services - Wireless Telecom Consumer - Services Consumer - Health & Fitness Consumer Staples - Household Supplies Energy - Utilities Energy - Integrated Oil & Gas Financial Services - Insurance Grocers - Traditional Hotels - C-corp Industrial - Misc Industrial - Tools And Hardware Internet - E-commerce Internet - B2B Services Internet - Ride Hailing & Delivery Leisure - Online Gambling Media - Digital Subscription Real Estate - Brokerage Restaurants - Quick Service Restaurants - Fast Casual Restaurants - Pubs Restaurants - Specialty Retail - Softlines Retail - Mass Merchants Retail - European Luxury Retail - Specialty Retail - Sports & Athletics Retail - Footwear Retail - Dept Stores Retail - Luxury Retail - Convenience Stores Retail - Hardlines Technology - Enterprise Software Technology - Electronics & Appliances Technology - Computer Hardware Utilities - Water Utilities

Use Cases

For Private Equity & Venture Capital Firms: - Deal Sourcing: Identify high-growth opportunities. - Due Diligence: Leverage transaction data to evaluate investment potential. - Portfolio Monitoring: Track performance post-investment with real-time data.

For Consumer Insights & Strategy Teams: - Market Dynamics: Compare sales trends, average transaction size, and customer loyalty. - Competitive Analysis: Benchmark market share and identify emerging competitors. - E-commerce vs. Brick & Mortar Trends: Assess channel performance and strategic opportunities. - Demographic & Geographic Insights: Uncover growth drivers by demo and geo segments.

For Investor Relations Teams: - Shareholder Insights: Monitor brand performance relative to competitors. - Real-Time Intelligence: Analyse sales and market dynamics for public and private companies. - M&A Opportunities: Evaluate market share and growth potential for strategic investments.

Key Benefits of ExactOne - Understand Market Share: Benchmark against competitors and uncover emerging players. - Analyse Customer Loyalty: Evaluate repeat purchase behavior and retention rates. - Track Growth Trends: Identify key drivers of sales by geography, demographic, and channel. - Granular Insights: Drill into transaction-level data or aggregated summaries for in-depth analysis.

With ExactOne, investors and corporate leaders gain actionable, real-time insights into consumer behaviour and market dynamics, enabling smarter decisions and sustained growth.

Datos brings to market anonymized, at scale, consolidated privacy-secured datasets with granularity rarely found in market. Datos offers access to the desktop and mobile browsing behavior for millions of users across the globe, packaged into clean, easy to understand data products and reports for use by our clients.

The Datos Keywords Feed is an aggregated accounting of all observed searches executed on up to nine major search properties worldwide, with both raw and projected statistics available.

Artificial intelligence (AI) algorithms are dramatically redefining the current drug discovery landscape by boosting the efficiency of its various steps. Still, their implementation often requires a certain level of expertise in AI paradigms and coding. This often prevents the use of these powerful methodologies by non-expert users involved in the design of new biologically active compounds. Here, the random matrix discriminant (RMD) algorithm, a high-performance AI method specifically tailored for the identification of new ligands, was implemented in a new fully automated tool, PyRMD. This ligand-based virtual screening tool can be trained using target bioactivity data directly downloaded from the ChEMBL repository without manual intervention. The software automatically splits the available training compounds into active and inactive sets and learns the distinctive chemical features responsible for the compounds’ activity/inactivity. PyRMD was designed to easily screen millions of compounds in hours through an automated workflow and intuitive input files, allowing fine tuning of each parameter of the calculation. Additionally, PyRMD features a wealth of benchmark metrics, to accurately probe the model performance, which were used here to gauge its predictive potential and limitations. PyRMD is freely available on GitHub (https://github.com/cosconatilab/PyRMD) as an open-source tool.

Silencio’s Anonymized Location Dataset offers unique insights into real-world human mobility, collected from a community of 1M+ actively opted-in users who voluntarily agree to share their data for commercial use. The dataset focuses exclusively on people movement patterns, aggregated into anonymized pedestrian and mobility flows.

We maintain a healthy and active dataset with: • 20–40K daily active users (DAU) • 300–400K monthly active users (MAU)

This enables us to provide consistent, fresh, and geographically balanced mobility data.

Our dataset has worldwide coverage, with particularly strong data density in: • Europe • Brazil • India • Nigeria • Philippines • Bangladesh • Pakistan • United States

Designed for: • Urban mobility studies • Transportation planning • Mobility apps and AI models • Smart city development

Silencio is built on privacy-first principles, collecting data only from users who explicitly opt-in to share and commercialize their data, in full compliance with GDPR and other global data protection regulations.

Data delivery options: • CSV exports • S3 bucket delivery • API (in development — open to early access discussions)

Our combination of real movement data, active user base, and ethical data practices makes this dataset ideal for any organization looking for privacy-compliant, real-world mobility insights.

This proposal establishes an industrially relevant methodology for operando characterization of homogeneous and heterogeneous reactions under harsh conditions in gas and liquid phases currently unavailable for regular users. The scientific cases will be based on two classes of novel catalytic systems: Ru-mediated defunctionalization of polyols to olefins and alkenylation of arenes via direct C-H activation over single-site Pd-catalysts. Initially, a spectral database of well-defined Pd and Ru compounds will be collected and used as a training set for machine learning. Then, we will step by step increase the complexity of experimental conditions from currently available cells to a reactor that can withstand up to 250°C and 50 bar, with the possibility to sample the gas phase and carefully dose liquid reactants. Finally, the ML-based system will be implemented and tested allowing for online evaluation of structural and catalytic data and automated refining of the reaction conditions.

The project explores the development of an AI model using computer vision for precise household waste classification, targeting plastics, cardboard, glass, cans, and paper. The literature review underscores the environmental concern in Costa Rica and examines AI techniques in solid waste management for enhanced efficiency and accuracy.The methodology employs a mixed approach, combining quantitative and applied elements. A carefully selected dataset and augmentation techniques are used to develop a robust AI model. Evaluation occurs in real household environments with active user participation. Results indicate good performance in classifying with positive user acceptance.

Active Data Warehousing Market Outlook

The global Active Data Warehousing market size was valued at approximately USD 15 billion in 2023 and is expected to reach around USD 40 billion by 2032, growing at a remarkable CAGR of 12%. This growth is primarily driven by the increasing demand for real-time data analytics and the expanding deployment of cloud-based solutions across various industries. Additionally, the rise in data volume and the necessity for businesses to make timely and data-driven decisions are significant growth factors for this market.

One of the major growth factors in the Active Data Warehousing market is the exponential rise in data generation from various sources, including social media, IoT devices, and enterprise-level applications. Businesses are increasingly recognizing the importance of leveraging this data to gain actionable insights and maintain a competitive edge. The ability to process and analyze data in real-time allows organizations to respond swiftly to market changes, customer needs, and operational challenges, thereby driving the demand for active data warehousing solutions.

The rapid adoption of cloud computing technologies is another significant factor fueling the growth of the Active Data Warehousing market. Cloud-based data warehousing solutions offer scalability, flexibility, and cost-efficiency, making them an attractive option for organizations of all sizes. The ease of integration with various data sources and the ability to scale resources up or down based on demand further enhance the appeal of cloud-based solutions. Consequently, many businesses are shifting from traditional on-premises data warehousing solutions to cloud-based platforms, contributing to market expansion.

Moreover, advancements in artificial intelligence (AI) and machine learning (ML) technologies are playing a crucial role in the evolution of active data warehousing. These technologies enable more sophisticated data processing and analytics, allowing businesses to uncover deeper insights and make more informed decisions. AI and ML algorithms can automate various analytical processes, reduce human intervention, and enhance the accuracy and speed of data analysis. This technological progression is expected to continue driving the adoption of active data warehousing solutions across different sectors.

Regionally, North America is expected to dominate the Active Data Warehousing market during the forecast period, owing to the presence of leading technology companies, high adoption of advanced analytics solutions, and a robust IT infrastructure. However, the Asia Pacific region is anticipated to witness the highest growth rate due to the increasing digital transformation initiatives, rising adoption of cloud services, and the expanding e-commerce and retail sectors in countries such as China and India. Europe is also expected to show significant growth, driven by stringent data regulations and the growing emphasis on data-driven decision-making.

Component Analysis

The Active Data Warehousing market is segmented by component into software, hardware, and services. The software segment encompasses various data warehousing tools and platforms that are essential for managing and analyzing large volumes of data. These software solutions include data integration tools, data quality tools, and analytics and BI (Business Intelligence) platforms. The continuous development of more advanced and user-friendly software solutions is a major driver of growth in this segment. Additionally, the integration of AI and ML capabilities into these software solutions is further enhancing their functionality and appeal.

The hardware segment primarily includes servers, storage systems, and networking equipment that are necessary for supporting data warehousing operations. With the increasing volume of data being generated, there is a growing need for robust and scalable hardware solutions to store, manage, and process this data efficiently. Innovations in hardware technology, such as high-performance computing (HPC) and the development of more powerful and energy-efficient servers, are contributing to the growth of this segment. Additionally, the rising adoption of hybrid and edge computing models is driving the demand for versatile and adaptable hardware solutions.

The services segment includes a wide range of professional services such as consulting, implementation, maintenance, and support services. These services are crucial for the successful deployment and ongoing management of data warehousing solutions. Consulting services help orga

In January 2025, deepseek.com attracted a total of 278 million visits. Male users accounted for over two-thirds. With a fraction of costs to develop its advanced large language model, the Chinese company Deepseek has rapidly emerged as a significant player in the global AI industry. Its chatbot app hit 20 million daily active users in just three weeks.

In 2020, there will be *** billion digital voice assistants being used in devices around the world. Forecasts suggest that by 2024, the number of digital voice assistants will reach *** billion units – a number higher than the world’s population. Virtual assistants Virtual assistants, an increasingly commonplace feature of many consumer electronics devices, can respond to commands, provide users with information, and assist in the control of other connected electronics. There are over *** million virtual assistant users in the United States alone, and the software is especially common in smartphones and smart speakers. As of 2019, Amazon’s Alexa was supported on around ****** different smart home devices around the world, providing an excellent example of just how popular the software has become. “Smart” everything Virtual assistants have become a key component of the smart device industry, being absolutely integral to the way that consumers interact with their devices. As the industry grows and its technology becomes more advanced, companies are increasingly searching for bigger and better uses of “smart” technology. Tech savvy consumers can now communicate with their connected homes and vehicles in much the same way that they can with their smartphones.