WiserBrand's Comprehensive Customer Call Transcription Dataset: Tailored Insights

WiserBrand offers a customizable dataset comprising transcribed customer call records, meticulously tailored to your specific requirements. This extensive dataset includes:

User ID and Firm Name: Identify and categorize calls by unique user IDs and company names. Call Duration: Analyze engagement levels through call lengths. Geographical Information: Detailed data on city, state, and country for regional analysis. Call Timing: Track peak interaction times with precise timestamps. Call Reason and Group: Categorised reasons for calls, helping to identify common customer issues. Device and OS Types: Information on the devices and operating systems used for technical support analysis. Transcriptions: Full-text transcriptions of each call, enabling sentiment analysis, keyword extraction, and detailed interaction reviews.

Our dataset is designed for businesses aiming to enhance customer service strategies, develop targeted marketing campaigns, and improve product support systems. Gain actionable insights into customer needs and behavior patterns with this comprehensive collection, particularly useful for Consumer Data, Consumer Behavior Data, Consumer Sentiment Data, Consumer Review Data, AI Training Data, Textual Data, and Transcription Data applications.

WiserBrand's dataset is essential for companies looking to leverage Consumer Data and B2B Marketing Data to drive their strategic initiatives in the English-speaking markets of the USA, UK, and Australia. By accessing this rich dataset, businesses can uncover trends and insights critical for improving customer engagement and satisfaction.

Cases:

1. Training Speech Recognition (Speech-to-Text) and Speech Synthesis (Text-to-Speech) Models WiserBrand's Comprehensive Customer Call Transcription Dataset is an excellent resource for training and improving speech recognition models (Speech-to-Text, STT) and speech synthesis systems (Text-to-Speech, TTS). Here’s how this dataset can contribute to these tasks:

Enriching STT Models: The dataset includes a wide variety of real-world customer service calls with diverse accents, tones, and terminologies. This makes it highly valuable for training speech-to-text models to better recognize different dialects, regional speech patterns, and industry-specific jargon. It could help improve accuracy in transcribing conversations in customer service, sales, or technical support.

Contextualized Speech Recognition: Given the contextual information (e.g., reasons for calls, call categories, etc.), it can help models differentiate between various types of conversations (technical support vs. sales queries), which would improve the model’s ability to transcribe in a more contextually relevant manner.

Improving TTS Systems: The transcriptions, along with their associated metadata (such as call duration, timing, and call reason), can aid in training Text-to-Speech models that mimic natural conversation patterns, including pauses, tone variation, and proper intonation. This is especially beneficial for developing conversational agents that sound more natural and human-like in their responses.

Noise and Speech Quality Handling: Real-world customer service calls often contain background noise, overlapping speech, and interruptions, which are crucial elements for training speech models to handle real-life scenarios more effectively.

1. Training AI Agents for Replacing Customer Service Representatives WiserBrand’s dataset can be incredibly valuable for businesses looking to develop AI-powered customer support agents that can replace or augment human customer service representatives. Here’s how this dataset supports AI agent training:

Customer Interaction Simulation: The transcriptions provide a comprehensive view of real customer interactions, including common queries, complaints, and support requests. By training AI models on this data, businesses can equip their virtual agents with the ability to understand customer concerns, follow up on issues, and provide meaningful solutions, all while mimicking human-like conversational flow.

Sentiment Analysis and Emotional Intelligence: The full-text transcriptions, along with associated call metadata (e.g., reason for the call, call duration, and geographical data), allow for sentiment analysis, enabling AI agents to gauge the emotional tone of customers. This helps the agents respond appropriately, whether it’s providing reassurance during frustrating technical issues or offering solutions in a polite, empathetic manner. Such capabilities are essential for improving customer satisfaction in automated systems.

Customizable Dialogue Systems: The dataset allows for categorizing and identifying recurring call patterns and issues. This means AI agents can be trained to recognize the types of queries that come up frequently, allowing them to automate routine tasks such as ...

By 2034, the AI Agents Data Analysis Market is expected to reach a valuation of USD 38.1 billion, expanding at a healthy CAGR of 38.2%.

The Visual AI Agents market is experiencing robust growth, driven by increasing adoption of AI across various sectors and advancements in computer vision technologies. The market, estimated at $5 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $25 billion by 2033. This expansion is fueled by several key factors. The surge in demand for automated visual data analysis across industries like healthcare (medical image analysis), manufacturing (quality control and predictive maintenance), retail (customer behavior analysis), and autonomous vehicles (object detection and navigation) is a primary driver. Furthermore, improvements in deep learning algorithms, the availability of large labeled datasets for training, and the decreasing cost of computing power are significantly accelerating market growth. Key players like Google, Amazon Web Services (AWS), Microsoft, and IBM are actively investing in research and development, fostering innovation and competition in this space. The market is segmented by application (e.g., image recognition, object detection, video analytics), deployment (cloud, on-premises), and industry vertical, each exhibiting unique growth trajectories. However, certain restraints exist. The high cost of implementation and maintenance, concerns about data privacy and security, and the need for specialized expertise to develop and deploy visual AI agents are limiting factors. Addressing these challenges through the development of more user-friendly interfaces, robust security protocols, and accessible training resources will be crucial for unlocking the full market potential. Despite these challenges, the long-term outlook for Visual AI Agents remains exceptionally positive, driven by continuous technological advancements and the expanding application scope across diverse sectors. The emergence of edge AI and the integration of visual AI agents into IoT devices are expected to further propel market expansion in the coming years.

AI Agents Market size was valued at USD 3.84 Billion in 2024 and is projected to reach USD 51.58 Billion by 2032, growing at a CAGR of 38.5% from 2025 to 2032.

AI Agents Market Drivers

Efficiency and Productivity: Businesses across industries are seeking to automate repetitive tasks, streamline workflows, and improve overall efficiency. AI agents offer the potential to automate complex processes, freeing up human employees for more strategic and creative work.

Cost Reduction: Automating tasks with AI agents can significantly reduce labor costs and operational expenses, making it an attractive option for businesses looking to optimize their resources.

Improved Natural Language Processing (NLP): Breakthroughs in NLP enable AI agents to understand and respond to human language more effectively, making them more capable of interacting with users in a natural and intuitive way.

Enhanced Machine Learning Algorithms: Advancements in machine learning algorithms allow AI agents to learn from data and improve their performance over time, making them more adaptable and intelligent.

The Autonomous Agents market is experiencing explosive growth, projected to reach a substantial size by 2033, driven by a remarkable Compound Annual Growth Rate (CAGR) of 57%. This rapid expansion is fueled by several key factors. The increasing adoption of cloud-based solutions across various industries streamlines deployment and reduces infrastructure costs, significantly boosting market penetration. Furthermore, the rising demand for automation in sectors like BFSI (Banking, Financial Services, and Insurance), IT & Telecom, and Healthcare, where autonomous agents offer enhanced efficiency and improved customer experiences, is a major driver. Large enterprises are leading the adoption curve, leveraging autonomous agents for complex tasks and data analysis. However, concerns regarding data security and privacy, along with the need for significant upfront investment in infrastructure and skilled personnel, pose potential restraints on market growth. The market is segmented by deployment type (cloud and on-premises), organization size (SMEs and large enterprises), and industry vertical (BFSI, IT & Telecom, Healthcare, Manufacturing, Transportation & Mobility, and others). The competition is fierce, with established players like SAS Institute, Infosys, IBM, Google, and Salesforce competing alongside emerging innovators in the space. Geographic distribution shows significant market presence across North America, Europe, and the Asia-Pacific region, with North America currently holding a leading market share. The future of the Autonomous Agents market hinges on overcoming technological hurdles and addressing regulatory concerns. Advancements in artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) will further enhance the capabilities of autonomous agents, expanding their applications in diverse fields. The integration of autonomous agents with existing enterprise systems will be crucial for seamless implementation. Focus will shift towards developing robust security protocols to mitigate privacy risks. The market’s growth trajectory will also depend on the successful adoption of autonomous agents by SMEs, which currently lags behind large enterprises. Strategic partnerships and mergers and acquisitions are expected to further shape the competitive landscape in the years to come. Continuous innovation in agent design, coupled with a focus on user experience and trust-building, will be key to unlocking the full potential of this transformative technology. This in-depth report provides a comprehensive analysis of the burgeoning Autonomous Agents Market, offering invaluable insights for businesses and investors seeking to navigate this rapidly evolving landscape. Covering the period from 2019 to 2033, with a focus on 2025, this report examines market dynamics, growth drivers, challenges, and future trends, projecting a market valued in the billions of dollars by 2033. The study encompasses detailed segmentation by deployment type (cloud, on-premises), organization size (SMEs, large enterprises), and industry vertical (BFSI, IT & Telecom, Healthcare, Manufacturing, Transportation & Mobility, and others). Key players such as SAS Institute Inc, Infosys Limited, Fair Isaac Corporation, Aptiv PLC, IBM Corporation, Google LLC, Nuance Communications, Salesforce com Inc, Microsoft Corporation, Affectiva Inc, Amazon Web Services Inc, Fetch.ai, Oracle Corporation, Intel Corporation, and SAP SE are profiled, providing a clear understanding of the competitive landscape. Key drivers for this market are: , Rising Number of AI Applications; Growing Presence of Parallel Computational Resources. Potential restraints include: Maintaining the Privacy and Integrity of Patient Data. Notable trends are: Transportation and Mobility Segments to Dominate the Market.

The market for Visual AI Agents is projected to reach $XXX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The growing adoption of AI technologies in various industries, such as customer service, healthcare, and manufacturing, is driving the demand for Visual AI Agents. These agents use computer vision and natural language processing (NLP) to understand and respond to human requests in a more intuitive and natural way, enhancing the user experience. Key trends shaping the market include the increasing use of cloud-based Visual AI Agents, the integration of advanced analytics and machine learning capabilities, and the growing focus on personalization and customization. Moreover, the rising demand for remote customer service and support is fueling the market's growth. However, factors such as privacy concerns, data security risks, and the need for continuous training and maintenance may restrain market growth to some extent. North America is expected to hold the largest market share due to the high adoption rate of AI technologies and the presence of major players. Asia Pacific is anticipated to witness significant growth, driven by the rapidly expanding tech industry and increasing internet penetration.

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AI Agent Platform Market Size 2025-2029

The AI agent platform market size is forecast to increase by USD 23.56 billion at a CAGR of 41.1% between 2024 and 2029.

The market is witnessing significant growth, driven by the rapid advancements in foundational AI models and reasoning capabilities. These advancements enable agents to learn and adapt more effectively, leading to improved performance and increased value for businesses. Security protocols and agent communication protocols are critical for maintaining data security. However, this progress also brings challenges, as the shift from single-purpose agents to collaborative multi-agent systems necessitates new approaches to ensuring reliability and mitigating agentic hallucinations. Collaborative systems, while offering increased efficiency and versatility, introduce complexities that must be addressed to prevent miscommunications and errors.
Additionally, prioritizing transparency and explainability in AI models will be crucial for building trust and fostering widespread adoption. In summary, the market is poised for growth, but companies must navigate the challenges of collaborative systems and ensure reliability to maximize their potential. Companies seeking to capitalize on the opportunities presented by the market must focus on developing robust, adaptive systems that can navigate these complexities and deliver reliable, accurate results. Deep learning algorithms and explainable AI improve agent performance and transparency.

What will be the Size of the AI Agent Platform Market during the forecast period?

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In the dynamic market, system architecture and model accuracy are paramount for task completion rate optimization. Monitoring dashboards provide real-time insights into model performance, enabling error rate analysis and precision and recall assessment. Data augmentation techniques enhance model training, while sentiment analysis and intent recognition improve user experience. Continuous deployment, model retraining, and model version control ensure AUC score maximization. Data security is paramount, with edge computing and cloud computing offering solutions. Performance tuning and infrastructure cost management are essential for maintaining optimal response time latency and compute resource utilization.

Data quality assessment and entity extraction are crucial for maintaining high user satisfaction scores. Alerting systems and dialogue management facilitate continuous integration and A/B testing, enabling prompt engineering and knowledge reasoning. Log analysis is essential for identifying and addressing performance issues, while F1 score evaluation offers a comprehensive assessment of model effectiveness. Computer vision and image recognition are transforming industries like healthcare and education. Cloud-based infrastructure and task delegation mechanisms facilitate seamless integration and execution of these intelligent agents.

How is this AI Agent Platform Industry segmented?

The AI agent platform 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.

Technology

  ML
  NLP
  Others


Type

  Single agent systems
  Multi agent systems


End-user

  Financial services
  Retail and eCommerce
  IT and telecommunication
  Healthcare
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Technology Insights

The ML segment is estimated to witness significant growth during the forecast period. The market is witnessing significant advancements, with machine learning (ML) serving as the foundational intelligence layer. ML models, particularly reinforcement learning, enable agents to autonomously navigate complex environments and optimize actions toward achieving specific goals. Agents learn from interaction feedback, refining strategies through trial and error, mimicking human learning. Sophisticated ML models enable agents to reason, plan long term, and self-correct. Advanced frameworks are increasingly integrated into platforms, enabling the handling of dynamic, unpredictable tasks. These solutions optimize intricate supply chains by predicting demand fluctuations, manage financial portfolios, and analyze market signals. Agent autonomy levels are expanding, with human-in-the-loop AI allowing for collaboration between humans and agents.

Data privacy measures are crucial, with federated learning enabling data processing on decentralized devices, preserving data c

Analysis of ‘L&I Apprenticeship Training Agent details’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/30584bee-b052-4f8e-8b7a-439ddb3e062a on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Updated monthly for all active Training Agents for Washington State registered apprenticeship programs. Use the Program ID and Program Occupation ID as the unique identifier to link data from other L&I Apprenticeship datasets.

--- Original source retains full ownership of the source dataset ---

The global market for Dynamic AI Agents is experiencing robust growth, driven by increasing demand for automated customer service, improved operational efficiency, and the need for personalized user experiences across various industries. While precise market sizing data isn't provided, considering similar AI-powered solutions and their growth trajectories, a reasonable estimation for the 2025 market size could be around $5 billion, with a Compound Annual Growth Rate (CAGR) of 20% projected for the forecast period (2025-2033). This growth is fueled by several key factors. Firstly, the shift towards cloud-based solutions offers scalability and cost-effectiveness, driving wider adoption among both large enterprises and SMEs. Secondly, advancements in Natural Language Processing (NLP) and Machine Learning (ML) are enabling more sophisticated and human-like interactions, improving customer satisfaction and reducing operational costs. Finally, the rising integration of Dynamic AI Agents into diverse applications, from chatbots and virtual assistants to personalized recommendations, is expanding market reach and application opportunities. The market segmentation reveals a significant share held by cloud-based solutions, reflecting the trend towards agility and reduced infrastructure management. Large enterprises currently dominate the application segment, leveraging the technology for streamlining complex workflows and optimizing customer interactions. However, the SME segment shows significant potential for future growth as adoption accelerates. Geographic analysis suggests that North America and Europe currently hold the largest market shares, owing to early adoption and technological advancements. However, the Asia-Pacific region is expected to witness the fastest growth due to increasing digitalization and a large pool of potential users. Despite this promising outlook, challenges such as data security concerns, integration complexities, and the need for ongoing maintenance and updates could potentially restrain market growth to some extent. The success of Dynamic AI Agent deployment hinges on addressing these challenges while continuing to innovate and improve user experience.

Simulation plays a crucial role in modern academic study, particularly in the field of artificial intelligence (AI). The simulation environment can mimic real-world scenarios, allowing the AI agent to learn, adapt, and make decisions in a controlled and safe setting. This thesis tackles two important problems in building the next generation of artificial general intelligence (AGI): how to efficiently train an AI agent with values and how to overcome the simulation to reality gap to bring the training results to real-world applications. The current studies of AI mainly consider learning about the potential or energy function (U), referring to understanding the impact of the outside environment. The U function helps the agent apprehend the physical world laws, natural potentials, and social norms. However, taking into account the value learning, usually representing modeling one's inside thinking, benefits the agent to derive its goals, intents, and social values. Our research shows that both U and V learning are equally important to the pathway to AGI. The learning of U is usually data-driven. It enables the agent to imitate and complete the task through statistical learning. By incorporating the value function, the agent can spontaneously specify a task plan and its behavior is more in line with human cognition and value.This thesis consists of three parts: (1) Potential function learning, which explores the process of acquiring knowledge or skills that are useful and practical for a particular purpose. (2) Value learning when learning the potential (U) function can not satisfy all the learning goals, which investigates situations where utility-based learning approaches might be limited or ineffective. (3) Combining U and V learning, which focuses on the integration of simulation-based learning and data-driven learning methods. We primarily focus on assessing the effectiveness of U learning within a simulated environment. Our investigation commences with agents operating in a controlled simulated setting, where the action space is intentionally kept small. Through rigorous testing and iterative refinement, we gradually expand the scope of our analysis to encompass agents dealing with increasingly complex and continuous action spaces. Upon achieving compelling results in the simulated realm, we proceed to the crucial next step: transferring the knowledge and expertise gained from the well-trained agents in the simulation space to real-world scenarios. This process entails adapting the learned policies, strategies, and decision-making capabilities of the agents to navigate the intricacies and uncertainties of genuine environments.

Autonomous AI and Autonomous Agents Market size was valued at USD 6.9 Billion in 2024 and is projected to reach USD 126.2 Billion by 2032, growing at a CAGR of 43.8 % from 2025 to 2032.

Key drivers of the Autonomous AI and Autonomous Agents Market include rapid advancements in machine learning, deep learning, and generative AI, enabling more sophisticated decision-making and automation. The growing demand for intelligent automation in industries such as healthcare, finance, manufacturing, and logistics is fueling adoption, driven by the need for efficiency, cost reduction, and real-time decision-making.

Additionally, the rise of IoT, 5G connectivity, and edge computing is enhancing AI autonomy by enabling real-time data processing and seamless interaction between AI-driven systems. Increasing investments in AI research, along with supportive government policies and regulations, are further accelerating market growth, making autonomous AI a key driver of digital transformation.

CoMP

Effective animal training depends on well-structured training plans that ensure consistent progress and measurable outcomes. However, the creation of such plans is often time-intensive, repetitive, and detracts from hands-on training. Recent advancements in generative AI powered by large language models (LLMs) provide potential solutions but frequently fail to produce actionable, individualized plans tailored to specific contexts. This limitation is particularly significant given the diverse tasks performed by dogs–ranging from working roles in military and police operations to competitive sports–and the varying training philosophies among practitioners. To address these challenges, a modular agentic workflow framework is proposed, leveraging LLMs while mitigating their shortcomings. By decomposing the training plan generation process into specialized building blocks–autonomous agents that handle subtasks such as structuring progressions, ensuring welfare compliance, and adhering to team-specific standard operating procedures (SOPs)—this approach facilitates the creation of specific, actionable plans. The modular design further allows workflows to be tailored to the unique requirements of individual tasks and philosophies. As a proof of concept, a complete training plan generation workflow is presented, integrating these agents into a cohesive system. This framework prioritizes flexibility and adaptability, empowering trainers to create customized solutions while leveraging generative AI's capabilities. In summary, agentic workflows bridge the gap between cutting-edge technology and the practical, diverse needs of the animal training community. As such, they could form a crucial foundation for advancing computer-assisted animal training methodologies.

AI Agent Evasion Dataset Overview The AI Agent Evasion Dataset is a comprehensive collection of 1000 prompts designed to train and evaluate large language models (LLMs) against advanced attacks targeting AI-driven systems, such as chatbots, APIs, and voice assistants. It addresses vulnerabilities outlined in the OWASP LLM Top 10, including prompt injection, data leakage, and unauthorized command execution. The dataset balances 70% malicious prompts (700 entries) with 30% benign prompts (300… See the full description on the dataset page: https://huggingface.co/datasets/darkknight25/AI_Agent_Evasion_Dataset.

The dataset contains the data collected for the research in the PhD thesis "Calculated Moves". Two types of data were collected. The first type are the results of agent-based air combat simulations, in which the agents learned by act by means of machine learning. The second type are the results of a validation study, in which we aimed to determine whether the learned behaviour was fit for use in real-world training simulations.

The Arcade Learning Environment (ALE) is an object-oriented framework that allows researchers to develop AI agents for Atari 2600 games. It is built on top of the Atari 2600 emulator Stella and separates the details of emulation from agent design.

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Quantum Computing For AI Market Size 2025-2029

The quantum computing for AI market size is forecast to increase by USD 614.6 million at a CAGR of 35.2% between 2024 and 2029.

The market is experiencing significant momentum, driven by continuous and rapid advancements in quantum hardware technology. This technological evolution is enabling the development of increasingly powerful quantum computers, which hold the potential to revolutionize Artificial Intelligence applications by solving complex problems much faster than classical computers. Another key trend in the market is the rise of integrated hybrid quantum-classical systems. These systems combine the strengths of both quantum and classical computing, allowing for the efficient processing of large data sets and the execution of complex algorithms.
Moreover, achieving fault tolerance in quantum systems remains a major challenge, requiring advanced error correction techniques to ensure the reliability and stability of quantum computations. Companies seeking to capitalize on the opportunities presented by the market must address these challenges effectively, investing in research and development to overcome hardware noise and develop robust fault tolerance strategies. Quantum data compression reduces storage requirements, and quantum deep learning enhances neural networks. However, the market faces challenges as well. One significant obstacle is pervasive hardware noise, which can lead to errors and inaccuracies in quantum computations.

What will be the Size of the Quantum Computing For 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.
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Quantum finance models are being developed to optimize financial portfolios, while quantum feature extraction enhances AI algorithms' performance. Quantum cryptography applications secure data transmission, and quantum risk management mitigates risks with higher precision. In the realm of natural language processing, quantum natural language models improve language understanding. Quantum circuit optimization streamlines AI workflows, and post-quantum cryptography ensures data security in a quantum world. Quantum reinforcement learning expedites the training of AI agents, and quantum algorithm complexity offers new insights into AI optimization.

Quantum search algorithms discover patterns in vast datasets, and quantum inspired algorithms mimic quantum phenomena for AI solutions. Quantum computing, a revolutionary technology, is transforming the Artificial Intelligence (AI) market dynamics with its potential to solve complex problems that classical computers cannot. Quantum AI applications span various industries, including materials science, computer vision, drug discovery, computational chemistry, and more. Quantum error correction ensures data reliability, and quantum generative models create realistic data. Quantum hardware acceleration boosts AI performance, and quantum recommendation systems personalize user experiences. Quantum software libraries facilitate quantum AI adoption, and quantum hardware advances fuel innovation.

How is this Quantum Computing For AI Industry segmented?

The quantum computing for 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.

Technology

  Superconducting qubits
  Trapped ions
  Photonic systems
  Spin qubits


Deployment

  On-premises
  Cloud-based


End-user

  Healthcare and life sciences
  BFSI
  Automotive and aerospace
  Defense and security
  Energy


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Technology Insights

The Superconducting qubits segment is estimated to witness significant growth during the forecast period. Quantum computing for Artificial Intelligence (AI) is a rapidly advancing field, driven by technological innovations such as quantum supremacy claims, quantum tomography, and quantum circuit design. Error correction codes and quantum cloud computing enable larger-scale quantum computations, while hybrid quantum-classical approaches combine the strengths of both quantum and classical computing. Quantum entanglement, a unique phenomenon in quantum mechanics, is harnessed for quantum machine learning and quantum information theory. Quantum optimization and resource estimation are essential for solving complex problems in various industries. Topological quantum computing and gate-based quantum computing offer distinct approaches to building quantum computers.

The market is experiencing significant growth

CVE Chat‑Style Multi‑Turn Cybersecurity Dataset (1999 – 2025)

  1. Project Overview

This repository hosts the largest publicly available chat‑style, multi‑turn cybersecurity dataset to date, containing ≈ 300 000 Common Vulnerabilities and Exposures (CVE) records published between 1999 and 2025. Each record has been meticulously parsed, enriched, and converted into a conversational format that is ideal for training and evaluating AI and AI‑Agent systems focused on… See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/All-CVE-Records-Training-Dataset.

AI Training Data | Annotated Checkout Flows for Retail, Restaurant, and Marketplace Websites Overview

Unlock the next generation of agentic commerce and automated shopping experiences with this comprehensive dataset of meticulously annotated checkout flows, sourced directly from leading retail, restaurant, and marketplace websites. Designed for developers, researchers, and AI labs building large language models (LLMs) and agentic systems capable of online purchasing, this dataset captures the real-world complexity of digital transactions—from cart initiation to final payment.

Key Features

Breadth of Coverage: Over 10,000 unique checkout journeys across hundreds of top e-commerce, food delivery, and service platforms, including but not limited to Walmart, Target, Kroger, Whole Foods, Uber Eats, Instacart, Shopify-powered sites, and more.

Actionable Annotation: Every flow is broken down into granular, step-by-step actions, complete with timestamped events, UI context, form field details, validation logic, and response feedback. Each step includes:

Page state (URL, DOM snapshot, and metadata)

User actions (clicks, taps, text input, dropdown selection, checkbox/radio interactions)

System responses (AJAX calls, error/success messages, cart/price updates)

Authentication and account linking steps where applicable

Payment entry (card, wallet, alternative methods)

Order review and confirmation

Multi-Vertical, Real-World Data: Flows sourced from a wide variety of verticals and real consumer environments, not just demo stores or test accounts. Includes complex cases such as multi-item carts, promo codes, loyalty integration, and split payments.

Structured for Machine Learning: Delivered in standard formats (JSONL, CSV, or your preferred schema), with every event mapped to action types, page features, and expected outcomes. Optional HAR files and raw network request logs provide an extra layer of technical fidelity for action modeling and RLHF pipelines.

Rich Context for LLMs and Agents: Every annotation includes both human-readable and model-consumable descriptions:

“What the user did” (natural language)

“What the system did in response”

“What a successful action should look like”

Error/edge case coverage (invalid forms, OOS, address/payment errors)

Privacy-Safe & Compliant: All flows are depersonalized and scrubbed of PII. Sensitive fields (like credit card numbers, user addresses, and login credentials) are replaced with realistic but synthetic data, ensuring compliance with privacy regulations.

Each flow tracks the user journey from cart to payment to confirmation, including:

Adding/removing items

Applying coupons or promo codes

Selecting shipping/delivery options

Account creation, login, or guest checkout

Inputting payment details (card, wallet, Buy Now Pay Later)

Handling validation errors or OOS scenarios

Order review and final placement

Confirmation page capture (including order summary details)

Why This Dataset?

Building LLMs, agentic shopping bots, or e-commerce automation tools demands more than just page screenshots or API logs. You need deeply contextualized, action-oriented data that reflects how real users interact with the complex, ever-changing UIs of digital commerce. Our dataset uniquely captures:

The full intent-action-outcome loop

Dynamic UI changes, modals, validation, and error handling

Nuances of cart modification, bundle pricing, delivery constraints, and multi-vendor checkouts

Mobile vs. desktop variations

Diverse merchant tech stacks (custom, Shopify, Magento, BigCommerce, native apps, etc.)

Use Cases

LLM Fine-Tuning: Teach models to reason through step-by-step transaction flows, infer next-best-actions, and generate robust, context-sensitive prompts for real-world ordering.

Agentic Shopping Bots: Train agents to navigate web/mobile checkouts autonomously, handle edge cases, and complete real purchases on behalf of users.

Action Model & RLHF Training: Provide reinforcement learning pipelines with ground truth “what happens if I do X?” data across hundreds of real merchants.

UI/UX Research & Synthetic User Studies: Identify friction points, bottlenecks, and drop-offs in modern checkout design by replaying flows and testing interventions.

Automated QA & Regression Testing: Use realistic flows as test cases for new features or third-party integrations.

What’s Included

10,000+ annotated checkout flows (retail, restaurant, marketplace)

Step-by-step event logs with metadata, DOM, and network context

Natural language explanations for each step and transition

All flows are depersonalized and privacy-compliant

Example scripts for ingesting, parsing, and analyzing the dataset

Flexible licensing for research or commercial use

Sample Categories Covered

Grocery delivery (Instacart, Walmart, Kroger, Target, etc.)

Restaurant takeout/delivery (Ub...

Advances in systems immunology, such as new biomarkers, offer the potential for highly personalized immunosuppression regimens that could improve patient outcomes. In the future, integrating all of this information with other patient history data will likely have to rely on artificial intelligence (AI). AI agents can help augment transplant decision making by discovering patterns and making predictions for specific patients that are not covered in the literature or in ways that are impossible for humans to anticipate by integrating vast amounts of data (e.g. trending across numerous biomarkers). Similar to other clinical decision support systems, AI may help overcome human biases or judgment errors. However, AI is not widely utilized in transplant to date. In this rapid review, we survey the methods employed in recent research in transplant-related AI applications and identify concerns related to implementing these tools. We identify three key challenges (bias/accuracy, clinical decision process/AI explainability, AI acceptability criteria) holding back AI in transplant. We also identify steps that can be taken in the near term to help advance meaningful use of AI in transplant (forming a Transplant AI Team at each center, establishing clinical and ethical acceptability criteria, and incorporating AI into the Shared Decision Making Model).