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Join our interactive powerhouse panel of industry leaders from MongoDB, Databricks, and DataRobot who are leveraging the power of data analytics and AI to drive innovation and business growth for customers. Gain firsthand insights into real-world examples revolutionizing industries. Discover use cases, success stories, and best practices for unlocking the true power of data-driven decision-making and AI innovation. Don't miss this opportunity to stay ahead of the curve and unlock the full potential of your organization's data strategies. Speakers: Debanjan Saha, David Meyer, Claude Robinson Watch more: All sessions from Google Cloud Next → https://goo.gle/next23 #GoogleCloudNext ANA214

The role of trust is an essential area of research for explainable AI, where trust is measured by the human's willingness to use the machine's suggestions. There are several existing studies that investigate and show that human-AI team performance on decision-making tasks improves when the AI explains its recommendations. However, most of these studies focus on binary decision-making tasks where the human and the machine have near-equal performance outcomes. This is different from a typical visualization scenario as analysts might want to explore the data and gather their own insights. In this project, we conduct user studies to understand the factors that influence trust in a data exploration setting where the task complements both the strengths of the human and the machine. As visual interfaces for data exploration already display a lot of information, we investigate the minimal amount of explanations needed for building trust and encouraging suggestion usage while minimizing information overload. We also seek to understand whether there truly is a correlation between the trust perceptions of the user and the usage of AI/ML suggestions. Explanations of the suggestions will be shown to users through a combination of keywords and confidence percentages.

Data practitioners spend much of their time on complex, fragmented and sometimes, repetitive tasks. This limits their ability to focus on strategic insights and maximize the value of their data. Gemini in BigQuery shifts this paradigm by providing AI and data analytics capabilities that help streamline your workflows across the entire data lifecycle. In this video, we'll explore how Google Gemini in BigQuery transforms the experience through assistance and automation. It empowers you with features such as intent-driven development with SQL and Python code assistance, semantic search of data assets, workload optimization, AI-driven data pipelines through Google Cloud BigQuery data transformation, a natural-language-based data journey experience with BigQuery data canvas, and more. Ready to transform your data analysis workflows? Discover how Gemini in BigQuery can unlock new levels of efficiency and productivity. Resources: Learn more in the documentation → https://goo.gle/4aGJ5u9 Read the blog → https://goo.gle/gemini-in-bq-blog Gemini → https://goo.gle/4aKqqxj Try Gemini in Vertex AI → https://goo.gle/4bKd193 Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech #gemini #googlecloud

We want to gain insights into the current state of the implemetation of ML and AI in the real estate industry, key drivers, challenges and opportunities related to new technologies and sustainability in the sector.

Discover how AI and machine learning transform big data into actionable insights, driving innovation, automation, and smarter decision-making.

Epidemics, such as Dengue and Influenza, remain significant threats to public health, particularly in densely populated areas. These diseases can spread rapidly, posing a challenge to early detection and containment. Existing methods for epidemic prediction often rely on basic surveillance and historical data, but these approaches have limitations, including a lack of real-time updates and the ability to predict disease trends with high accuracy. This project aims to leverage artificial intelligence (AI) and machine learning techniques to predict the infection status of individuals based on blood sample data and visualize epidemic spread patterns. Using medical datasets containing blood parameters such as WBC count, CRP levels, platelet count, and age, the AI model can classify individuals as infected, cured, or deceased, and determine whether the cause is Dengue or Influenza. Machine learning algorithms like Logistic Regression, AdaBoost, ANN, Random Forest, LGBM, and Decision Trees have been applied to the data, resulting in high prediction accuracy. The system also integrates real-time visualizations, showing infection statistics, recovery trends, and mortality rates, and it utilizes population data to predict the regional spread of the epidemic. This project represents a significant advancement over traditional methods by providing a robust, AI-based solution for epidemic prediction and management.

This open access book explains how leading business organizations attempt to achieve the responsible and ethical use of artificial intelligence (AI) and other advanced information technologies. These technologies can produce tremendous insights and benefits. But they can also invade privacy, perpetuate bias, and otherwise injure people and society. To use these technologies successfully, organizations need to implement them responsibly and ethically. The question is: how to do this? Data ethics management, and this book, provide some answers. The authors interviewed and surveyed data ethics managers at leading companies. They asked why these experts see data ethics as important and how they seek to achieve it. This book conveys the results of that research on a concise, accessible way. Much of the existing writing on data and AI ethics focuses either on macro-level ethical principles, or on micro-level product design and tooling. The interviews showed that companies need a third component: data ethics management. This third element consists of the management structures, processes, training and substantive benchmarks that companies use to operationalize their high-level ethical principles and to guide and hold accountable their developers. Data ethics management is the connective tissue makes ethical principles real. It is the focus of this book. This book should be of use to organizations that wish to improve their own data ethics management efforts, legislators and policymakers who hope to build on existing management practices, scholars who study beyond compliance business behavior, and members of the public who want to understand better the threats that AI poses and how to reduce them.

The rapid growth of digital data has forced businesses to rethink how they manage and utilize their information.