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Introducing the AI, Misinformation, and Policy Seminar Series

NYU Center for Data Science

The AI, Misinformation, and Policy Seminar Series (AMPol) at the Center for Data Science explores this critical research area, featuring speakers working in the intersecting fields of data science, machine learning, and misinformation. To access the lecture slides, please visit Emily Saltz Lecture Slides. by Meryl Phair

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Combat AI-Powered Threats with Cybersecurity Simulations & Other Practices

Smart Data Collective

AI has arrived in several business spheres. While the rest of the world is discussing its impact and dealing with changes in workflows, cybersecurity experts have long dealt with the use of AI in malicious attacks. Despite this experience, AI’s increasing sophistication has always resulted in security experts playing catch up.

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Blending Theory and Utility: The Vision and Impact of CDS’s MaD Group

NYU Center for Data Science

Some people want both, and those people, if attending NYU, join the Math and Data research group at the Center for Data Science (CDS) , which, thanks to the ever-broader applicability of AI, is now working on some of the most important problems currently facing humanity. This is an enormously complex — and ambitious — endeavor.

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When Should AI Step Aside? Understanding Cultural Values in AI Systems

NYU Center for Data Science

CDS Faculty Fellow Umang Bhatt l eading a practical workshop on Responsible AI at Deep Learning Indaba 2023 in Accra In Uganda’s banking sector, AI models used for credit scoring systematically disadvantage citizens by relying on traditional Western financial metrics that don’t reflect local economic realities.

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Spatial Intelligence: Why GIS Practitioners Should Embrace Machine Learning- How to Get Started.

Towards AI

Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. Created by the author with DALL E-3 Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst?

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What are some ethical considerations when using Generative AI

Dataconomy

As generative AI becomes increasingly integrated into various industries, it is essential to consider the ethical implications of its use. What are some ethical considerations when using generative AI? Addressing these issues is crucial for ensuring that generative AI serves as a beneficial and responsible tool in our society.

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Data Science Journey Walkthrough – From Beginner to Expert

Smart Data Collective

Some of the applications of data science are driverless cars, gaming AI, movie recommendations, and shopping recommendations. Data scientists use algorithms for creating data models. Whereas in machine learning, the algorithm understands the data and creates the logic. Domain experts of all fields use it. Reinforcement.