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According to the World Health Organization, more than one billion people worldwide have disabilities. The field of disability studies defines disability through a social lens, which considers people disabled to the extent that society creates accessibility barriers.
This workshop will cover how NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. You will learn how to use GPU-accelerated tools to conduct data science faster, leading to more scalable, reliable, and cost-effective results!
This workshop will cover how NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. You will learn how to use GPU-accelerated tools to conduct data science faster, leading to more scalable, reliable, and cost-effective results!
Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.
Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.
For example, computer vision might give people who are blind a better sense of the visual world, speech recognition and translation technologies might offer real-time captioning for people who are hard of hearing, and new robotic systems might augment the capabilities of people with mobility restrictions. Considering the needs of users with disabilities can help technologists identify high-impact challenges whose solutions can advance the state of AI for all users. At the same time, ethical challenges such as inclusion, bias, privacy, error, expectation setting, simulated data, and social acceptability must be considered. In this lecture, I will define these seven challenges, provide examples of how they relate to AI for Accessibility technologies, and discuss future considerations in this space.