Read about AI anxiety, model cards, our new developer portal and more. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
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Model cards serve as transparency tools, benefiting everyone involved in the AI life cycle, from creators and modelers to decision makers. Just as a nutrition label lists a food’s ingredients, a model card details an AI model’s training data, development process, accuracy, model drift, related fairness assessments and governance details. This transparency helps identify the model’s strengths and limitations, promoting trust and encouraging responsible and ethical use by all stakeholders. Learn more about model cards and why they are so important to model development.
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AI anxiety is the unease and stress people feel about AI’s growing influence in daily life. As AI becomes more pervasive, concerns about job security, privacy and ethical implications rise, leading to a mix of fear and uncertainty. Understanding AI and its impacts can help alleviate these worries. By educating ourselves and staying informed, we can better navigate the challenges and opportunities AI presents, turning anxiety into informed decision-making and strategic adaptation. Read more in this Insights article to explore how AI anxiety is shaping our world.
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Attention SAS developers: Say goodbye to the old SAS Developer Portal and hello to the new one! After two years of hard work, the revamped portal is live and better than ever. We've streamlined access and packed it with new features to make your experience seamless. Check out the fresh design and stay tuned for even more updates. Curious about what makes a great developer portal? Read more to dive into its components and discover how it benefits you.
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How does where you live impact your health and lifespan? On the latest episode of the Health Pulse podcast, Alex Maiersperger explores these questions with Dr. Joyonna Gamble-George from Yale School of Public Health and Dr. Karriem Watson, Chief Engagement Officer of the NIH All of Us Research Program. They discuss their personal journeys into research, the role of precision medicine and how the All of Us initiative is using big data to address health disparities across diverse populations.
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Despite the explosion of data in our digital world, AI is facing a surprising challenge: data scarcity. It's not a shortage of data; rather, there is a lack of suitable, high-quality data for AI training. Enter synthetic data – a game-changer created by generative AI. This article explores how synthetic data fills gaps, unlocks business growth and addresses privacy concerns. Read why synthetic data is essential for your AI-driven future and the ethical challenges that come with it.
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Large language models (LLMs) are reshaping AI with their ability to generate human-like responses, but they come with challenges like managing toxicity, bias and bad actors. Curious how to tackle these issues? This blog post dives into what organizations need to know about mitigating harmful content and ensuring responsible AI development. From using advanced SAS tools to human oversight, learn how to protect your AI systems and enhance trust. Check out the full story to explore this topic further.
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