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There's no doubt that AI adoption is exploding. Demand for AI and machine learning specialists is expected to grow 40% by 2027, creating 1 million jobs (World Economic Forum, Future of Jobs 2023 Report). With this growth comes increased awareness and responsibility. Read on to learn more about generative AI and responsible innovation.
You've probably seen the impact of generative AI at home, work, or school. If you've kicked off a creative process, outlined a new approach to a problem, written example code, or used a generative AI tool a few times, you know the hype around generative AI is a bit overblown. Generative AI has great potential for practical applications, but it's important to know when it's useful and when it's not.
Generative AI, as part of a broader analytics and AI strategy, is changing the world. Not much is known about how these techniques work. Data scientists can make better use of these tools by understanding the models behind the machines and how to combine these techniques with other techniques in the analytics and AI toolbox. Understanding a bit about the types of GenAI systems, synthetic data generation, transformers, and large-scale language models, can help you use these methods smarter and more effectively, and prevent you from trying to shoehorn generative AI into places where it won't be useful.
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Free e-learning courses from SAS
Generative AI with SAS
For analytics professionals who want to know more than just how to write prompts in an LLM, SAS has developed a free e-learning course, “Generative AI Using SAS.” Check it out if you want to learn a bit about how Generative AI works and how to integrate it into your analytics lifecycle.
Knowing how to use generative AI is not enough; it is equally important to know how to develop AI systems responsibly. AI of any kind, but especially generative AI, can pose risks to business, humanity, the environment, and more. In some cases, AI risks are negligible, while in other cases they are unacceptable. There are countless real-world examples that demonstrate both the importance of assessing and mitigating bias and risk, and the need for trustworthy AI.
Responsible Innovation and Trustworthy AI
SAS has developed a separate free e-learning course, “Responsible Innovation and Trustworthy AI,” for data scientists, business leaders, analysts, consumers and audiences of AI systems. Anyone implementing AI must have a fundamental understanding of the principles of trustworthy AI, including transparency, accountability and human-centricity.
The European Union Artificial Intelligence Act of March 2024 and the U.S. Executive Order on Safe, Secure and Trustworthy Artificial Intelligence of October 2023 increase the urgency of building trustworthy AI. Just as the GDPR has ushered in industry-wide reforms around data privacy since 2016, the EU AI law will affect companies in the EU as well as those doing business with EU citizens.
In other words, almost everyone. While the idea of legislation makes some business leaders uneasy, it's great to see governments taking the risks and opportunities of AI seriously. Such regulations are designed to protect everyone from unacceptable high-risk AI systems, and to encourage responsible innovation with low-risk AI to make the world a better place.
Expand your AI knowledge by taking both SAS' Generative AI Using SAS and Responsible Innovation and Trustworthy AI.
To learn how generative AI works and how it can be integrated into the analytics lifecycle, you also need to gain an understanding of the principles of trustworthy AI.
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