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There’s no doubt that AI adoption is booming, and demand for AI and Machine Learning Specialists is expected to grow by 40%, or 1 million jobs, by 2027 (World Economic Forum, 2023 Future of Jobs Report). With this growth also comes awareness and responsibility. Read on to learn more about Generative AI and Responsible Innovation.
You have seen the impact of generative AI at home, at work, or in school. Whether it’s kick-starting the creative process, outlining a new approach to a problem, or making some sample code, if you’ve used generative AI tools a few times, then you know that the hype around Generative AI, is more than a little overstated. It has enormous potential for practical use, but it is important to know when it is and is not useful.
Generative AI, as part of a broader analytics and AI strategy, is transforming the world. Less well-known is how those techniques work. A data scientist can make better use of these tools by understanding the models behind the machine, and how to combine these techniques with others in the analytics and AI toolbox. Understanding a bit about types of GenAI systems, synthetic data generation, transformers, and large language models helps to enable smarter, more effective use of the methods, and hopefully prevents you trying to cram generative AI into places where it’s not likely to be helpful.
Want to learn more?
The Free E-Learning Course’s by SAS
Generative AI Using SAS
SAS developed the free e-learning course, Generative AI Using SAS, for analytics professionals who need to know more than how to write a prompt in an LLM. If you want to learn a bit about how generative AI works and how it can be integrated into the analytics lifecycle, then check it out.
Knowing how to use generative AI is not enough; it is just as important to know how to develop AI systems responsibly. Any sort of AI, and especially generative AI, may pose risks for business, for humanity, for the environment, and more. Sometimes the risks of AI are negligible, and sometimes they are unacceptable. There are myriad real-world examples illustrating both the importance of assessing and mitigating bias and risk, as well as the need for trustworthy AI.
Responsible Innovation and Trustworthy AI
SAS developed another free e-learning course, Responsible Innovation and Trustworthy AI, for data scientists, business leaders, analysts, consumers, and targets of AI systems. Anyone who implements AI should have a fundamental understanding of the principles of trustworthy AI, including transparency, accountability, and human-centricity.
The urgency to build trustworthy AI is growing with the passage of the European Union Artificial Intelligence Act in March 2024 and the US Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence in October 2023. Just as GDPR has ushered in industry-wide reforms in data privacy since 2016, the EU AI Act impacts not only companies in the EU, but companies that do business with EU citizens.
In other words, nearly all of us. While the idea of legislation makes some business leaders uncomfortable, it’s great to see governments take seriously the risks and opportunities of AI. Such regulations are designed to keep everyone safe from unacceptable and high-risk AI systems, while encouraging the responsible innovation of low risk AI to make the world better.
Expand your AI knowledge by taking both Generative AI Using SAS and Responsible Innovation and Trustworthy AI from SAS.
In order to learn how generative AI works and how it can be integrated into the analytics lifecycle, we must also gather an understanding of the principles of trustworthy AI.
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