Want to get into AI? Start with a real problem worth solving

How building ethical AI and evolving developer skills will meet tomorrow’s challenges.

Dr. Ruth Akintunde, Generative AI lead at SAS, sat down with the All Things Open team to share how she’s using artificial intelligence to improve health, security, and emotional well-being, and why the future of development depends on how we build and use AI today. 

Editor’s note: “The opinions expressed are Dr. Ruth Akintunde’s own and do not represent the views of SAS Institute Inc.”

Ruth’s journey with AI started back in 2015, when she built a linear regression model to solve a real-world business problem. That early curiosity led her to a PhD in computer science and eventually to leading AI efforts at SAS. She now focuses on applying generative AI and computer vision to challenges like early disease detection, emotional stress monitoring, and even assessing spiritual well-being. She sees AI not as a replacement for humans but as a tool to amplify our capacity to serve and protect.

Throughout the interview, Ruth emphasized the need for strong infrastructure, ethical guardrails, and clear strategies to unlock AI’s full potential. She highlighted the importance of data privacy and bias mitigation in projects involving health records, IoT, and surveillance data. Tools like large language models (LLMs) can provide insights from raw data like text or CCTV footage, but without the right frameworks in place, she cautions that progress could be uneven.

For developers interested in AI, Ruth offers practical advice: Start with a data problem that matters to you. Her learning path included courses on Coursera, building real projects, and embracing tools like ChatGPT to explore solutions. She encourages developers to build portfolios and learn the math behind machine learning to feel more confident in choosing the right tools. As AI continues to evolve, she believes developers will take on new roles, perhaps even as “AI integrators,” blending technical skills with smart automation.

Read more: Build better with AI: Lessons from real-world GenAI projects

Key takeaways

• Start with a problem: Ruth recommends identifying a data challenge you care about and letting that curiosity guide your AI learning journey.
• Ethics are essential: From privacy to bias, developers must factor ethical concerns into every AI project.
• Developers will adapt: AI may not replace developers, but it will redefine roles and require new skills in prompt design, integration, and critical thinking.

Conclusion

Ruth’s perspective is a thoughtful reminder that AI is not just about technology, it’s about impact. Whether you’re a developer experimenting with new tools or someone just starting out, her story highlights how persistence, purpose, and ethics can guide meaningful work in open source and beyond.

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