Many people want to get into artificial intelligence (AI) because it's a trending field. That wasn't the case for me. In fact, up to about a year ago, I had no interest in working with AI. However, that all changed near the end of 2022.
Before I get into the type of AI project I'm working on, I want to start by saying, I love to learn new things. It's been that way for me since as far back as kindergarten. Back then, I'd spend many nap hours reading National Geographic magazines or thumbing through books on dinosaurs or solar systems. If that wasn't enough, both my parents were librarians, so I built make-believe castles using books I had finished reading!
Fast forward to 2022, professionally, I've been involved with communications, community management, and program building. I've also been involved in a side project, researching prebuilt AI products and their practical implementation in everyday business contexts.
The project I'm working on now has been more like a passion project that involves developing a risk management program for a friend. My role has been leading the research and implementation of prebuilt AI products to pinpoint risks and optimize business processes for his business. For me, that meant learning an entirely new field -- an adventure!
Working on this project meant constant research, testing, and discovering how AI components intersect within the context of everyday business operations. A significant challenge for me was learning how to enable data models to interact with each other while applying human thinking across each connecting point. That learning brought me to a very important question...
"How do you infuse cultural relevance into data modeling?"
In simple terms, data models are like individual brains. The data they're fed is like thoughts and memories given to algorithms to produce results. These results help make decisions, spot patterns and do real-world tasks. But machines and algorithms struggle with adapting to different cultures. This can create biases in data, causing problems like marginalization, stereotypes, exclusion, misrepresentation, and poor decision-making in crucial areas.
My goal is to learn more about how we can infuse cultural relevance into data models. For me, this isn't just about enhancing business efficiency but also ensuring that AI-driven solutions in diverse sectors like financial services, education, technology, healthcare, global tourism, and energy consider and represent a broader spectrum of our world.
As I gain a stronger understanding of the ties between culture and data, I'll also continue to explore data augmentation and the use of generative AI to simulate and predict a diverse range of real-world scenarios.
As you can see, there's a lot that comes with learning and deploying AI systems -- especially when you're learning from scratch without formal studies in these areas. For me, that means I'm able to share my learning experiences and knowledge with newly interested audiences. This touches on another passion of mine which is simplifying complex or sophisticated information for everyone to understand. Right now, my focus is helping companies of all sizes understand how to piece together AI systems using pre-built solutions, and then scale from there.
That being said, I'd like to thank you for reading about my work. Whether you're a business owner, a college professor, an aspiring professional, or someone without prior knowledge of AI, I'd love to connect to share more about this project.
If you'd like to collaborate on a project or ask questions about my work, send me a message on LinkedIn. Let's get a conversation started!