In episode 2 of "This is AI," hosts EJ, Erik Brown, and Ryan Elmore discuss the application of generative AI across industries, exploring the challenges organizations face when choosing between pre-built and custom solutions. They emphasize aligning AI with organizational goals and explore real-world examples, including a global food and beverage leader enhancing customer experiences. The episode delves into prompt engineering, fine-tuning, and data governance, offering insights into navigating AI adoption for impactful results.
If we look at generative AI, it's kind of the opposite of that, where the model is static. It's pre-trained, generative pre-trained model as GPT. Now the data is coming in is the non-static piece. It's changing. You can put anything towards it. You can put different categorizations. You can ask it different questions in different ways. But the model stays the same. The data is what changes. So it's kind of a different way of thinking about how machine learning is done.
Generative AI and chat GPT are creating a buzz, building on concepts that have been around for years. It's important to clarify the different subsets of AI and understand their applications. Generative AI has become a catalyst, creating something new and driving interest in AI, but it’s essential to know when to use different types of AI.
I think what folks can look forward to as we think about the coming episodes here is what are use cases? How do I actually use this sort of technology in my products and services? I have a spicy take and I think that people need to stop talking about generative AI use cases. Like we need to strike that from our lexicon because you should be talking about use cases.