Creating tested, reliable AI applications

Beyond Gen AI

Chris Benson and Daniel Whitenack explore the vast landscape of AI technologies beyond generative AI, emphasizing their continued productivity and significance despite limited media attention. Chris highlights the potential of deep reinforcement learning, especially when combined with robotics, as a productive area often overshadowed by generative models 1. Daniel adds that current generative models, while not the best for specific tasks like time series forecasting, can effectively orchestrate workflows using existing tools like Facebook's Prophet 1. He also notes the challenges in advancing AI models, such as the delayed release of GPT-5, which underscores the difficulty in achieving the next leap in AI functionality 2.

There's a lot more to AI than just the Gen AI models. You know, they've gotten all the spotlight the last couple of years, but there's a lot you can do.

--- Chris Benson

Chris and Daniel agree that even if AI development stalls, the existing models and open-source options provide ample opportunities for innovation and integration across various sectors 2.

   

Potential Roadblocks

Daniel Whitenack discusses the potential roadblocks in AI development, particularly the gap between expectations and reality in model advancements. He believes that the current AI building blocks, both generative and non-generative, are sufficient to transform certain cultural elements, even if future generational leaps are not yet anticipated 3. Chris Benson concurs, reflecting on past technological shifts and the transformative potential of existing AI tools 3. The conversation also touches on practical steps for AI development, with a focus on using familiar tools like PostgreSQL to build AI applications without needing to learn new technologies 4.

The building blocks of what we have with AI, whether that be gen AI or non gen AI, are enough to transform certain elements of our culture.

--- Daniel Whitenack

Chris and Daniel emphasize the importance of leveraging open-source tools and existing knowledge to advance AI projects, highlighting the accessibility and potential of current technologies 4.