AI is more than GenAI

Parameter Optimization

Parameter optimization is crucial for enhancing AI model accuracy and performance. Daniel Whitenack explains how developers can iteratively test various parameter values to find the most accurate results. This process involves using example inputs and outputs to train models, which then use the optimized parameters for inference or prediction 1. He highlights the importance of both the software function and the parameter set in forming a model, noting that confusion often arises around model licensing due to differing approaches to code and data 2.

I could try that set of parameters for all of my example inputs and outputs, and just choose the one that gives me the best results, in other words, the most accurate results.

--- Daniel Whitenack

This iterative process is a hallmark of the 2010 to 2017 era of machine learning, where practitioners often curated data and collaborated with domain experts to refine models.

   

AI Model Integration

AI model integration involves deploying models within systems for practical use, often requiring a blend of different AI types. Daniel discusses the challenges of using large models in high-throughput environments and emphasizes starting with foundation models for efficiency 3. He notes the evolving roles in AI deployment, where domain experts increasingly interact directly with models, reducing the need for intermediaries like data scientists 3.

The reality is that these are all software functions, parameterized software functions at different scales, most definitely, but software functions that need infrastructure to operate.

--- Daniel Whitenack

Understanding the AI landscape requires recognizing the historical progression of AI methodologies and their integration into current systems, highlighting the ongoing relevance of traditional AI techniques alongside generative models 4.