Explaining AI explainability

Generative Process

Generative synthesis is a transformative process that refines neural networks into more compact and efficient versions. Sheldon Fernandez explains that this technology involves an inquisitor-generator pair, which takes an existing neural network and generates optimized versions that maintain accuracy while enhancing speed 1. This approach allows AI practitioners to either start with a public model or refine their own pre-trained networks, offering flexibility and efficiency in AI development.

   

AI Comparison

Generative synthesis stands out from AutoML and meta-learning by emphasizing human-AI collaboration. Sheldon highlights that while AutoML automates model selection across vast search spaces, generative synthesis involves human intuition to refine AI models 2. He notes, "You need a human in the loop to couple the laborious intelligence of AI with your own intuition as a human being," underscoring the importance of human insight in AI processes 3.

   

Practical Uses

The practical applications of generative synthesis are vast, offering tools that simplify deep learning while retaining human creativity. Daniel Whitenack and Sheldon discuss how this technology can streamline AI development by providing optimized neural networks that are easier to interpret and apply 3. Sheldon explains, "You're giving me a neural network, which is a graph, and you're getting a much more compact graph as an output," highlighting the efficiency gains 1.