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Recent Posts

Part 5: Neo-ML: Two Ways to Recognise a Flower – An Introduction to Convolutional Neural Networks (CNNs)

The article compares two flower classification models: a simple decision tree using the Iris dataset and a convolutional neural network (CNN) trained on images. The first model makes decisions based on measurable attributes, achieving high accuracy with minimal data. In contrast, the CNN analyzes pixel data, needing vast training resources. Both models have unique strengths, highlighting that the best choice depends on the problem at hand.

Computer Vision: Just Point a Camera at It. Right?

The author shares a weekend project of building a CNN image recognition system for identifying five flower types. The narrative transitions to Castellan Wines, where CEO Marco seeks AI for defect detection on a bottling line. Data scientist Clare emphasizes tailored solutions: classical methods for measurable tasks and deep learning for variable damage detection, highlighting engineering decisions over technology hype. Her blueprint outlines three detection strategies, underscoring the need for controlled environments and ongoing system validation.

To Understand Data is to Understand AI

The AI industry struggles with data quality due to systemic issues that prioritize modeling over data work. A paper highlighted that 92% of AI practitioners faced data cascades, resulting from cultural and educational frameworks that undervalue data integrity. To address this, the industry must reward data excellence and treat data as integral to AI development.

Part 4: Neo-ML — More Isn’t Better. A Rule for Tokenomics

The author reflects on a principle learned early in their career: don’t over-engineer solutions based on assumptions of bigger is better. Data should dictate model size, as larger models don’t necessarily yield better outcomes. The importance of understanding data requirements is emphasized, particularly in the context of LLMs and operational costs.