Why Simpler, Smaller Data is Often Better

No single algorithm is universally superior. Simpler models are less susceptible to being fooled by noisy or adversarial inputs. They don't overreact to spurious correlations in the data.

The "Reasoned Rules" Revolution:

The idea that more data and complex algorithms automatically lead to better decisions is being challenged. Simpler algorithms built on a small, carefully chosen set of variables and common-sense logic or "reasoned rules" – can be surprisingly effective. These rules often perform just as well as, or even better than, complex models drowning in data.

Why? Complex models, especially those using machine learning, are prone to "overfitting." They become so tailored to the specific nuances of their training data that they fail to generalize well to new, real-world scenarios. Think of it like studying for a test by memorizing the answers instead of understanding the concepts. You might ace the test, but you'll struggle with similar problems phrased differently. Simple models, on the other hand, are less susceptible to overfitting and are often more robust in practice.

The Power of Interpretability:

Another critical advantage of simpler approaches is interpretability. The whitepaper on "Interpreting AI" highlights the crucial difference between "black-box" models, where the reasoning is opaque, and "white-box" models, where the logic is transparent. In critical applications like medicine or finance, understanding why an AI makes a particular prediction is paramount. Simple models are inherently more interpretable, allowing humans to understand and trust the AI's decisions.

This approach resonates with the foundational principles of AI, echoing the work of pioneers like Hinton, Bengio, LeCun, and McCulloch. It aligns with Hinton's logic-first approach, emphasizing the importance of formal reasoning and knowledge representation. It addresses Bengio's call for systems that can discover abstractions, enabling the model to learn from data and generalize to new situations. It incorporates LeCun's emphasis on visual cognition, leveraging visual representations to enhance understanding and reasoning. Finally, it echoes McCulloch's focus on finding solutions without excessive mathematical complexity, prioritizing simplicity and interpretability.

A solid simple model, especially in a complex high stakes domain like Human Capital Management (HCM), incorporates these elements:

• Symbolic Representation: This is key. Instead of directly feeding raw data (like employee records) into a statistical model, the data is transformed into symbols that represent meaningful concepts. Simpler models are often more cost-effective and easier to explain to non-technical stakeholders, fostering trust and adoption.

• Common Sense Knowledge Base: The model is equipped with a set of common-sense rules and facts about the world and the HCM domain.

• Logical Reasoning Engine: This is the core of the model. It takes the symbolic representations and the common-sense knowledge and uses logical inference to answer questions, make predictions, or solve problems.

• Visual Cognition Model: Visual reasoning is crucial to represent relationships and hierarchies visually. Visualizations can make the model's reasoning more intuitive and easier for humans to understand.