AI & Intelligence
Supervised learning has always been a conversation between humans and machines. What has changed is how much the machine is asking.
Gopal Bhat, Chief Strategy Officer1 min read
As AI agents are built for specific industries, human input during data preparation remains the mechanism that keeps a model aligned with human values and preferences.
The first generation of supervised learning ran on binary and categorical labels. Is this image a cat or a dog? Is this email spam or not? A human decided, the machine learned the boundary, and the quality of the decision set the ceiling on the model.
Computer vision raised the bar. Object detection, the automotive industry’s core problem, needed bounding boxes. Understanding a scene needed pixel-level segmentation masks and written scene descriptions. Annotation became skilled work, and data preparation became a discipline in its own right.
Large language models and generative AI changed the question again. For tasks like translation there is no single right label, only better and worse answers. Reinforcement learning from human feedback asks people to compare and rank outputs, and the model learns a preference rather than a category. The human is no longer labelling data; the human is supervising judgment.
The tools have changed at every stage. The role of the person has not. As AI agents are developed for specific industries, human input during training-data preparation remains essential to aligning those systems with human values and preferences. The organisations that treat that input as expert work, and organise it accordingly, will build the models that people trust.
In this insight
Visual explainer
Binary and categorical labels
Cat or dog, spam or not
Bounding boxes and segmentation
Object detection and scene understanding
Human feedback (RLHF)
Ranking outputs, teaching preference
Aligned AI
Models that reflect human values
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