Learning by Doing
Imagine a world where your GenAI learned continuously from your interactions instead of staying static after training. More than the 'memories' that it captures, think evolution.
A new wave of AI, from former Google and Apple researchers, is designed to continuously learn from user interactions, to do just that. The promise is systems that refine accuracy, adapt to edge cases, and reduce hallucinations through iterative, real-world feedback loops.
Learning by doing, just like humans.
The Wired story spotlights a startup model architecture that gets better because you used it capturing interaction data, identifying mistakes, and retraining or adapting in near real-time, effectively running a perpetual learning loop tied to actual usage patterns.
This is the shift from trained intelligence to living systems.
Adaptive AI already points in this direction with models that evolve with new data, adjust decisions dynamically, and improve continuously without full retraining cycles. AI's biggest challeng, hallucinations, come from static pattern predictions. Continuous learning systems aim to close that gap using real-world correction signals.
WHY IT MATTERS
If AI learns faster than your workforce, it becomes the workforce.
These systems don’t just automate known tasks, they learn the unknowns, especially edge cases that typically require senior humans.
Accuracy and consistency lead to greater trust in the outputs, which in turn will give comfort in greater risk taking. It is Iterative training at scale and speed, focusing on errors and weak spots to materially improve performance over time and outlearn your best (human) experts.
WHAT TO WATCH FOR
→ Enterprise tools that remember, revise, and self-correct
→ Edge-case learning pipelines that automatically retrain the model
→ Declining hallucination rates in production, not just benchmarks
→ Agentic workflows where AI iterates autonomously
LIMITATIONS
There’s a catch. Personalization and continuous learning can introduce new errors, including bias reinforcement and personalized hallucinations where systems align to user history over objective truth. If enough input argues black is white, then the model will believe it too.
There’s also the unresolved challenge of model drift, privacy, and control. Who governs what the system learns?
Although the vision is compelling, the operational reality is in beta.
SOURCE
https://www.ibm.com/think/news/llm-hallucination-human-cognition
BESCI AI OPINION
Your 'home' GenAI tracks your interests, what you like and puts it into 'memories'. It uses this to please you. It is like the annoying adverts on social media which show lawnmowers for 2 months after you search something to do with a lawn.
At an organisational level, not all learning is good learning. The big question will be what you iterate, and what you don't. Who curates the learning?
