Approaches
3 stories filed under Approaches on Beyond Market Intelligence. The newest of them: “Unlocking Data Potential by Combining Labelled and Unlabelled Insights”, “Three Criteria for Choosing Skills Over Sub-Agents in AI Design”, and “Exploring world models in robot learning through JEPA's practical promise”. Most of your data sits unlabeled, and that's not a problem to solve, it's an opportunity to leverage. Choosing the right building block for AI systems often comes down to fit, not flash. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every Approaches story on Beyond Market Intelligence, newest first.
Unlocking Data Potential by Combining Labelled and Unlabelled Insights
Most of your data sits unlabeled, and that's not a problem to solve, it's an opportunity to leverage. Semi-supervised learning sits between the fully labeled and the entirely unlabeled, using a small amount of guidance to make sense of the larger, messier whole. This primer walks through the algorithms that make that possible and, just as importantly, where they stumble. It's a practical read for anyone who wants to get more from their data without drowning in annotation costs.
Three Criteria for Choosing Skills Over Sub-Agents in AI Design
Choosing the right building block for AI systems often comes down to fit, not flash. In a recent Azure Architecture blog post, lead engineer Kishorekumar Pattabiraman lays out practical criteria for deciding between skills and sub-agents, with reusability and long-term maintainability leading the way. It's a grounded take that cuts through the noise, favoring simplicity over cleverness. For those weighing similar trade-offs, this is the kind of clarity that helps teams move forward.
Exploring world models in robot learning through JEPA's practical promise
Questioning a promising paradigm is how good research gets done. JEPA-style models are compelling, but the recent evangelism from Yann LeCun warrants scrutiny. The biggest downside isn't the architecture itself; it's the overconfidence in its superiority over LLMs and RL. Those systems, for all their flaws, have proven scalable. JEPA's reliance on energy-based learning and latent variables introduces significant training instability challenges that are often glossed over.