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In a crisis like a pandemic, time stands still. Policymakers rush to chart a way out, trying to establish some consensus truth about the situation and the best path forward. The “open science” movement rallies to offer predictive models, but the models are seemingly limitless in number, of uncertain provenance and questionable suitability, and they don’t agree. Understanding and extending complex model code requires deep expertise and takes weeks at best, especially when there’s little transparency into the original intent and assumptions.
Uncharted has been working on applications of knowledge-graph augmented Retrieval-Augmented Generation (RAG) for months now and couldn’t be more excited about its potential.
When it comes to productivity tools, we’ve reached the maximum complexity of graphical user interfaces (GUIs): cascading menus, endless dialogue boxes, a plethora of buttons. When you need a search engine or a user-generated tutorial to figure out how to change a setting in your email, slide deck, or image editor, the interface is no longer discoverable. So if using a GUI is now like trying to determine how to write a query in SQL, what’s the way back to discoverability?
Is your holiday wish list full of eco-friendly gadgets? Even if you’ve been decidedly nice, supply chain fragility may impact what ends up under your tree.
Understanding and organizing a summation of knowledge is hard. Large language models (LLMs) can help, but words alone are not enough.