prakasha | Revenue & Trends

The hottest Substack posts of prakasha

And their main takeaways

A brief history of computational language understanding dates back to collaboration between linguists and computer scientists.
Language models like ChatGPT use word embeddings to predict and generate text, allowing for effective context analysis.
Neural networks, like Transformers, have revolutionized NLP tasks, enabling advancements in machine translation and language understanding.

Defining a common metrics layer in the data stack can benefit organizations by centralizing metric definitions for easy reference by all users.
When refactoring for a metrics layer, clean abstractions are key to ensuring common parts are expressed in one place and variable parts are supplied as needed.
Encapsulating both the semantic and query generation layers in a metrics layer can lead to a powerful tool for standardizing metric definitions and query generation across different tools.

Curiosity is essential for better business outcomes through data exploration.
Encourage and reward curiosity within your organization to drive innovation.
Modern data stacks can support curiosity by allowing granular data exploration and fostering a culture of asking questions.