AI can help us learn about the Olympics and analyze different aspects, like who won medals and their physical attributes. It starts with basic questions and gets more complicated over time.
While AI is good at remembering information and summarizing it, it struggles with reasoning about things it hasn't seen before. This means it can't always come up with new insights without the right data.
For businesses, using AI with their private data can lead to smarter insights and faster decisions. It's important to combine human knowledge with AI to make the best use of available information.
A metrics layer helps standardize how companies measure data, making it easier for everyone to understand what is important. It can automate calculations, like rolling averages, which saves time and reduces confusion.
Traditional business intelligence tools often lose useful underlying information, which makes it hard to understand how certain metrics were created. More context is needed to ensure decisions are well-informed and based on complete data.
HyperArc offers a solution by capturing the team's insights and reasoning during analysis. It helps keep track of not just the final metrics, but also the thought process behind them, making it easier to revisit and understand decisions in the future.
Business Intelligence (BI) needs both good models and great data to be effective with AI. Without quality data, AI can't really show its true power.
Many BI tools only focus on successful outcomes, like specific metrics, while ignoring the complete journey of discovery. This limited data can lead to missing important insights.
To improve AI's effectiveness in BI, we should include a wider range of experiences and exploration paths, not just successful queries. This fuller picture can help create better AI training sets.
Semantic ABI helps organize data from Ethereum transactions better. Instead of dealing with lots of confusing tables, it allows you to get a clear view of the data directly.
By using Semantic ABI, you can easily combine data from different sources without complex joins. This saves time and makes analysis simpler.
The library supports features like adding extra meaning to data and finding matches in transactions more efficiently. It's designed to help with analyzing Web3 data easily.