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Dan McCreary: Jellyfish, Flatworms, and the AI-Ready Enterprise – Episode 1

Content + AI

NOTE

The Consensus of Knowledge

The math behind embeddings involves representing data in 150 to 200 dimensions of similarity, allowing data scientists and machine learning to figure out how to utilize these embeddings. Similar to how our brains connect similar concepts through neurons, embeddings help find similarities between data points. Progress in understanding the brain, neural networks, and knowledge graphs leads to a consensus of knowledge that highlights the importance of correctly representing data for making better decisions and empowering intelligent agents. The goal is to have every knowledge worker supported by 100 intelligent agents, but this is hindered if data remains trapped in individual spreadsheets on desktops, emphasizing the architect's role in ensuring data accessibility.

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