Resolution analogy & learning loop

Evan Kaplan (@EvanKaplan, CEO @InfluxDB) talks about Physical AI and the evolving and emerging technologies required to bring AI to physical locations and activities. 

SHOW: 979

SHOW TRANSCRIPT: The Cloudcast #979 Transcript

SHOW VIDEO: https://youtube.com/@TheCloudcastNET 

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SHOW NOTES:

• InfluxData homepage
• Evan on The Cloudcast #394
• SpaceNews article on Time Series and AI in Space
• Time Series is critical to Physical AI

Topic 1 - Welcome back to the show, Evan. Give everyone a brief introduction.

Topic 2 - We last spoke in 2019, and our goal with that show was to give everyone an introduction to time series databases. There’s a link in the show notes for those who want to go back and get a refresher. But, if folks aren’t up to speed, give everyone a quick definition of time series and its impacts in recent years

Topic 3 - First, we need to discuss Physical AI. What is Physical AI, and how is it different from, say, GenAI or Agentic AI? It seems that AI in the mainstream equates LLMs with AI, but that isn’t correct. We are talking about deterministic AI, not probabilistic solutions. Can you explain to everyone the difference and why it matters?

Topic 4 - Why is the concept of time series so crucial to Physical AI? Additionally, you provided a great analogy comparing time series data collection to low-resolution and high-resolution images. Can you explain to everyone why this is so important?

Topic 5 - Let’s talk about some use cases. How and where does this intersection of Physical AI and time series impact organizations the most? Is this specific to certain industries (robotics, aerospace, IoT, etc.) or specific collection mechanisms (telemetry, sensor data, etc.)

Topic 6 - Are we shifting with AI to a state that is less reactive and more proactive with an active intelligence?

Topic 7 - What kind of demands do real-time, modern workflows and data streaming place on the infrastructure? When I think of time series, I think of real-time data, which means ultra-low latency and processing near the source, among other things. 

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