Sign up to save your podcasts
Or
Share Understanding information with AI
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Understanding information with AI
Interpreting different types of information is a task humans
are inherently good at with just a little guidance and, from there, conclusions
can be drawn and connections made. Artificial intelligence by contrast requires
much more training but is capable of analyzing information and building
connections in wholly different ways than humans, allowing for a novel
perspective on key data.
In this episode, host Spencer Acain is joined by Dr. James
Loach, head of research at Senseye Predictive Maintenance to explore the ways
AI can analyze and interpret industrial data, the similarities between text
generating LLMs and Senseye’s own time series foundation models. James also
delves into what it took to make the time series foundation model a reality.
In this episode you will learn:
· The
similarities between text and time series data (00:36)
· Challenges
of building a time series foundation model (05:50)
View all episodes
By Siemens Digital Industry Software
5
11 ratings
Interpreting different types of information is a task humans
are inherently good at with just a little guidance and, from there, conclusions
can be drawn and connections made. Artificial intelligence by contrast requires
much more training but is capable of analyzing information and building
connections in wholly different ways than humans, allowing for a novel
perspective on key data.
In this episode, host Spencer Acain is joined by Dr. James
Loach, head of research at Senseye Predictive Maintenance to explore the ways
AI can analyze and interpret industrial data, the similarities between text
generating LLMs and Senseye’s own time series foundation models. James also
delves into what it took to make the time series foundation model a reality.
In this episode you will learn:
· The
similarities between text and time series data (00:36)
· Challenges
of building a time series foundation model (05:50)