In this episode I continue the conversation from the previous one, about failing machine learning models.
When data scientists have access to the distributions of training and testing datasets it becomes relatively easy to assess if a model will perform equally on both datasets. What happens with private datasets, where no access to the data can be granted?
At fitchain we might have an answer to this fundamental problem.
Chatting with ChatGPT: Pros and Cons of Advanced Language AI (Ep. 215)
Accelerating Perception Development with Synthetic Data (Ep. 214)
Edge AI applications for military and space [RB] (Ep. 213)
From image to 3D model (Ep. 212)
Machine learning is physics (Ep. 211)
Autonomous cars cannot drive. Here is why. (Ep. 210)
Evolution of data platforms (Ep. 209)
[RB] Is studying AI in academia a waste of time? (Ep. 208)
Private machine learning done right (Ep. 207)
Edge AI for applications in military and space (Ep. 206)
[RB] What are generalist agents and why they can change the AI game (Ep. 205)
LIDAR, cameras and autonomous vehicles (Ep. 204)
Predicting Out Of Memory Kill events with Machine Learning (Ep. 203)
Is studying AI in academia a waste of time? (Ep. 202)
Zero-Cost Proxies: How to find the best neural network without training (Ep. 201)
Online learning is better than batch, right? Wrong! (Ep. 200)
What are generalist agents and why they can change the AI game (Ep. 199)
Streaming data with ease. With Chip Kent from Deephaven Data Labs (Ep. 198)
Learning from data to create personalized experiences with Matt Swalley from Omneky (Ep. 197)
State of Artificial Intelligence 2022 (Ep. 196)
Create your
podcast in
minutes
It is Free
Insight Story: Tech Trends Unpacked
Zero-Shot
Fast Forward by Tomorrow Unlocked: Tech past, tech future
The Unbelivable Truth - Series 1 - 26 including specials and pilot
A Prairie Home Companion: News from Lake Wobegon