Training data quality in machine learning
Subject in Technology
Training data quality in machine learning has come up in 1 episode across 1 podcast in TMT, including Moonshots with Peter Diamandis. 2 people have made recorded remarks about it.
What people said
Alexander Wissner-GrossComputer scientist and founder of Reified, co-host of Moonshots
Wissner-Gross warns that proprietary enterprise data has value but a limited shelf life, citing Bloomberg GPT as a cautionary example.
“I do think there is value in internal enterprise data, but it has a shelf life”
Moonshots with Peter Diamandis: Should we slow down AI progress? | MOONSHOTS #288 (Sep 11, 2026) · Listen from 18:05
Wissner-Gross argues that historically AI grand challenges have been solved by assembling the right dataset rather than by algorithmic innovation.
“the solutions to all of the grand challenges in AI historically over the past 30 years have actually been the result of putting together the correct data set, not the right algorithm”
Moonshots with Peter Diamandis: Should we slow down AI progress? | MOONSHOTS #288 (Sep 11, 2026) · Listen from 8:57
Salim IsmailFounder of OpenExO and author on exponential organizations, co-host of Moonshots
Ismail recounts a company whose spun-out data subsidiary was valued at roughly four times the parent company's value.
“the company's worth about $10 billion, about $8 billion, and the data subsidiary got valued at $32 billion”
Moonshots with Peter Diamandis: Should we slow down AI progress? | MOONSHOTS #288 (Sep 11, 2026) · Listen from 15:49
Podcasts
- Moonshots with Peter Diamandis · 1 episode
Episodes
Should we slow down AI progress? | MOONSHOTS #288Moonshots with Peter Diamandis · Sep 11, 2026 · 2 h 44 min
The Moonshots panel and Emad Mostaque debate AI safety resignations, P(doom), the Navier-Stokes solution, data as moat, GPU economics and AI-designed longevity drugs.