Data as a Bottleneck

Shayan highlights a critical issue in AI development: the scarcity of quality data. The conversation delves into the challenges of labeling data, emphasizing the need for automation to reduce time and cost. Additionally, they explore the social implications of data labeling jobs, revealing how workers face downward pressure and limited career advancement, which raises ethical concerns in the industry.

In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
635: The Perils of Manually Labeling Data for Machine Learning Models — with Shayan Mohanty
Related Questions
- Is less labeled data needed for training machine learning models according to the episode The Fallacy of "Ground Truth" with Shayan Mohanty - #576 and the clip Active Learning Insights?
- What is the role of AI in data engineering as discussed in the episode The Fallacy of "Ground Truth" with Shayan Mohanty - #576 and the clip Data Over Models?

Data as a Bottleneck

In this clip

From this podcast

Super Data Science: ML & AI Podcast with Jon Krohn

635: The Perils of Manually Labeling Data for Machine Learning Models — with Shayan Mohanty

Related Questions

Is less labeled data needed for training machine learning models according to the episode The Fallacy of "Ground Truth" with Shayan Mohanty - #576 and the clip Active Learning Insights?

What is the role of AI in data engineering as discussed in the episode The Fallacy of "Ground Truth" with Shayan Mohanty - #576 and the clip Data Over Models?