635: The Perils of Manually Labeling Data for Machine Learning Models

<p>Hand labeling data and information bias: Jon Krohn speaks with Watchful CEO Shayan Mohanty about the pitfalls of data analysis when bias comes into the equation (spoiler alert: it always does), the importance of the Chomsky hierarchy in data management, and the importance of simulation engines for returning real-time results to users.</p><p>This episode is brought to you by Iterative (<a href='https://iterative.ai/'>iterative.ai</a>), your mission control center for machine learning. Interested in sponsoring a SuperDataScience Podcast episode? Visit <a href='http://jonkrohn.com/podcast'>JonKrohn.com/podcast</a> for sponsorship information.<br/><br/></p><p>In this episode you will learn:<br/>• Why bias in general is good [04:06]<br/>• The arguments against hand labeling [09:47]<br/>• How Shayan solves the problem of labeling at his company [24:26]<br/>• Misconceptions concerning hand-labeled data [43:25]<br/>• What the Chomsky hierarchy is [52:38]<br/>• Watchful’s high-performance simulation engine [1:04:51]<br/>• What Shayan looks for in his new hires [1:08:15]<br/><br/></p><p>Additional materials: <a href='https://gate.sc/?url=http%3A%2F%2Fwww.superdatascience.com%2F635&amp;token=dfc36e-1-1670922502924'>www.superdatascience.com/635</a></p>