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The Role of Data in Advancing AI: Insights from Expert Jerome Pasquero

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Manage episode 396945382 series 3496315
Content provided by Louis-François Bouchard. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Louis-François Bouchard or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

A new episode with Jerome Pasquero, a Machine Learning Director at Sama, a leading company for data annotation solutions, where we dive into the role of data in AI's evolution. We explore the nuances of data annotation, the ethical implications of data in AI, and how data is shaping the future of technology. Don't miss Jerome Pasquero's insights on the intersection of data and AI!

►Jerome Pasquero: https://www.linkedin.com/in/jeromepasquero/

►Twitter: https://twitter.com/Whats_AI

►My Newsletter (My AI updates and news clearly explained): https://louisbouchard.substack.com/

►Support me on Patreon: https://www.patreon.com/whatsai

►Join Our AI Discord: https://discord.gg/learnaitogether

Timestamps:

00:00:00 Coming up in the conversation

00:01:34 Jerome shares about his background

00:04:07 How did Jerome get into the data field?

00:05:23 AI back in the days of 2000s

00:07:20 Back then, what piqued Jerome's interest the most in AI?

00:08:40 Using AI to try to mimic human comprehension

00:12:47 Present challenges and the prospective outlook of computer vision

00:14:54 Using Humans vs. ML Models to Annotate Data

00:17:46 Jerome's perspective on Constitutional AI or RLAIF

00:24:52 Impact of LLM and AI on the Job market

00:26:27 Is the AI revolution bigger than previous tech revolutions?

00:28:35 Will there be something more interesting than AGI?

00:31:15 Dealing with complex annotation tasks and different perspectives

00:33:33 Dealing with biases

00:36:18 Using a single annotator vs. multiple annotators on the same data

00:37:49 Synthetically generated data

00:40:47 Scaling quality assurance for large datasets

00:42:46 When is machine learning better at annotation than human annotators?

00:45:34 Reduction of Humans-in-the-loop due to the constant evolution of AI

00:46:42 Data Requirements for Training Autonomous Vehicles

00:51:43 Sensors for transferring human driving skills to Autonomous cars

00:53:20 Why don’t we build only autonomous subway system?

00:55:26 Use of AI in the vision industry and example of vision technology used in our daily life

01:00:17 The potential of haptics and its link with AI

  continue reading

33 episodes

Artwork
iconShare
 
Manage episode 396945382 series 3496315
Content provided by Louis-François Bouchard. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Louis-François Bouchard or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

A new episode with Jerome Pasquero, a Machine Learning Director at Sama, a leading company for data annotation solutions, where we dive into the role of data in AI's evolution. We explore the nuances of data annotation, the ethical implications of data in AI, and how data is shaping the future of technology. Don't miss Jerome Pasquero's insights on the intersection of data and AI!

►Jerome Pasquero: https://www.linkedin.com/in/jeromepasquero/

►Twitter: https://twitter.com/Whats_AI

►My Newsletter (My AI updates and news clearly explained): https://louisbouchard.substack.com/

►Support me on Patreon: https://www.patreon.com/whatsai

►Join Our AI Discord: https://discord.gg/learnaitogether

Timestamps:

00:00:00 Coming up in the conversation

00:01:34 Jerome shares about his background

00:04:07 How did Jerome get into the data field?

00:05:23 AI back in the days of 2000s

00:07:20 Back then, what piqued Jerome's interest the most in AI?

00:08:40 Using AI to try to mimic human comprehension

00:12:47 Present challenges and the prospective outlook of computer vision

00:14:54 Using Humans vs. ML Models to Annotate Data

00:17:46 Jerome's perspective on Constitutional AI or RLAIF

00:24:52 Impact of LLM and AI on the Job market

00:26:27 Is the AI revolution bigger than previous tech revolutions?

00:28:35 Will there be something more interesting than AGI?

00:31:15 Dealing with complex annotation tasks and different perspectives

00:33:33 Dealing with biases

00:36:18 Using a single annotator vs. multiple annotators on the same data

00:37:49 Synthetically generated data

00:40:47 Scaling quality assurance for large datasets

00:42:46 When is machine learning better at annotation than human annotators?

00:45:34 Reduction of Humans-in-the-loop due to the constant evolution of AI

00:46:42 Data Requirements for Training Autonomous Vehicles

00:51:43 Sensors for transferring human driving skills to Autonomous cars

00:53:20 Why don’t we build only autonomous subway system?

00:55:26 Use of AI in the vision industry and example of vision technology used in our daily life

01:00:17 The potential of haptics and its link with AI

  continue reading

33 episodes

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