James Cham is a co-founder and partner at Bloomberg Beta, an early-stage venture firm that invests in machine learning and the future of work, the intersection between business and technology.

James explains how his approach to investing in AI has developed over the last decade, which signals of success he looks for in the ever-adapting world of venture startups (tip: look for the "gradient of admiration"), and why it's so important to demystify ML for executives and decision-makers.

Lukas and James also discuss how new technologies create new business models, and what the ethical considerations of a world where machine learning is accepted to be possibly fallible would be like.

Show notes (transcript and links): http://wandb.me/gd-james-cham

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⏳ Timestamps:

0:00 Intro

0:46 How investment in AI has changed and developed

7:08 Creating the first MI landscape infographics

10:30 The impact of ML on organizations and management

17:40 Demystifying ML for executives

21:40 Why signals of successful startups change over time

27:07 ML and the emergence of new business models

37:58 New technology vs new consumer goods

39:50 What James considers when investing

44:19 Ethical considerations of accepting that ML models are fallible

50:30 Reflecting on past investment decisions

52:56 Thoughts on consciousness and Theseus' paradox

59:08 Why it's important to increase general ML literacy

1:03:09 Outro

1:03:30 Bonus: How James' faith informs his thoughts on ML

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Connect with James:

📍 Twitter: https://twitter.com/jamescham

📍 Bloomberg Beta: https://github.com/Bloomberg-Beta/Manual

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Links:

📍 "Street-Level Algorithms: A Theory at the Gaps Between Policy and Decisions" by Ali Alkhatib and Michael Bernstein (2019): https://doi.org/10.1145/3290605.3300760

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💬 Host: Lukas Biewald

📹 Producers: Cayla Sharp, Angelica Pan, Lavanya Shukla

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