AI should be understood as a powerful tool for writing software, not as a person, to avoid common misconceptions.
The correct output of an AI system is determined by its intended purpose, making the definition of success subjective.
AI systems are reliable workers that execute instructions literally, so poorly defined objectives can lead to unintended and foolish outcomes.
AI development simplifies coding to just an objective and a dataset, requiring developers to consciously add back the thoughtfulness that traditional coding demanded.
Expect AI systems to make mistakes and build in safety nets, adopting a site reliability engineering (SRE) approach to mitigate failures.
Models can easily memorize training data, so you must test them on a separate, pristine dataset to ensure they have learned to generalize.
A model can achieve high accuracy by learning unintended patterns, like a background object, rather than the intended subject.
Datasets are like textbooks created by humans and inevitably reflect the implicit values and biases of their authors.
Building teams with diverse perspectives, backgrounds, and life experiences is a mandatory requirement for identifying and mitigating bias in AI systems.
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Ismaning, Germany
28:05Toju Duke
35:16Ankit Patel
24:23Balázs Kiss
33:24Laura Moritz, Nina Levchuk, Laura Möller & Nica Huestegge
48:01Jan Zawadzki
29:30Kilian Kluge & Isabel Bär
28:27PJ Hagerty
28:54Torsten Stiller
Jobs that call for the skills explored in this talk.
Ai-driven
Charing Cross, United Kingdom
Collaboration Betters The World GmbH
Apple Inc.
Cupertino, United States of America
AI Powered LLC
Apple Inc.
Seattle, United States of America
