De-Noising AI: Why Quality Beats Quantity for Humans and Machines Alike

Presented by

Alex Fink, Silicon Valley Expact, Founder & CEO | Otherweb

About this talk

Machine learning is the most efficient known way to create software models that solve real-world problems. When trained correctly, ML models can know more than any human who ever lived, explain or summarize anything, and find patterns in the data that world-class experts have been missing for decades. But what happens when you train these incredible artificial brains by feeding them noise? In this talk, we will explore both directions of this noise problem: - What does AI "learn" when you train it in an unsupervised way, using data that hasn't been scrubbed beforehand? - What do humans learn when the information that surrounds them is 90% noise, including noise that was produced by AI? In the process, we will work together to articulate a new paradigm of AI that applies to everything from news media to business intelligence tools -- a paradigm that emphasizes quality over quantity, and focuses on removing bad inputs to produce better outputs. About the speaker Alex Fink is the Founder and CEO of the Otherweb, a Public Benefit Corporation that uses AI to help people read news and commentary, listen to podcasts and search the web without paywalls, clickbait, ads, autoplaying videos, affiliate links, or any other junk. The Otherweb is available as an app (ios and android), a website, a newsletter, or a standalone browser extension.
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