Signal, Noise and the Coming Era of AI Curation

Oct 11, 2020 at 19:00 UTCUpdated Oct 12, 2020 at 14:51 UTC.On this "Speaking of Bitcoin" episode, join hosts Adam B. Levine, Stephanie Murphy, Jonathan Mohan and special guest Martin Rerak, creator of AllYourFeeds.com, for a look at how "AI curation" is being used to figure out what's useful information and what's just fluff.

For more episodes and free early access before our regular releases, subscribe with Apple Podcasts, Spotify, Pocketcasts, Google Podcasts, Castbox, Stitcher, RadioPublica, iHeartRadio or RSS.This episode is sponsored by Crypto.com, Nexo.io and Elliptic.

Biased AI.While unsettling on the surface, the idea of bias within an AI is not as controversial as you might imagine - it's almost required.

As humans, we each have our own experiences and preferences which shape our viewpoint and our biases.

Modern artificial intelligence consumes "Training material" curated by humans to learn what's right or wrong for its particular task.

Once trained, AI can help us with those tasks and is at its most useful when it's "Instincts" match whomever it is working on behalf of.

When Google trained an AI to help with hiring, the data around past and current employees led it to believe that an ideal "Google engineer" wouldn't have a woman's college on their academic transcript.

For Google, their past records did not match their future ambitions and so bias was a problem.

I've developed patent-pending AI technology that assists with audio editing, and here the idea of bias is critical.

For more episodes and free early access before our regular releases, subscribe with Apple Podcasts, Spotify, Pocketcasts, Google Podcasts, Castbox, Stitcher, RadioPublica, iHeartRadio or RSS.Disclosure.