







In this episode of Future Knowledge, Alexis Durante-Tierney and Helen Vogelsong-Donahue, hosts of the Born Digital podcast, explore how growing up under constant surveillance may be changing the wa...
Izabella Kaminska on Twitter / X
The problem of our time is not merely information overload. It is that the time required to construct a coherent understanding of reality is becoming longer than the time during which that understanding remains valid.— Izabella Kaminska (@izakaminska) October 16, 2026
Beware the Power of Prediction | Carissa Véliz | TED
Kari Jordan, Ph.D.
Advancing the power of data to improve the social and economic lives of all people.

Landslide / Holdfast
Notes on what’s happening to our ability to collectively know things, and a look ahead to what this community is especially well positioned to do to support and revitalize our info ecosystems and the humans inside them.

Changing Minds: Computers, Learning, and Literacy
An impassioned guide to how computers can fundamentally change how we learn and think.Andrea diSessa's career as a scholar, technologist, and teacher has b

Stewarding the stories that power tries to silence
"2026 will mark the rise of algorithmic witnessing — a movement that uses AI not to replace journalists, but to extend the reach of the communities they serve."

AI’s Next Chapter Requires Human Expertise
The future of AI doesn’t just require technical skills; it demands deep human expertise, writes Annie K. Lamar.

Prophecy: Prediction, Power, and the Fight for the Future, from Ancient Oracles to AI
From an award-winning University of Oxford professor comes a brilliant, urgent new look at prophecies--the predictions that determine our lives, from our personal finances and the quality of our healthcare to the news and social media we consume and the produces foisted upon us

Eight Lessons from the Distributed Podcast So Far
Looking back over the 2019 run of the Distributed podcast, one is struck by the wealth of insights our guests have shared in each and every episode. We’ve spoken to CEOs, activists, lawyers, author…

The Expert Trap and the Next War
what forecasting failure tells us about the future

The Intelligence Curse
This series examines the incoming crisis of human irrelevance and provides a map towards a future where people remain the masters of their destiny.

Gazing Back at the Surveillance Cameras That Watch Us (Published 2018)
These photographers explored the implications of a culture of pervasive monitoring.

How did Leopold do? Evaluating Situational Awareness's predictions
In June 2024, Leopold Aschenbrenner published Situational Awareness, a 165-page essay predicting AGI by 2027, trillion-dollar compute clusters, an in…
Episode 5: Metaphors for AI, with Shannon Vallor
This week, Adam talks with Shannon Vallor, a philosopher of AI, about the metaphors we use to talk about AI and ourselves. They also dunk on libertarians and San Francisco billboards, and consider what kind of hope we can have for the future of technology.Messages: • Support the show at patreon.com/DreamingAgainstTheMachine • Follow us on Bluesky and Instagram • Subscribe to the show wherever you get your podcasts! • Check out other great podcasts from Multitude like Wow if TrueAbout the show:Dreaming Against the Machine is a podcast about envisioning a realistic and hopeful future. Each week, the show’s host, journalist and astrophysicist Dr. Adam Becker, will have an earnest (and entertaining!) conversation with a guest about possible futures, seen through the lenses of history, science, and culture. In a world where tech oligarchs and their power fantasies are driving visions of the future, Dreaming Against the Machine aims to take back the terms of the public conversation about what our world can and should be.

After the feed with Eli Pariser, Ben Smith, and Jasmine Sun
Ever thought we acquire generalizable knowledge by discarding details and compressing our experiences? In a new BBS paper, @sabinasloman.bsky.social and I argue otherwise, proposing a novel way of studying human learning inspired by double descent in ML. Disagree? Propose a commentary by May 15 :)