In a world where AI shapes much of our media landscape, are we becoming unwitting allies in the spread of misinformation? In The Misinformation Effect, our (slightly annoying) AI hosts dive heart first into how AI doesn’t just generate fake news—it relies on real people to amplify it. Listen in as we explore the surprising ways humans become "super spreaders" of AI-driven disinformation and what can be done to break the cycle.

Brought to you by Virtual Story Lab - empower.virtualstorylab.com

Sources:

Aïmeur, E., Amri, S. and Brassard, G., 2023. Fake news, disinformation and misinformation in social media: a review. Social Network Analysis and Mining, 13(1), p.30. Available at: https://link.springer.com/article/10.1007/s13278-023-01028-5

Bashardoust, A., Feuerriegel, S. and Shrestha, Y.R., 2024. Comparing the willingness to share for human-generated vs. AI-generated fake news. arXiv preprint arXiv:2402.07395. Available at: https://arxiv.org/abs/2402.07395

James Cameron, the mind behind Terminator, once warned about rogue AI—but now, he's backing it to revolutionize film. In this episode, our intrepid (and slightly annoying) AI hosts ponder the ins and outs of how AI could reshape creative industries, cutting costs and boosting creativity, yet facing a minefield of copyright issues. Is scraping online art for AI training fair use or just theft? And who really owns what AI creates? Buckle up as we navigate this tangled web of innovation, ethics, and ownership.

Brought to you by Virtual Story Lab - empower.virtualstorylab.com

Sources:

Desai, D.R. and Riedl, M., 2024. Between Copyright and Computer Science: The Law and Ethics of Generative AI. Northwestern University.

Lees, D., 2024. AI could transform film visual effects. But first, the technology needs to address copyright debate. The Conversation. Available at: https://theconversation.com/ai-could-transform-film-visual-effects-but-first-the-technology-needs-to-address-copyright-debate-240348

As we the people drown in data, experts warn that within the next couple of years, AI could face a data drought. Models like GPT-4 have been trained on trillions of words, but the next generation will demand even more—quadrillions, quintillions—numbers so large the zeros disappear into the distance. But where will all this data come from? Who owns it? Who controls it? In this episode, our intrepid (and slightly annoying) AI hosts delve into the implications of AI’s insatiable appetite for data and what it means for future development. They discuss, amongst other things, the impact on our data identities, privacy, and copyright laws that increasingly look like they are no longer fit for purpose.

Brought to you by Virtual Story Lab - empower.virtualstorylab.com

Sources:

Longpré, L. (2024). "Consent in Crisis: The Rapid Decline of the AI Data Commons." arXiv preprint. Available at: https://arxiv.org/abs/2407.14933

Woodie, A. (2024). "Are We Running Out of Training Data for GenAI?" Big Data Wire. Available at: https://www.bigdatawire.com/2024/07/26/are-we-running-out-of-training-data-for-genai