We created software that could generate human-like text output quickly and easily. Now we're dealing with the societal upheaval it's caused. What are the risks and rewards, and what can we learn about language from these large language models? Daniel — joined by Caitlin Green — has a chat with Dr Christopher Summerfield, author of These Strange New Minds: How AI Learned to Talk and What It Means. Timestamps 00:00 Start 00:46 Intros: Generative A.I. concerns 04:15 Shout out to our patrons! 05:03 News: AP Style Guide defines COUPLE 10:35 News: Men do vocal fry more 14:59 News: Uptalk from 1890 16:01 News: Is Singlish up? 22:22 Related or Not: Bonkers Mélange editon, theme from Ste 23:41 Related or Not: population, discombobulate, bobbin 29:09 Related or Not: goggle, goo-goo, agog 36:09 Related or Not: once, ounce, pounce, lynx 41:55 Interview with Christopher Summerfield: Do you like A.I.? 44:21 Consequences of AI: Will we know nothing, or know everything? 47:03 Are LLMs just spicy autocorrect? 48:44 Are LLMs simply regurgitating their training data? 49:51 LLMs are getting better fast 52:33 On consciousness and intentionality 55:58 Do LLMs (or humans) understand? 58:58 The Chinese Room 01:01:00 Should we avoid anthropomorphising language around LLM behaviour? 01:04:02 Why we dismiss LLMs 01:07:26 Accelerationists, anti-hypers, and X-risk: Which are you? 01:09:49 Safety, privacy, and security 01:14:29 The magic wand of policy 01:20:18 Fixing the hallucination problem 01:27:36 Goals of the book 01:31:18 Word of the Week: liminal 01:39:59 Word of the Week: pink slime journalism 01:44:44 Word of the Week: waste colonialism 01:48:13 Quick words: hot-washing, eppy, shoulder surgfing, news-jacking, bio-break 01:51:37 Word of the Week: wario 01:55:02 The Reads 02:01:06 Outtake: That time when a siren went off in Hedvig's Parisian hotel, mid-recording Video for this episode: https://youtu.be/94SbeM0KpWw

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