This week, Dana, Julia, and Stephen start by discussing the film, Women Talking. Then they chat about the new U.K. import Traitors with Slate's own Carl Wilson. Finally, they finish by talking ChatGPT and the coming of AI chatbots. Dana: Werner Herzog is in his somber, elegiac mode with The Fire Within: A Requiem for Katia and Maurice Krafft. Not to be confused with Fire of Love, about the same people, but made by Sara Dosa.

This week, Dana and Julia are joined by Slate writer Dan Kois. They start by discussing HBO's new series, The Last of Us, a video game adaptation with culture editor and writer at The New Yorker, Alex Barasch. Then they discuss the French film, Saint Omer, shortlisted for Best International Film at the 2023 Oscars. Finally, they finish by talking about Dan's essay on how the Trunchbull, the formidable villain of Roald Dahl's 1988 novel, Matilda is still evolving. Dan: Two books publishing this week, an anti-romantic comedy, Really Good, Actually by Monica Heisey, about a young woman in Toronto failing to deal with her divorce, and a novel by Matthew Salesses titled The Sense of Wonder about the ways Asian Americans navigate the worlds of sports and entertainment when everything is stacked against them.

After teasing her new single with lyrics scrawled in lipstick on Instagram, where she reigns supreme, Selena Gomez has released "Bad Liar." If it reminds you of the Talking Heads a lot more than you ever thought a Selena Gomez song would, that's on purpose. Songwriter Justin Tranter, who worked on the song with Gomez and songwriter Julia Michaels, explained how the Tina Weymouth bassline ended up on the track Variety. "Selena and Julia are Talking Heads-obsessed. So when we all got together in one room, Julia suggested, 'Why don't we just write over the bassline from "Psycho Killer?"'

So, you want to create a hugely successful machine-learning startup? Or you've been asked to start investigating ML for your firm? Well, you'd better get programming – but what language should you use? No languages have been designed specifically with ML in mind, but some do lend themselves to the task. Developers experimenting with machine learning will spend most of their time processing data sets, running them against a machine-learning algorithm, and then classifying them again until the results seem right.

A futuristic novel, "2084" centers on a self-driving car named Winston that lives in a world where humans have reversed global warming. His owner is a math teacher named Julia. Children from every state are invited to participate in a national art competition as a result of the incredibly well-endowed federal arts budget. The winners travel to Washington, D.C., and meet the President of the United States, who is a very kind woman and also a doctor. Written in Seussian rhyme, this children's book is filled with many cute animals that live wherever they like, not necessarily on a farm!

If you don't know, Julia is "a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments." Julia is fast, and enjoys support from and integration with the Jupyter notebook environment. Julia can call C directly without a wrapper, integrates top tier open source C and Fortran code into its Base library, and can easily call Python as well. Julia is built for parallel and cloud computing, and has particular interest from the analytics and scientific computing communities. According to KDnuggets' most recent analytics software poll, Julia placed 8th on the list of most used programming languages.

How can a robot tell when it — rather than another agent — is making an utterance or performing an action? This is rather tricky and also very important for human-robot (or even robot-robot) interaction. Here we outline our beginning attempt to deal with this issue.

This paper describes Convex, a convex optimization modeling framework in Julia. Convex translates problems from a user-friendly functional language into an abstract syntax tree describing the problem. This concise representation of the global structure of the problem allows Convex to infer whether the problem complies with the rules of disciplined convex programming (DCP), and to pass the problem to a suitable solver. These operations are carried out in Julia using multiple dispatch, which dramatically reduces the time required to verify DCP compliance and to parse a problem into conic form. Convex then automatically chooses an appropriate backend solver to solve the conic form problem.