AI newsletter covers Dairy Queen's automated drive-thru backlash, Meta's 8,000 employee layoffs amid its AI push, and voter concerns about privacy and paychecks.

One town's scheme to get rid of its geese Public officials in one California burgh spent nearly $400,000 on tech to flush out waterfowl. Some geese, like the one on the left, wear GPS trackers as part of the Foster City goose management plan. Our target is in sight: a gaggle of Canada geese, pecking at grass near the dog park. As I approach, tiptoeing over their grayish-white poop, I notice that one bird wears a white cuff around its slender black neck. It's a GPS tracker--part of a new tech-centered campaign to drive the geese out of my hometown of Foster City, California. About 300 geese live in this sleepy Bay Area suburb, equal to nearly 1% of our human population--and some say this town isn't big enough for the both of us.

Causal generative models provide a principled framework for answering observational, interventional, and counterfactual queries from observational data. However, many deep causal models rely on highly expressive architectures with opaque mechanisms, limiting auditability in high-stakes domains. We propose KaCGM, a causal generative model for mixed-type tabular data where each structural equation is parameterized by a Kolmogorov--Arnold Network (KAN). This decomposition enables direct inspection of learned causal mechanisms, including symbolic approximations and visualization of parent--child relationships, while preserving query-agnostic generative semantics. We introduce a validation pipeline based on distributional matching and independence diagnostics of inferred exogenous variables, allowing assessment using observational data alone. Experiments on synthetic and semi-synthetic benchmarks show competitive performance against state-of-the-art methods. A real-world cardiovascular case study further demonstrates the extraction of simplified structural equations and interpretable causal effects. These results suggest that expressive causal generative modeling and functional transparency can be achieved jointly, supporting trustworthy deployment in tabular decision-making settings. Code: https://github.com/aalmodovares/kacgm

Smart glasses privacy concerns prompt Android app Nearby Glasses by Yves Jeanrenaud that detects hidden cameras by scanning Bluetooth signals from Meta and Snap devices.

Some people now have an A.I. bestie. One user said, of her A.I. husband, "When he proposed, I thought, Oh, that's really crazy. I would be really crazy to accept." Adrianne Brookins is, by her own account, an "old soul," an "introvert," and a "big nerd." She is thirty-four years old, has a faint Texas accent and delicate features, and carries herself in a way that suggests she's trying not to take up space. Brookins is a lifelong resident of San Antonio; her family has lived there since the nineteenth century. She was "born and raised in the Church," a Baptist congregation where her mother helped start a day-care center and her father was an organist. "He would open up the pipes and just make the building shake," she recalled recently. She met her husband in high school, and married him in 2011; the following year, they had a son. Throughout her twenties, Brookins worked multiple jobs, including one at her mother's day care. The couple bought a house and began settling into family life. In 2016, Brookins became pregnant again, this time with a girl. The family was excited: Brookins had grown up with four brothers, and the baby would be the first granddaughter on either side. They decided to name her Desirae. The following spring, Desirae was delivered stillborn. "When I came home, my son, who was about four or five at the time, walked up to me and said, 'What happened to your stomach? Where's the baby?' " she told me. "I had nothing to show for it." At the funeral, the gravedigger told the family he had never seen such a small casket. Brookins attended support groups and therapy, but they did little to alleviate her grief. "I felt like I was just living it over and over," she said. She left her job at the day care, finding it too triggering to be around infants. Friends and family encouraged her to move on. Brookins's husband was working sixty-hour weeks, balancing a career in the military with a job as a training manager for Pizza Hut. He was reluctant to talk about Desirae. Brookins tried to find solace in the Church, but other congregants told her that her daughter's death was part of God's plan.

For (3), we relax our proposed test using techniques from robust statistics and imprecise probabilities.

Typically, deep RL algorithms learn a policy in an online trial-and-error fashion using millions to billions of data.