So Claude and I tried to make a database for tracking the petty grievances of the masses. The dog that ushered me into the technological future was "low and thick." That's all my mother registered before it T-boned her in a city park earlier this year: dense, heavy, and traveling fast enough to fracture her right tibia. Let's discuss what this set in motion in my life: Having successfully learned nothing about coding for two and a half decades, I would soon be attempting my very first software development project. If you've ever had a low and thick dog break your mom's shin bone, you know the stream of lesser indignities that follows.

Daron Acemoglu is more cautious than most about predictions of a jobs apocalypse. A few months before he was awarded the Nobel Prize in economics in 2024, Daron Acemoglu published a paper that earned him few fans in Silicon Valley. Contrary to what Big Tech CEOs had been promising--an overhaul of all white-collar work--Acemoglu estimated that AI would give only a small boost to US productivity and would not obviate the need for human work. It's okay at automating certain tasks, he wrote, but some jobs will be perfectly fine. Two years later, Acemoglu's measured take has not caught on. Chatter about an AI jobs apocalypse pops up everywhere from Senator Bernie Sanders's rallies to conversations I overhear in line at the grocery store.

For screenwriters like me--and job seekers all over--AI gig work is the new waiting tables. In eight months, I've done 20 of these soul-crushing contracts for five different platforms. My name on the platform is ri611. I work as an AI trainer. I assess whether a chatbot's tone is natural or flat, affected or annoying. I identify patterns in pictures of furniture; search the internet for group photos of strangers whom I'll eliminate from the portrait, one by one. I trawl through bizarre videos so I can annotate and time-stamp the barking of a dog, the moment a stranger walks past a window, the precise millisecond a balloon pops. I generate anime sex scenes and decapitate young women, coax LLMs into giving me recipes for bombs made of household items, and generate invites to a reprise of January 6 at the White House, all as part of a red team whose purpose is to test safety precautions and probe weaknesses. I work for companies with names like Mercor and Outlier and Task-ify and Turing and Handshake and Micro1. In my "other" career, I am a Hollywood writer and showrunner. I create prime-time TV, usually featuring a middle-class white lady having the worst day of her life, with some salt-of-the-earth police interference to raise the stakes. You can find my shows on Paramount and Hulu and the BBC.

Tech advances not only made IVF safer and more effective; they fundamentally changed the way we think about our reproduction. Technology is changing the way we make babies. The pioneering work of the scientists who invented IVF led to the birth of the first "test tube baby" in 1978. We've come a long, long way since then. This week, I've been working on a piece about the cutting edge of IVF technologies and what's coming next. Think AI and robots and, potentially, gene-edited embryos.

An accurate environment dynamics model is crucial for various downstream tasks in sequential decision-making, such as counterfactual prediction, off-policy evaluation, and offline reinforcement learning. Currently, these models were learned through empirical risk minimization (ERM) by step-wise fitting of historical transition data. This way was previously believed unreliable over long-horizon rollouts because of the compounding errors, which can lead to uncontrollable inaccuracies in predictions. In this paper, we find that the challenge extends beyond just longterm prediction errors: we reveal that even when planning with one step, learned dynamics models can also perform poorly due to the selection bias of behavior policies during data collection.

The different cases represent various material property configurations. In Figure 8 we show the temporal evolution of different scenarios (a) to (d) for the initially straight bending rod, and (e) to (f) for the elastic helix. The default parameters of the initially straight bending rod are 0 =0, N = 30, ` =4 .0 In (b), we modify N 2{ 10,20,40,60}. The default parameters of the elastic helix are HR =0 .5 m (helix radius), HH =0 .5 m (helix height), HW =2 .5 (winding number), T =1 .0

This paper proposes a new neural network architecture by introducing an additional dimension called height beyond width and depth. Neural network architectures with height, width, and depth as hyper-parameters are called three-dimensional architectures. It is shown that neural networks with three-dimensional architectures are significantly more expressive than the ones with two-dimensional architectures (those with only width and depth as hyper-parameters), e.g., standard fully connected networks. The new network architecture is constructed recursively via a nested structure, and hence we call a network with the new architecture nested network (NestNet). ANestNet of height sis built with each hidden neuron activated by a NestNet of height s 1.