The purpose of this article is twofold. Firstly, we use the next-token probabilities given by a language model to explicitly define a $[0,1]$-enrichment of a category of texts in natural language, in the sense of Bradley, Terilla, and Vlassopoulos. We consider explicitly the terminating conditions for text generation and determine when the enrichment itself can be interpreted as a probability over texts. Secondly, we compute the M\"obius function and the magnitude of an associated generalized metric space $\mathcal{M}$ of texts using a combinatorial version of these quantities recently introduced by Vigneaux. The magnitude function $f(t)$ of $\mathcal{M}$ is a sum over texts $x$ (prompts) of the Tsallis $t$-entropies of the next-token probability distributions $p(-|x)$ plus the cardinality of the model's possible outputs. The derivative of $f$ at $t=1$ recovers a sum of Shannon entropies, which justifies seeing magnitude as a partition function. Following Leinster and Schulman, we also express the magnitude function of $\mathcal M$ as an Euler characteristic of magnitude homology and provide an explicit description of the zeroeth and first magnitude homology groups.

Harnessing the potential computational advantage of quantum computers for machine learning tasks relies on the uploading of classical data onto quantum computers through what are commonly referred to as quantum encodings. The choice of such encodings may vary substantially from one task to another, and there exist only a few cases where structure has provided insight into their design and implementation, such as symmetry in geometric quantum learning. Here, we propose the perspective that category theory offers a natural mathematical framework for analyzing encodings that respect structure inherent in datasets and learning tasks. We illustrate this with pedagogical examples, which include geometric quantum machine learning, quantum metric learning, topological data analysis, and more. Moreover, our perspective provides a language in which to ask meaningful and mathematically precise questions for the design of quantum encodings and circuits for quantum machine learning tasks.

A group of veterans inspired by the need to keep schools and public spaces safer have created a new technology they say can detect guns and send out alerts before shots are ever fired. Active shooter situations have played out across the country – a gunman opened fire inside a Florida high school, shots rang out at a Texas Walmart and multiple people were shot to death in an office building in Virginia Beach. The nation's most recent school shooting happened Thursday morning – when a 16-year-old high school student in Santa Clarita, California, opened fire in the campus quad, shooting five classmates and killing two. What if the gun was detected early – so early, the shooter was never able to get inside to hurt anyone? The technology to do that exists, and only WUSA9 was there when it was tested in Northern Virginia.

The Tesla self-driving car has prompted debate online after the man was seen on the I-5 highway near Santa Clarita, California sleeping while driving. A Tesla driver was filmed asleep at the wheel as the semi-autonomous vehicle cruised on its own down a highway in Massachusetts over the weekend. At least, that's what video captured Sunday appears to show. Twitter user Dakota Randall posted a 28-second clip of the incident which occurred along the Massachusetts Turnpike. Teslas are sick, I guess?" The post has since received over 422,000 views, 600 Retweets and almost 2,000 likes. The video appears to show a driver using Tesla's advanced driver assistance system called Autopilot. Teslas are sick, I guess? Randall told local media that he blew his car's horn in an attempt to wake the driver, but it didn't work. "At no point did I feel like I was in danger until after the fact, when I thought'Wow, I was just driving next to somebody who was completely asleep on the Mass Pike of all places, like one of the most dangerous roads I can imagine," Randall told WHDH in Newton, Massachusetts. "But yeah, the car stayed the same speed in the same way on the highway, and yeah, it didn't change at all.

Have you ever felt guilty about leaving your dog home alone, wishing there was a way to calm it? Amazon's Audible may have the answer - a good book. Dog behavior expert Cesar Millan has teamed up with the firm to create audiobooks for dogs, which use human voices to tell stories to dogs when nobody is at home, keeping then company. Dog behavior expert Cesar Millan (pictured) teamed up with audible to conduct their own study to see what impact audiobooks have on dogs when their owners are away. By using a speaker along with one of the audiobooks, users can play one of a range of audible's audiobooks, with recommended titles including: According to a demonstration video by Millan, having to be away from our dogs can be a stressful situation, joined by guilt and shame for having to be away from our pets for long periods of time, for example when going to work.

We've already seen it played on the toilet, to say nothing of park benches, beds, airports, party vans, e-sports arenas and ad hoc nighttime games of underpass hoops. And you'll soon be able to play Nintendo's imminent $299 hybrid TV/mobile Switch game console, due March 3, in even stranger locales. "Imagine driving in the middle of the desert and seeing a couch, TV and video game system in the middle of nowhere," writes Nintendo in a press blurb, "or skiing down the side of a mountain and seeing another one of these surprising setups sitting there in the snow, ready for a round of gaming." That's right, the desert, into which Nintendo says it'll deposit a living room on February 23. Blue Cloud Movie Ranch in Santa Clarita, California, to be geo-spatially precise, a place that's been everything from an Afghan village in American Sniper to an Iraqi village in an episode of Arrested Development.