GTs directly connect nodes, using the graph structure asasoft bias through positionalencoding[37].

The Industry I Offered to Take Less Money to Get Hired. In a rough hiring market, a growing number of younger, female job seekers have begun "lowballing" their salary expectations. I know this because I did it myself. If it feels impossible to get hired in today's job market, it's because it is. Greenhouse, a hiring software firm, estimates that when someone applies for a job, they now have a 0.4 percent chance of being hired--meaning you have a better chance of getting into Harvard than securing employment.

A major Verizon outage appeared to impact customers across the United States starting around noon ET on Wednesday. Calls to Verizon customers from other carriers may also be impacted. Customers of the telecom giant Verizon began reporting cellular outages around the United States beginning around noon ET on Wednesday, saying they could not complete calls and did not have access to mobile data. Verizon broadband internet customers are also reporting issues. AT&T and T-Mobile customers also began reporting service outages in the same timeframe, however these reports may be linked to the Verizon outage.

A Multinomial Logit (MNL) model is composed of a finite universe of items $[n]=\{1,..., n\}$, each assigned a positive weight. A query specifies an admissible subset -- called a slate -- and the model chooses one item from that slate with probability proportional to its weight. This query model is also known as the Plackett-Luce model or conditional sampling oracle in the literature. Although MNLs have been studied extensively, a basic computational question remains open: given query access to slates, how efficiently can we learn weights so that, for every slate, the induced choice distribution is within total variation distance $\varepsilon$ of the ground truth? This question is central to MNL learning and has direct implications for modern recommender system interfaces. We provide two algorithms for this task, one with adaptive queries and one with non-adaptive queries. Each algorithm outputs an MNL $M'$ that induces, for each slate $S$, a distribution $M'_S$ on $S$ that is within $\varepsilon$ total variation distance of the true distribution. Our adaptive algorithm makes $O\left(\frac{n}{\varepsilon^{3}}\log n\right)$ queries, while our non-adaptive algorithm makes $O\left(\frac{n^{2}}{\varepsilon^{3}}\log n \log\frac{n}{\varepsilon}\right)$ queries. Both algorithms query only slates of size two and run in time proportional to their query complexity. We complement these upper bounds with lower bounds of $Ω\left(\frac{n}{\varepsilon^{2}}\log n\right)$ for adaptive queries and $Ω\left(\frac{n^{2}}{\varepsilon^{2}}\log n\right)$ for non-adaptive queries, thus proving that our adaptive algorithm is optimal in its dependence on the support size $n$, while the non-adaptive one is tight within a $\log n$ factor.

When everyone looks too perfect to trust, weirdness becomes the most convincing sign you're real. If my dating app profile were made with A.I., my nose would be smaller, my teeth whiter. My eyes would be equally hooded, or not hooded at all, and my skin smoother. Men wouldn't make a game out of guessing whether I'm neurodivergent or Jewish. My gaze would be coquettish, my aura obvious, my entire essence ratcheted down a notch or several.

Users Elon Musk's Chatbot Is Making Child Sexual Abuse Images for Users. An app the U.S. and U.K. governments use has devolved into a source of A.I. porn. Lawmakers are keeping dangerously mum. Enter your email to receive alerts for this author. You can manage your newsletter subscriptions at any time.

Jurisprudence Brett Kavanaugh Is Trying to Walk Back "Kavanaugh Stops." Justice Brett Kavanaugh does not seem happy that his name has become synonymous with racist immigration enforcement. In September, the justice wrote that Hispanic residents' "apparent ethnicity" could be a "relevant factor" in federal agents' decision to stop them and demand proof of citizenship. Immigration and Customs Enforcement and Customs and Border Protection promptly seized upon his opinion as a license to stop any Hispanic person on the basis of race--often with excessive, even sadistic force --and detain them until they proved their lawful presence. Law professor Anil Kalhan termed these encounters "Kavanaugh stops," and the name swiftly caught on as evidence mounted that they had become standard practice across the country.