Musk's company denies that its driving assistance system is to blame. The family of a woman killed after a Tesla, which was operating using an automated driving assistance system according to authorities, crashed into her home is suing both the company and the driver of the vehicle. As reported by, a lawsuit was filed in Harris County District Court by Jennifer and Justin Barbour, the daughter and son-in-law of the 76-year-old victim, Martha Avila. It accuses Tesla of a design defect, and the car's owner, Michael Butler, 44, of negligence. Butler's Tesla Model 3 allegedly collided with Avila's Katy, Texas, home at around 8pm on June 19, at which time she was standing in her front room.

Estimating heterogeneous treatment effects (HTEs) is crucial for personalized decision-making. However, this task is challenging in survival analysis, which includes time-to-event data with censored outcomes (e.g., due to study dropout). In this paper, we propose a toolbox of orthogonal survival learners to estimate HTEs from time-to-event data under censoring. Our learners have three main advantages: (i) we show that learners from our toolbox are guaranteed to be orthogonal and thus robust with respect to nuisance estimation errors; (ii) our toolbox allows for incorporating a custom weighting function, which can lead to robustness against different types of low overlap, and (iii) our learners are \emph{model-agnostic} (i.e., they can be combined with arbitrary machine learning models). We instantiate the learners from our toolbox using several weighting functions and, as a result, propose various neural orthogonal survival learners. Some of these coincide with existing survival learners (including survival versions of the DRand R-learner), while others are novel and further robust w.r.t.

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The pair is expected to leave the nest in early July. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Sandy going out on a limb to practice her wingers. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

A campaign event in Los Angeles, California, for a proposed'billionaires tax', on 18 February. A campaign event in Los Angeles, California, for a proposed'billionaires tax', on 18 February. Despite more than double the needed number of signatures to qualify for ballot, there's uncertainty it'll make it to voters Nick Robins-Early and Dara Kerr here, filling in for your usual host Blake Montgomery who is out on vacation. We'll be talking about the fight over a proposed billionaire tax in California, the UK's social media ban and SpaceX making a big buy in the AI arms race. The California wealth tax showdown comes to a head this week.

We examine hypothesis testing within a principal-agent framework, where a strategic agent, holding private beliefs about the effectiveness of a product, submits data to a principal who decides on approval. The principal employs a hypothesis testing rule, aiming to pick a p-value threshold that balances false positives and false negatives while anticipating the agent's incentive to maximize expected profitability. Building on prior work, we develop a game-theoretic model that captures how the agent's participation and reporting behavior respond to the principal's statistical decision rule. Despite the complexity of the interaction, we show that the principal's errors exhibit clear monotonic behavior when segmented by an efficiently computable critical p-value threshold, leading to an interpretable characterization of their optimal p-value threshold.

In practice, many models from a function class can fit a dataset almost equally well. This collection of near-optimal models is known as the Rashomon set. Prior work has shown that the Rashomon set offers flexibility in choosing models aligned with secondary objectives like interpretability or fairness. However, it is unclear how far this flexibility extends to different trustworthy criteria, especially given that most trustworthy machine learning systems today still rely on complex specialized optimization procedures. Is the Rashomon set all you need for trustworthy model selection?

President Trump probably can't get rid of her yet, but FCC commissioner Anna Gomez still checks her email every day to see if he has. Until then, she wants to stand up for the First Amendment. If you've given much thought to the Federal Communications Commission in recent years, it probably had something to do with Brendan Carr . The group's chairman since 2025, Carr has been on an ongoing, public rampage against freedom of speech: he's gone after late-night hosts like Jimmy Kimmel, threatened to revoke broadcast licenses over Iran war coverage, and targeted networks for their DEI policies. Disturbing as Carr's rhetoric and actions have been, he does count at least one opponent within the agency: Commissioner Anna Gomez, currently the lone Democrat among three FCC commissioners, has been vocal about the damage she thinks the agency is doing to American press freedom--and has repeatedly urged the public and the press, namely major networks like ABC, CBS, and NBC, to fight back. In May, Commissioner Gomez penned a stunning public letter to Disney CEO Josh D'Amaro, wherein she warned that the company--which owns ABC--was being subjected to "a sustained, coordinated campaign of censorship and control, carried out through the weaponization of the FCC's authority as a federal regulator and aimed at pressuring a free and independent press." Gomez urged D'Amaro to fight the actions her own agency was taking, adding that "this is a fight worth having, and one that I am confident you will win." I wanted to talk to Commissioner Gomez about that bold letter, the risks she sees for the media and the American public under the Trump administration, and how she works alongside a chairman with whom she disagrees so fiercely. Gomez, whose FCC term ends this month, was generous enough to sit down and talk about all of it. You can read our conversation below, or listen to it on the podcast platform of your choice. KATIE DRUMMOND: Welcome, Commissioner Gomez. Thank you for being here. It's great to be here. I want to start, before we talk more about Disney and your letter and all the rest of it, with a very basic question for our listeners. What is your agency's basic role?

Greens senator Sarah Hanson-Young says Australia can't allow tech firms to'drain our power and water'. Greens senator Sarah Hanson-Young says Australia can't allow tech firms to'drain our power and water'. Australia'sleepwalking' into AI crisis and'tech bro free-for-all', says Greens senator Sarah Hanson Young's warning comes as David Pocock urges government to prevent firms using Australian content to train AI models Tue 23 Jun 2026 06.21 EDTFirst published on Tue 23 Jun 2026 05.16 EDT His call came as the Greens senator Sarah Hanson-Young called for a moratorium on the building and approval of new datacentres in Australia until "we get the regulations right". She said the nation was "sleepwalking" into an AI crisis and could hand tech companies a greenlight "to drain our power and water". Pocock used Senate question time on Tuesday to ask the government about intense lobbying from AI proprietors over possible new rules and regulations for Australian-made content - including suggestions Labor would create a new "carve out" or extend existing licensing arrangements.

Firms' algorithm development practices are often homogeneous. Whether firms train algorithms on similar data or rely on similar pre-trained models, the result is correlated predictions. In the context of personalized pricing, correlated algorithms can be viewed as a means to collude among competing firms, but whether or not this conduct is legal depends on the mechanisms of achieving collusion. We investigate the precise mechanisms through a formal game-theoretic model. Indeed, we find that (1) higher correlation diminishes consumer welfare and (2) as consumers become more price sensitive, firms are increasingly incentivized to compromise on the accuracy of their predictions in exchange for coordination. We demonstrate our theoretical results in a stylized empirical study where two firms compete using personalized pricing algorithms. Our results demonstrate a new mechanism for achieving collusion through correlation, which allows us to analyze its legal implications. Correlation through algorithms is a new frontier of anti-competitive behavior that is largely unconsidered by US antitrust law.