Gift Ideas For Everyone On Your List Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable @Home In My Bag Safety Net Look Up Versus Trending Now Say More Creator Playbook Mashable Selects Getting Technical with Mashable All Series AT&T's $177 million class-action settlement payout is coming, but probably won't change your life Several million dollars divided by several million people actually won't leave you with much. Amanda Yeo is an Assistant Editor at Mashable, covering entertainment, culture, tech, science, and social good. Based in Australia, she writes about everything from video games and K-pop to movies and gadgets. AT&T settled a $177 million class-action lawsuit last year, agreeing to pay customers whose personal information was stolen in two data breaches. A Texas district court has since approved the settlement agreement on Oct. 2. However, even if you were impacted, this doesn't mean a windfall is coming your way.

Mashable Selects Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Safety Net Versus Creator Playbook In My Bag Trending Now Back to School Good Connection: Uplifting stories for a digital age Switch Off All Series If you have an iPhone, Apple might owe you money. Here's how to claim it. The tech giant has agreed to pay a $250 million settlement to iPhone owners. Amanda Yeo is an Assistant Editor at Mashable, covering entertainment, culture, tech, science, and social good. Based in Australia, she writes about everything from video games and K-pop to movies and gadgets.

You Know Who Really Hates AI? Insurance Claims Adjusters Of the Glassdoor reviews from claims adjusters that mentioned AI, a staggering 98 percent were negative. "AI is just a tool," one person tells WIRED. "It should never be given the keys." On the job review platform Glassdoor, one faction hates artificial intelligence more than any other. "Pushing AI to the point that you are asking humans not to use their thoughts and brains is such a turn off," reads one representative review.

Sam Altman wants Americans to share in AI's wealth. The proposal may be more revealing as a political narrative than as a policy plan. OpenAI CEO Sam Altman's oft-discussed promise that Americans will share in the wealth AI creates was in the news again last week. On Thursday, the reported that Altman is in talks with President Trump about giving the US government a 5% stake in OpenAI. In some ways, Altman's plan is old news. He wrote about a more radical version of this back in 2021, proposing that companies above a certain valuation (not just AI companies) pay 2.5% of their market value each year into a fund that sends Americans annual disbursements.

Traditional Home Insurance Is Collapsing. A new, AI-assisted model of insurance is quietly exploding in disaster-prone areas--and may be coming for FEMA too. Is it the answer to climate change, or a trap? In 2019, when the worst flooding in recorded history spread across the entire Mississippi River basin, Colin Wellenkamp's phone rang for weeks. Wellenkamp runs a nonprofit called the Mississippi River Cities & Towns Initiative, which coordinates between mayors' offices in more than 100 river communities from northern Minnesota to southern Louisiana. As he describes it, his headquarters served as "one big virtual situation room" for relief agencies and municipalities up and down the central US.

What to Know About Sony's $7.85 Million PlayStation Settlement Are you eligible for a payout? Probably, but it might take a while and will likely be pretty small. Sony, owner of the PlayStation brand, has been accused of antitrust practices. The lawsuit was originally settled in 2024 but was rejected twice during the approval process. Last week, a judge approved a preliminary reopening of the settlement.

How to claim Verizon's $20 outage credit He gave millions in seed funding. Part of a lawsuit accusing OpenAI of abandoning its non-profit status claims Musk is owed anywhere from $79 billion to $134 billion in damages for the "wrongful gains" of OpenAI and Microsoft. Musk claims in the filing that he's entitled to a chunk of the company's recent $500 billion valuation, after contributing $38 million in "seed funding" during the AI company's early years. It wasn't just money -- according to the filing, Musk helped advise on key employee recruitment, introductions with business contacts and startup advice. If this sounds familiar, it's because the lawsuit dates back to March 2024.

How to claim Verizon's $20 outage credit The latest filing in the lawsuit claims that Musk deserves anywhere from $79 billion to $134 billion from wrongful gains. We now have some idea of what's at stake in the longstanding feud between Elon Musk and OpenAI. As first reported by, the latest filing, as part of a lawsuit that accuses the AI giant of abandoning its non-profit status, claims that Musk is owed anywhere between $79 billion and $134 billion in damages from the wrongful gains of OpenAI and Microsoft. Musk claimed in the filing that he's entitled to a portion of OpenAI's recent valuation at $500 billion, after contributing $38 million in seed funding during the AI company's startup years. Along with providing roughly 60 percent of the nonprofit's seed funding, Musk offered recruiting of key employees, introductions with business contacts and startup advice, according to the filing.

Neural Information Processing Systems http://nips.cc/

Canonical algorithms for multi-armed bandits typically assume a stationary reward environment where the size of the action space (number of arms) is small. More recently developed methods typically relax only one of these assumptions: existing non-stationary bandit policies are designed for a small number of arms, while Lipschitz, linear, and Gaussian process bandit policies are designed to handle a large (or infinite) number of arms in stationary reward environments under constraints on the reward function. In this manuscript, we propose a novel policy to learn reward environments over a continuous space using Gaussian interpolation. We show that our method efficiently learns continuous Lipschitz reward functions with $\mathcal{O}^*(\sqrt{T})$ cumulative regret. Furthermore, our method naturally extends to non-stationary problems with a simple modification. We finally demonstrate that our method is computationally favorable (100-10000x faster) and experimentally outperforms sliding Gaussian process policies on datasets with non-stationarity and an extremely large number of arms.