A US Food and Drug Administration (FDA) advisory panel has voted to loosen restrictions on controversial peptides that have become popular online, but have not been well researched for human usage. The panel narrowly voted to allow specialised pharmacies to produce drugs including BPC-157, TB-500 and KPV, a major regulatory hurdle towards making them available via prescription. Peptides are small proteins that our bodies normally produce and that have long been used to treat medical conditions, including diabetes. But unregulated injectable peptides have exploded in the online wellness community - including among many young influencers - since GLP-1s (weight loss drugs) became mainstream. The FDA does not have to follow the panel's recommendations, but often does.

Despite positioning itself as an anti-hookup app, users tell WIRED that Goose has fake profiles, harsh acceptance standards, and problems with inclusivity. When Erick Hall heard that Goose, a new "anti-algorithm" dating app, was less focused on hookups, he was drawn to its mission. The New York City -based OnlyFans creator, who has over 800,000 followers on X, explains he has to hook up "all the time" for work. Because Goose was marketing itself as an alternative to that, he was curious what it had to offer. During the sign-up process he chose a few selfies where he was fully clothed; in one, wearing a black shirt, a baseball cap, and blue jeans, he pulled his shirt upward showing off his abs.

UK fears a'triple whammy': oversized investment in AI stocks, slower adoption of AI than predicted and the breakneck pace of AI's development The UK wants a piece of the mammoth global investment in AI but fears it as well. In the coming weeks, the Bank of England is planning to ease capital rules to help encourage more lending. But the central bank simultaneously expressed concerns that there are too many loans going to investors like hedge funds, who are using that money to buy up AI stocks. The central bank's moves reflect the country's global position: hoping to catch up to the US and China in the AI race, struggling to mobilize its resources to do so, and too wary of the risks to go full bore. UK banking regulators have recently been under enormous pressure to do more to stimulate growth, my colleague Kalyeena Makortoff this week reports.

Gowanus the media puppet probably shouldn't even be talking to me. Made of literal garbage--his origin story is that he was born in a dumpster in his namesake neighborhood in Brooklyn--he's the media representative for the Summer of Ludd, a Luddite festival that took place in New York earlier this month. The festival, which WIRED attended, included everything from workshops on how to flirt IRL to an evidence box, where people could submit testimonies on how Big Tech has negatively impacted their lives. Its rules were simple: Be present. No phones, recordings, or photographs allowed. So, philosophically speaking, it is somewhat contrary to Gowanus' beliefs to be in a podcast recording studio at Condé Nast's Manhattan offices. But he's pragmatic, telling us he wants to reach people, so he's willing to meet them where they're at. Still, he has some conditions--presented to me on a handwritten contract. Namely, that we not clip short-form video of the show, in an effort to encourage people to engage with the full interview. In a compromise, we agree to only clip Gowanus explaining the contract. You might be wondering why Summer of Ludd and its movement are represented by a puppet. It's a nod to the original Luddites, British textile workers who anonymously organized against being replaced by technology during the Industrial Revolution in the early 19th century. While the term "Luddite" has since become a derogatory way to refer to someone who opposes technology, there's a renaissance happening--and it's surprisingly being heavily embraced by Gen Z. Gowanus offers anonymity to the people behind the growing trend. I was curious about how being a modern-day Luddite works in practical terms--even organizing this interview was a challenge, because the Summer of Ludd folks weren't necessarily quick to reply to emails. And I wanted to know why the first generation to ever grow up totally online seems to be leading the charge on having less screen time.

Erling Haaland Is Everywhere at the World Cup. He's become an internet character perpetuated by fans and AI. Last week, somewhere amid the World Cup frenzy, a now-viral video circulated of Norwegian striker Erling Haaland mid-mouthful in a restaurant, glancing left and flinching at his own reflection. One post on X sharing the video racked up more than 31 million views in mere days. Fact checkers traced the footage to a slapstick skit by the Chinese comedian Jin Long, posted to TikTok in mid-June .

Plus: The US has allowed Anthropic to release Mythos 5 to "trusted" orgs. There are plenty of useful things a metric can reveal. There are even more that it can obscure or corrupt. Like a lot of people bitten by the self-quantifying bug, I started gathering personal data to pursue a nebulous collection of goals and desires. I wanted to feel better physically and emotionally, get outside more, and bring order to the messiness and uncertainty of my daily existence. But external metrics and data can never capture what's truly important.

Increasing demand for Large Language Models (LLMs) services imposes substantial deployment and computation costs on providers. LLM routing offers a cost-efficient solution by directing queries to the optimal LLM based on model and query features. However, existing works primarily focus on offline scenarios and struggle to adapt to online settings with high query volume and constrained token budgets. In this work, we introduce the first training-free algorithm for online routing scenarios. Our algorithm leverages approximate nearest neighbor search to efficiently estimate query features and performs a one-time optimization over a small set of initial queries to learn a routing strategy that guides future routing. We provide theoretical guarantees demonstrating that our algorithm achieves a competitive ratio of 1 o(1)under natural assumptions, which is further validated by extensive experiments across 3 benchmark datasets and 8 baselines, showing an average improvement of 3.55 in overall performance, 1.85 in cost efficiency, and nearly 4.25 in throughput. Our code is available at https://github.com/fzwark/PORT.

We propose a novel online Gaussian process (GP) model that is capable of capturing long-term memory in sequential data in an online learning setting. Our model, Online HiPPO Sparse Variational Gaussian Process (OHSVGP), leverages the HiPPO (High-order Polynomial Projection Operators) framework, which is popularized in the RNN domain due to its long-range memory modeling capabilities. We interpret the HiPPO time-varying orthogonal projections as inducing variables with timedependent orthogonal polynomial basis functions, which allows the SVGP inducing variables to memorize the process history. We show that the HiPPO framework fits naturally into the interdomain GP framework and demonstrate that the kernel matrices can also be updated online in a recurrence form based on the ODE evolution of HiPPO. We evaluate OHSVGP with online prediction for 1D time series, continual learning in discriminative GP model for data with multidimensional inputs, and deep generative modeling with sparse Gaussian process variational autoencoder, showing that it outperforms existing online GP methods in terms of predictive performance, long-term memory preservation, and computational efficiency.

The field of learning-augmented algorithms seeks to use ML techniques on past instances of a problem to inform an algorithm designed for a future instance. In this paper, we introduce a novel model for learning-augmented algorithms inspired by online learning. In this model, we are given a sequence of instances of a problem and the goal of the learning-augmented algorithm is to use prior instances to propose a solution to a future instance of the problem. The performance of the algorithm is measured by its average performance across all the instances, where the performance on a single instance is the ratio between the cost of the algorithm's solution and that of an optimal solution for that instance. We apply this framework to the classic k-median clustering problem, and give an efficient learning algorithm that can approximately match the average performance of the best fixed k-median solution in hindsight across all the instances. We also experimentally evaluate our algorithm and show that its empirical performance is close to optimal, and also that it automatically adapts the solution to a dynamically changing sequence.

In this work, we study offline convex optimization with smooth objectives, where the classical Nesterov's Accelerated Gradient (NAG) method achieves the optimal accelerated convergence. Extensive research has aimed to understand NAG from various perspectives, and a recent line of work approaches this from the viewpoint of online learning and online-to-batch conversion, emphasizing the role of optimistic online algorithms for acceleration. In this work, we contribute to this perspective by proposing novel optimistic online-to-batch conversions that incorporate optimism theoretically into the analysis, thereby significantly simplifying the online algorithm design while preserving the optimal convergence rates. Specifically, we demonstrate the effectiveness of our conversions through the following results: (i) when combined with simple online gradient descent, our optimistic conversion achieves the optimal accelerated convergence; (ii) our conversion also applies to strongly convex objectives, and by leveraging both optimistic online-to-batch conversion and optimistic online algorithms, we achieve the optimal accelerated convergence rate for strongly convex and smooth objectives, for the first time through the lens of online-to-batch conversion; (iii) our optimistic conversion can achieve universality to smoothness -- applicable to both smooth and non-smooth objectives without requiring knowledge of the smoothness coefficient -- and remains efficient as non-universal methods by using only one gradient query in each iteration. Finally, we highlight the effectiveness of our optimistic online-to-batch conversions by a precise correspondence with NAG.