Instead of clicking on a bunch of random website links, I was reading an AI summary positioned at the top of my search results and sometimes clicking through to double-check the accuracy of the output. The next evolution of Search that Google is building asks for even less active participation from users. You're really the most involved at the start of the journey, and that's it. You tell the agents what you want to know, and they do the clicking and even calling on your behalf. Rather than you going off on some online adventure, it's the agent that's hoovering up anything it can find and bouncing between different sites.

Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or factual lookup, where much simpler and smaller specialized models excel. In this paper, we show that LMs can teach themselves to use external tools via simple APIs and achieve the best of both worlds. We introduce Toolformer, a model trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction. This is done in a self-supervised way, requiring nothing more than a handful of demonstrations for each API. We incorporate a range of tools, including a calculator, a Q&A system, a search engine, a translation system, and a calendar. Toolformer achieves substantially improved zero-shot performance across a variety of downstream tasks, often competitive with much larger models, without sacrificing its core language modeling abilities.

PCWorld argues that Bing serves as a superior alternative to AI-heavy search engines by prioritizing human-authored content over automated summaries. AI search engines like Google's AI Mode often hide original sources and provide misleading information, with traffic to publishers dropping significantly.

Background: Determining an adequate sample size is essential for developing reliable and generalisable clinical prediction models, yet practical guidance on selecting appropriate methods remains limited. Existing analytical and simulation-based approaches often rely on restrictive assumptions and focus on mean-based criteria. We present and validate pmsims, an R package that uses Gaussian process surrogate modelling to provide a flexible and computationally efficient simulation-based framework for sample size determination across diverse prediction settings. Methods: We conducted a comprehensive simulation study with two aims. First, we compared three search engines implemented in pmsims: a Gaussian process-based adaptive method, a deterministic bisection method, and a hybrid approach, across binary, continuous, and survival outcomes. Second, we benchmarked the best-performing pmsims engine against existing analytical (pmsampsize) and simulation-based (samplesizedev) methods, evaluating recommended sample sizes, computational time, and achieved performance on large independent validation datasets. Results: The Gaussian process-based method consistently produced the most stable sample size estimates, particularly in low-signal, high-dimensional settings. In benchmarking, pmsims achieved performance close to prespecified targets across all outcome types, matching simulation-based approaches and outperforming analytical methods in more challenging scenarios. Conclusions: pmsims provides an efficient and flexible framework for principled sample size planning in clinical prediction modelling, requiring fewer model evaluations than non-adaptive simulation approaches.

The power of digital platforms is at the center of major ongoing policy and regulatory efforts. To advance existing debates, we designed and executed an experiment to measure the performative power of online search providers. Instantiated in our setting, performative power quantifies the ability of a search engine to steer web traffic by rearranging results. To operationalize this definition we developed a browser extension that performs unassuming randomized experiments in the background. These randomized experiments emulate updates to the search algorithm and identify the causal effect of different content arrangements on clicks. Analyzing tens of thousands of clicks, we discuss what our robust quantitative findings say about the power of online search engines, using the Google Shopping antitrust investigation as a case study. More broadly, we envision our work to serve as a blueprint for how the recent definition of performative power can help integrate quantitative insights from online experiments with future investigations into the economic power of digital platforms.

Presearch's "Doppelgänger" is trying to help people discover adult creators rather than use nonconsensual deepfakes. For three days in February, porn star Alix Lynx flew to Miami for her first exclusive creator gathering where she was in full grind mode: shooting Reels and talking strategy with other creators. "It was kind of like SoHo House for OnlyFans girls," she says of the experience, which is called The Circle and drew more than a dozen sex workers, including Remy LaCroix and Forrest Smith. Lynx, who is a former webcam model turned OnlyFans starlet, has a combined 2 million followers across Instagram, TikTok, and X . She joined OnlyFans in 2017 with "the luxury of having my own following," she says, but those numbers haven't always translated to subscriptions. It's why she was in Miami.

Beyond mistakes or nonsense, deliberately bad information being injected into AI search summaries is leading people down potentially harmful paths. These days, rather than showing you the traditional list of links when you run a search query, Google is intent on throwing up AI Overviews instead: synthesized summaries of information scraped off the web, with some word-prediction magic added, and packaged together in a way to sound as accurate and reliable as possible. We've written before about some of the problems with these AI Overviews, which regularly contain mistakes or nonsense, and of course rip off the work of the human writers who actually know the answers to the questions you're putting into Google. There's another problem though--these AI answers can actually be dangerous. As with every other new technology through history, scams are now making their way into AI Overviews as well, apparently injecting Google's AI answers with fraudulent phone numbers that you shouldn't trust.