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.

PCWorld reports that Google Search's AI Mode now incorporates Personal Intelligence, mining data from Gmail and Google Photos to deliver customized search results and travel recommendations. This development matters as it represents Google's push toward highly personalized AI experiences while users retain control over which services connect and how data is used. Google assures that personal data from these services won't be used to train its AI models, addressing privacy concerns as the feature expands beyond Gemini chatbot. Earlier this month, Google said its Gemini AI chatbot will be getting to know you a lot better thanks to a new Personal Intelligence feature that scours your digital life. Now, the company has announced that Personal Intelligence is also coming to Google Search's AI Mode . By connecting Gmail and Google Photos to Personal Intelligence, for example, the search engine's AI Mode will be able to provide you with search results tailored specifically to you. For example, AI Mode will use hotel bookings in your Gmail inbox and old travel photos in your Google Photos albums to recommend activities for an upcoming holiday.

When you purchase through links in our articles, we may earn a small commission. Here's how to get rid of them Are you tired of AI summaries in Google Search? Want the search results to look like they used to? Here are some tricks you can use. Unless you live under a rock, you've probably seen that Google Search has been showing "AI Overviews" at the top of its search results.

The unbiased learning to rank (UL TR) problem has been greatly advanced by recent deep learning techniques and well-designed debias algorithms.

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.

Absence of large-scale labeled data in the practitioner's target domain can be a bottleneck to applying machine learning algorithms in practice. Transfer learning is a popular strategy for leveraging additional data to improve the downstream performance, but finding the most relevant data to transfer from can be challenging. Neural Data Server (NDS), a search engine that recommends relevant data for a given downstream task, has been previously proposed to address this problem (Yan et al., 2020). NDS uses a mixture of experts trained on data sources to estimate similarity between each source and the downstream task. Thus, the computational cost to each user grows with the number of sources and requires an expensive training step for each data provider.To address these issues, we propose Scalable Neural Data Server (SNDS), a large-scale search engine that can theoretically index thousands of datasets to serve relevant ML data to end users. SNDS trains the mixture of experts on intermediary datasets during initialization, and represents both data sources and downstream tasks by their proximity to the intermediary datasets. As such, computational cost incurred by users of SNDS remains fixed as new datasets are added to the server, without pre-training for the data providers.We validate SNDS on a plethora of real world tasks and find that data recommended by SNDS improves downstream task performance over baselines. We also demonstrate the scalability of our system by demonstrating its ability to select relevant data for transfer outside of the natural image setting.

Tussles between websites and scrapers are not new. Almost since there has been a web to scrape, people have been scraping it and using the data to make search engines, caches and archives, analytics platforms, research datasets, and more. And for almost as long, some websites have objected and tried to stop the scraping with a mix of technical and legal measures. Broadly speaking, scrapers cause two kinds of problems for websites. First, they create bad traffic: millions of automated requests that no human will ever see.

'There are a lot of people that are scared to even talk about what's going on because it is their livelihood,' says Jim Delmage who runs the blog and YouTube channel Sip and Feast with his wife, Tara. 'There are a lot of people that are scared to even talk about what's going on because it is their livelihood,' says Jim Delmage who runs the blog and YouTube channel Sip and Feast with his wife, Tara. Google AI summaries are ruining the livelihoods of recipe writers: 'It's an extinction event' T his past March, when Google began rolling out its AI Mode search capability, it began offering AI-generated recipes. The recipes were not all that intelligent. The AI had taken elements of similar recipes from multiple creators and Frankensteined them into something barely recognizable.