Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Depop is known as an online marketplace for secondhand clothes--but for the Gen-Z fashionistas who congregate there, it's also a community. With more than 56 million registered users and over 68 million items for sale, it's a big community, to be sure.

Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Trending Now Mashable Selects Creator Playbook Back to School Good Connection: Uplifting stories for a digital age Switch Off Mashable Voices Safety Net Versus All Series Amazon's giving Prime members $1,500 to buy a car through Amazon Autos -- here's how to cash in The offer is available nationwide, but you'll need to be a Prime member purchasing a new vehicle. Tabitha Britt is an award-winning freelance journalist, editor, and SEO/AEO strategist. Aside from reviewing dating apps and sex toys for Mashable, Tabitha is also the founding editor-in-chief of DO YOU ENDO -- a digital magazine by individuals with endometriosis, for individuals with endometriosis. She has a Master's degree in Creative Publishing and Critical Journalism from The New School for Social Research and is a grad of Sextech School. You can find more of her work in various online publications, including,, and .

Is the war entering a new phase? 'Long-range sanctions': Ukraine hopes strikes cause Russia fear and losses Then come indistinguishable shouts of male voices, followed by a crescendo - a loud sound like scissors tearing through wrapping paper. "Like an orchestra, every part has to work together in order for us to achieve effective strikes," says Colonel Ray, commander of the Kairos Battalion of the Magyars Birds Brigade. The drone, roughly the length of a compact SUV with a wingspan of nearly 6 metres (19.7 feet), climbs into the night. Where exactly it is headed is known only to him.

We're finally getting, poised to be one of the largest gaming releases in history. It's the end of an era for marveling at all the stuff we got before . The game is officially, truly, really, finally something people can pay actual money for. And it's bound to be a biggie. That would be a feat, but not one without precedent for the series.

Modern data marketplaces and data sharing consortia increasingly rely on incentive mechanisms to encourage agents to contribute data. However, schemes that reward agents based on the quantity of submitted data are vulnerable to manipulation, as agents may submit fabricated or low-quality data to inflate their rewards. Prior work has proposed comparing each agent's data against others' to promote honesty: when others contribute genuine data, the best way to minimize discrepancy is to do the same. Yet prior implementations of this idea rely on very strong assumptions about the data distribution (e.g.

We argue that the machine learning value chain is structurally unsustainable due to an economic data processing inequality: each state in the data cycle from inputs to model weights to synthetic outputs refines technical signal but strips economic equity from data generators. We show, by analyzing seventy-three public data deals, that the majority of value accrues to aggregators, with documented creator royalties rounding to zero and widespread opacity of deal terms. This is not just an economic welfare concern: as data and its derivatives become economic assets, the feedback loop that sustains current learning algorithms is at risk. We identify three structural faults--missing provenance, asymmetric bargaining power, and nondynamic pricing--as the operational machinery of this inequality. In our analysis, we trace these problems along the machine learning value chain and propose an Equitable Data-Value Exchange (EDVEX) Framework to enable a minimal market that benefits all participants. Finally, we outline research directions where our community can make concrete contributions to data deals and contextualize our position with related and orthogonal viewpoints.

Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers, buyers or content viewers on one side and producers, sellers or content-creators on the other. Consumer side measurement of the impact of a treatment variant can be done via simple online A/B testing. Producer side measurement is more challenging because the producer experience depends on the treatment assignment of the consumers. Existing approaches for producer side measurement are either based on graph cluster-based randomization or on certain treatment propagation assumptions. The former approach results in low-powered experiments as the producer-consumer network density increases and the latter approach lacks a strict notion of error control. In this paper, we propose (i) a quantification of the quality of a producer side experiment design, and (ii) a new experiment design mechanism that generates high-quality experiments based on this quantification.

Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers, buyers or content viewers on one side and producers, sellers or content-creators on the other. Consumer side measurement of the impact of a treatment variant can be done via simple online A/B testing. Producer side measurement is more challenging because the producer experience depends on the treatment assignment of the consumers. Existing approaches for producer side measurement are either based on graph cluster-based randomization or on certain treatment propagation assumptions. The former approach results in low-powered experiments as the producer-consumer network density increases and the latter approach lacks a strict notion of error control. In this paper, we propose (i) a quantification of the quality of a producer side experiment design, and (ii) a new experiment design mechanism that generates high-quality experiments based on this quantification.

Valuation methods of data and machine learning (ML) models are essential to the establishment of AI marketplaces.

WIRED spoke with the Zoomer founders of a platform where AI agents hire humans to do real-world tasks. Their pitch: People would love to have a clanker as their boss. For centuries, people have catastrophized about robots taking away jobs. On February 1, the paradigm shifted: bots are jobs. Now, 518,284 humans--and rapidly counting--are offering their labor to AI agents on a new online marketplace called RentAHuman . There are classifieds to count pigeons in Washington ($30/hour); deliver CBD gummies ($75/hour); play exhibition badminton ($100/hour); and anything else you could possibly imagine that a disembodied agent couldn't do.