If you can't be bothered to fill out your credit card and address details when shopping for jeans online, the Nate app sounds like a service you might want. The company bills itself as an "artificial intelligence startup" that uses AI to auto-fill customer information for $1 per transaction, saving shoppers a few minutes when completing purchases through the Nate app. But instead of using high-tech methods to complete purchases, Nate transactions were often handled manually by workers in the Philippines, according to a deep dive by The Information. Speaking to two people with direct access to Nate's internal data, The Information reports that "the share of transactions Nate handled manually rather than automatically ranged between 60 percent and 100 percent" throughout 2021. One person with knowledge of fundraising efforts told the outlet that the company didn't share its manual process with some potential investors while the company was trying to raise money.

If you can't be bothered to fill out your credit card and address details when shopping for jeans online, the Nate app sounds like a service you might want. The company bills itself as an "artificial intelligence startup" that uses AI to auto-fill customer information for $1 per transaction, saving shoppers a few minutes when completing purchases through the Nate app. But instead of using high-tech methods to complete purchases, Nate transactions were often handled manually by workers in the Philippines, according to a deep dive by The Information. Speaking to two people with direct access to Nate's internal data, The Information reports that "the share of transactions Nate handled manually rather than automatically ranged between 60 percent and 100 percent" throughout 2021. One person with knowledge of fundraising efforts told the outlet that the company didn't share its manual process with some potential investors while the company was trying to raise money.

Waymo is reportedly rolling out additional safety measures for its self-driving vehicle fleets, reintroducing safety drivers and installing cameras to monitor driver fatigue. The Information reports that these changes were put into place due to safety concerns, and they come after a handful of recent traffic incidents. Within the last few weeks, Waymo has put safety drivers back behind the wheels of its more advanced vehicles, which have been operating without such drivers for some time, and across its broader fleet, the company has added co-drivers to its daytime shifts as well as its night time shifts. The co-drivers are part of Waymo's effort to keep its safety drivers alert, and The Information reports that the company has also been installing cameras aimed at drivers' faces for the purpose of monitoring when they might be nodding off. In June, a safety driver appeared to fall asleep while behind the wheel of one of Waymo's Pacificas, causing an accident after he inadvertently turned off the driving software.

Reports have been swirling around Google's development of a subscription-based game streaming service, but a flurry of secret meetings at recent conferences has raised speculation it could soon be revealed. The service, codenamed Yeti, would put Google at the forefront of a new part of the videogame business that lets people play games as they're being streamed, rather than using downloads or disks, a report said. The firm met with big video game companies at the Game Developers Conference in March and took meetings at the E3 gaming expo in Los Angeles a few weeks ago, it has been claimed. It is also believed to be considering buying existing games development studios to bolster the plans. Google is developing a subscription-based game streaming service that could work either on its Chromecast or a Google-made console.

With new rivals on the market, Amazon has to do something to help its Alexa devices compete. Until now, Amazon has not given third-party developers access to what you say to the voice assistant. According to sources reported by The Information, Amazon is currently looking at opening up this private transcript data to its developers, which could help them build better voice apps for Alexa. It would also raise serious privacy concerns for users. It's a delicate balancing act between user privacy and developer access, of course, but with rivals like Google and Apple getting into the smart speaker game, Amazon needs to keep its early lead.

The modern web critically depends on aggregation of information from self-interested agents, for example opinion polls, product ratings, or crowdsourcing. We consider a setting where multiple objects (questions, products, tasks) are evaluated by a group of agents. We first construct a minimal peer prediction mechanism that elicits honest evaluations from a homogeneous population of agents with different private beliefs. Second, we show that it is impossible to strictly elicit honest evaluations from a heterogeneous group of agents with different private beliefs. Nevertheless, we provide a modified version of a divergence-based Bayesian Truth Serum that incentivizes agents to report consistently, making truthful reporting a weak equilibrium of the mechanism.

We consider settings where a collective intelligence is formed by aggregating information contributed from many independent agents, such as product reviews, community sensing, or opinion polls. We propose a novel mechanism that elicits both private signals and beliefs. The mechanism extends the previous versions of the Bayesian Truth Serum (the original BTS, the RBTS, and the multi-valued BTS), by allowing small populations and non-binary private signals, while not requiring additional assumptions on the belief updating process. For priors that are sufficiently smooth, such as Gaussians, the mechanism allows signals to be continuous.

Several mechanisms have been proposed for incentivizing truthful reports of a private signals owned by rational agents, among them the peer prediction method and the Bayesian truth serum. The robust Bayesian truth serum (RBTS) for small populations and binary signals is particularly interesting since it does not require a common prior to be known to the mechanism. We further analyze the problem of the common prior not known to the mechanism and give several results regarding the restrictions that need to be placed in order to have an incentive-compatible mechanism. Moreover, we construct a Bayes-Nash incentive-compatible scheme called multi-valued RBTS that generalizes RBTS to operate on both small populations and non-binary signals.

Peer prediction mechanisms allow the truthful elicitation of private signals (e.g., experiences, or opinions) in regard to a true world state when this ground truth is unobservable. The original peer prediction method is incentive compatible for any number of agents n >= 2, but relies on a common prior, shared by all agents and the mechanism. The Bayesian Truth Serum (BTS) relaxes this assumption. While BTS still assumes that agents share a common prior, this prior need not be known to the mechanism. However, BTS is only incentive compatible for a large enough number of agents, and the particular number of agents required is uncertain because it depends on this private prior. In this paper, we present a robust BTS for the elicitation of binary information which is incentive compatible for every n >= 3, taking advantage of a particularity of the quadratic scoring rule. The robust BTS is the first peer prediction mechanism to provide strict incentive compatibility for every n >= 3 without relying on knowledge of the common prior. Moreover, and in contrast to the original BTS, our mechanism is numerically robust and ex post individually rational.

RESTful services on the Web expose information through retrievable resource representations that represent self-describing descriptions of resources, and through the way how these resources are interlinked through the hyperlinks that can be found in those representations. This basic design of RESTful services means that for extracting the most useful information from a service, it is necessary to understand a service's representations, which means both the semantics in terms of describing a resource, and also its semantics in terms of describing its linkage with other resources. Based on the Resource Linking Language (ReLL), this paper describes a framework for how RESTful services can be described, and how these descriptions can then be used to harvest information from these services. Building on this framework, a layered model of RESTful service semantics allows to represent a service's information in RDF/OWL. Because REST is based on the linkage between resources, the same model can be used for aggregating and interlinking multiple services for extracting RDF data from sets of RESTful services.