Google Turns a Selfie Video Into Your Account's Spare Key The next time you're locked out of your Google account, you can use your face as part of the account recovery process. Google has a new way for users to sign in to their accounts: a selfie video. If you've ever lost your device and gotten locked out of your Google account, you know it can feel like a desperate, Kafkaesque process to recover access and get back in--especially for people who run their digital lives, from email messages to calendar appointments, on Google software. This new sign-in option is an additional way to unlock your Google account, like a spare key hidden in the bushes. "We always recommend that you set up more than one option," says Claire Forszt, a product manager at Google who focuses on identity and engagement.

With "blessings" from the original Jibo founders, iKairos is a wearable or desk-mounted "AI journal" that turns your family moments into AI images. Jibo was a cute, social robot that sat in one place in your home. Built in 2014, Jibo was meant to be a robot people brought into their lives before the smart-home industry had even really taken off. It could not interact with objects or move from its spot, but it could wiggle and talk in a way that felt endearing. It was adorable, but not competent or in demand enough to last--it was ahead of its time and died too soon .

Is the All-New Range Rover GT Stepping on Jaguar's Tail? It's "the most car-like Range Rover ever created," but will this all-electric grand tourer spoil Jaguar's Type 01 party? In what looks to be a radical departure from its existing lineup, Range Rover has confirmed an entirely new fifth vehicle: the Range Rover GT, an all-electric grand tourer built on the company's Electrified Modular Architecture (EMA), the same platform that will underpin other midsized cars for the brand. Range Rover has only shared camouflaged prototype pictures for now, as the GT is supposedly still undergoing final testing, ahead of a full launch expected later this year. That means details on this completely new model are scant to say the least: no price, no range, and no power figures have been disclosed.

Neural Information Processing Systems http://nips.cc/

Label noise and class imbalance are two major issues coexisting in real-world datasets. To alleviate the two issues, state-of-the-art methods reweight each instance by leveraging a small amount of clean and unbiased data. Yet, these methods overlook class-level information within each instance, which can be further utilized to improve performance. To this end, in this paper, we propose Generalized Data Weighting (GDW) to simultaneously mitigate label noise and class imbalance by manipulating gradients at the class level. To be specific, GDW unrolls the loss gradient to class-level gradients by the chain rule and reweights the flow of each gradient separately.

This paper focuses on the state estimation problem in distributed sensor networks, where intermittent packet dropouts, corrupted observations, and unknown noise covariances coexist. To tackle this challenge, we formulate the joint estimation of system states, noise parameters, and network reliability as a Bayesian variational inference problem, and propose a novel variational Bayesian adaptive Kalman filter (VB-AKF) to approximate the joint posterior probability densities of the latent parameters. Unlike existing AKF that separately handle missing data and measurement outliers, the proposed VB-AKF adopts a dual-mask generative model with two independent Bernoulli random variables, explicitly characterizing both observable communication losses and latent data authenticity. Additionally, the VB-AKF integrates multiple concurrent multiple observations into the adaptive filtering framework, which significantly enhances statistical identifiability. Comprehensive numerical experiments verify the effectiveness and asymptotic optimality of the proposed method, showing that both parameter identification and state estimation asymptotically converge to the theoretical optimal lower bound with the increase in the number of sensors.

We also gathered milk yield data and outdoor weather conditions.

A challenge in mobile sensing is to overcome the location uncertainty of its sensors.