Ring faces privacy backlash over its AI-powered'Search Party' feature, which uses outdoor cameras to track lost dogs and is enabled by default. PCWorld reports that Ring ended its Flock partnership but remains committed to expanding'Search Party' despite surveillance concerns from its Super Bowl ad. A leaked email from Ring founder Jamie Siminoff suggests the AI tracking feature may extend beyond pets to broader applications. Ring's been in damage-control mode ever since its now-infamous "lost dog" Super Bowl ad, furiously spinning the sinister imagery of digital "bounding boxes" locking in on a wayward pooch and a simulated aerial view of dozens of homes scanning the neighborhood. Rather than giving off warm fuzzies--your Ring camera can help find lost dogs!--the Super Bowl ad gave off serious "big brother" vibes to many viewers.

Fungal diseases are a major threat to the global banana supply. Breakthroughs, discoveries, and DIY tips sent six days a week. The popular fruit is threatened by a fungal disease called Fusarium wilt of banana (FWB), which blocks the flow of nutrients and makes it wilt. In the 1950s, the pathogen even made one species-Gros Michel bananas-functionally extinct. Fear not though, scientists are on it.

When a SpaceX rocket failure set the skies aflame over western Europe last February, no-one was sure if the debris was also polluting our atmosphere. Now scientists are directly linking the uncontrolled rocket re-entry to a plume of lithium measured less than 100km above Earth. It is the first time researchers have drawn a direct link between a known piece of space debris crashing to Earth and pollution levels. They warn that as SpaceX chief Elon Musk pledges to launch one million satellites in the coming years, this contamination could be the tip of the iceberg. The scientists were already investigating the problem of pollution from space debris when they realised a SpaceX Falcon 9 had failed in flight.

Recursion is the fundamental paradigm to finitely describe potentially infinite objects.

After a Falcon 9 rocket stage burned up in the atmosphere, vaporised lithium and other metals drifted over Europe. A SpaceX rocket that burned up after re-entering the atmosphere unleashed a plume of vaporised metals over Europe, a type of pollution that is expected to increase as spacecraft and satellites multiply. The upper stage of a Falcon 9, which is designed to splash down in the Pacific Ocean for possible re-use, lost control due to engine failure and fell from orbit over the north Atlantic in February 2025. We're finally solving the puzzle of how clouds will affect our climate People across Europe saw fiery debris streaking through the sky, some of which crashed behind a warehouse in Poland. Seeing the news, Robin Wing at the Leibniz Institute of Atmospheric Physics in Germany and his colleagues turned on their lidar, an instrument for atmospheric sensing.

Bandit learning is characterized by the tension between long-term exploration and short-term exploitation. However, as has recently been noted, in settings in which the choices of the learning algorithm correspond to important decisions about individual people (such as criminal recidivism prediction, lending, and sequential drug trials), exploration corresponds to explicitly sacrificing the well-being of one individual for the potential future benefit of others. In such settings, one might like to run a ``greedy'' algorithm, which always makes the optimal decision for the individuals at hand --- but doing this can result in a catastrophic failure to learn. In this paper, we consider the linear contextual bandit problem and revisit the performance of the greedy algorithm. We give a smoothed analysis, showing that even when contexts may be chosen by an adversary, small perturbations of the adversary's choices suffice for the algorithm to achieve ``no regret'', perhaps (depending on the specifics of the setting) with a constant amount of initial training data. This suggests that in slightly perturbed environments, exploration and exploitation need not be in conflict in the linear setting.

Microsoft has a new plan to prove what's real and what's AI online A new proposal calls on social media and AI companies to adopt strict verification, but the company hasn't committed to following its own recommendations. There are the high-profile cases you may easily spot, like when White House officials recently shared a manipulated image of a protester in Minnesota and then mocked those asking about it. Other times, it slips quietly into social media feeds and racks up views, like the videos that Russian influence campaigns are currently spreading to discourage Ukrainians from enlisting. It is into this mess that Microsoft has put forward a blueprint, shared with, for how to prove what's real online. An AI safety research team at the company recently evaluated how methods for documenting digital manipulation are faring against today's most worrying AI developments, like interactive deepfakes and widely accessible hyperrealistic models. It then recommended technical standards that can be adopted by AI companies and social media platforms.

Wegiveasmoothed analysis, showing that evenwhen contexts may be chosen by an adversary, small perturbations of the adversary's choices suffice for the algorithm to achieve "no regret", perhaps (depending on the specifics of the setting) with a constant amount of initial training data.

We propose a Bayesian decision making framework for control of Markov Decision Processes (MDPs) with unknown dynamics and large, possibly continuous, state, action, and parameter spaces in data-poor environments. Most of the existing adaptive controllers for MDPs with unknown dynamics are based on the reinforcement learning framework and rely on large data sets acquired by sustained direct interaction with the system or via a simulator. This is not feasible in many applications, due to ethical, economic, and physical constraints. The proposed framework addresses the data poverty issue by decomposing the problem into an offline planning stage that does not rely on sustained direct interaction with the system or simulator and an online execution stage. In the offline process, parallel Gaussian process temporal difference (GPTD) learning techniques are employed for near-optimal Bayesian approximation of the expected discounted reward over a sample drawn from the prior distribution of unknown parameters. In the online stage, the action with the maximum expected return with respect to the posterior distribution of the parameters is selected. This is achieved by an approximation of the posterior distribution using a Markov Chain Monte Carlo (MCMC) algorithm, followed by constructing multiple Gaussian processes over the parameter space for efficient prediction of the means of the expected return at the MCMC sample. The effectiveness of the proposed framework is demonstrated using a simple dynamical system model with continuous state and action spaces, as well as a more complex model for a metastatic melanoma gene regulatory network observed through noisy synthetic gene expression data.