Online personalized recommendation services are generally hosted in the cloud where users query the cloud-based model to receive recommended input such as merchandise of interest or news feed. State-of-the-art recommendation models rely on sparse and dense features to represent users' profile information and the items they interact with. Although sparse features account for 99% of the total model size, there was not enough attention paid to the potential information leakage through sparse features. These sparse features are employed to track users' behavior, e.g., their click history, object interactions, etc., potentially carrying each user's private information. Sparse features are represented as learned embedding vectors that are stored in large tables, and personalized recommendation is performed by using a specific user's sparse feature to index through the tables. Even with recently-proposed methods that hides the computation happening in the cloud, an attacker in the cloud may be able to still track the access patterns to the embedding tables. This paper explores the private information that may be learned by tracking a recommendation model's sparse feature access patterns. We first characterize the types of attacks that can be carried out on sparse features in recommendation models in an untrusted cloud, followed by a demonstration of how each of these attacks leads to extracting users' private information or tracking users by their behavior over time.

We present a method for controlling a swarm using its spectral decomposition -- that is, by describing the set of trajectories of a swarm in terms of a spatial distribution throughout the operational domain -- guaranteeing scale invariance with respect to the number of agents both for computation and for the operator tasked with controlling the swarm. We use ergodic control, decentralized across the network, for implementation. In the DARPA OFFSET program field setting, we test this interface design for the operator using the STOMP interface -- the same interface used by Raytheon BBN throughout the duration of the OFFSET program. In these tests, we demonstrate that our approach is scale-invariant -- the user specification does not depend on the number of agents; it is persistent -- the specification remains active until the user specifies a new command; and it is real-time -- the user can interact with and interrupt the swarm at any time. Moreover, we show that the spectral/ergodic specification of swarm behavior degrades gracefully as the number of agents goes down, enabling the operator to maintain the same approach as agents become disabled or are added to the network. We demonstrate the scale-invariance and dynamic response of our system in a field relevant simulator on a variety of tactical scenarios with up to 50 agents. We also demonstrate the dynamic response of our system in the field with a smaller team of agents. Lastly, we make the code for our system available.

Climate change, population growth, and water scarcity present unprecedented challenges for agriculture. This project aims to forecast soil moisture using domain knowledge and machine learning for crop management decisions that enable sustainable farming. Traditional methods for predicting hydrological response features require significant computational time and expertise. Recent work has implemented machine learning models as a tool for forecasting hydrological response features, but these models neglect a crucial component of traditional hydrological modeling that spatially close units can have vastly different hydrological responses. In traditional hydrological modeling, units with similar hydrological properties are grouped together and share model parameters regardless of their spatial proximity. Inspired by this domain knowledge, we have constructed a novel domain-inspired temporal graph convolution neural network. Our approach involves clustering units based on time-varying hydrological properties, constructing graph topologies for each cluster, and forecasting soil moisture using graph convolutions and a gated recurrent neural network. We have trained, validated, and tested our method on field-scale time series data consisting of approximately 99,000 hydrological response units spanning 40 years in a case study in northeastern United States. Comparison with existing models illustrates the effectiveness of using domain-inspired clustering with time series graph neural networks. The framework is being deployed as part of a pro bono social impact program. The trained models are being deployed on small-holding farms in central Texas.

To be successful in single source domain generalization, maximizing diversity of synthesized domains has emerged as one of the most effective strategies. Many of the recent successes have come from methods that pre-specify the types of diversity that a model is exposed to during training, so that it can ultimately generalize well to new domains. However, na\"ive diversity based augmentations do not work effectively for domain generalization either because they cannot model large domain shift, or because the span of transforms that are pre-specified do not cover the types of shift commonly occurring in domain generalization. To address this issue, we present a novel framework that uses adversarially learned transformations (ALT) using a neural network to model plausible, yet hard image transformations that fool the classifier. This network is randomly initialized for each batch and trained for a fixed number of steps to maximize classification error. Further, we enforce consistency between the classifier's predictions on the clean and transformed images. With extensive empirical analysis, we find that this new form of adversarial transformations achieve both objectives of diversity and hardness simultaneously, outperforming all existing techniques on competitive benchmarks for single source domain generalization. We also show that ALT can naturally work with existing diversity modules to produce highly distinct, and large transformations of the source domain leading to state-of-the-art performance.

More than three years ago, this editor sat down with Sam Altman for a small event in San Francisco soon after he'd left his role as the president of Y Combinator to become CEO of the AI company he co-founded in 2015 with Elon Musk and others, OpenAI. At the time, Altman described OpenAI's potential in language that sounded outlandish to some. Altman said, for example, that the opportunity with artificial general intelligence -- machine intelligence that can solve problems as well as a human -- is so great that if OpenAI managed to crack it, the outfit could "maybe capture the light cone of all future value in the universe." He said that the company was "going to have to not release research" because it was so powerful. Asked if OpenAI was guilty of fear-mongering -- Musk has repeatedly called all organizations developing AI to be regulated -- Altman talked about the dangers of not thinking about "societal consequences" when "you're building something on an exponential curve."

"We're entering an era in which our enemies can make anyone say anything at any point in time." In this viral video from 2018, actor-writer Jordan Peele projected his voice into former President Obama's moving lips. Peele's PSA on'deepfakes,' audio and video altered with the intent to mislead, was the first time many people heard of synthetic data. It won't be the last. Today, synthetic data are everywhere, driving some of AI's most innovative applications.

For Sunday's mission, the payloads include the Rashid lunar rover from the Mohammed Bin Rashid Space Center in Dubai; a two-wheeled "transformable lunar robot" from JAXA, the Japanese space agency; a test module for a solid-state battery from NGK Spark Plug Company; an artificial intelligence flight computer; and 360-degree cameras from Canadensys Aerospace. As a vestige of its Lunar X Prize heritage, it is also carrying a panel engraved with the names of people who provided crowdfunding support and a music disc with a song performed by the Japanese rock band Sakanaction. The Japanese company's lander is not the only passenger on Sunday's flight. A secondary payload on the Falcon 9 is a small NASA mission, Lunar Flashlight, which is to enter an elliptical orbit around the moon and use an infrared laser to probe the deep, dark craters at the moon's polar regions. Much like some other recent moon missions, M1 is taking a circuitous, energy-efficient trip to the moon and will not land, in the Atlas Crater in the Northern Hemisphere of the moon, until late April. The fuel-efficient trajectory allows the mission to pack in more payload and carry less fuel.

During the early years of the Cold War, an array of underwater microphones monitoring for sounds of Russian submarines captured something otherworldly in the depths of the North Atlantic. The haunting sounds came not from enemy craft, nor aliens, but humpback whales, a species that, at the time, humans had hunted almost to the brink of extinction. Years later, when environmentalist Roger Payne obtained the recordings from U.S. Navy storage and listened to them, he was deeply moved. The whale songs seemed to reveal majestic creatures that could communicate with one another in complex ways. If only the world could hear these sounds, Payne reasoned, the humpback whale might just be saved from extinction. When Payne released the recordings in 1970 as the album Songs of the Humpback Whale, he was proved right. It was played at the U.N. general assembly, and it inspired Congress to pass the 1973 endangered species act. By 1986, commercial whaling was banned under international law.

PARIS: From "intelligent" vacuum cleaners and driverless cars to advanced techniques for diagnosing diseases, artificial intelligence has burrowed its way into every arena of modern life. Its promoters reckon it is revolutionising human experience, but critics stress that the technology risks putting machines in charge of life-changing decisions. Regulators in Europe and North America are worried. The European Union is likely to pass legislation next year -- the AI Act -- aimed at reining in the age of the algorithm. The United States recently published a blueprint for an AI Bill of Rights and Canada is also mulling legislation.

CAPE CANAVERAL, Fla. -- A Tokyo company aimed for the moon with its own private lander Sunday, blasting off atop a SpaceX rocket with the United Arab Emirates' first lunar rover and a toylike robot from Japan that's designed to roll around up there in the gray dust. It will take nearly five months for the lander and its experiments to reach the moon. The company ispace designed its craft to use minimal fuel to save money and leave more room for cargo. By contrast, NASA's Orion crew capsule with test dummies took five days to reach the moon last month. The lunar flyby mission ends Sunday with a Pacific splashdown.