SAVE 20: The Amazon Echo Spot is on sale for just 59.99, down from 79.99 as of June 2. That's the lowest price its seen all year. Not to mention, it comes with a free WiZ smart bulb. One of the newest additions to the Amazon Echo family, the Echo Spot serves as a fancy alarm clock to help you wake up and wind down each day with smart features. If your morning routine could use a boost, now's a great time to grab one, as it's down to its lowest price this year.

Artificial intelligence startup Anthropic has hit 3 billion in annualized revenue, marking a 200% increase in just five months, according to a Friday report from Reuters. Anthropic's annualized revenue -- or its total projected earnings over the course of the year, assuming its current rate of income continues -- was close to 1 billion in December, according to the Reuters report, which cited anonymous sources close to the matter. It crossed the 2 billion threshold in late March and reached 3 billion last month. Also: Anthropic's free Claude 4 Sonnet aced my coding tests - but its paid Opus model somehow didn't Founded in 2021 by siblings Dario and Daniela Amodei, both former OpenAI employees, Anthropic has built its business model around its Claude family of generative AI chatbots. The company has also positioned itself as a leader in the responsible deployment of powerful AI tools.

From spring to fall, every weekend -- and even more frequently if it rains -- a couple of hours go into maintaining the lawn. What if you had all those hours back to yourself without worrying that your yard would turn into a jungle? This is the problem Segway's Navimow i series robot lawnmowers aim to solve. Available at a discounted price of 849* (regular price 999), the Navimow i105N can mow up to 1/8 of an acre without needing your input for anything. For larger lawns there is also the currently discounted 1,099* (usual price 1,299) Navimow i110N, which can mow up to 1/4 of an acre on a single charge.

Training and inference with large machine learning models that far exceed the memory capacity of individual devices necessitates the design of distributed architectures, forcing one to contend with communication constraints. We present a framework for distributed computation over a quantum network in which data is encoded into specialized quantum states. We prove that for models within this framework, inference and training using gradient descent can be performed with exponentially less communication compared to their classical analogs, and with relatively modest overhead relative to standard gradient-based methods. We show that certain graph neural networks are particularly amenable to implementation within this framework, and moreover present empirical evidence that they perform well on standard benchmarks. To our knowledge, this is the first example of exponential quantum advantage for a generic class of machine learning problems that hold regardless of the data encoding cost. Moreover, we show that models in this class can encode highly nonlinear features of their inputs, and their expressivity increases exponentially with model depth. We also delineate the space of models for which exponential communication advantages hold by showing that they cannot hold for linear classification. Communication of quantum states that potentially limit the amount of information that can be extracted from them about the data and model parameters may also lead to improved privacy guarantees for distributed computation. Taken as a whole, these findings form a promising foundation for distributed machine learning over quantum networks.

We introduce a fairness-aware dataset for job recommendation in advertising, designed to foster research in algorithmic fairness within real-world scenarios. It was collected and prepared to comply with privacy standards and business confidentiality. An additional challenge is the lack of access to protected user attributes such as gender, for which we propose a solution to obtain a proxy estimate. Despite being anonymized and including a proxy for a sensitive attribute, our dataset preserves predictive power and maintains a realistic and challenging benchmark. This dataset addresses a significant gap in the availability of fairnessfocused resources for high-impact domains like advertising - the actual impact being having access or not to precious employment opportunities, where balancing fairness and utility is a common industrial challenge. We also explore various stages in the advertising process where unfairness can occur and introduce a method to compute a fair utility metric for the job recommendations in online systems case from a biased dataset. Experimental evaluations of bias mitigation techniques on the released dataset demonstrate potential improvements in fairness and the associated trade-offs with utility.

SAVE 61%: As of June 2, you can get the Amazon Echo Buds (2nd gen) with active noise cancellation for just 54.99, down from 139.99, at Amazon. If you're looking for an affordable pair of earbuds that'll help you block out your surroundings on your morning commute, during a workout, or just in the office, we found a pretty good deal at Amazon. As of June 2, you can get the Amazon Echo Buds (2nd gen) with active noise cancellation for just 54.99, down from 139.99, at Amazon. Not too bad for ANC earbuds released in 2021. You can get the 2023 Amazon Echo Buds (no ANC) for 34.99, down from 49.99, if you're looking for something a little bit cheaper.

Centred on content modification and style preservation, Scene Text Editing (STE) remains a challenging task despite considerable progress in text-to-image synthesis and text-driven image manipulation recently. GAN-based STE methods generally encounter a common issue of model generalization, while Diffusion-based STE methods suffer from undesired style deviations. To address these problems, we propose TextCtrl, a diffusion-based method that edits text with prior guidance control. Our method consists of two key components: (i) By constructing finegrained text style disentanglement and robust text glyph structure representation, TextCtrl explicitly incorporates Style-Structure guidance into model design and network training, significantly improving text style consistency and rendering accuracy.

Data assimilation is a vital component in modern global medium-range weather forecasting systems to obtain the best estimation of the atmospheric state by combining the short-term forecast and observations. Recently, AI-based data assimilation approaches have attracted increasing attention for their significant advantages over traditional techniques in terms of computational consumption. However, existing AI-based data assimilation methods can only handle observations with a specific resolution, lacking the compatibility and generalization ability to assimilate observations with other resolutions. Considering that complex real-world observations often have different resolutions, we propose the Fourier Neural Processes (FNP) for arbitrary-resolution data assimilation in this paper. Leveraging the efficiency of the designed modules and flexible structure of neural processes, FNP achieves state-of-the-art results in assimilating observations with varying resolutions, and also exhibits increasing advantages over the counterparts as the resolution and the amount of observations increase. Moreover, our FNP trained on a fixed resolution can directly handle the assimilation of observations with out-of-distribution resolutions and the observational information reconstruction task without additional fine-tuning, demonstrating its excellent generalization ability across data resolutions as well as across tasks.

Federated learning (FL) has rapidly evolved as a promising paradigm that enables collaborative model training across distributed participants without exchanging their local data. Despite its broad applications in fields such as computer vision, graph learning, and natural language processing, the development of a data projection model that can be effectively used to visualize data in the context of FL is crucial yet remains heavily under-explored. Neighbor embedding (NE) is an essential technique for visualizing complex high-dimensional data, but collaboratively learning a joint NE model is difficult. The key challenge lies in the objective function, as effective visualization algorithms like NE require computing loss functions among pairs of data.

Image restoration has experienced significant advancements due to the development of deep learning. Nevertheless, it encounters challenges related to ill-posed problems, resulting in deviations between single model predictions and ground-truths. Ensemble learning, as a powerful machine learning technique, aims to address these deviations by combining the predictions of multiple base models. Most existing works adopt ensemble learning during the design of restoration models, while only limited research focuses on the inference-stage ensemble of pre-trained restoration models. Regression-based methods fail to enable efficient inference, leading researchers in academia and industry to prefer averaging as their choice for post-training ensemble.