There's a good chance learning a new language is one of your New Year's resolutions, unless you're hoping Google Translate will be enough for your next international adventure. Either way, you'll need a reliable method to guide you through speaking and understanding the foreign language of your choosing. Fortunately, we're no longer confined to flashcards and textbooks as you can learn using your phone from the comfort of your couch. Many of the best language learning apps today offer a multi-tier approach, with AI-powered conversations, extensive vocab libraries and even podcasts you can listen to to help you master your target language. Whether you're just starting because you're just trying to understand what Bad Bunny means when he says "un verano en Nueva Yol," or you want to brush up on your Korean before that planned vacation, there's a language learning app to suit your needs.

Sometimes, all that's stopping you is a language barrier. If you're ready to tear that down and interact with more of the world, Babbel is ready to serve as your passport. Imagine learning the entirety of one semester of Spanish in just 15 hours. Researchers from City University of New York recently assessed Babbel's Spanish courses and discovered that the novice learners "acquired knowledge equivalent to one Spanish semester in 15h." That impressive distinction is easy to believe once you see Babbel's process.

For years, we've been told the future belongs to tech jobs, coding boot camps and college degrees that leave young Americans saddled with debt. But while artificial intelligence is shaking up white-collar professions, there's one sector AI won't be replacing anytime soon: blue-collar skilled trades. Let's face it, when your septic system blows up are you the one who is going to clean up the mess? That's right -- while office workers worry about ChatGPT taking their jobs, the demand for electricians, plumbers, welders, and mechanics is skyrocketing. Companies are desperate for skilled workers, wages are soaring, and many of these careers offer six-figure salaries without the need for a four-year degree.

Markowicz previously explained why they wrote the book in a Fox News Digital opinion piece, noting that in 2021, then-Democratic Virginia gubernatorial candidate Terry McAuliffe said, "I don't think parents should be telling schools what they should teach." "Taken on its own, the comment might even be benign. Sure, parental involvement in education had always been a prediction of student success. A 2010 study called'Parent Involvement and Student Academic Performance: A Multiple Mediational Analysis' by researchers at the Warren Alpert Medical School of Brown University and the University of North Carolina at Greensboro found'children whose parents are more involved in their education have higher levels of academic performance than children whose parents are involved to a lesser degree." But should parents be designing a curriculum?

Continual learning (CL) refers to the ability to continuously learn and accumulate new knowledge while retaining useful information from past experiences. Although numerous CL methods have been proposed in recent years, it is not straightforward to deploy them directly to real-world decision-making problems due to their computational cost and lack of uncertainty quantification. To address these issues, we propose CL-BRUNO, a probabilistic, Neural Process-based CL model that performs scalable and tractable Bayesian update and prediction. Our proposed approach uses deep-generative models to create a unified probabilistic framework capable of handling different types of CL problems such as task- and class-incremental learning, allowing users to integrate information across different CL scenarios using a single model. Our approach is able to prevent catastrophic forgetting through distributional and functional regularisation without the need of retaining any previously seen samples, making it appealing to applications where data privacy or storage capacity is of concern. Experiments show that CL-BRUNO outperforms existing methods on both natural image and biomedical data sets, confirming its effectiveness in real-world applications.

This article reports on the third iteration of a survey of computerized tools and technologies taught as part of postgraduate translation training programmes. While the survey was carried out under the aegis of the EMT Network, more than half of responses are from outside that network. The results show the responsiveness of programmes to innovations in translation technology, with increased compulsory inclusion of machine translation, post-editing, and quality evaluation, and a rapid response to the release of generative tools. The flexibility required during the Covid-19 pandemic has also led to some lasting changes to programmes. While the range of tools being taught has continued to expand, programmes seem to be consolidating their core offering around cloud-based software with cost-free academic access. There has also been an increase in the embedding of professional contexts and workflows associated with translation technology. Generic file management and data security skills have increased in perceived importance, and legal and ethical issues related to translation data have also become more prominent. In terms of course delivery the shift away from conventional labs identified in EMT2017 has accelerated markedly, no doubt partly driven by the pandemic, accompanied by a dramatic expansion in the use of students' personal devices.

The rapid advancement of large language models (LLMs) has spurred significant interest in tool learning, where LLMs are augmented with external tools to tackle complex tasks. However, existing tool environments face challenges in balancing stability, scalability, and realness, particularly for benchmarking purposes. To address this problem, we propose MirrorAPI, a novel framework that trains specialized LLMs to accurately simulate real API responses, effectively acting as "mirrors" to tool environments. Using a comprehensive dataset of request-response pairs from 7,000+ APIs, we employ supervised fine-tuning and chain-of-thought reasoning to enhance simulation fidelity. MirrorAPI achieves superior accuracy and stability compared to state-of-the-art methods, as demonstrated by its performance on the newly constructed MirrorAPI-Bench and its integration into StableToolBench.

The level set estimation problem seeks to identify regions within a set of candidate points where an unknown and costly to evaluate function's value exceeds a specified threshold, providing an efficient alternative to exhaustive evaluations of function values. Traditional methods often use sequential optimization strategies to find $\epsilon$-accurate solutions, which permit a margin around the threshold contour but frequently lack effective stopping criteria, leading to excessive exploration and inefficiencies. This paper introduces an acquisition strategy for level set estimation that incorporates a stopping criterion, ensuring the algorithm halts when further exploration is unlikely to yield improvements, thereby reducing unnecessary function evaluations. We theoretically prove that our method satisfies $\epsilon$-accuracy with a confidence level of $1 - \delta$, addressing a key gap in existing approaches. Furthermore, we show that this also leads to guarantees on the lower bounds of performance metrics such as F-score. Numerical experiments demonstrate that the proposed acquisition function achieves comparable precision to existing methods while confirming that the stopping criterion effectively terminates the algorithm once adequate exploration is completed.

--Convolutional neural networks (CNNs) have succeeded remarkably in various computer vision tasks. However, they are not intrinsically explainable. While feature-level understanding of CNNs reveals where the models looked, concept-based explainability methods provide insights into what the models saw. However, their assumption of linear reconstructability of image activations fails to capture the intricate relationships within these activations. Their Fidelity-only approach to evaluating global explanations also presents a new concern. For the first time, we address these limitations with the novel Transformative Nonlinear Concept Explainer (TraNCE) for CNNs. Unlike linear reconstruction assumptions made by existing methods, TraNCE captures the intricate relationships within the activations. This study presents three original contributions to the CNN explain-ability literature: (i) An automatic concept discovery mechanism based on variational autoencoders (V AEs). This transformative concept discovery process enhances the identification of meaningful concepts from image activations. Based on the investigations on publicly available datasets, we prove that a valid decomposition of a high-dimensional image activation should follow a non-linear reconstruction, contributing to the explainer's efficiency. We also demonstrate quantitatively that, besides accuracy, consistency is crucial for the meaningfulness of concepts and human trust. The code is available at https://github.com/daslimo/TrANCE ONVOLUTIONAL neural networks (CNNs) are widely used in computer vision, achieving notable success in visual classification tasks [1], [2]. However, understanding them at a human level remains a major challenge in artificial intelligence (AI), raising significant concerns about their explainability, especially in promoting ethical AI [3]- [5].

Large language models (LLMs) achieve strong performance across diverse tasks, largely driven by high-quality web data used in pre-training. However, recent studies indicate this data source is rapidly depleting. Synthetic data emerges as a promising alternative, but it remains unclear whether synthetic datasets exhibit predictable scalability comparable to raw pre-training data. In this work, we systematically investigate the scaling laws of synthetic data by introducing SynthLLM, a scalable framework that transforms pre-training corpora into diverse, high-quality synthetic datasets. Our approach achieves this by automatically extracting and recombining high-level concepts across multiple documents using a graph algorithm. Key findings from our extensive mathematical experiments on SynthLLM include: (1) SynthLLM generates synthetic data that reliably adheres to the rectified scaling law across various model sizes; (2) Performance improvements plateau near 300B tokens; and (3) Larger models approach optimal performance with fewer training tokens. For instance, an 8B model peaks at 1T tokens, while a 3B model requires 4T. Moreover, comparisons with existing synthetic data generation and augmentation methods demonstrate that SynthLLM achieves superior performance and scalability. Our findings highlight synthetic data as a scalable and reliable alternative to organic pre-training corpora, offering a viable path toward continued improvement in model performance.