Switch 2 games are on sale through Jan. 5 The two-armed, five-fingered robot named CLOiD is supposed to help with tasks around the home. LG will present a named CLOiD at in Las Vegas. With humanoid robotics sure to feature heavily at this year's tech conference, LG has teased its home assistant before a full unveiling in January. The company says CLOiD's two articulated arms with five individually actuated fingers are designed to help with a variety of household tasks. However, LG has not yet given a specific example of a task CLOiD can handle.

A new op-ed explores how we can live and work with artificial intelligence without losing our humanity, which is featured atop the Fox News AI Newsletter.

Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications. Though the impact and novelty of innovations expressed in patent data are difficult to measure through traditional means, machine learning offers a promising set of techniques for evaluating novelty, summarizing contributions, and embedding semantics. In this paper, we introduce the Harvard USPTO Patent Dataset (HUPD), a large-scale, well-structured, and multi-purpose corpus of English-language patent applications filed to the United States Patent and Trademark Office (USPTO) between 2004 and 2018. With more than 4.5 million patent documents, HUPD is two to three times larger than comparable corpora. Unlike other NLP patent datasets, HUPD contains the inventor-submitted versions of patent applications, not the final versions of granted patents, allowing us to study patentability at the time of filing using NLP methods for the first time.

The remarkable growth and significant success of machine learning have expanded its applications into programming languages and program analysis. However, a key challenge in adopting the latest machine learning methods is the representation of programming languages which has a direct impact on the ability of machine learning methods to reason about programs. The absence of numerical awareness, aggregate data structure information, and improper way of presenting variables in previous representation works have limited their performances. To overcome the limitations and challenges of current program representations, we propose a novel graph-based program representation called PERFOGRAPH. PERFOGRAPH can capture numerical information and the aggregate data structure by introducing new nodes and edges. Furthermore, we propose an adapted embedding method to incorporate numerical awareness.These enhancements make PERFOGRAPH a highly flexible and scalable representation that can effectively capture programs' intricate dependencies and semantics. Consequently, it serves as a powerful tool for various applications such as program analysis, performance optimization, and parallelism discovery. Our experimental results demonstrate that PERFOGRAPH outperforms existing representations and sets new state-of-the-art results by reducing the error rate by 7.4% (AMD dataset) and 10% (NVIDIA dataset) in the well-known Device Mapping challenge. It also sets new state-of-the-art results in various performance optimization tasks like Parallelism Discovery and Numa and Prefetchers Configuration prediction.

Data shift is a phenomenon present in many real-world applications, and while there are multiple methods attempting to detect shifts, the task of localizing and correcting the features originating such shifts has not been studied in depth. Feature shifts can occur in many datasets, including in multi-sensor data, where some sensors are malfunctioning, or in tabular and structured data, including biomedical, financial, and survey data, where faulty standardization and data processing pipelines can lead to erroneous features. In this work, we explore using the principles of adversarial learning, where the information from several discriminators trained to distinguish between two distributions is used to both detect the corrupted features and fix them in order to remove the distribution shift between datasets. We show that mainstream supervised classifiers, such as random forest or gradient boosting trees, combined with simple iterative heuristics, can localize and correct feature shifts, outperforming current statistical and neural network-based techniques.

When you purchase through links in our articles, we may earn a small commission. I failed at spotting AI slop videos. I did far worse than I expected. Fake videos used to stand out immediately to me. And thanks to the fine folks over at NPR, I have the quiz results to prove it.

How the candy makers at Hammond's Candies have made the sweet treats for over 100 years. Decembmer 26 is National Candy Cane Day. Breakthroughs, discoveries, and DIY tips sent every weekday. Candy canes are a holiday staple with roots dating back to the 1600s. The story suggests that in 1670, a choirmaster in Cologne, Germany, gave children these sugary sticks shaped like a shepherd's staff for the long nativity church service.

The widespread adoption of Transformer architectures in various data modalities has opened new avenues for the applications in molecular modeling. Nevertheless, it remains elusive that whether the Transformer-based architecture can do molecular modeling as good as equivariant GNNs. In this paper, by designing Interatomic Positional Encoding (IPE) thatparameterizes atomic environments as Transformer's positional encodings,we propose Geoformer, a novel geometric Transformer to effectively model molecular structures for various molecular property prediction. We evaluate Geoformer on several benchmarks, including the QM9 dataset and the recently proposed Molecule3D dataset.

Elon Musk sits ahead of Google's co-founder Larry Page and the Amazon founder, Jeff Bezos, in the overall rankings of the world's wealthiest billionaire. Elon Musk sits ahead of Google's co-founder Larry Page and the Amazon founder, Jeff Bezos, in the overall rankings of the world's wealthiest billionaire. Elon Musk's net worth increased by nearly 50% to $645bn with founders of Google and Amazon also seeing huge wealth gains Fri 26 Dec 2025 08.42 ESTLast modified on Fri 26 Dec 2025 21.30 EST A stock market boom in artificial intelligence companies has added more than half a trillion dollars to the wealth of America's tech barons in the past year, data shows. The top 10 US founders and bosses of some of the world's largest technology companies saw their finances swell to nearly $2.5tn, up from $1.9tn, in the year to Christmas Eve, according to figures from Bloomberg. Elon Musk, already the world's richest man, has again proved to be one of biggest winners as the AI gold-rush has pushed US stock markets to record highs.

OpenAI GPT-5.2 replaces GPT-5.1 with behind-the-scenes improvements for presentations, spreadsheets and image interpretation, though users may struggle to notice differences.