Off-policy evaluation can leverage logged data to estimate the effectiveness of new policies in e-commerce, search engines, media streaming services, or automatic diagnostic tools in healthcare. However, the performance of baseline off-policy estimators like IPS deteriorates when the logging policy significantly differs from the evaluation policy. Recent work proposes sharing information across similar actions to mitigate this problem. In this work, we propose an alternative estimator that shares information across similar contexts using clustering. We study the theoretical properties of the proposed estimator, characterizing its bias and variance under different conditions. We also compare the performance of the proposed estimator and existing approaches in various synthetic problems, as well as a real-world recommendation dataset. Our experimental results confirm that clustering contexts improves estimation accuracy, especially in deficient information settings.

The new Alexa is powered by a more responsive AI. (iStock) Amazon is taking Alexa to the next level with the help of AI. Amazon just announced Alexa, an updated assistant powered by generative AI. The idea is to make Alexa more human, so she can help you control all your devices and get more done. The U.S. Alexa launch is set to happen over the next few weeks, and will start with the Echo Show 8, 10, 15, and 21 devices. It can have more in-depth conversations, understand colloquial expressions and think through complex ideas.

SHOPPING – Contains affiliated content. Products featured in this Shopping Finder article are selected by our shopping writers. If you make a purchase using links on this page, Dailymail.co.uk will earn an affiliate commission. Whether it's the 3,499 Vision Pro or the 7,199 Mac Pro, many of Apple's products come with hefty price-tags. The 599 iPhone 16e is the latest in Apple's'budget' smartphone line, and is the successor to the iPhone SE.

Sentiment Analysis (SA) is instrumental in understanding peoples viewpoints facilitating social media monitoring recognizing products and brands and gauging customer satisfaction. Consequently SA has evolved into an active research domain within Natural Language Processing (NLP). Many approaches outlined in the literature devise intricate frameworks aimed at achieving high accuracy, focusing exclusively on either binary sentiment classification or fine-grained sentiment classification. In this paper our objective is to fine-tune the pre-trained BERT model with Bidirectional LSTM (BiLSTM) to enhance both binary and fine-grained SA specifically for movie reviews. Our approach involves conducting sentiment classification for each review followed by computing the overall sentiment polarity across all reviews. We present our findings on binary classification as well as fine-grained classification utilizing benchmark datasets. Additionally we implement and assess two accuracy improvement techniques Synthetic Minority Oversampling Technique (SMOTE) and NLP Augmenter (NLPAUG) to bolster the models generalization in fine-grained sentiment classification. Finally a heuristic algorithm is employed to calculate the overall polarity of predicted reviews from the BERT+BiLSTM output vector. Our approach performs comparably with state-of-the-art (SOTA) techniques in both classifications. For instance in binary classification we achieve 97.67% accuracy surpassing the leading SOTA model NB-weighted-BON+dv-cosine by 0.27% on the renowned IMDb dataset. Conversely for five-class classification on SST-5 while the top SOTA model RoBERTa+large+Self-explaining attains 55.5% accuracy our model achieves 59.48% accuracy surpassing the BERT-large baseline by 3.6%.

Text-to-image diffusion models offer powerful image editing capabilities. To edit real images, many methods rely on the inversion of the image into Gaussian noise. A common approach to invert an image is to gradually add noise to the image, where the noise is determined by reversing the sampling equation. This process has an inherent tradeoff between reconstruction and editability, limiting the editing of challenging images such as highly-detailed ones. Recognizing the reliance of text-to-image models inversion on a text condition, this work explores the importance of the condition choice. We show that a condition that precisely aligns with the input image significantly improves the inversion quality. Based on our findings, we introduce Tight Inversion, an inversion method that utilizes the most possible precise condition -- the input image itself. This tight condition narrows the distribution of the model's output and enhances both reconstruction and editability. We demonstrate the effectiveness of our approach when combined with existing inversion methods through extensive experiments, evaluating the reconstruction accuracy as well as the integration with various editing methods.

Fake news poses global risks by influencing elections and spreading misinformation, making detection critical. Existing NLP and supervised Machine Learning methods perform well under cross-validation but struggle to generalise across datasets, even within the same domain. This issue stems from coarsely labelled training data, where articles are labelled based on their publisher, introducing biases that token-based models like TF-IDF and BERT are sensitive to. While Large Language Models (LLMs) offer promise, their application in fake news detection remains limited. This study demonstrates that meaningful features can still be extracted from coarsely labelled data to improve real-world robustness. Stylistic features-lexical, syntactic, and semantic-are explored due to their reduced sensitivity to dataset biases. Additionally, novel social-monetisation features are introduced, capturing economic incentives behind fake news, such as advertisements, external links, and social media elements. The study trains on the coarsely labelled NELA 2020-21 dataset and evaluates using the manually labelled Facebook URLs dataset, a gold standard for generalisability. Results highlight the limitations of token-based models trained on biased data and contribute to the scarce evidence on LLMs like LLaMa in this field. Findings indicate that stylistic and social-monetisation features offer more generalisable predictions than token-based methods and LLMs. Statistical and permutation feature importance analyses further reveal their potential to enhance performance and mitigate dataset biases, providing a path forward for improving fake news detection.

Warner Bros Discovery recently shut down a trio of game studios, including the well-regarded Monolith Productions. This has put one of the coolest game mechanics of the 2010s in limbo. Middle-earth: Shadow of Mordor's excellent Nemesis system is locked behind a patent owned by Warner Bros all the way until 2036, according to reporting by Eurogamer. The Nemesis system was featured in both 2014's Shadow of Mordor and the follow-up Middle-earth: Shadow of War. Simply put, it's a gameplay mechanic in which enemies remember previous encounters with the protagonist.

Science fiction, from The Jetsons to the Marvel Cinematic Universe, is replete with humanoid robots. But for a long time in the real world, such robots have been a novelty at best and a punchline at worst. Somehow, though, in the last few years, things have shifted. More than a handful of companies are developing humanoid robots, and these technological simulacra have begun popping up in automobile factories and shipping outfits. Some firms are even promising household robots. Still, the most important question has yet to be satisfyingly answered: what is the point?

Two cross-party committees of MPs have urged the government to prioritise ensuring that creators are fairly remunerated for their creative work over making it easy to train artificial intelligence models. The MPs argued there needed to be more transparency around the vast amounts of data used to train generative AI models, and urged the government not to press ahead with plans to require creators to opt out of having their data used. The chair of the culture, media and sport committee, Caroline Dinenage, said there had been a "groundswell of concern from across the creative industries" in response to the proposals, which "illustrates the scale of the threat artists face from artificial intelligence pilfering the fruits of their hard-earned success without permission". She added that making creative works "fair game unless creators say so" was akin to "burglars being allowed into your house unless there's a big sign on your front door expressly telling them that thievery isn't allowed". The letter warned that without this, "the biggest impact would be felt by the long tail of creators and journalists already operating under financial constraints".

Amazon held its first major product event of the year on Wednesday and, as expected, it was largely about Alexa. The company first announced its next-gen, AI-powered voice assistant back in 2023, but technical issues forced Amazon to delay its formal unveiling and rollout. An Alexa upgrade means that Amazon has a swathe of new devices ready to support the latest version of the voice assistant. Amazon's hardware chief, Panos Panay, and his devices and services team were at the event to show off Alexa . Here's a rundown of everything Amazon announced at its first devices event of 2025: After lots (and lots) of boring rambling about generative AI from Amazon CEO Andy Jassy at Wednesday's event, Panay took the mic to start sharing the actual news. Alexa is the name of the company's upgraded voice assistant.