The alleged criminal organisation is suspected of using "advanced technologies" to aid its activities, including virtual private networks, foreign cloud servers and artificial intelligence software, an EPPO report says.

Demand forecasts are the crucial basis for numerous business decisions, ranging from inventory management to strategic facility planning. While machine learning (ML) approaches offer accuracy gains, their interpretability and acceptance are notoriously lacking. Addressing this dilemma, we introduce Hierarchical Neural Additive Models for time series (HNAM). HNAM expands upon Neural Additive Models (NAM) by introducing a time-series specific additive model with a level and interacting covariate components. Covariate interactions are only allowed according to a user-specified interaction hierarchy. For example, weekday effects may be estimated independently of other covariates, whereas a holiday effect may depend on the weekday and an additional promotion may depend on both former covariates that are lower in the interaction hierarchy. Thereby, HNAM yields an intuitive forecasting interface in which analysts can observe the contribution for each known covariate. We evaluate the proposed approach and benchmark its performance against other state-of-the-art machine learning and statistical models extensively on real-world retail data. The results reveal that HNAM offers competitive prediction performance whilst providing plausible explanations.

This paper proposes a practically efficient algorithm with optimal theoretical regret which solves the classical network revenue management (NRM) problem with unknown, nonparametric demand. Over a time horizon of length $T$, in each time period the retailer needs to decide prices of $N$ types of products which are produced based on $M$ types of resources with unreplenishable initial inventory. When demand is nonparametric with some mild assumptions, Miao and Wang (2021) is the first paper which proposes an algorithm with $O(\text{poly}(N,M,\ln(T))\sqrt{T})$ type of regret (in particular, $\tilde O(N^{3.5}\sqrt{T})$ plus additional high-order terms that are $o(\sqrt{T})$ with sufficiently large $T\gg N$). In this paper, we improve the previous result by proposing a primal-dual optimization algorithm which is not only more practical, but also with an improved regret of $\tilde O(N^{3.25}\sqrt{T})$ free from additional high-order terms. A key technical contribution of the proposed algorithm is the so-called demand balancing, which pairs the primal solution (i.e., the price) in each time period with another price to offset the violation of complementary slackness on resource inventory constraints. Numerical experiments compared with several benchmark algorithms further illustrate the effectiveness of our algorithm.

Amazon is removing Just Walk Out tech from all of its Fresh grocery stores in the US, as reported by The Information. The self-checkout system relies on a host of cameras, sensors and good old-fashioned human eyeballs to track what people leave the store with, charging the customers accordingly. The technology has been plagued by issues from the onset. Most notably, Just Walk Out merely presents the illusion of automation, with Amazon crowing about generative AI and the like. Here's where the smoke and mirrors come in.

Recent advancements in Large Language Models (LLMs) have significantly increased their presence in human-facing Artificial Intelligence (AI) applications. However, LLMs could reproduce and even exacerbate stereotypical outputs from training data. This work introduces the Multi-Grain Stereotype (MGS) dataset, encompassing 51,867 instances across gender, race, profession, religion, and stereotypical text, collected by fusing multiple previously publicly available stereotype detection datasets. We explore different machine learning approaches aimed at establishing baselines for stereotype detection, and fine-tune several language models of various architectures and model sizes, presenting in this work a series of stereotypes classifier models for English text trained on MGS. To understand whether our stereotype detectors capture relevant features (aligning with human common sense) we utilise a variety of explanainable AI tools, including SHAP, LIME, and BertViz, and analyse a series of example cases discussing the results. Finally, we develop a series of stereotype elicitation prompts and evaluate the presence of stereotypes in text generation tasks with popular LLMs, using one of our best performing previously presented stereotypes detectors. Our experiments yielded several key findings: i) Training stereotype detectors in a multi-dimension setting yields better results than training multiple single-dimension classifiers.ii) The integrated MGS Dataset enhances both the in-dataset and cross-dataset generalisation ability of stereotype detectors compared to using the datasets separately.

Here are a few areas where companies say that the latest A.I. technology is being used in ways that could influence productivity, pulled from interviews, earnings calls and financial filings. Employees spend a lot of time trying to figure out human resources-related questions. Companies have been investing in generative A.I. to help answer those queries more quickly. At Walmart, the largest retailer in the United States with 1.6 million workers, the company's employee app has a section called "My Assistant," which is backed by generative A.I. The feature uses the technology to quickly answer questions like, "Do I have dental coverage?",

Grounded text generation, encompassing tasks such as long-form question-answering and summarization, necessitates both content selection and content consolidation. Current end-to-end methods are difficult to control and interpret due to their opaqueness. Accordingly, recent works have proposed a modular approach, with separate components for each step. Specifically, we focus on the second subtask, of generating coherent text given pre-selected content in a multi-document setting. Concretely, we formalize Fusion-in-Context (FiC) as a standalone task, whose input consists of source texts with highlighted spans of targeted content. A model then needs to generate a coherent passage that includes all and only the target information. Our work includes the development of a curated dataset of 1000 instances in the reviews domain, alongside a novel evaluation framework for assessing the faithfulness and coverage of highlights, which strongly correlate to human judgment. Several baseline models exhibit promising outcomes and provide insightful analyses. This study lays the groundwork for further exploration of modular text generation in the multi-document setting, offering potential improvements in the quality and reliability of generated content. Our benchmark, FuseReviews, including the dataset, evaluation framework, and designated leaderboard, can be found at https://fusereviews.github.io/.

Fraudsters are using the latest AI technology to churn out masses of high-quality fake IDs in just minutes, a report has warned. Yoti, which provides facial estimation systems for British supermarkets and pubs to check users are over-18, said the forgeries were so'sophisticated' they were hard to spot. The British firm highlighted an underground website called Onlyfake that used the technology behind chatbots to create'highly convincing' AI-generated IDs for just 12. With a reported 20,000 being produced every day, an investigation found they were good enough to bypass an online trading platform's strict verification system. Security experts said supermarkets, pubs, and airports would also need to be on ' red alert' - warning many were'woefully unprepared to deal with this threat'.

Amazon just launched an app that lets people sign up for its palm recognition service without having to head to an in-store kiosk. The Amazon One app uses a smartphone's camera to take a photo of a palm print to set up an account. Once signed up, you can pay for stuff by using just your hand, ending the tyranny of having to carry a smartphone, cash or a burdensome plastic card. The tech uses generative AI to analyze a palm's vein structure, turning the data into a "unique numerical, vector representation" which is recognized by scanning machines at retail locations. You'll have to add a payment method within the app to get started and upload a photo of your ID for the purpose of age verification.

Amazon's Big Spring Sale has officially ended but a few deals are still going strong. While this latest event wasn't on the level of a Prime Day or a Black Friday sale, over the past week we found decent savings on some of the gadgets and devices we recommend. Now that the sale is done, the pickings are a little slimmer, but that doesn't mean you're out of luck completely. If you didn't take advantage of the sale while it was live, or if you've still got some shopping left to do, consider this list your last chance to reap the discounts from Amazon's latest sale. Here are the best Amazon Spring Sale discounts on tech we love that you can still get today. A single AirTag is on sale for 24, which is 5 off and close to its record low price. These are the best Bluetooth trackers for those with iOS devices since they use the vast Find My network to keep track of your belongings.