Privacy-protected microdata are often the desired output of a differentially private algorithm since microdata is familiar and convenient for downstream users. However, there is a statistical price for this kind of convenience. We show that an uncertainty principle governs the trade-off between accuracy for a population of interest (``sum query'') vs. accuracy for its component sub-populations (``point queries''). Compared to differentially private query answering systems that are not required to produce microdata, accuracy can degrade by a logarithmic factor. For example, in the case of pure differential privacy, without the microdata requirement, one can provide noisy answers to the sum query and all point queries while guaranteeing that each answer has squared error $O(1/\epsilon^2)$. With the microdata requirement, one must choose between allowing an additional $\log^2(d)$ factor ($d$ is the number of point queries) for some point queries or allowing an extra $O(d^2)$ factor for the sum query. We present lower bounds for pure, approximate, and concentrated differential privacy. We propose mitigation strategies and create a collection of benchmark datasets that can be used for public study of this problem.

Federated learning is a distributed learning paradigm where multiple agents, each only with access to local data, jointly learn a global model. There has recently been an explosion of research aiming not only to improve the accuracy rates of federated learning, but also provide certain guarantees around social good properties such as total error. One branch of this research has taken a game-theoretic approach, and in particular, prior work has viewed federated learning as a hedonic game, where error-minimizing players arrange themselves into federating coalitions. This past work proves the existence of stable coalition partitions, but leaves open a wide range of questions, including how far from optimal these stable solutions are. In this work, we motivate and define a notion of optimality given by the average error rates among federating agents (players).

Having your favourite TV show or movie interrupted by adverts is already frustrating, but things could soon be getting worse for Netflix users. At its'Upfront' event on Wednesday, the streaming giant revealed that it would be incorporating adverts made with'generative AI'. Arriving in 2026, these AI-generated adverts will begin to appear not only during mid-content breaks but also when users press pause. And the only way to get rid of these annoying intrusions will be to pay for the more expensive ad-free subscriptions. But in a further twist, Netflix says AI would be used'instantly marry advertisers' ads with the worlds of our shows'.

Aspect-based sentiment analysis (ABSA) is a crucial task in information extraction and sentiment analysis, aiming to identify aspects with associated sentiment elements in text. However, existing ABSA datasets are predominantly English-centric, limiting the scope for multilingual evaluation and research. To bridge this gap, we present M-ABSA, a comprehensive dataset spanning 7 domains and 21 languages, making it the most extensive multilingual parallel dataset for ABSA to date. Our primary focus is on triplet extraction, which involves identifying aspect terms, aspect categories, and sentiment polarities. The dataset is constructed through an automatic translation process with human review to ensure quality. We perform extensive experiments using various baselines to assess performance and compatibility on M-ABSA. Our empirical findings highlight that the dataset enables diverse evaluation tasks, such as multilingual and multi-domain transfer learning, and large language model evaluation, underscoring its inclusivity and its potential to drive advancements in multilingual ABSA research.

The manuscript proposes a dimensionality reduction method called "fair PCA". The proposed study is based on the observation that, in a data model containing more than one data category ("population" as called by authors), the projection learnt by PCA may yield different reconstruction errors for different populations. This may impair the performance of machine learning algorithms that have access to dimensionality-reduced data obtained via PCA. To address this problem, the authors propose a variant of the PCA algorithm that minimizes the total deviation between the error of the learnt projection and the error of the optimal projection for each population. Quality: The paper is based on an interesting idea with an interesting motivation. The technical content of the paper is of satisfactory depth.

Measuring sales performance is a crucial aspect of running a successful business. Accurately tracking and analyzing sales data helps companies understand their strengths and weaknesses, perform forecasts, identify trends, and make informed decisions that drive growth. In this article, I will illuminate how some simple statistical models can be used for measuring sales performance. Whether it is a small or enterprise sales team, simple quantitative techniques can be used to provide valuable sales insights or draw attention to areas of need. After reading this article, you will see various examples how simple models are applied in real life scenarios. Note: All the images in the article were generated by Artificial Intelligence using Stable Diffusion 2.x.

Most marketers know that good marketing software is the most important tool in any successful campaign. However, adding an extra dimension to the strategy and using AI-powered marketing tools can make your job easier and better. In recent years, many new startups have switched to AI and machine learning to offer better products aligned with the AI fever that has beaten all of us. And thanks to these advancements and the emergence of startups providing AI solutions, the way we work is fundamentally changing. Also, customers' needs are constantly changing. Digital marketers need to be on the cutting edge so they can effectively tackle new tasks. If you're thinking of starting a business, do it in 2023.

This AI allows you to send personalized messages to all your LinkedIn contacts. Create a CSV with a list of people you are interested in, the AI checks their latest posts or the company they work for and creates a specific message for each user answering their comment. The personalization is very good, I mean it does not use standard phrases, but rather responds exactly on the topic being discussed. You can also customize emails, but maybe it's better to use ChatGPT for that. You can use a temporary mail.

The recent flooding of social media feeds with AI-generated "portraits" derived from databases of artists' work has renewed conversation over data ownership and the potential power AI has to supplant livelihoods in the future. The 22 million individuals and counting who have already handed over their images to the Lensa application might be fine to receive the myriad of AI-illustrated images in exchange for their data. But the fundamental rights, principles, and freedoms users are giving up during this exchange remains largely unchecked. In Web3 technology circles, much promises have been made of decentralized technologies to open up the possibility for individual ownership and monetization of data, returning power to "creators." This reflects the political ethos held by Blockchain proponents like Etherum co-founder Joe Lubin, who ostensibly seek to supplant the existing power structures of finance through "permissionless" consensus-based transaction data structures.

Netflix officially launched its £4.99-a-month subscription service in the UK last week, with 30 second adverts shown during films and TV shows. 'Basic with Adverts' is £11 cheaper each month than the streamer's most expensive package, as viewers will be subjected to four to five minutes of adverts per hour. And there's another catch - many of Netflix's most popular movies and programmes are blocked from this option. This includes the sitcoms New Girl and Brooklyn Nine-Nine, dramas Suits and House of Cards, and movies Paddington and The Imitation Game. Netflix explained on its website: 'Some TV shows and movies aren't available to watch with the Basic with ads plan because of licensing restrictions. 'These titles will have a lock icon when you search or browse Netflix.'