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).

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.

Somewhere in Mabushi, a crossroad area between the inner and outer districts of Abuja, Nigeria, Uniccon Group, a two-year-old Nigerian technology firm, has built a humanoid: a 6-foot-tall multilingual human-like robot called Omeife. From an idea that was conceptualised in 2020 to a back-and-forth construction--slow wins and quick-succession learning--that stretched across two years, Omeife, built as a female Igbo character that understands and speaks eight different languages, is now a product ready to meet the world. Powered by sophisticated artificial intelligence algorithms developed in-house by the company's team of scientists, Omeife has a deep understanding of African culture and behavioural patterns. Speaking to TechCabal about the project over a call, Chuks Ekwueme, who founded the company in 2020 and serves as its CEO, revealed that the humanoid also has a real time understanding of its environment including active listening and the ability to focus on a specific conversation thread as it's happening. "It's not just multilingual, it has the ability to switch languages and interact with specific gestures--hand illustrations, smile and other bodily gestures--that match the tone of the conversation," said Ekwueme.

ODSC East 2018 is one of the largest applied data science conferences in the world. Our speakers include some of the core contributors to many open source tools, libraries, and languages. Attend ODSC East 2018 and learn the latest AI & data science topics, tools, and languages from some of the best and brightest minds in the field. See schedule for many more.. The largest applied data science conference is now 4 days including 2 full training days for even more talks, trainings, and workshops vested in 8 focused courses.

This article summarizes 16 agent strategies that were designed for the 2002 Trading Agent Competition. Agent architects use numerous general-purpose AI techniques, including machine learning, planning, partially observable Markov decision processes, Monte Carlo simulations, and multiagent systems. Ultimately, the most successful agents were primarily heuristic based and domain specific. It would be quite a daunting task to manually monitor prices and make bidding decisions at all web sites currently offering the camera--especially if accessories such as a flash and a tripod are sometimes bundled with the camera and sometimes auctioned separately. However, for the next generation of trading agents, autonomous bidding in simultaneous auctions will be a routine task.

We examine the design space of auction mechanisms and identify three core activities that structure this space. Formal parameters qualifying the performance of core activities enable precise specification of auction rules. This specification constitutes an auction description language that can be used in the implementation of configurable marketplaces. The specification also provides a framework for organizing previous work and identifying new possibilities in auction design. Given that many multiagent systems involve the allocation of resources, it is natural that the connection between AI and economics has become a common theme in AI. This emphasis is also certainly influenced by the automation of commercial activities on the internet and the potential benefits of intelligent software support for these economic activities. Auctions are central to this confluence of research agendas because they represent a class of basic mechanisms by which economic systems compute the outcome of ...

We describe one such competition, the Trading Agent Competition for Supply Chain Management (TAC SCM). We discuss its significance in the context of today's global market economy as well as AI research, the ways in which it breaks away from limiting assumptions made in prior work, and some of the advances it has engendered over the past six years. TAC SCM requires autonomous supply chain entities, modeled as agents, to coordinate their internal operations while concurrently trading in multiple dynamic and highly competitive markets. Since its introduction in 2003, the competition has attracted more than 150 entries and brought together researchers from AI and beyond in the form of 75 competing teams from 25 different countries. Yet the real-time demands of many domains do not lend themselves to traditional assumptions of rationality (Simon 1979, Wellman 1996).