Since the provider's goal of maximizing revenue can only be achieved

This paper investigates simultaneous preference and metric learning from a crowd of respondents. A set of items represented by $d$-dimensional feature vectors and paired comparisons of the form ``item $i$ is preferable to item $j$'' made by each user is given. Our model jointly learns a distance metric that characterizes the crowd's general measure of item similarities along with a latent ideal point for each user reflecting their individual preferences. This model has the flexibility to capture individual preferences, while enjoying a metric learning sample cost that is amortized over the crowd. We first study this problem in a noiseless, continuous response setting (i.e., responses equal to differences of item distances) to understand the fundamental limits of learning. Next, we establish prediction error guarantees for noisy, binary measurements such as may be collected from human respondents, and show how the sample complexity improves when the underlying metric is low-rank. Finally, we establish recovery guarantees under assumptions on the response distribution. We demonstrate the performance of our model on both simulated data and on a dataset of color preference judgements across a large number of users.

Since the provider's goal of maximizing revenue can only be achieved

Current AI writing support tools are largely designed for individuals, complicating collaboration when co-writers must leave the shared workspace to use AI and then communicate and reintegrate results. We propose integrating AI agents directly into collaborative writing environments. Our prototype makes AI use transparent and customisable through two new shared objects: agent profiles and tasks. Agent responses appear in the familiar comment feature. In a user study (N=30), 14 teams worked on writing projects during one week. Interaction logs and interviews show that teams incorporated agents into existing norms of authorship, control, and coordination, rather than treating them as team members. Agent profiles were viewed as personal territory, while created agents and outputs became shared resources. We discuss implications for team-based AI interaction, highlighting opportunities and boundaries for treating AI as a shared resource in collaborative work.

This paper investigates simultaneous preference and metric learning from a crowd of respondents. A set of items represented by d -dimensional feature vectors and paired comparisons of the form item i is preferable to item j '' made by each user is given. Our model jointly learns a distance metric that characterizes the crowd's general measure of item similarities along with a latent ideal point for each user reflecting their individual preferences. This model has the flexibility to capture individual preferences, while enjoying a metric learning sample cost that is amortized over the crowd. We first study this problem in a noiseless, continuous response setting (i.e., responses equal to differences of item distances) to understand the fundamental limits of learning.

Esri has released a new web application for users that want to integrate deep learning into their imagery workflows. Deep Learning Studio, available with the release of ArcGIS Enterprise 11, offers a collaborative environment where multiple users can work together on a image-based project that includes deep learning. With the app, multiple users can work on a single project and perform deep learning tasks, such as collecting training samples, train deep learning models and run inferencing at scale. The app combines multiple things at once: a frontend experience to deep learning tasks that are part of backend raster analytics, a collaborative environment that divides otherwise tedious deep learning tasks over multiple users and a complete end-to-end deep learning workflow, that is offered through a user-friendly project wizard (figure 3). Although the app requires no local software installations, it requires both ArcGIS Enterprise and ArcGIS Image Server, as these provide the data and analytics tools that are accessed through the app.

Machine Learning, while being a very powerful technology, appears to know some drawbacks. For example the huge need for data and the possible attacks over a neural networks, discussed later, are hopefully to be answered with some Privacy Preserving Federated Learning. First of all, let's quickly discuss what Federated Learning is. Today, machine learning is widely used by anyone who wants to understand their dataset deeper and model some classification/regression tasks. However, to achieve the best model possible, one needs a lot of data, which may be difficult to gather in one place.

Ensemble techniques--wherein a model is composed of multiple (possibly) weaker models--are prevalent nowadays within the field of machine learning (ML). Well-known methods such as bagging [1], boosting [2], and stacking [3] are ML mainstays, widely (and fruitfully) deployed on a daily basis. Generally speaking, there are two types of ensemble methods, the first generating models in sequence--e.g., AdaBoost [2]--the latter in a parallel manner--e.g., random forests [4] and evolutionary algorithms [5]. AdaBoost (Adaptive Boosting) is an ML meta-algorithm that is used in conjunction with other types of learning algorithms to improve performance. The output of so-called "weak learners" is combined into a weighted sum that represents the final output of the boosted classifier.

Netflix moochers beware, you may have to start paying for your own account. At a technology event in Las Vegas, a software firm Synamedia unveiled their Orwellian AI system which has been created to track down any account that has been sharing login information with another. The software works by analyzing geolocation data to determine which accounts are logged in at any one time and from where. By doing so they can see who is sharing their credentials and where. It can even decipher your location type like your home, work or if you are accessing from a hotel or vacation location.

We consider a source node that communicates the most up-to-date status packets to multiple users (see Figure 1). We are interested in the average age of information (AoI) [1]-[3] at the users, for a system in which the source node samples an underlying timevarying process and schedules the transmission of the sample values over imperfect links. The AoI at each user at any point in time can simply be defined as the amount of time elapsed since the most recent status update at that user was generated. Most of the earlier work on AoI consider queue-based models, in which the status updates arrive at the source node randomly following a memoryless Poisson process, and are stored in a buffer before being transmitted to the destination [2], [3]. Instead, in the so-called generate-at-will model [1], [4]-[7], also considered in this paper, the status updates of the underlying process of interest can be generated at any time by the source node. AoI in multi-user networks has been studied in [6]- [11]. It is shown in [8] that the scheduling problem for the age minimization is NPhard in general. Scheduling transmissions to multiple receivers is investigated in [7], focusing on a perfect transmission medium, and the optimal scheduling algorithm is shown to be threshold-type. Average AoI has also been studied when status updates over unreliable multi-access channels [10] and multi-cast networks [11] are considered.