Artificial intelligence is having profound impacts on the media industry, from the way advertisers buy from search and social platforms to speeding up elements of the creative process. Now, programmatic ad buyers are using AI-based algorithms to get more value from the open marketplace. More advertisers have been adopting a programmatic tool called custom algorithms recently, four agency and brand media buying sources told Adweek. Two sources, including brands like The Hershey Company, are currently in the process of making custom algorithms a pillar of their programmatic strategy, while two have been using them for the past couple of years. Of course, the programmatic ecosystem is no stranger to algorithms, they are its raison d'être.

Pulmonary pathologies can be uniquely detectable from the study of the voice signal. Current screening techniques for COVID-19 are limited in both accuracy and frequency in time. Custom Adaboost and CNN architectures are employed and compared for the detection of COVID-19 from smartphone recordings. Acoustic features are identified as voice biomarkers for COVID-19; the RASTA filtering is a noise-robust, effective domain. COVID-positive and recovered subjects can be discriminated from healthy subjects.

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Multi-object tracking (MOT) in video analysis is increasingly in demand in many industries, such as live sports, manufacturing, surveillance, and traffic monitoring. For example, in live sports, MOT can track soccer players in real time to analyze physical performance such as real-time speed and moving distance. Previously, most methods were designed to separate MOT into two tasks: object detection and association. The object detection task detects objects first. The association task extracts re-identification (re-ID) features from image regions for each detected object, and links each detected object through re-ID features to existing tracks or creates a new track.

The seminaïve algorithm can be used to materialise all consequences of a datalog program, and it also forms the basis for algorithms that incrementally update a materialisation as the input facts change. Certain (combinations of) rules, however, can be handled much more efficiently using custom algorithms. To integrate such algorithms into a general reasoning approach that can handle arbitrary rules, we propose a modular framework for computing and maintaining a materialisation. We split a datalog program into modules that can be handled using specialised algorithms, and we handle the remaining rules using the seminaïve algorithm. We also present two algorithms for computing the transitive and the symmetric-transitive closure of a relation that can be used within our framework. Finally, we show empirically that our framework can handle arbitrary datalog programs while outperforming existing approaches, often by orders of magnitude.

Artificial intelligence(AI) and machine learning(ML) are growing in popularity in the technology ecosystem. Just a few months ago, it was relatively simple to keep up with the developments in the AI and ML markets. Today, that seems like a daunting task for most technologists as the space have been evolving incredibly fast. The explosion of AI and ML platforms have created a very crowded market in which is very hard to distinguish signal from noise. However, despite the large number of AI technologies and startups, we can identify a few main categories that provide a good taxonomy to better understand the AI-ML markets.