In machine learning, crowdsourcing is an economical way to label a large amount of data. However, the noise in the produced labels may deteriorate the accuracy of any classification method applied to the labelled data. We propose an enhanced nearest neighbor classifier (ENN) to overcome this issue. Two algorithms are developed to estimate the worker quality (which is often unknown in practice): one is to construct the estimate based on the denoised worker labels by applying the $k$NN classifier to the expert data; the other is an iterative algorithm that works even without access to the expert data. Other than strong numerical evidence, our proposed methods are proven to achieve the same regret as its oracle version based on high-quality expert data. As a technical by-product, a lower bound on the sample size assigned to each worker to reach the optimal convergence rate of regret is derived.

If I'd known a movie version of Uncharted was soon coming out, I would have been a bit more guarded about admitting I'm a huge fan of the game to my editor. I am a fan, but I'm also a parent now, and I can't just leave the house on a whim for some entertainment. I need to hire a nanny to watch my kid, and like ordering popcorn and Twizzlers, that factors into the cost of catching a flick. This was this the first movie we've seen in a theater since Musa was born. I'm still waiting for the new Spider-Man to make it to streaming platforms, which is the only way my wife and I get to scratch our movie itch these days.

Dr. Dorel Iosif is a Board Director and CEO with Cognisium, a tech executive marketplace headquartered in Australia. He held senior executive roles with KBR, WorleyParsons, PwC and Advisian Management Consulting. Dr Iosif started his career in Israel with the Technion Institute of Technology and continued in Australia with BHPBilliton and the University of Melbourne. He holds a Ph.D in applied mathematics from the University of Melbourne and studied Corporate Level Strategy - Executive Program at Harvard Business School. Dorel worked in Australia, USA, Europe and the Middle East.

Data Scientists to join the Data Insights team at Zapier. Data Insights is responsible for driving impactful insight, experimentation, and quantitative research at Zapier. We work across Product, Growth & Revenue, Marketing, and Support, steering our business stakeholders to make data-informed decisions and deepening business understanding of opportunities and risks. Our Data Scientists are semi-embedded into different business zones, developing tight-knit thought partnerships with key stakeholders. We're hiring for a range of zones, so if you are a creative quantitative analyst interested in helping to grow a product that helps the world automate their work so they can get back to living, this may be the right challenge for you!

Meta wants you to understand anyone, from anywhere, no matter which language they speak. To achieve this the company is looking to build a universal, instantaneous speech translator, capable of translating any language to any other language -- including languages that are primarily spoken. Mark Zuckerberg announced this goal during an AI-focused event Wednesday, describing it as a key step toward a world-encompassing metaverse. "The ability to communicate with anyone in any language -- that's a superpower people have dreamed of forever, and AI is going to deliver that in our lifetimes." Meta's ambitious universal translation project is part of a broader push to build out the company's translation capabilities for the metaverse.

Last year, researchers at Data Science Nigeria noted that engineers looking to train computer-vision algorithms could choose from a wealth of data sets featuring Western clothing, but there were none for African clothing. The team addressed the imbalance by using AI to generate artificial images of African fashion--a whole new data set from scratch. Such synthetic data sets--computer-generated samples with the same statistical characteristics as the genuine article--are growing more and more common in the data-hungry world of machine learning. These fakes can be used to train AIs in areas where real data is scarce or too sensitive to use, as in the case of medical records or personal financial data. The idea of synthetic data isn't new: driverless cars have been trained on virtual streets.

The adaptive processing of structured data is a long-standing research topic in machine learning that investigates how to automatically learn a mapping from a structured input to outputs of various nature. Recently, there has been an increasing interest in the adaptive processing of graphs, which led to the development of different neural network-based methodologies. In this thesis, we take a different route and develop a Bayesian Deep Learning framework for graph learning. The dissertation begins with a review of the principles over which most of the methods in the field are built, followed by a study on graph classification reproducibility issues. We then proceed to bridge the basic ideas of deep learning for graphs with the Bayesian world, by building our deep architectures in an incremental fashion. This framework allows us to consider graphs with discrete and continuous edge features, producing unsupervised embeddings rich enough to reach the state of the art on several classification tasks. Our approach is also amenable to a Bayesian nonparametric extension that automatizes the choice of almost all model's hyper-parameters. Two real-world applications demonstrate the efficacy of deep learning for graphs. The first concerns the prediction of information-theoretic quantities for molecular simulations with supervised neural models. After that, we exploit our Bayesian models to solve a malware-classification task while being robust to intra-procedural code obfuscation techniques. We conclude the dissertation with an attempt to blend the best of the neural and Bayesian worlds together. The resulting hybrid model is able to predict multimodal distributions conditioned on input graphs, with the consequent ability to model stochasticity and uncertainty better than most works. Overall, we aim to provide a Bayesian perspective into the articulated research field of deep learning for graphs.

The bizarre promotional video promises "Face analysis based on best of breed Artificial Intelligence algorithms for Business Intelligence and Digital Signage applications." What follows is footage of a woman pushing her hair behind her ears, a man grimacing and baring his teeth, and an actor in a pinstripe suit being slapped in the face against a green screen. Digitally overlayed on each person's face are colored outlines of rectangles with supposed measurements displayed: "F 25 happiness," "caucasian_latin," "M 38 sadness." The commercial reel advertises just one of the many video analytics tools available for download on an app store monitored by the Internet of Things startup Azena, itself a project from the German kitchen appliance maker Bosch. Bosch, known more for its line of refrigerators, ovens, and dishwashers, also develops and sells an entire suite of surveillance cameras.

Most of us have a music, movie or video-game library – possibly all three – but few have an art collection or even know what their favourite works of art are. Next year, that will change as art moves from the inaccessible to the everyday, thanks to AI. Art hasn't felt accessible to many for a long time. Our main experience of it involves visiting galleries and museums or feeling out our depth in art history classes. At a gallery, we spend a couple of hours looking at a lot of seemingly important pieces, but then we leave and the artworks stay where they are. They don't draw us in, like a favourite album, movie or video game, and we know we can't afford to take them home with us.

The United States has issued fresh sanctions on alleged members of an illicit network financing Yemen's Houthi rebels, citing the group's involvement in the continuing war in Yemen and recent drone and missile attacks on Washington's Gulf allies. In a statement on Wednesday, the US Department of the Treasury said the network "has transferred tens of millions of dollars to Yemen via a complex international network of intermediaries in support of the Houthis' attacks". The new sanctions target alleged front companies and ships that the US says worked with a branch of Iran's Islamic Revolutionary Guard Corps to smuggle petroleum and other commodities around the Middle East, Asia and Africa to help fund the Houthis. "Despite pleas to negotiate an end to this devastating conflict, Houthi leaders continue to launch missile and unmanned aerial vehicle attacks against Yemen's neighbors, killing innocent civilians, while millions of Yemeni civilians remain displaced and hungry," Treasury Under-secretary Brian E Nelson said in the statement. The Houthi rebels have ramped up their missile and drone attacks against Saudi Arabia and started directly targeting the UAE in recent weeks, but the penalties appeared to fall short of the tougher measures that the Saudis and Emiratis, key strategic partners of the US, had sought from the Biden administration.