Recent years have brought a stream of exciting developments in the field of Business Process Management (BPM). We have seen a breathtaking uptake of business process automation technology, such as Robotic Process Automation (RPA). We have witnessed the rise of process mining, and promising evolutions in the areas of predictive process analytics and digital process twins. In the eyes of a business analyst, each of these technologies offers compelling opportunities to enhance operational excellence. However, if we look at these technologies in isolation, it is easy to miss the bigger picture and the wider space of opportunities that these technologies open when used jointly rather than applied in individual projects or silos.

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!

George Orwell's novel 1984 got one thing wrong. A surveillance state will not have people watching people, as the Stasi did in East Germany. Computers will be the ones watching people. Technology lets you perform surveillance at an industrial scale. This is already happening in China, where facial recognition software is being used by law enforcement for catching relatively minor offenders such as jaywalkers to enabling much more disturbing activities such as tracking Uyghurs.

The success of machine learning for a wide range of applications has come with serious costs. The largest deep neural networks can have hundreds of billions of parameters that need to be tuned to mammoth datasets. This computationally intensive training process can cost millions of dollars, as well as large amounts of energy and associated carbon. Inference, the subsequent application of a trained model to new data, is less demanding for each use, but for widely used applications, the cumulative energy use can be even greater. "Typically there will be more energy spent on inference than there is on training," said David Patterson, Professor Emeritus at the University of California, Berkeley, and a Distinguished Engineer at Google, who in 2017 shared ACM's A.M. Turing Award.

Deakin University says it welcomes the announcement from Hanwha Defense Australia, and the Australian Government, that its new armoured vehicle centre of excellence will be constructed at Avalon Airport. Deakin Vice-Chancellor Professor Iain Martin said the Geelong-based centre would cement the University's close connection with Hanwha, facilitating investment in local research expertise and the development of high-tech jobs. It's expected the new bespoke production facility will create a minimum of 300 jobs, spread across construction, acquisition and maintenance, as well as generating ongoing support opportunities for Australian industry until the late 2040s. Deakin and Hanwha Defense Australia signed a Memorandum of Understanding (MoU) last year, an agreement to drive collaboration in modelling and simulation, machine learning applications and human performance. "Deakin and Hanwha share a common goal in the development of emerging technologies and advanced manufacturing, with a focus on growing the local economy through education and employment opportunities," Professor Martin said.

VPR is a fundamental task for autonomous navigation as it enables a robot to localize itself in the workspace when a known location is detected. Although accuracy is an essential requirement for a VPR technique, computational and energy efficiency are not less important for real-world applications. CNN-based techniques archive state-of-the-art VPR performance but are computationally intensive and energy demanding. Binary neural networks (BNN) have been recently proposed to address VPR efficiently. Although a typical BNN is an order of magnitude more efficient than a CNN, its processing time and energy usage can be further improved. In a typical BNN, the first convolution is not completely binarized for the sake of accuracy. Consequently, the first layer is the slowest network stage, requiring a large share of the entire computational effort. This paper presents a class of BNNs for VPR that combines depthwise separable factorization and binarization to replace the first convolutional layer to improve computational and energy efficiency. Our best model achieves state-of-the-art VPR performance while spending considerably less time and energy to process an image than a BNN using a non-binary convolution as a first stage.

A personalized customer experience has become the basis for competitive advantage. However, providing personalization requires more than just a technological fix. Businesses must design intelligent experience engines, which assemble high-quality, end-to-end customer experiences using AI powered by customer data. Brinks is a 163-year-old business well-known for its fleet of armored trucks. The company also licenses its brand to a lesser-known, independently operated sister company, Brinks Home. The Dallas-based smart-home-technology business has struggled to gain brand recognition commensurate with the Brinks name. It competes against better-known systems from ADT, Google Nest, and Ring, and although it has earned stellar reviews from industry analysts and customers, its market share is only 2%.

Within the last decade, there has been growing awareness that causal inference can improve scientific research in many disciplines as interpretability and robustness become increasingly important (Doshi-Velez and Kim, 2017; Roscher et al., 2020; Marcinkevičs and Vogt, 2020; Moraffah et al., 2020). Causality is a crucial factor for gaining insights into the decision process of algorithms, which has many use cases such as avoiding bias and discrimination (Mehrabi et al., 2019), improving user experience (Zhou and Fu, 2007) and gathering biological insights (Angermueller et al., 2016). If the causal relation between variables is known, causality can be used to study the interaction between statistical units such as estimating the average effect of treatments (Imbens and Rubin, 2015; Holland, 1986), analyze their mediation (Berzuini et al., 2012), detect the root causes of anomalies (Janzing et al., 2019) or quantifying the causal influence of variables in a system (Janzing et al., 2013, 2020).

You didn't think Netflix would leave its TikTok-style comedy feed on phones, did you? Sure enough, the company is launching a test that brings the Fast Laughs feature to TVs. Opt in and you'll get a flurry of hopefully funny clips from Netflix shows, movies and (of course) comedy specials. Find something you enjoy and you can watch the whole affair or add it to your watch list. The addition is "slowly" deploying to subscribers in English-speaking countries including the US, Canada, UK, Ireland, Australia and New Zealand.

Markov chain Monte Carlo (MCMC) methods are often used in clustering since they guarantee asymptotically exact expectations in the infinite-time limit. In finite time, though, slow mixing often leads to poor performance. Modern computing environments offer massive parallelism, but naive implementations of parallel MCMC can exhibit substantial bias. In MCMC samplers of continuous random variables, Markov chain couplings can overcome bias. But these approaches depend crucially on paired chains meetings after a small number of transitions. We show that straightforward applications of existing coupling ideas to discrete clustering variables fail to meet quickly. This failure arises from the "label-switching problem": semantically equivalent cluster relabelings impede fast meeting of coupled chains. We instead consider chains as exploring the space of partitions rather than partitions' (arbitrary) labelings. Using a metric on the partition space, we formulate a practical algorithm using optimal transport couplings. Our theory confirms our method is accurate and efficient. In experiments ranging from clustering of genes or seeds to graph colorings, we show the benefits of our coupling in the highly parallel, time-limited regime.