Humans tell stories to make sense of the world and communicate their understanding of what happens. Storytelling supposes to be able to sort out which events are worth telling, deciding on a level of detail for describing events, selecting among possible causes the ones which are deemed worth telling. It also supposes to make use of an affective machinery for capturing an audience's attention (emotional contagion, suspense elicitation...). In the act of storytelling, structural and affective phenomena are thus combined with communicative goals in mind. This combination has indeed shown its effectiveness in this respect: the phenomenon of narrative transportation (the experience of being immersed in a story) has been linked to persuasion [27]. The narrative paradigm therefore provides an appropriate framework, in which causal reasoning about the situations narrated [53] is combined with narrative devices to encourage the audience's emotional involvement [51], to study and model how opinion is formed and evolves. Building a framework for reasoning about and unveiling storytelling mechanics could pave the way for intellectual selfdefense supporting tools, enabling citizens to arm themselves against hostile disinformation or influence campaigns. Previous works in structural narratology have studied the way stories are conveyed to their audience and seminal work from (for instance) Genette [25] or Propp [45] have previously served as the backbone inspiration for computational narrative models and storytelling systems [43].

Over the past several decades, conventional spectral analysis techniques of polluted white dwarfs have become powerful tools to learn about the geology and chemistry of extrasolar bodies. Despite their proven capabilities and extensive legacy of scientific discoveries, these techniques are however still limited by their manual, time-intensive, and iterative nature. As a result, they are susceptible to human errors and are difficult to scale up to population-wide studies of metal pollution. This paper seeks to address this problem by presenting cecilia, the first Machine Learning (ML)-powered spectral modeling code designed to measure the metal abundances of intermediate-temperature (10,000$\leq T_{\rm eff} \leq$20,000 K), Helium-rich polluted white dwarfs. Trained with more than 22,000 randomly drawn atmosphere models and stellar parameters, our pipeline aims to overcome the limitations of classical methods by replacing the generation of synthetic spectra from computationally expensive codes and uniformly spaced model grids, with a fast, automated, and efficient neural-network-based interpolator. More specifically, cecilia combines state-of-the-art atmosphere models, powerful artificial intelligence tools, and robust statistical techniques to rapidly generate synthetic spectra of polluted white dwarfs in high-dimensional space, and enable accurate ($\lesssim$0.1 dex) and simultaneous measurements of 14 stellar parameters -- including 11 elemental abundances -- from real spectroscopic observations. As massively multiplexed astronomical surveys begin scientific operations, cecilia's performance has the potential to unlock large-scale studies of extrasolar geochemistry and propel the field of white dwarf science into the era of Big Data. In doing so, we aspire to uncover new statistical insights that were previously impractical with traditional white dwarf characterisation techniques.

Artificial intelligence has created an alcoholic beverage that tastes like the melted candy found at the bottom of your grandma's handbag - and it got a three out of 10 in taste. This drink, called Grandma's Revenge, is just one recipe generated by the technology, which was asked to make cocktails based on names given by TikTok users. Grandma's Revenge features brandy, sherry and port, while another called Burning Inferno is made with vodka and Tabasco sauce. TikTok account Mob recently shared videos showing him following the instructions to make each drink based on what the AI churned out. TikTok account Mob shared two videos showing cocktails generated by AI.

Since ML algorithms have proven their success in many different applications, there is also a big interest in privacy preserving (PP) ML methods for building models on sensitive data. Moreover, the increase in the number of data sources and the high computational power required by those algorithms force individuals to outsource the training and/or the inference of a ML model to the clouds providing such services. To address this, we propose a secure 3-party computation framework, CECILIA, offering PP building blocks to enable complex operations privately. In addition to the adapted and common operations like addition and multiplication, it offers multiplexer, most significant bit and modulus conversion. The first two are novel in terms of methodology and the last one is novel in terms of both functionality and methodology. CECILIA also has two complex novel methods, which are the exact exponential of a public base raised to the power of a secret value and the inverse square root of a secret Gram matrix. We use CECILIA to realize the private inference on pre-trained RKNs, which require more complex operations than most other DNNs, on the structural classification of proteins as the first study ever accomplishing the PP inference on RKNs. In addition to the successful private computation of basic building blocks, the results demonstrate that we perform the exact and fully private exponential computation, which is done by approximation in the literature so far. Moreover, they also show that we compute the exact inverse square root of a secret Gram matrix up to a certain privacy level, which has not been addressed in the literature at all. We also analyze the scalability of CECILIA to various settings on a synthetic dataset. The framework shows a great promise to make other ML algorithms as well as further computations privately computable by the building blocks of the framework.

This post contains spoilers for Outer Range and Yellowstone. Some viewers of Outer Range's first season may have been focused on parsing the Amazon Prime series's Lost-style mysteries: What is up with the big, swirling time hole in Royal Abbott's pasture? Why did Rebecca Abbott, his daughter-in-law, vanish without a trace? What does Autumn, the charismatic hippie camping on Royal's land, want with the Abbott family? I had a different question about the sci-fi Western: What the heck is this show doing with Taylor Sheridan's megahit Yellowstone?

In this post, we summarise the final three invited talks from the International Conference on Machine Learning (ICML). These presentations covered: how machine learning can complement randomised controlled trials, encoding and decoding speech, and molecular science. Esther's work centres on the use of randomised controlled trials (RCT) and she runs policy experiments with the aim of understanding which policies work and which don't. Her work is particularly focussed on reducing poverty. Work of this type involves many causal questions, for which there are often many competing ideas. Such is the field that there is no real guidance for theory; experiments are needed to determine successful policies.

In this special guest feature, Cecilia Pizzurro, Senior Director, Strategic Data Projects at LOGICnow, discusses the convergence of data/machine learning and cybersecurity, and the idea that these two are playing off of each other in a more meaningful way than ever before. Cecilia leads a team of data scientists and software engineers in Cambridge (US) and Newcastle (UK). These teams use machine learning and big data analytics to find business value in the vast amount of customer data gathered from LOGICnow's products. She was also the co-founder and CTO of the The Dolomite Group, a South American mining consortium, pioneering machine learning and big data analyses to improve mining efficiency and reduce environmental impact in Peru. This company is currently finalizing its acquisition by a Chilean mining company.

During the 2016 Collision Conference held in New Orleans, our Content Strategist Cecilia Haynes interviewed conference speaker Dr. Tyrone Grandison. At the time of the interview, he was the Deputy Chief Data Officer at the U.S. Department of Commerce. Tyrone is currently the Chief Information Officer for the Institute for Health Metrics and Evaluation. Coming fresh off his talk on "Data science, apps and civic responsibility", Cecilia was thrilled to chat with Tyrone all about the democratization of data and how open data can help anyone build innovative products and services. I saw your talk and I thought you would be the the perfect person to reach out to.

In this special guest feature, Cecilia Pizzurro, Senior Director, Strategic Data Projects at LOGICnow, discusses the convergence of data/machine learning and cybersecurity, and the idea that these two are playing off of each other in a more meaningful way than ever before. Cecilia leads a team of data scientists and software engineers in Cambridge (US) and Newcastle (UK). These teams use machine learning and big data analytics to find business value in the vast amount of customer data gathered from LOGICnow's products. She was also the co-founder and CTO of the The Dolomite Group, a South American mining consortium, pioneering machine learning and big data analyses to improve mining efficiency and reduce environmental impact in Peru. This company is currently finalizing its acquisition by a Chilean mining company.