Thirty years ago, a peace-loving Austrian theologian spoke to Peter Thiel about the apocalyptic theories of Nazi jurist Carl Schmitt. They've been a road map for the billionaire ever since. For a full two years now, the billionaire has been on the circuit, spreading his biblically inflected ideas about doomsday through a set of variably and sometimes visibly perplexed interviewers. He has chatted onstage with the economist podcaster Tyler Cowen about the (the scriptural term for "that which withholds" the end times); traded some very awkward on-camera silences with the New York Times columnist Ross Douthat; and is, at this very moment, in the midst of delivering a four-part, off-the-record lecture series about the Antichrist in San Francisco. Depending on who you are, you may find it hilarious, fascinating, insufferable, or horrifying that one of the world's most powerful men is obsessing over a figure from sermons and horror movies. But the ideas and influences behind these talks are key to understanding how Thiel sees his own massive role in the world--in politics, technology, and the fate of the species. And to really grasp Thiel's katechon-and-Antichrist schtick, you need to go back to the first major lecture of his doomsday road show--which took place on an unusually hot day in Paris in 2023. No video cameras recorded the event, and no reporters wrote about it, but I've been able to reconstruct it by talking to people who were there. The venue was a yearly conference of scholars devoted to Thiel's chief intellectual influence, the late French-American theorist René Girard. On the evening of the unpublicized lecture, dozens of Girardian philosophers and theologians from around the world filed into a modest lecture hall at the Catholic University of Paris. And from the dais, Thiel delivered a nearly hourlong account of his thoughts on Armageddon--and all the things he believed were "not enough" to prevent it. By Thiel's telling, the modern world is scared, way too scared, of its own technology. Our "listless" and "zombie" age, he said, is marked by a growing hostility to innovation, plummeting fertility rates, too much yoga, and a culture mired in the "endless Groundhog Day of the worldwide web." But in its neurotic desperation to avoid technological Armageddon--the real threats of nuclear war, environmental catastrophe, runaway AI--modern civilization has become susceptible to something even more dangerous: the Antichrist. According to some Christian traditions, the Antichrist is a figure that will unify humanity under one rule before delivering us to the apocalypse. For Thiel, its evil is pretty much synonymous with any attempt to unite the world. "How might such an Antichrist rise to power?" Thiel asked.

The realization that AI-driven decision-making is indispensable in today's fast-paced and ultra-competitive marketplace has raised interest in industrial machine learning (ML) applications significantly. The current demand for analytics experts vastly exceeds the supply. One solution to this problem is to increase the user-friendliness of ML frameworks to make them more accessible for the non-expert. Automated machine learning (AutoML) is an attempt to solve the problem of expertise by providing fully automated off-the-shelf solutions for model choice and hyperparameter tuning. This paper analyzed the potential of AutoML for applications within business analytics, which could help to increase the adoption rate of ML across all industries. The H2O AutoML framework was benchmarked against a manually tuned stacked ML model on three real-world datasets. The manually tuned ML model could reach a performance advantage in all three case studies used in the experiment. Nevertheless, the H2O AutoML package proved to be quite potent. It is fast, easy to use, and delivers reliable results, which come close to a professionally tuned ML model. The H2O AutoML framework in its current capacity is a valuable tool to support fast prototyping with the potential to shorten development and deployment cycles. It can also bridge the existing gap between supply and demand for ML experts and is a big step towards automated decisions in business analytics. Finally, AutoML has the potential to foster human empowerment in a world that is rapidly becoming more automated and digital.

We present a large real-world dataset obtained from monitoring a smart company facility over the course of six years, from 2018 to 2023. The dataset includes energy consumption data from various facility areas and components, energy production data from a photovoltaic system and a combined heat and power plant, operational data from heating and cooling systems, and weather data from an on-site weather station. The measurement sensors installed throughout the facility are organized in a hierarchical metering structure with multiple sub-metering levels, which is reflected in the dataset. The dataset contains measurement data from 72 energy meters, 9 heat meters and a weather station. Both raw and processed data at different processing levels, including labeled issues, is available. In this paper, we describe the data acquisition and post-processing employed to create the dataset. The dataset enables the application of a wide range of methods in the domain of energy management, including optimization, modeling, and machine learning to optimize building operations and reduce costs and carbon emissions.

Consider a binary classification problem solved using a feed-forward artificial neural network (ANN). Let the ANN be composed of a ReLU layer and several linear layers (convolution, sum-pooling, or fully connected). We assume the network was trained with high accuracy. Despite numerous suggested approaches, interpreting an artificial neural network remains challenging for humans. For a new method of interpretation, we construct a bridge between a simple ANN and logic. As a result, we can analyze and manipulate the semantics of an ANN using the powerful tool set of logic. To achieve this, we decompose the input space of the ANN into several network partition cells. Each network partition cell represents a linear combination that maps input values to a classifying output value. For interpreting the linear map of a partition cell using logic expressions, we suggest minterm values as the input of a simple ANN. We derive logic expressions representing interaction patterns for separating objects classified as 1 from those classified as 0. To facilitate an interpretation of logic expressions, we present them as binary logic trees.

This paper explores the integration of Explainable Automated Machine Learning (AutoML) in the realm of financial engineering, specifically focusing on its application in credit decision-making. The rapid evolution of Artificial Intelligence (AI) in finance has necessitated a balance between sophisticated algorithmic decision-making and the need for transparency in these systems. The focus is on how AutoML can streamline the development of robust machine learning models for credit scoring, while Explainable AI (XAI) methods, particularly SHapley Additive exPlanations (SHAP), provide insights into the models' decision-making processes. This study demonstrates how the combination of AutoML and XAI not only enhances the efficiency and accuracy of credit decisions but also fosters trust and collaboration between humans and AI systems. The findings underscore the potential of explainable AutoML in improving the transparency and accountability of AI-driven financial decisions, aligning with regulatory requirements and ethical considerations.

A rigorous formalization of desired system requirements is indispensable when performing any verification task. This often limits the application of verification techniques, as writing formal specifications is an error-prone and time-consuming manual task. To facilitate this, we present nl2spec, a framework for applying Large Language Models (LLMs) to derive formal specifications (in temporal logics) from unstructured natural language. In particular, we introduce a new methodology to detect and resolve the inherent ambiguity of system requirements in natural language: we utilize LLMs to map subformulas of the formalization back to the corresponding natural language fragments of the input. Users iteratively add, delete, and edit these sub-translations to amend erroneous formalizations, which is easier than manually redrafting the entire formalization. The framework is agnostic to specific application domains and can be extended to similar specification languages and new neural models. We perform a user study to obtain a challenging dataset, which we use to run experiments on the quality of translations. We provide an open-source implementation, including a web-based frontend.

It's not just salespeople, traders, compliance professionals and people formatting pitchbooks who risk losing their banking jobs to technology. It turns out that private equity professionals do too. A new study by a professor at one of France's top finance universities explains how. Professor Thomas Åstebro at Paris-based HEC says private equity firms are using artificial intelligence (AI) to push the limits of human cognition and to support decision-making. He found that funds that have embraced AI are using decision support systems (DSS) across the investment decision-making process, including to source potential targets for investments before rivals.

Knowledge base completion (KBC) methods aim at inferring missing facts from the information present in a knowledge base (KB) by estimating the likelihood of candidate facts. In the prevailing evaluation paradigm, models do not actually decide whether a new fact should be accepted or not but are solely judged on the position of true facts in a likelihood ranking with other candidates. We argue that consideration of binary predictions is essential to reflect the actual KBC quality, and propose a novel evaluation paradigm, designed to provide more transparent model selection criteria for a realistic scenario. We construct the data set FB14k-QAQ where instead of single facts, we use KB queries, i.e., facts where one entity is replaced with a variable, and construct corresponding sets of entities that are correct answers. We randomly remove some of these correct answers from the data set, simulating the realistic scenario of real-world entities missing from a KB. This way, we can explicitly measure a model's ability to handle queries that have more correct answers in the real world than in the KB, including the special case of queries without any valid answer. The latter especially contrasts the ranking setting. We evaluate a number of state-of-the-art KB embeddings models on our new benchmark. The differences in relative performance between ranking-based and classification-based evaluation that we observe in our experiments confirm our hypothesis that good performance on the ranking task does not necessarily translate to good performance on the actual completion task. Our results motivate future work on KB embedding models with better prediction separability and, as a first step in that direction, we propose a simple variant of TransE that encourages thresholding and achieves a significant improvement in classification F1 score relative to the original TransE.

Artificial intelligence and other technologies hold great promise for the insurance industry but are not without issues, such as adoption and security, according to a panel of insurance and technology industry executives speaking at the Insurance Information Institute's Joint Industry Forum in New York on Thursday. Andrew Robinson, co-CEO for Groundspeed Analytics Inc. in Atlanta, left his life in the insurance industry "to join an early-stage insurtech company because I saw the great potential technology offered. I'm a full believer in the enablement of our industry and the role that technology is now playing, particularly AI and big data." Technology by itself, however, is not an answer to all challenges faced by the insurance industry, according to Sean Ringsted, executive vice president, chief digital officer and chief risk officer for Chubb Ltd. "For me and Chubb, we couldn't be more excited about the possibility of AI and big data and how it can be a force for good," with a couple of caveats, he said. "It's not going to do everything by itself; you've got to bring other technologies and capabilities to bear on that," Mr. Ringsted said, adding "there's always going to be a place for a human touch."

"They were trying to run their operation from that physical board," says Ted Schmitt, principal business development manager for conservation technology at Vulcan, the Seattle-based philanthropic tech company founded by Microsoft cofounder Paul Allen (who died on October 15), which partnered with the park to help it move to the company's digital system, called EarthRanger, in April. "They all know that poaching goes up during a full moon, for obvious reasons," says Schmitt. "But what they don't know, and they all expect, is that there are patterns like that latent in the data that they just can't pull out. That's the promise of machine learning…it's going to let them be proactive." The machine learning is still in early stages of development, but some analytic tools are already in use. A new heat map feature, for example, first tested at Grumeti Game Reserve in Tanzania and Liwonde National Park in Malawi, showed that most incidents were happening near the borders of each park, so rangers could focus on those areas with the highest risk.