Although the existing max-value entropy search (MES) is based on the widely celebrated notion of mutual information, its empirical performance can suffer due to two misconceptions whose implications on the exploration-exploitation trade-off are investigated in this paper. These issues are essential in the development of future acquisition functions and the improvement of the existing ones as they encourage an accurate measure of the mutual information such as the rectified MES (RMES) acquisition function we develop in this work. Unlike the evaluation of MES, we derive a closed-form probability density for the observation conditioned on the max-value and employ stochastic gradient ascent with reparameterization to efficiently optimize RMES. As a result of a more principled acquisition function, RMES shows a consistent improvement over MES in several synthetic function benchmarks and real-world optimization problems.

We introduce in this survey the major concepts, models, and algorithms proposed so far to infer causal relations from observational time series, a task usually referred to as causal discovery in time series. To do so, after a description of the underlying concepts and modelling assumptions, we present different methods according to the family of approaches they belong to: Granger causality, constraint-based approaches, noise-based approaches, score-based approaches, logic-based approaches, topology-based approaches, and difference-based approaches. We then evaluate several representative methods to illustrate the behaviour of different families of approaches. This illustration is conducted on both artificial and real datasets, with different characteristics. The main conclusions one can draw from this survey is that causal discovery in times series is an active research field in which new methods (in every family of approaches) are regularly proposed, and that no family or method stands out in all situations. Indeed, they all rely on assumptions that may or may not be appropriate for a particular dataset.

MadJax is a tool for generating and evaluating differentiable matrix elements of high energy scattering processes. As such, it is a step towards a differentiable programming paradigm in high energy physics that facilitates the incorporation of high energy physics domain knowledge, encoded in simulation software, into gradient based learning and optimization pipelines. MadJax comprises two components: (a) a plugin to the general purpose matrix element generator MadGraph that integrates matrix element and phase space sampling code with the JAX differentiable programming framework, and (b) a standalone wrapping API for accessing the matrix element code and its gradients, which are computed with automatic differentiation. The MadJax implementation and example applications of simulation based inference and normalizing flow based matrix element modeling, with capabilities enabled uniquely with differentiable matrix elements, are presented.

World Endurance Championship (WEC) racing events are characterised by a relevant performance gap among competitors. The fastest vehicles category, consisting in hybrid vehicles, has to respect energy usage constraints set by the technical regulation. Considering absence of competitors, i.e. traffic conditions, the optimal energy usage strategy for lap time minimisation is typically computed through a constrained optimisation problem. To the best of our knowledge, the majority of state-of-the-art works neglects competitors. This leads to a mismatch with the real world, where traffic generates considerable time losses. To bridge this gap, we propose a new framework to offline compute optimal strategies for the powertrain energy management considering competitors. Through analysis of the available data from previous events, statistics on the sector times and overtaking probabilities are extracted to encode the competitors' behaviour. Adopting a multi-agent model, the statistics are then used to generate realistic Monte Carlo (MC) simulation of their position along the track. The simulator is then adopted to identify the optimal strategy as follows. We develop a longitudinal vehicle model for the ego-vehicle and implement an optimisation problem for lap time minimisation in absence of traffic, based on Genetic Algorithms. Solving the optimisation problem for a variety of constraints generates a set of candidate optimal strategies. Stochastic Dynamic Programming is finally implemented to choose the best strategy considering competitors, whose motion is generated by the MC simulator. Our approach, validated on data from a real stint of race, allows to significantly reduce the lap time.

In our previous work, we introduced the rule-based Bayesian Regression, a methodology that leverages two concepts: (i) Bayesian inference, for the general framework and uncertainty quantification and (ii) rule-based systems for the incorporation of expert knowledge and intuition. The resulting method creates a penalty equivalent to a common Bayesian prior, but it also includes information that typically would not be available within a standard Bayesian context. In this work, we extend the aforementioned methodology with grammatical evolution, a symbolic genetic programming technique that we utilise for automating the rules' derivation. Our motivation is that grammatical evolution can potentially detect patterns from the data with valuable information, equivalent to that of expert knowledge. We illustrate the use of the rule-based Evolutionary Bayesian learning technique by applying it to synthetic as well as real data, and examine the results in terms of point predictions and associated uncertainty.

What if there's a quick way to screen molecules and predict their reactivity and other properties? Certainly this will make drug and material design much faster because chemists could then focus more on the most promising compounds instead of trying them all. This is what the Merck Molecular Activity Challenge somehow illustrates. Here, the goal is to predict biological activities of different molecules, both on- and off-target, given numerical descriptors generated from their chemical structures. In other words, we have to predict whether a certain molecule will become highly active towards the intended target and "inert" to others (thereby minimal or zero side effects).

DURHAM, NC -- Whether preventing explosions on electrical grids, spotting patterns among past crimes, or optimizing resources in the care of critically ill patients, Duke University computer scientist Cynthia Rudin wants artificial intelligence (AI) to show its work. Especially when it's making decisions that deeply affect people's lives. While many scholars in the developing field of machine learning were focused on improving algorithms, Rudin instead wanted to use AI's power to help society. She chose to pursue opportunities to apply machine learning techniques to important societal problems, and in the process, realized that AI's potential is best unlocked when humans can peer inside and understand what it is doing. Now, after 15 years of advocating for and developing "interpretable" machine learning algorithms that allow humans to see inside AI, Rudin's contributions to the field have earned her the $1 million Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (AAAI).

Technological advancements continue to create new opportunities in Ontario's electricity sector, and many businesses are turning to artificial intelligence (AI) to enhance their operations. If you're an organization that is currently using AI, or exploring the use of it, the IESO wants to hear from you. By taking a short survey, you can help inform a whitepaper the IESO is developing that will provide insight into the opportunities AI can provide for Ontario's electricity market participants and ratepayers. The whitepaper is the latest in the Innovation and Sector Evolution White Paper Series, and an overview was presented to stakeholders at a webinar held yesterday. The survey is a key first step that will provide a foundational understanding of where various segments of Ontario's electricity sector are in their AI journey, and where they expect to be in five years.

The Internet of the future could be written by bots, but will that make it better or worse? Experts at the Copenhagen Institute for Future Studies (CIFS) are raising questions about AI-generated content, and how it could come to dominate the metaverse and other digital locations. CIFS expert Timothy Shoup estimates that 99 percent to 99.9 percent of the internet's content will be AI-generated by 2025 to 2030, especially if models like OpenAI's GPT-3 achieve wider adoption. "The internet would be completely unrecognizable," Shoup told colleague Sofie Hvitved. As its capabilities advance, the idea is that AI could start to generate entire online worlds, along with all the stuff that inhabits them -- not to mention all the online material that's currently mostly made by humans.

Maëva Ghonda is a scientist born in Kinshasa, the great capital city of the Democratic Republic of Congo (DRC). Maëva is the editor-in-chief of the IEEE Quantum Computing Newsletter, the host of the Quantum AI Series Podcast, and the chair of the Quantum AI Institute. As a research scientist, her work is centered on technological innovations -- i.e. Quantum Computing, Artificial Intelligence and Machine Learning -- to tackle challenges in Pharma and Healthcare (e.g. Maëva Ghonda's passion for quantum computing ignited while working as Joint Quantum Institute Scholar.