Antimicrobial resistance (AMR) is a growing public health threat, estimated to cause over 10 million deaths per year and cost the global economy 100 trillion USD by 2050 under status quo projections. These losses would mainly result from an increase in the morbidity and mortality from treatment failure, AMR infections during medical procedures, and a loss of quality of life attributed to AMR. Numerous interventions have been proposed to control the development of AMR and mitigate the risks posed by its spread. This paper reviews key aspects of bacterial AMR management and control which make essential use of data technologies such as artificial intelligence, machine learning, and mathematical and statistical modelling, fields that have seen rapid developments in this century. Although data technologies have become an integral part of biomedical research, their impact on AMR management has remained modest. We outline the use of data technologies to combat AMR, detailing recent advancements in four complementary categories: surveillance, prevention, diagnosis, and treatment. We provide an overview on current AMR control approaches using data technologies within biomedical research, clinical practice, and in the "One Health" context. We discuss the potential impact and challenges wider implementation of data technologies is facing in high-income as well as in low- and middle-income countries, and recommend concrete actions needed to allow these technologies to be more readily integrated within the healthcare and public health sectors.

The rise of AI in human contexts places new demands on automated systems to be transparent and explainable. We examine some anthropomorphic ideas and principles relevant to such accountablity in order to develop a theoretical framework for thinking about digital systems in complex human contexts and the problem of explaining their behaviour. Structurally, systems are made of modular and hierachical components, which we abstract in a new system model using notions of modes and mode transitions. A mode is an independent component of the system with its own objectives, monitoring data, and algorithms. The behaviour of a mode, including its transitions to other modes, is determined by functions that interpret each mode's monitoring data in the light of its objectives and algorithms. We show how these belief functions can help explain system behaviour by visualising their evaluation as trajectories in higher-dimensional geometric spaces. These ideas are formalised mathematically by abstract and concrete simplicial complexes. We offer three techniques - a framework for design heuristics, a general system theory based on modes, and a geometric visualisation - and apply them in three types of human-centred systems.

In recent years there has been substantial growth in the capabilities of systems designed to generate text that mimics the fluency and coherence of human language. From this, there has been considerable research aimed at examining the potential uses of these natural language generators (NLG) towards a wide number of tasks. The increasing capabilities of powerful text generators to mimic human writing convincingly raises the potential for deception and other forms of dangerous misuse. As these systems improve, and it becomes ever harder to distinguish between human-written and machine-generated text, malicious actors could leverage these powerful NLG systems to a wide variety of ends, including the creation of fake news and misinformation, the generation of fake online product reviews, or via chatbots as means of convincing users to divulge private information. In this paper, we provide an overview of the NLG field via the identification and examination of 119 survey-like papers focused on NLG research. From these identified papers, we outline a proposed high-level taxonomy of the central concepts that constitute NLG, including the methods used to develop generalised NLG systems, the means by which these systems are evaluated, and the popular NLG tasks and subtasks that exist. In turn, we provide an overview and discussion of each of these items with respect to current research and offer an examination of the potential roles of NLG in deception and detection systems to counteract these threats. Moreover, we discuss the broader challenges of NLG, including the risks of bias that are often exhibited by existing text generation systems. This work offers a broad overview of the field of NLG with respect to its potential for misuse, aiming to provide a high-level understanding of this rapidly developing area of research.

There are many different methods in the literature for local explanation of machine learning results. However, the methods differ in their approaches and often do not provide same explanations. In this paper, we consider two recent methods: Integrated Gradients (Sundararajan, Taly, & Yan, 2017) and Baseline Shapley (Sundararajan and Najmi, 2020). The original authors have already studied the axiomatic properties of the two methods and provided some comparisons. Our work provides some additional insights on their comparative behavior for tabular data. We discuss common situations where the two provide identical explanations and where they differ. We also use simulation studies to examine the differences when neural networks with ReLU activation function is used to fit the models.

In this article, we develop a modular framework for the application of Reinforcement Learning to the problem of Optimal Trade Execution. The framework is designed with flexibility in mind, in order to ease the implementation of different simulation setups. Rather than focusing on agents and optimization methods, we focus on the environment and break down the necessary requirements to simulate an Optimal Trade Execution under a Reinforcement Learning framework such as data pre-processing, construction of observations, action processing, child order execution, simulation of benchmarks, reward calculations etc. We give examples of each component, explore the difficulties their individual implementations \& the interactions between them entail, and discuss the different phenomena that each component induces in the simulation, highlighting the divergences between the simulation and the behavior of a real market. We showcase our modular implementation through a setup that, following a Time-Weighted Average Price (TWAP) order submission schedule, allows the agent to exclusively place limit orders, simulates their execution via iterating over snapshots of the Limit Order Book (LOB), and calculates rewards as the \$ improvement over the price achieved by a TWAP benchmark algorithm following the same schedule. We also develop evaluation procedures that incorporate iterative re-training and evaluation of a given agent over intervals of a training horizon, mimicking how an agent may behave when being continuously retrained as new market data becomes available and emulating the monitoring practices that algorithm providers are bound to perform under current regulatory frameworks.

The US Court of Appeals for the Federal Circuit affirmed on August 5 that only a natural person--not an artificial intelligence system--can be an inventor. Artificial Intelligence (AI) technology is widely applied as a tool in different technical areas, such as machine learning, image processing, and speech recognition. More complex AI technology can create new products or processes with little or no human help. If an AI system can independently create something new, can it be designated as an inventor? The Federal Circuit finally settled this issue--affirming decisions of the US Patent and Trademark Office (USPTO) and Eastern District of Virginia that an AI system cannot be an inventor.

Calling the top of the current wave of inflation has been a painful exercise for economists and central bankers, who have been proven wrong time and again during the past year. But data on Wednesday, which showed that some measures of inflation had cooled in the world's two largest economies, was likely to rekindle a debate about whether the worst might be over after a year of torrid price growth. United States consumer prices did not rise in July compared with June due to a sharp drop in the cost of petrol, delivering much-needed relief to American consumers on edge after steady prices climbs during the past two years. And China's factory-gate inflation slowed to a 17-month low on an annual basis while consumer prices rose less than expected. After wrongly predicting last year that high inflation would be transitory, most central bankers, including the US Federal Reserve, have stopped trying to put an exact date on when they expect current price growth to peak.

A team based at Princeton University has accurately simulated the initial steps of ice formation by applying artificial intelligence (AI) to solving equations that govern the quantum behavior of individual atoms and molecules. The resulting simulation describes how water molecules transition into solid ice with quantum accuracy. This level of accuracy, once thought unreachable due to the amount of computing power it would require, became possible when the researchers incorporated deep neural networks, a form of artificial intelligence, into their methods. The study was published in the journal Proceedings of the National Academy of Sciences. "In a sense, this is like a dream come true," said Roberto Car, Princeton's Ralph W. *31 Dornte Professor in Chemistry, who co-pioneered the approach of simulating molecular behaviors based on the underlying quantum laws more than 35 years ago.

Today Insilico Medicine announced the completion of a second closing of its Series D round, led by Prosperity7 Ventures, the diversified growth fund of Saudi Aramco Ventures, bringing the total Series D financing to $95 million. Other global investors with expertise in the biopharmaceutical and life sciences sectors also participated. The financing brought in Prosperity7 as a new investor, alongside current investors in the Series D round, including a large, diversified asset management firm on the US West Coast, B Capital Group, Warburg Pincus, BHR Partners, Qiming Venture Partners, Deerfield, Pavilion Capital, BOLD Capital Partners, and WS Investment Company. Insilico's founder and CEO, Alex Zhavoronkov, PhD, also invested in the Series D round. Insilico Medicine plans to grow its presence in Saudi Arabia, building on the recent investment from Prosperity7.

It's an increasingly common experience: You wander into the kitchen, quietly muttering under your breath, when you hear a disembodied feminine voice say, "I'm sorry, I didn't quite catch that." We can all agree that Alexa's tendency to eavesdrop is, at times, a little creepy. But is it possible to harness that ability to improve our health? That's the question that researcher David Simon and his coauthors sought to answer in a recent paper published in Cell Press. Simon, a legal ethicist at Harvard University, and his team imagined a hypothetical near-future scenario in which Alexa came equipped with the power to diagnose cognitive conditions like Alzheimer's and dementia simply by analyzing an elder person's speech patterns.