In a companion paper (Beckers et al. 2022), we defined a qualitative notion of harm: either harm is caused, or it is not. For practical applications, we often need to quantify harm; for example, we may want to choose the lest harmful of a set of possible interventions. We first present a quantitative definition of harm in a deterministic context involving a single individual, then we consider the issues involved in dealing with uncertainty regarding the context and going from a notion of harm for a single individual to a notion of "societal harm", which involves aggregating the harm to individuals. We show that the "obvious" way of doing this (just taking the expected harm for an individual and then summing the expected harm over all individuals can lead to counterintuitive or inappropriate answers, and discuss alternatives, drawing on work from the decision-theory literature.

Automatic extraction of narrative elements from text, combining narrative theories with computational models, has been receiving increasing attention over the last few years. Previous works have utilized the oral narrative theory by Labov and Waletzky to identify various narrative elements in personal stories texts. Instead, we direct our focus to informational texts, specifically news stories. We introduce NEAT (Narrative Elements AnnoTation) - a novel NLP task for detecting narrative elements in raw text. For this purpose, we designed a new multi-label narrative annotation scheme, better suited for informational text (e.g. news media), by adapting elements from the narrative theory of Labov and Waletzky (Complication and Resolution) and adding a new narrative element of our own (Success). We then used this scheme to annotate a new dataset of 2,209 sentences, compiled from 46 news articles from various category domains. We trained a number of supervised models in several different setups over the annotated dataset to identify the different narrative elements, achieving an average F1 score of up to 0.77. The results demonstrate the holistic nature of our annotation scheme as well as its robustness to domain category.

In this paper we present SynKB, an open-source, automatically extracted knowledge base of chemical synthesis protocols. Similar to proprietary chemistry databases such as Reaxsys, SynKB allows chemists to retrieve structured knowledge about synthetic procedures. By taking advantage of recent advances in natural language processing for procedural texts, SynKB supports more flexible queries about reaction conditions, and thus has the potential to help chemists search the literature for conditions used in relevant reactions as they design new synthetic routes. Using customized Transformer models to automatically extract information from 6 million synthesis procedures described in U.S. and EU patents, we show that for many queries, SynKB has higher recall than Reaxsys, while maintaining high precision. We plan to make SynKB available as an open-source tool; in contrast, proprietary chemistry databases require costly subscriptions.

We introduce MyStyle, a personalized deep generative prior trained with a few shots of an individual. MyStyle allows to reconstruct, enhance and edit images of a specific person, such that the output is faithful to the person's key facial characteristics. Given a small reference set of portrait images of a person (~100), we tune the weights of a pretrained StyleGAN face generator to form a local, low-dimensional, personalized manifold in the latent space. We show that this manifold constitutes a personalized region that spans latent codes associated with diverse portrait images of the individual. Moreover, we demonstrate that we obtain a personalized generative prior, and propose a unified approach to apply it to various ill-posed image enhancement problems, such as inpainting and super-resolution, as well as semantic editing. Using the personalized generative prior we obtain outputs that exhibit high-fidelity to the input images and are also faithful to the key facial characteristics of the individual in the reference set. We demonstrate our method with fair-use images of numerous widely recognizable individuals for whom we have the prior knowledge for a qualitative evaluation of the expected outcome. We evaluate our approach against few-shots baselines and show that our personalized prior, quantitatively and qualitatively, outperforms state-of-the-art alternatives.

Recent research shows that supervised learning can be an effective tool for designing nearoptimal feedback controllers for high-dimensional nonlinear dynamic systems. But the behavior of neural network controllers is still not well understood. In particular, some neural networks with high test accuracy can fail to even locally stabilize the dynamic system. To address this challenge we propose several novel neural network architectures, which we show guarantee local asymptotic stability while retaining the approximation capacity to learn the optimal feedback policy semi-globally. The proposed architectures are compared against standard neural network feedback controllers through numerical simulations of two high-dimensional nonlinear optimal control problems: stabilization of an unstable Burgers-type partial differential equation, and altitude and course tracking for an unmanned aerial vehicle. The simulations demonstrate that standard neural networks can fail to stabilize the dynamics even when trained well, while the proposed architectures are always at least locally stabilizing. Moreover, the proposed controllers are found to be close to optimal in testing.

The White House this morning unveiled what it's colloquially calling an "AI Bill of Rights," which aims to establish tenets around the ways AI algorithms should be deployed as well as guardrails on their applications. In five bullet points crafted with feedback from the public, companies like Microsoft and Palantir and human rights and AI ethics groups, the document lays out safety, transparency and privacy principles that the Office of Science & Technology Policy (OSTP) -- which drafted the AI Bill of Rights -- argues will lead to better outcomes while mitigating harmful real-life consequences. The AI Bill of Rights mandates that AI systems be proven safe and effective through testing and consultation with stakeholders, in addition to continuous monitoring of the systems in production. It explicitly calls out algorithmic discrimination, saying that AI systems should be designed to protect both communities and individuals from biased decision-making. And it strongly suggests that users should be able to opt out of interactions with an AI system if they choose, for example in the event of a system failure.

Sometimes we invent them by accident, like with Silly Putty. But far more often, making useful materials is a tedious and expensive process of trial and error. Scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory have recently demonstrated an automated process for identifying and exploring promising new materials by combining machine learning (ML) -- a type of artificial intelligence -- and high performance computing. The new approach could help accelerate the discovery and design of useful materials. Using the single element carbon as a prototype, the algorithm predicted the ways in which atoms order themselves under a wide range of temperatures and pressures to make up different substances.

In the cybersecurity sector, artificial intelligence testing is crucial. This is because AI has the potential to help cybersecurity overcome some of its major obstacles. And there are many obstacles, including the incapacity of many organizations to stay on top of the numerous new risks and attacks that emerge as the internet and technological usage increase. AI-powered cybersecurity is expected to change how we respond to cyber attacks. Because of its capacity to study and learn from enormous volumes of data, artificial intelligence will be crucial in identifying sophisticated threats.

In a metal workshop in the industrial city of Kryvyi Rih in southern Ukraine, a homemade anti-drone system waits to be mounted on a military pick-up truck. The contraption -- a heavy machine gun welded to steel tubes -- is one of several do-it-yourself weapons that are proving to be valuable additions to the Ukraine war effort. "We have the skills and the equipment, and we don't lack ideas," said Sergey Bondarenko in the workshop near the southern front. The well-built 39-year-old with a long black beard is a local leader of the territorial defence, a unit of the Ukrainian army. The device will be accompanied by shock absorbers, for more stability and precision, Bondarenko told AFP beside the anti-drone prototype.

To address this ever-growing issue, the White House today released a Blueprint for an AI Bill of Rights. This outlines five principles that should guide the design, use and deployment of automated systems to protect Americans in this age of AI.