Agent-based and activity-based models for simulating transportation systems have attracted significant attention in recent years. Few studies, however, include a detailed representation of active modes of transportation - such as walking and cycling - at a city-wide level, where dominating motorised modes are often of primary concern. This paper presents an open workflow for creating a multi-modal agent-based and activity-based transport simulation model, focusing on Greater Melbourne, and including the process of mode choice calibration for the four main travel modes of driving, public transport, cycling and walking. The synthetic population generated and used as an input for the simulation model represented Melbourne's population based on Census 2016, with daily activities and trips based on the Victoria's 2016-18 travel survey data. The road network used in the simulation model includes all public roads accessible via the included travel modes. We compared the output of the simulation model with observations from the real world in terms of mode share, road volume, travel time, and travel distance. Through these comparisons, we showed that our model is suitable for studying mode choice and road usage behaviour of travellers.

To treat others as one would wish to be treated is a common formulation of the golden rule (GR). Yet, despite its prevalence as an axiom throughout history, no transfer of this moral philosophy into computational systems exists. In this paper we consider how to algorithmically operationalise this rule so that it may be used to measure sentences such as the boy harmed the girl, and categorise them as fair or unfair. For the purposes of the paper, we define a fair act as one that one would be accepting of if it were done to oneself. A review and reply to criticisms of the GR is made. We share the code for the digitisation of the GR, and test it with a list of sentences. Implementing it within two language models, the USE, and ALBERT, we find F1 scores of 78.0, 85.0, respectively. A suggestion of how the technology may be implemented to avoid unfair biases in word embeddings is made - given that individuals would typically not wish to be on the receiving end of an unfair act, such as racism, irrespective of whether the corpus being used deems such discrimination as praiseworthy.

Structured and grounded representation of text is typically formalized by closed information extraction, the problem of extracting an exhaustive set of (subject, relation, object) triplets that are consistent with a predefined set of entities and relations from a knowledge base schema. Most existing works are pipelines prone to error accumulation, and all approaches are only applicable to unrealistically small numbers of entities and relations. We introduce GenIE (generative information extraction), the first end-to-end autoregressive formulation of closed information extraction. GenIE naturally exploits the language knowledge from the pre-trained transformer by autoregressively generating relations and entities in textual form. Thanks to a new bi-level constrained generation strategy, only triplets consistent with the predefined knowledge base schema are produced. Our experiments show that GenIE is state-of-the-art on closed information extraction, generalizes from fewer training data points than baselines, and scales to a previously unmanageable number of entities and relations. With this work, closed information extraction becomes practical in realistic scenarios, providing new opportunities for downstream tasks. Finally, this work paves the way towards a unified end-to-end approach to the core tasks of information extraction. Code and models available at https://github.com/epfl-dlab/GenIE.

The selected landing site should be at a low slope with respect to the planetary equipotential surface, free of hazards, and also be located such that it satisfies mission objectives. The Apollo Lunar missions allowed the lander pilot to steer towards a manually selected landing site by manipulating a control stick until the designated landing site (DLS) appeared in a window fixed reticle [1], but the actual trajectory flown to reach that point in a manner consistent with a soft landing was automated by the guidance and control system. The Chinese Chang'e 3 mission [2] used coarse hazard detection from greyscale images during the powered descent, and once the lander reached an altitude of 100 m, hovered while a hazard free landing site was selected, after which the lander diverted horizontally until directly above the selected landing site, and then continued straight down to the surface. Finally, the Morpheus project [3] demonstrated integration of flash LIDAR based hazard detection with guidance and control on Earth using a hazard field with a mix of terrain hazards and safe landing sites. During the powered descent the landing site detection system will likely change the DLS as distance to surface decreases and resolution increases. Consequently, a suitable guidance and control system should be capable of multiple divert maneuvers during the powered descent phase.

Given the growing use of Artificial Intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we initiate a discussion about the ethical and responsible use of AI. In fact, much can be learned from other domains where AI was introduced, often with the best of intentions, yet often led to unintended societal consequences, such as hard coding racial bias in the criminal justice system or increasing economic inequality through the financial system. A common misconception is that the environmental sciences are immune to such unintended consequences when AI is being used, as most data come from observations, and AI algorithms are based on mathematical formulas, which are often seen as objective. In this article, we argue the opposite can be the case. Using specific examples, we demonstrate many ways in which the use of AI can introduce similar consequences in the environmental sciences. This article will stimulate discussion and research efforts in this direction. As a community, we should avoid repeating any foreseeable mistakes made in other domains through the introduction of AI. In fact, with proper precautions, AI can be a great tool to help {\it reduce} climate and environmental injustice. We primarily focus on weather and climate examples but the conclusions apply broadly across the environmental sciences.

Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types. However, information and knowledge evolve continuously, and temporal dynamics emerge, which are expected to influence future situations. In temporal knowledge graphs, time information is integrated into the graph by equipping each edge with a timestamp or a time range. Embedding-based methods have been introduced for link prediction on temporal knowledge graphs, but they mostly lack explainability and comprehensible reasoning chains. Particularly, they are usually not designed to deal with link forecasting -- event prediction involving future timestamps. We address the task of link forecasting on temporal knowledge graphs and introduce TLogic, an explainable framework that is based on temporal logical rules extracted via temporal random walks. We compare TLogic with state-of-the-art baselines on three benchmark datasets and show better overall performance while our method also provides explanations that preserve time consistency. Furthermore, in contrast to most state-of-the-art embedding-based methods, TLogic works well in the inductive setting where already learned rules are transferred to related datasets with a common vocabulary.

Large, AI-powered language models (LLMs) like OpenAI's GPT-3 have enormous potential in the enterprise. For example, GPT-3 is now being used in over 300 apps by thousands of developers to produce more than 4.5 billion words per day. And Naver, the company behind the eponymous search engine Naver, is employing LLMs to personalize search results on the Naver platform -- following on the heels of Bing and Google. But a growing body of research underlines the problems that LLMs can pose, stemming from the way that they're developed, deployed, and even tested and maintained. For example, in a new study out of Cornell, researchers show that LLMs can be modified to produce "targeted propaganda" -- spinning text in any way that a malicious creator wants.

Washington, DC – The United States is sending a "dangerous and misleading message" by failing to hold any US military personnel responsible for a Kabul drone attack that killed 10 civilians, including seven children, human rights advocates have said. Calls for accountability for the deadly bombing on August 29 grew on Tuesday, a day after US media outlets first reported that US Defense Secretary Lloyd Austin had accepted a recommendation from top commanders not to punish any members of the military. Rights groups also urged President Joe Biden's administration to do more to help the survivors of the attack in the Afghan capital to relocate to the US. The bombing targeted the car of Zemari Ahmadi, who worked for US-based aid organisation Nutrition and Education International (NEI), killing him and nine of his family members. "I've been beseeching the US government to evacuate directly-impacted family members and NEI employees for months because their security situation is so dire," Steven Kwon, founder and president of NEI, said in a statement.

Although artificial intelligence has been the subject of academic research since the 1950s and has been used commercially in some industries for decades, it is still in its infancy across much of the broader economy. The rapid adoption of this technology, along with the unique privacy, security and liability issues associated with it, has created opportunities for lawyers to help their clients capture its economic value while ensuring its use is ethical and legal. However, before advising clients on AI issues, lawyers should have some basic technical knowledge to answer questions about legal compliance. Machine learning algorithms are incredibly complex, learning billions of rules from datasets and applying those rules to arrive at an output recommendation. Even the most precise and well-designed AI systems are probabilistic in nature, guaranteeing that the system will, at some point, produce an incorrect result.

This article appears in the November/December 2021 issue of The American Prospect magazine. The term "artificial intelligence" is widely recognized by researchers as less a technically precise descriptor than an aspirational project that comprises a growing collection of data-centric technologies. The recent AI trend kicked off around 2010, when a combination of increased computing power and massive troves of web data reanimated interest in decades-old techniques. It wasn't the algorithms that were new as much as the concentrated resources and the surveillance business models capable of collecting, storing, and processing previously unfathomable amounts of data. In other words, so-called "advances" in AI celebrated over the last decade are primarily the product of significantly concentrated data and computing resources that reside in the hands of a few large tech corporations like Amazon, Facebook, and Google.