The essential task of urban planning is to generate the optimal land-use configuration of a target area. However, traditional urban planning is time-consuming and labor-intensive. Deep generative learning gives us hope that we can automate this planning process and come up with the ideal urban plans. While remarkable achievements have been obtained, they have exhibited limitations in lacking awareness of: 1) the hierarchical dependencies between functional zones and spatial grids; 2) the peer dependencies among functional zones; and 3) human regulations to ensure the usability of generated configurations. To address these limitations, we develop a novel human-instructed deep hierarchical generative model.

This year, we've seen the introduction of powerful generative AI systems that have the ability to create images and text on demand. At the same time, regulators are on the move. Europe is in the middle of finalizing its AI regulation (the AI Act), which aims to put strict rules on high-risk AI systems. Canada, the UK, the US, and China have all introduced their own approaches to regulating high-impact AI. But general-purpose AI seems to be an afterthought rather than the core focus.

Amazon's marketing material boasts that more than 7,500 government agencies worldwide use its cloud computing service AWS. Some of its investors fear those contracts include projects that see the company's technology contribute to human rights violations. Today a collective of 50 organizations working on digital and human rights called the Athena Coalition filed a proposal asking Amazon shareholders to force the company to investigate possible human rights violations by government clients. Athena works with owners of stock in the company who have the right to file shareholder resolutions on corporate governance. The proposal will be put to a vote at Amazon's annual meeting next year.

Whenever there is any mention of ethics in the context of AI, the topic of bias & fairness often follows. Similarly, whenever there is any mention of training and testing machine learning models, the trade-off between bias & variance features heavily. But do these two mentions of bias refer to the same thing? In order for machines to learn these patterns, especially in "supervised learning", they go through a training process whereby an algorithm extracts patterns from a training dataset, typically in an iterative manner. It then tests its predictions on an unseen (out-of-sample) test dataset to validate if the patterns it had learnt from the training dataset are valid. Bias: The action of supporting or opposing a particular person or thing in an unfair way, because of allowing personal opinions to influence your judgment.

This volume contains revised versions of the papers selected for the third volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.

The application of Natural Language Processing (NLP) to specialized domains, such as the law, has recently received a surge of interest. As many legal services rely on processing and analyzing large collections of documents, automating such tasks with NLP tools emerges as a key challenge. Many popular language models, such as BERT or RoBERTa, are general-purpose models, which have limitations on processing specialized legal terminology and syntax. In addition, legal documents may contain specialized vocabulary from other domains, such as medical terminology in personal injury text. Here, we propose LegalRelectra, a legal-domain language model that is trained on mixed-domain legal and medical corpora. We show that our model improves over general-domain and single-domain medical and legal language models when processing mixed-domain (personal injury) text. Our training architecture implements the Electra framework, but utilizes Reformer instead of BERT for its generator and discriminator. We show that this improves the model's performance on processing long passages and results in better long-range text comprehension.

As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant through self-improvement, without any human labels identifying harmful outputs. The only human oversight is provided through a list of rules or principles, and so we refer to the method as 'Constitutional AI'. The process involves both a supervised learning and a reinforcement learning phase. In the supervised phase we sample from an initial model, then generate self-critiques and revisions, and then finetune the original model on revised responses. In the RL phase, we sample from the finetuned model, use a model to evaluate which of the two samples is better, and then train a preference model from this dataset of AI preferences. We then train with RL using the preference model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a result we are able to train a harmless but non-evasive AI assistant that engages with harmful queries by explaining its objections to them. Both the SL and RL methods can leverage chain-of-thought style reasoning to improve the human-judged performance and transparency of AI decision making. These methods make it possible to control AI behavior more precisely and with far fewer human labels.

Michael Huppe is President & CEO of SoundExchange, an adjunct music law professor, published author, frequent contributor and lecturer. In the not-too-distance future, when a new recording artist seizes the spotlight with hit songs, a huge social media following and sold-out venues, it won't be a human being. It'll be a performer whose lyrics, melodies and voice are solely created by artificial intelligence (AI). We're already seeing hints of this with virtual artists such as metaverse avatars, hybrid performers that rely on a combination of AI and human talent. Beyond music, there's also been the emergence of AI products that create realistic digital images based on a natural language sentence provided by the user.

Did you know that in 2021: • A patent application named an Artificial Intelligence (AI) as the inventor and in at least 1 country- it is listed as an inventor; • AI is being used by the US Patent & Trademark Office (USPTO) to aid in the patent search process; and • a case involving a monkey that took a selfie may guide us in statutory construction? It's a brave new world and Darin Klemchuk is here to walk us through it. Darin is the managing partner and founder of the law firm, Klemchuk, LLP. He has handled more than 500 intellectual property disputes, including over 40 patent infringement cases and hundreds of IP enforcement actions. Darin represents Fortune 500 corporations, growing companies that benefit from broad intellectual property counseling, and startup companies with disruptive technologies. Additionally at the end of the podcast at the 36:04 mark, the ILS gives a tribute to one of our own, Tom Wilson.

Welcome to insideBIGDATA's annual technology predictions round-up! The big data industry has significant inertia moving into 2023. In order to give our valued readers a pulse on important new trends leading into next year, we here at insideBIGDATA heard from all our friends across the vendor ecosystem to get their insights, reflections and predictions for what may be coming. We were very encouraged to hear such exciting perspectives. Even if only half actually come true, Big Data in the next year is destined to be quite an exciting ride. There are many reasons why a customer would choose to implement their architecture on multiple clouds whether it's technology, market, or business-driven. When this happens, many times this leads to transactional and operational data being stored on multiple cloud platforms. The challenge this brings is how to gain insight into these without resorting to implementing multiple disparate data platforms. Historically data virtualization tools have been ...