Artificial Intelligence promises an exciting future and tremendous growth, provided that legal professionals able to navigate their business in this novel environment. Many companies make massive investments in artificial intelligence (AI), and more and more AI products and technologies are being launched by companies that are not traditional software companies. This signals a transition where traditional engineering companies invest in software capabilities and position AI as a critical way to disrupt their markets and gain market share. That transition does not come without challenges for legal teams. Lawyers need to keep abreast of new and fast-evolving technologies and familiarise themselves with novel technical concepts like "machine learning" or "black box AI".

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The processing of legal texts has been developing as an emerging field in natural language processing (NLP). Legal texts contain unique jargon and complex linguistic attributes in vocabulary, semantics, syntax, and morphology. Therefore, the development of text simplification (TS) methods specific to the legal domain is of paramount importance for facilitating comprehension of legal text by ordinary people and providing inputs to high-level models for mainstream legal NLP applications. While a recent study proposed a rule-based TS method for legal text, learning-based TS in the legal domain has not been considered previously. Here we introduce an unsupervised simplification method for legal texts (USLT). USLT performs domain-specific TS by replacing complex words and splitting long sentences. To this end, USLT detects complex words in a sentence, generates candidates via a masked-transformer model, and selects a candidate for substitution based on a rank score. Afterward, USLT recursively decomposes long sentences into a hierarchy of shorter core and context sentences while preserving semantic meaning. We demonstrate that USLT outperforms state-of-the-art domain-general TS methods in text simplicity while keeping the semantics intact.

Predictive Coding Networks (PCNs) aim to learn a generative model of the world. Given observations, this generative model can then be inverted to infer the causes of those observations. However, when training PCNs, a noticeable pathology is often observed where inference accuracy peaks and then declines with further training. This cannot be explained by overfitting since both training and test accuracy decrease simultaneously. Here we provide a thorough investigation of this phenomenon and show that it is caused by an imbalance between the speeds at which the various layers of the PCN converge. We demonstrate that this can be prevented by regularising the weight matrices at each layer: by restricting the relative size of matrix singular values, we allow the weight matrix to change but restrict the overall impact which a layer can have on its neighbours. We also demonstrate that a similar effect can be achieved through a more biologically plausible and simple scheme of just capping the weights.

Ariel Koren, a Google employee who became a face of worker protests against the company's contract with the Israeli government, announced her resignation yesterday. The Jewish marketing manager says she faced retaliation from management and some colleagues for expressing pro-Palestinian views within the company. In October she joined other Google and Amazon employees in public opposition to Project Nimbus, a $1.2 billion contract for Google and Amazon to provide cloud computing to Israel, including its defense ministry. She says that Google later gave her an ultimatum: Agree to move to Brazil within 17 days or lose her job. Training documents leaked to the Intercept show Project Nimbus providing Israel with access to Google's cloud AI services, including face and expression detection, video analysis, and sentiment analysis.

This role requires you to design and implement end-to-end Machine Learning (ML) and Natural Language Processing (NLP) models and systems to drive business impact. You partner with cross-functional stakeholders and customers to frame business problems as ML problems, prototype solutions effectively, and implement production-grade ML systems and the backend software systems they support to provide end-to-end five-star user experiences. Given you are constructing the foundation on which our global data infrastructure will be built, you need to pay close attention to detail and maintain a forward-thinking outlook as well as scrappiness for the present needs. You thrive in a fast-paced, iterative, but heavily test-driven development environment, with full ownership to design features from scratch to impact the business and the accountability that comes along. Responsibilities:Scoping: Actively participate in customer engagements and partner with cross-functional stakeholders (legal product ...

Artificial intelligence (AI) and machine learning (ML) are two technologies that are revolutionizing the industrial sector. The manufacturing area is no exception. Developing a Smart Factory is an opportunity to be competitive, to optimize timelines and make product design and production more efficient. Quality, worker safety, and sustainability are the fundamental pieces where these technologies can participate in the redesign towards high productivity, much safer, and more sustainable manufacturing. Manufacturing companies that are committed to finding their applications, understanding market trends and changes, to remain competitive.

When most people think of artificial intelligence (AI), they think of smarty-pants robots that can service our every whim. While real robots may be in the cards, the future of AI will also revolutionize the way we work (in real life and in the metaverse). In fact, AI is already in your workplace: You use AI when you use Google Maps to find your way to an off-site meeting (perhaps in a self-driving car?), or when you use spell-check for a report. The current state of AI and the future of AI goes far beyond simplifying mundane tasks, however. Artificial intelligence, or computers that are taught to "think" like humans, can make us healthier, less stressed and happier through advancements in medicine, manufacturing and more.

This paper introduces Probabilistic Deduction (PD) as an approach to probabilistic structured argumentation. A PD framework is composed of probabilistic rules (p-rules). As rules in classical structured argumentation frameworks, p-rules form deduction systems. In addition, p-rules also represent conditional probabilities that define joint probability distributions. With PD frameworks, one performs probabilistic reasoning by solving Rule-Probabilistic Satisfiability. At the same time, one can obtain an argumentative reading to the probabilistic reasoning with arguments and attacks. In this work, we introduce a probabilistic version of the Closed-World Assumption (P-CWA) and prove that our probabilistic approach coincides with the complete extension in classical argumentation under P-CWA and with maximum entropy reasoning. We present several approaches to compute the joint probability distribution from p-rules for achieving a practical proof theory for PD. PD provides a framework to unify probabilistic reasoning with argumentative reasoning. This is the first work in probabilistic structured argumentation where the joint distribution is not assumed form external sources.

Decisions impacting human lives are increasingly being made or assisted by automated decision-making algorithms. Many of these algorithms process personal data for predicting recidivism, credit risk analysis, identifying individuals using face recognition, and more. While potentially improving efficiency and effectiveness, such algorithms are not inherently free from bias, opaqueness, lack of explainability, maleficence, and the like. Given that the outcomes of these algorithms have a significant impact on individuals and society and are open to analysis and contestation after deployment, such issues must be accounted for before deployment. Formal audits are a way of ensuring algorithms meet the appropriate accountability standards. This work, based on an extensive analysis of the literature and an expert focus group study, proposes a unifying framework for a system accountability benchmark for formal audits of artificial intelligence-based decision-aiding systems. This work also proposes system cards to serve as scorecards presenting the outcomes of such audits. It consists of 56 criteria organized within a four-by-four matrix composed of rows focused on (i) data, (ii) model, (iii) code, (iv) system, and columns focused on (a) development, (b) assessment, (c) mitigation, and (d) assurance. The proposed system accountability benchmark reflects the state-of-the-art developments for accountable systems, serves as a checklist for algorithm audits, and paves the way for sequential work in future research.