The Guardian has blocked OpenAI from using its content to power artificial intelligence products such as ChatGPT. Concerns that OpenAI is using unlicensed content to create its AI tools have led to writers bringing lawsuits against the company and creative industries calling for safeguards to protect their intellectual property. The Guardian has confirmed that it has prevented OpenAI from deploying software that harvests its content. Generative AI technology – the term for products that generate convincing text, image and audio from simple human prompts – has dazzled the public since a breakthrough version of its ChatGPT chatbot launched last year. However, fears have arisen about the potential mass-production of disinformation and the way in which such tools are built.

Legal Judgment Prediction (LJP) aims to predict judgment outcomes based on case description. Several researchers have developed techniques to assist potential clients by predicting the outcome in the legal profession. However, none of the proposed techniques were implemented in Arabic, and only a few attempts were implemented in English, Chinese, and Hindi. In this paper, we develop a system that utilizes deep learning (DL) and natural language processing (NLP) techniques to predict the judgment outcome from Arabic case scripts, especially in cases of custody and annulment of marriage. This system will assist judges and attorneys in improving their work and time efficiency while reducing sentencing disparity. In addition, it will help litigants, lawyers, and law students analyze the probable outcomes of any given case before trial. We use a different machine and deep learning models such as Support Vector Machine (SVM), Logistic regression (LR), Long Short Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) using representation techniques such as TF-IDF and word2vec on the developed dataset. Experimental results demonstrate that compared with the five baseline methods, the SVM model with word2vec and LR with TF-IDF achieve the highest accuracy of 88% and 78% in predicting the judgment on custody cases and annulment of marriage, respectively. Furthermore, the LR and SVM with word2vec and BiLSTM model with TF-IDF achieved the highest accuracy of 88% and 69% in predicting the probability of outcomes on custody cases and annulment of marriage, respectively.

Recent strides in Large Language Models (LLMs) have saturated many NLP benchmarks (even professional domain-specific ones), emphasizing the need for novel, more challenging novel ones to properly assess LLM capabilities. In this paper, we introduce a novel NLP benchmark that poses challenges to current LLMs across four key dimensions: processing long documents (up to 50K tokens), utilizing domain specific knowledge (embodied in legal texts), multilingual understanding (covering five languages), and multitasking (comprising legal document to document Information Retrieval, Court View Generation, Leading Decision Summarization, Citation Extraction, and eight challenging Text Classification tasks). Our benchmark comprises diverse legal NLP datasets from the Swiss legal system, allowing for a comprehensive study of the underlying Non-English, inherently multilingual, federal legal system. Despite recent advances, efficiently processing long documents for intense review/analysis tasks remains an open challenge for language models. Also, comprehensive, domain-specific benchmarks requiring high expertise to develop are rare, as are multilingual benchmarks. This scarcity underscores our contribution's value, considering most public models are trained predominantly on English corpora, while other languages remain understudied, particularly for practical domain-specific NLP tasks. Our benchmark allows for testing and advancing the state-of-the-art LLMs. As part of our study, we evaluate several pre-trained multilingual language models on our benchmark to establish strong baselines as a point of reference. Despite the large size of our datasets (tens to hundreds of thousands of examples), existing publicly available models struggle with most tasks, even after in-domain pretraining. We publish all resources (benchmark suite, pre-trained models, code) under a fully permissive open CC BY-SA license.

The technology has increasingly commanded the legal system's attention, and as such office began seeking public comments on Wednesday about some of AI's thorniest issues (via Ars Technica). "The crucial question appears to be whether the'work' is basically one of human authorship, with the computer merely being an assisting instrument, or whether the traditional elements of authorship in the work (literary, artistic, or musical expression or elements of selection, arrangement, etc.) were actually conceived and executed not by man but by a machine," the USCO wrote. Although the issue is far from resolved, several cases have hinted at where the boundaries may fall. On the other hand, a Federal judge recently rejected an attempt to register AI-generated art which had no human intervention other than its inciting text prompt. Sarah Silverman is among the high-profile plaintiffs suing OpenAI and Meta for allegedly training ChatGPT and LLaMA (respectively) on their written work -- in her case, her 2010 memoir The Bedwetter. OpenAI also faces a class-action lawsuit over using scraped web data to train its viral chatbot.

Over the past year or so, you've probably had conversations with friends, family, and coworkers about the rise of generative AI capable of making convincing text and imagery--but perhaps also about the hype and fear swirling around the technology. A poll out this week finds that worry over harmful effects of AI is outpacing the wow of helpful AI. A majority of Americans say their concern about artificial intelligence in daily life outweighs their excitement about it, according to a Pew Research Center survey of more than 11,000 US adults. The results come at a time when a growing number of people are paying attention to news about AI in their daily lives. Pew has run this survey twice before and reports that the number of people more concerned than excited about AI jumped from 37 percent in 2021 to 52 percent this month.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Kelly McKernan's acrylic and watercolor paintings are bold and vibrant, often featuring feminine figures rendered in bright greens, blues, pinks and purples. The style, in the artist's words, is "surreal, ethereal … dealing with discomfort in the human journey." The word "human" has a special resonance for McKernan these days.

"A Recent Entrance to Paradise" is a pixelated pastoral scene of train tracks running under a moss-flecked bridge. It was, according to its creator's creator, drawn and named in 2012 by an artificial intelligence called DABUS (Device for the Autonomous Bootstrapping of Unified Sentience). Thaler is appealing the decision. Thaler, a Missouri-based inventor and AI researcher, has become something of a serial litigant on behalf of DABUS. Judges have swatted away similar lawsuits in the European Union, the United States, and, eventually, on appeal, in Australia.

TRAVEL PLANS – President Joe Biden set to depart from tradition on upcoming 9/11 anniversary. SENSELESS SLAYING – Judge outraged by teen killer's explanation for brutal murder of 16-year-old girlfriend. WORDS OF WISDOM – Donald Trump has some advice for 2024 rival Vivek Ramaswamy. IDALIA'S WRATH – Monster storm leaves path of destruction after bulldozing through coastal states. 'GRAVE ISSUES' – Commissioner resigns over city's squalid conditions, sends scathing letter to Dem mayor.

China's Baidu has rolled out its ChatGPT rival ERNIE Bot to the public, in a major leap for the country's tech sector as it aims to cash in on the artificial intelligence gold rush. The Chinese government introduced new regulations this month for AI developers, aiming to allow them to stay in the race with the likes of ChatGPT maker OpenAI and Microsoft while tightly controlling information online. ERNIE Bot is the first domestic AI app to be fully available to the public in China. It is not available outside the country. "We are thrilled to share that ERNIE Bot is now fully open to the general public starting August 31," Baidu said in a statement on Thursday.

The aim of Machine Unlearning (MU) is to provide theoretical guarantees on the removal of the contribution of a given data point from a training procedure. Federated Unlearning (FU) consists in extending MU to unlearn a given client's contribution from a federated training routine. Current FU approaches are generally not scalable, and do not come with sound theoretical quantification of the effectiveness of unlearning. In this work we present Informed Federated Unlearning (IFU), a novel efficient and quantifiable FU approach. Upon unlearning request from a given client, IFU identifies the optimal FL iteration from which FL has to be reinitialized, with unlearning guarantees obtained through a randomized perturbation mechanism. The theory of IFU is also extended to account for sequential unlearning requests. Experimental results on different tasks and dataset show that IFU leads to more efficient unlearning procedures as compared to basic re-training and state-of-the-art FU approaches.