Through a systematization of generative AI (GenAI) stakeholder goals and expectations, this work seeks to uncover what value different stakeholders see in their contributions to the GenAI supply line. This valuation enables us to understand whether fair use advocated by GenAI companies to train model progresses the copyright law objective of promoting science and arts. While assessing the validity and efficacy of the fair use argument, we uncover research gaps and potential avenues for future works for researchers and policymakers to address.

We have concluded that a new law is needed,

Meta's shares rose in after-hours trading on Wednesday off the back of a strong earnings report that comes as the company is spending heavily on AI tools. The company's stock price grew around 5% following the report, which revealed the company outperformed analysts' expectations for its second quarter. Meta, which owns Facebook, Instagram and WhatsApp, reported 39.07bn in revenue and 5.16 earnings per share. Both results outpaced market predictions of around 38bn in revenue and 4.7 per share, while the company also reported 8.47bn in capital expenditures – lower than analysts expected. "We had a strong quarter, and Meta AI is on track to be the most used AI assistant in the world by the end of the year," Mark Zuckerberg, Meta's CEO, claimed in a statement.

Authorship obfuscation aims to disguise the identity of an author within a text by altering the writing style, vocabulary, syntax, and other linguistic features associated with the text author. This alteration needs to balance privacy and utility. While strong obfuscation techniques can effectively hide the author's identity, they often degrade the quality and usefulness of the text for its intended purpose. Conversely, maintaining high utility tends to provide insufficient privacy, making it easier for an adversary to de-anonymize the author. Thus, achieving an optimal trade-off between these two conflicting objectives is crucial. In this paper, we propose TAROT: Task-Oriented Authorship Obfuscation Using Policy Optimization, a new unsupervised authorship obfuscation method whose goal is to optimize the privacy-utility trade-off by regenerating the entire text considering its downstream utility. Our approach leverages policy optimization as a fine-tuning paradigm over small language models in order to rewrite texts by preserving author identity and downstream task utility. We show that our approach largely reduce the accuracy of attackers while preserving utility. We make our code and models publicly available.

The deployment of artificial intelligence (AI) applications has accelerated rapidly. AI enabled technologies are facing the public in many ways including infrastructure, consumer products and home applications. Because many of these technologies present risks either in the form of physical injury, or bias, potentially yielding unfair outcomes, policy makers must consider the need for oversight. Most policymakers, however, lack the technical knowledge to judge whether an emerging AI technology is safe, effective, and requires oversight, therefore policy makers must depend on expert opinion. But policymakers are better served when, in addition to expert opinion, they have some general understanding of existing guidelines and regulations. This work provides an overview [the landscape] of AI legislation and directives at the international, U.S. state, city and federal levels. It also reviews relevant business standards, and technical society initiatives. Then an overlap and gap analysis are performed resulting in a reference guide that includes recommendations and guidance for future policy making.

As artificial intelligence systems grow more powerful, there has been increasing interest in "AI safety" research to address emerging and future risks. However, the field of AI safety remains poorly defined and inconsistently measured, leading to confusion about how researchers can contribute. This lack of clarity is compounded by the unclear relationship between AI safety benchmarks and upstream general capabilities (e.g., general knowledge and reasoning). To address these issues, we conduct a comprehensive meta-analysis of AI safety benchmarks, empirically analyzing their correlation with general capabilities across dozens of models and providing a survey of existing directions in AI safety. Our findings reveal that many safety benchmarks highly correlate with upstream model capabilities, potentially enabling "safetywashing" -- where capability improvements are misrepresented as safety advancements. Based on these findings, we propose an empirical foundation for developing more meaningful safety metrics and define AI safety in a machine learning research context as a set of clearly delineated research goals that are empirically separable from generic capabilities advancements. In doing so, we aim to provide a more rigorous framework for AI safety research, advancing the science of safety evaluations and clarifying the path towards measurable progress.

Crime situations are race against time. An AI-assisted criminal investigation system, providing prompt but precise legal counsel is in need for police officers. We introduce LAPIS (Language Model Augmented Police Investigation System), an automated system that assists police officers to perform rational and legal investigative actions. We constructed a finetuning dataset and retrieval knowledgebase specialized in crime investigation legal reasoning task. We extended the dataset's quality by incorporating manual curation efforts done by a group of domain experts. We then finetuned the pretrained weights of a smaller Korean language model to the newly constructed dataset and integrated it with the crime investigation knowledgebase retrieval approach. Experimental results show LAPIS' potential in providing reliable legal guidance for police officers, even better than the proprietary GPT-4 model. Qualitative analysis on the rationales generated by LAPIS demonstrate the model's reasoning ability to leverage the premises and derive legally correct conclusions.

Large autoregressive generative models have emerged as the cornerstone for achieving the highest performance across several Natural Language Processing tasks. However, the urge to attain superior results has, at times, led to the premature replacement of carefully designed task-specific approaches without exhaustive experimentation. The Coreference Resolution task is no exception; all recent state-of-the-art solutions adopt large generative autoregressive models that outperform encoder-based discriminative systems. In this work,we challenge this recent trend by introducing Maverick, a carefully designed - yet simple - pipeline, which enables running a state-of-the-art Coreference Resolution system within the constraints of an academic budget, outperforming models with up to 13 billion parameters with as few as 500 million parameters. Maverick achieves state-of-the-art performance on the CoNLL-2012 benchmark, training with up to 0.006x the memory resources and obtaining a 170x faster inference compared to previous state-of-the-art systems. We extensively validate the robustness of the Maverick framework with an array of diverse experiments, reporting improvements over prior systems in data-scarce, long-document, and out-of-domain settings. We release our code and models for research purposes at https://github.com/SapienzaNLP/maverick-coref.

The voice mode is powered by OpenAI's new GPT-4o model, which combines voice, text, and vision capabilities. To gather feedback, the company is initially launching the chatbot to a "small group of users" paying for ChatGPT Plus, but it says it will make the bot available to all ChatGPT Plus subscribers this fall. OpenAI says it will notify customers who are part of the first rollout wave in the ChatGPT app and provide instructions on how to use the new model. The new voice feature, which was announced in May, is being launched a month later than originally planned because the company said it needed more time to improve safety features, such as the model's ability to detect and refuse unwanted content. The company also said it was preparing its infrastructure to offer real-time responses to millions of users.

Mr. Moore is not alone. In a dozen interviews with editors and other Hollywood craftspeople, almost all worried that A.I. had either begun displacing them or could soon do so. As it happens, these workers belong to a labor union, the International Alliance of Theatrical Stage Employees (IATSE), which can negotiate A.I. protections on their behalf, as actors' and writers' unions did during last year's strikes. Yet their union recently approved a contract, by a large margin, that clears the way for studios to require employees to use the technology, just as Mr. Moore and his colleagues have feared. Some labor experts believe that the protections negotiated by the union, like regular meetings with studios on A.I., may be the best it could do during an industrywide downturn.