According to Bloomberg and The New York Times, Meta is in talks with the likes of Keegan-Michael Key, Awkwafina and Dame Judi Dench, among others, for its AI projects. The company apparently intends to incorporate their voices into a conversational generative AI-slash-digital assistant called MetaAI, which is rumored to be like Siri and Google Assistant, which could live within Facebook, Meta hardware, and all the other parts of the multimillion-dollar social network company. The actors' representatives are still negotiating for stricter limits, though SAG-AFTRA has reportedly agreed on terms with Meta. SAG-AFTRA, if you recall, fought for provisions to protect actors from the threat of job loss due to AI. Didn't Meta already do something like this? Yes. During its Connect event last year, the company also introduced a chatbot platform with 28 "characters" voiced by celebrities, including Snoop Dogg, Paris Hilton, Dwyane Wade and Kendall Jenner.

Will it be easier to retreat to a replicant of a deceased partner than to navigate the confusing and painful realities of human relationships? Indeed, the AI companionship provider Replika was born from an attempt to resurrect a deceased best friend and now provides companions to millions of users. Even the CTO of OpenAI warns that AI has the potential to be "extremely addictive." We're seeing a giant, real-world experiment unfold, uncertain what impact these AI companions will have either on us individually or on society as a whole. Will Grandma spend her final neglected days chatting with her grandson's digital double, while her real grandson is mentored by an edgy simulated elder?

Talent hunting is heating up in the Japanese semiconductor industry, which is booming thanks to efforts to enhance national economic security and the spread of generative artificial intelligence systems. While projects to build big factories are underway in Hokkaido and the Kyushu region, job information company Recruit has reported a jump in job openings for chip engineers. Annual salaries offered to experienced workers have also risen. According to a survey by Recruit Agent, a job information website for those seeking to change jobs, openings for engineer jobs related to the production of semiconductors or chipmaking equipment in fiscal 2023 jumped 14.24-fold from 10 years before.

Artificial Intelligence (AI) is increasingly acknowledged as a vital component for the advancement and competitiveness of modern organizations, including small and medium enterprises (SMEs). However, the adoption of AI technologies in SMEs faces significant barriers, primarily related to cost, lack of technical skills, and employee acceptance. This study proposes a comprehensive, phased framework designed to facilitate the effective adoption of AI in SMEs by systematically addressing these barriers. The framework begins with raising awareness and securing commitment from leadership, followed by the adoption of low-cost, general-purpose AI tools to build technical competence and foster a positive attitude towards AI. As familiarity with AI technologies increases, the framework advocates for the integration of task-specific AI tools to enhance efficiency and productivity. Subsequently, it guides organizations towards the in-house development of generative AI tools, providing greater customization and control. Finally, the framework addresses the development of discriminative AI models to meet highly specific and precision-oriented tasks. By providing a structured and incremental approach, this framework ensures that SMEs can navigate the complexities of AI integration effectively, driving innovation, efficiency, and competitive advantage. This study contributes to the field by offering a practical, prescriptive framework tailored to the unique needs of SMEs, facilitating the successful adoption of AI technologies and positioning these organizations for sustained growth in a competitive landscape.

This paper presents and evaluates a new retrieval augmented generation (RAG) and large language model (LLM)-based artificial intelligence (AI) technique: rubric enabled generative artificial intelligence (REGAI). REGAI uses rubrics, which can be created manually or automatically by the system, to enhance the performance of LLMs for evaluation purposes. REGAI improves on the performance of both classical LLMs and RAG-based LLM techniques. This paper describes REGAI, presents data regarding its performance and discusses several possible application areas for the technology.

Foundation models (FMs) provide societal benefits but also amplify risks. Governments, companies, and researchers have proposed regulatory frameworks, acceptable use policies, and safety benchmarks in response. However, existing public benchmarks often define safety categories based on previous literature, intuitions, or common sense, leading to disjointed sets of categories for risks specified in recent regulations and policies, which makes it challenging to evaluate and compare FMs across these benchmarks. To bridge this gap, we introduce AIR-Bench 2024, the first AI safety benchmark aligned with emerging government regulations and company policies, following the regulation-based safety categories grounded in our AI risks study, AIR 2024. AIR 2024 decomposes 8 government regulations and 16 company policies into a four-tiered safety taxonomy with 314 granular risk categories in the lowest tier. AIR-Bench 2024 contains 5,694 diverse prompts spanning these categories, with manual curation and human auditing to ensure quality. We evaluate leading language models on AIR-Bench 2024, uncovering insights into their alignment with specified safety concerns. By bridging the gap between public benchmarks and practical AI risks, AIR-Bench 2024 provides a foundation for assessing model safety across jurisdictions, fostering the development of safer and more responsible AI systems.

Following a report from The Wall Street Journal that claims OpenAI has been sitting on a tool that can spot essays written by ChatGPT with a high degree of accuracy, the company has shared a bit of information about its research into text watermarking -- and why it hasn't released its detection method. According to The Wall Street Journal's report, debate over whether the tool should be released has kept it from seeing the light of day, despite it being "ready." In an update published on Sunday to a May blog post, spotted by TechCrunch, OpenAI said, "Our teams have developed a text watermarking method that we continue to consider as we research alternatives." The company said watermarking is one of multiple solutions, including classifiers and metadata, that it has looked into as part of "extensive research on the area of text provenance." According to OpenAI, it "has been highly accurate" in some situations, but doesn't perform as well when faced with certain forms of tampering, "like using translation systems, rewording with another generative model, or asking the model to insert a special character in between every word and then deleting that character."

Calls to use open generative language models in academic research have highlighted the need for reproducibility and transparency in scientific research. However, the impact of generative AI extends well beyond academia, as corporations and public interest organizations have begun integrating these models into their data science pipelines. We expand this lens to include the impact of open models on organizations, focusing specifically on fact-checking organizations, which use AI to observe and analyze large volumes of circulating misinformation, yet must also ensure the reproducibility and impartiality of their work. We wanted to understand where fact-checking organizations use open models in their data science pipelines; what motivates their use of open models or proprietary models; and how their use of open or proprietary models can inform research on the societal impact of generative AI. To answer these questions, we conducted an interview study with N=24 professionals at 20 fact-checking organizations on six continents. Based on these interviews, we offer a five-component conceptual model of where fact-checking organizations employ generative AI to support or automate parts of their data science pipeline, including Data Ingestion, Data Analysis, Data Retrieval, Data Delivery, and Data Sharing. We then provide taxonomies of fact-checking organizations' motivations for using open models and the limitations that prevent them for further adopting open models, finding that they prefer open models for Organizational Autonomy, Data Privacy and Ownership, Application Specificity, and Capability Transparency. However, they nonetheless use proprietary models due to perceived advantages in Performance, Usability, and Safety, as well as Opportunity Costs related to participation in emerging generative AI ecosystems. Our work provides novel perspective on open models in data-driven organizations.

We present ShieldGemma, a comprehensive suite of LLM-based safety content moderation models built upon Gemma2. These models provide robust, state-of-the-art predictions of safety risks across key harm types (sexually explicit, dangerous content, harassment, hate speech) in both user input and LLM-generated output. By evaluating on both public and internal benchmarks, we demonstrate superior performance compared to existing models, such as Llama Guard (+10.8\% AU-PRC on public benchmarks) and WildCard (+4.3\%). Additionally, we present a novel LLM-based data curation pipeline, adaptable to a variety of safety-related tasks and beyond. We have shown strong generalization performance for model trained mainly on synthetic data. By releasing ShieldGemma, we provide a valuable resource to the research community, advancing LLM safety and enabling the creation of more effective content moderation solutions for developers.

Recently, the integration of mobile edge computing (MEC) and generative artificial intelligence (GAI) technology has given rise to a new area called mobile edge generation and computing (MEGC), which offers mobile users heterogeneous services such as task computing and content generation. In this letter, we investigate the joint communication, computation, and the AIGC resource allocation problem in an MEGC system. A latency minimization problem is first formulated to enhance the quality of service for mobile users. Due to the strong coupling of the optimization variables, we propose a new deep reinforcement learning-based algorithm to solve it efficiently. Numerical results demonstrate that the proposed algorithm can achieve lower latency than two baseline algorithms.