In a statement released on its website, OpenAI said this new model, which would replace GPT-4 technology, will bring the company closer to achieving "AGI," or artificial general intelligence, a hotly contested idea that refers to computers matching the power of human brains.

This is not just a prediction. A few successful companies have begun to publicly experiment with the notion of an A.I. leader, even if at the moment it might largely be a branding exercise. A.I. has been hyped as the solution to all corporate problems for about 18 months now, ever since OpenAI rolled out ChatGPT in November 2022. Silicon Valley put 29 billion last year into generative A.I. and is selling it hard. Even in its current rudimentary form, A.I. that mimics human reasoning is finding a foothold among distressed companies with little to lose and lacking strong leadership.

OpenAI has created a new Safety and Security Committee less than two weeks after the company dissolved the team tasked with protecting humanity from AI's existential threats. This latest iteration of the group responsible for OpenAI's safety guardrails will include two board members and CEO Sam Altman, raising questions about whether the move is little more than self-policing theatre amid a breakneck race for profit and dominance alongside partner Microsoft. The Safety and Security Committee, formed by OpenAI's board, will be led by board members Bret Taylor (Chair), Nicole Seligman, Adam D'Angelo and Sam Altman (CEO). The new team follows co-founder Ilya Sutskever's and Jan Leike's high-profile resignations, which raised more than a few eyebrows. Their former "Superalignment Team" was only created last July.

OpenAI says it is setting up a safety and security committee and has begun training a new AI model to supplant the GPT-4 system that underpins its ChatGPT chatbot. The San Francisco startup said in a blogpost on Tuesday that the committee will advise the full board on "critical safety and security decisions" for its projects and operations. The safety committee arrives as debate swirls around AI safety at the company, which was thrust into the spotlight after a researcher, Jan Leike, resigned and leveled criticism at OpenAI for letting safety "take a backseat to shiny products". The OpenAI co-founder and chief scientist Ilya Sutskever also resigned, and the company disbanded the "superalignment" team focused on AI risks that they jointly led. OpenAI said it had "recently begun training its next frontier model" and its AI models led the industry on capability and safety, though it made no mention of the controversy.

Tokyo police have arrested a 25-year-old man for allegedly creating malware using generative artificial intelligence tools available for free online. Ryuki Hayashi from Kawasaki was arrested by the Metropolitan Police Department on suspicion of the unauthorized creation of electronic records. According to the police, there have been very few cases in which police took law-enforcement action over the creation of malware using generative AI technology. Hayashi, who has admitted to the allegations, was quoted as saying that he wanted to "earn easy money" and that he thought he "could do anything" if he used AI. Hayashi is suspected of creating the malware in March last year by combining designs of illegal malware programs obtained through the use of interactive generative AI tools.

Comprehensive clinical documentation is crucial for effective healthcare delivery, yet it poses a significant burden on healthcare professionals, leading to burnout, increased medical errors, and compromised patient safety. This paper explores the potential of generative AI (Artificial Intelligence) to streamline the clinical documentation process, specifically focusing on generating SOAP (Subjective, Objective, Assessment, Plan) and BIRP (Behavior, Intervention, Response, Plan) notes. We present a case study demonstrating the application of natural language processing (NLP) and automatic speech recognition (ASR) technologies to transcribe patient-clinician interactions, coupled with advanced prompting techniques to generate draft clinical notes using large language models (LLMs). The study highlights the benefits of this approach, including time savings, improved documentation quality, and enhanced patient-centered care. Additionally, we discuss ethical considerations, such as maintaining patient confidentiality and addressing model biases, underscoring the need for responsible deployment of generative AI in healthcare settings. The findings suggest that generative AI has the potential to revolutionize clinical documentation practices, alleviating administrative burdens and enabling healthcare professionals to focus more on direct patient care.

Generative AI systems are increasingly capable of expressing emotions via text and imagery. Effective emotional expression will likely play a major role in the efficacy of AI systems -- particularly those designed to support human mental health and wellbeing. This motivates our present research to better understand the alignment of AI expressed emotions with the human perception of emotions. When AI tries to express a particular emotion, how might we assess whether they are successful? To answer this question, we designed a survey to measure the alignment between emotions expressed by generative AI and human perceptions. Three generative image models (DALL-E 2, DALL-E 3 and Stable Diffusion v1) were used to generate 240 examples of images, each of which was based on a prompt designed to express five positive and five negative emotions across both humans and robots. 24 participants recruited from the Prolific website rated the alignment of AI-generated emotional expressions with a text prompt used to generate the emotion (i.e., "A robot expressing the emotion amusement"). The results of our evaluation suggest that generative AI models are indeed capable of producing emotional expressions that are well-aligned with a range of human emotions; however, we show that the alignment significantly depends upon the AI model used and the emotion itself. We analyze variations in the performance of these systems to identify gaps for future improvement. We conclude with a discussion of the implications for future AI systems designed to support mental health and wellbeing.

Recent advancements in generative artificial intelligence (AI) have transformed collaborative work processes, yet the impact on team performance remains underexplored. Here we examine the role of generative AI in enhancing or replacing traditional team dynamics using a randomized controlled experiment with 435 participants across 122 teams. We show that teams augmented with generative AI significantly outperformed those relying solely on human collaboration across various performance measures. Interestingly, teams with multiple AIs did not exhibit further gains, indicating diminishing returns with increased AI integration. Our analysis suggests that centralized AI usage by a few team members is more effective than distributed engagement. Additionally, individual-AI pairs matched the performance of conventional teams, suggesting a reduced need for traditional team structures in some contexts. However, despite this capability, individual-AI pairs still fell short of the performance levels achieved by AI-assisted teams. These findings underscore that while generative AI can replace some traditional team functions, more comprehensively integrating AI within team structures provides superior benefits, enhancing overall effectiveness beyond individual efforts.

With the enhancement in the field of generative artificial intelligence (AI), contextual question answering has become extremely relevant. Attributing model generations to the input source document is essential to ensure trustworthiness and reliability. We observe that when large language models (LLMs) are used for contextual question answering, the output answer often consists of text copied verbatim from the input prompt which is linked together with "glue text" generated by the LLM. Motivated by this, we propose that LLMs have an inherent awareness from where the text was copied, likely captured in the hidden states of the LLM. We introduce a novel method for attribution in contextual question answering, leveraging the hidden state representations of LLMs. Our approach bypasses the need for extensive model retraining and retrieval model overhead, offering granular attributions and preserving the quality of generated answers. Our experimental results demonstrate that our method performs on par or better than GPT-4 at identifying verbatim copied segments in LLM generations and in attributing these segments to their source. Importantly, our method shows robust performance across various LLM architectures, highlighting its broad applicability. Additionally, we present Verifiability-granular, an attribution dataset which has token level annotations for LLM generations in the contextual question answering setup.

Large language models have gained considerable interest for their impressive performance on various tasks. Within this domain, ChatGPT and GPT-4, developed by OpenAI, and the Gemini, developed by Google, have emerged as particularly popular among early adopters. Additionally, Mixtral by Mistral AI and Claude by Anthropic are newly released, further expanding the landscape of advanced language models. These models are viewed as disruptive technologies with applications spanning customer service, education, healthcare, and finance. More recently, Mistral has entered the scene, captivating users with its unique ability to generate creative content. Understanding the perspectives of these users is crucial, as they can offer valuable insights into the potential strengths, weaknesses, and overall success or failure of these technologies in various domains. This research delves into the responses generated by ChatGPT, GPT-4, Gemini, Mixtral and Claude across different Conversational QA corpora. Evaluation scores were meticulously computed and subsequently compared to ascertain the overall performance of these models. Our study pinpointed instances where these models provided inaccurate answers to questions, offering insights into potential areas where they might be susceptible to errors. In essence, this research provides a comprehensive comparison and evaluation of these state of-the-art language models, shedding light on their capabilities while also highlighting potential areas for improvement