OpenAI, the company behind ChatGPT, is exploring whether users should be allowed to create artificial intelligence-generated pornography and other explicit content with its products. While the company stressed that its ban on deepfakes would continue to apply to adult material, campaigners suggested the proposal undermined its mission statement to produce "safe and beneficial" AI. OpenAI, which is also the developer of the DALL-E image generator, revealed it was considering letting developers and users "responsibly" create what it termed not-safe-for-work (NSFW) content through its products. OpenAI said this could include "erotica, extreme gore, slurs, and unsolicited profanity". It said: "We're exploring whether we can responsibly provide the ability to generate NSFW content in age-appropriate contexts … We look forward to better understanding user and societal expectations of model behaviour in this area."

TikTok is ramping up its efforts to automatically label AI-generated content in its app, even when it was created with third-party tools. The company announced plans to support content credentials, a kind of digital watermark that indicates the use of generative AI. TikTok's rules already require creators to disclose "realistic" AI-generated content. But that policy can be difficult for the company to enforce, particularly when creators use other companies' AI tools. But because content credentials are increasingly used across the AI industry, TikTok's new automated labels should be able to address some of those gaps. Often described as a "nutrition label for digital content," content credentials attach "tamper-evident metadata" that can trace the origins of an image and AI tools that were used to edit it along the way.

AI intent alignment, ensuring that AI produces outcomes as intended by users, is a critical challenge in human-AI interaction. The emergence of generative AI, including LLMs, has intensified the significance of this problem, as interactions increasingly involve users specifying desired results for AI systems. In order to support better AI intent alignment, we aim to explore human strategies for intent specification in human-human communication. By studying and comparing human-human and human-LLM communication, we identify key strategies that can be applied to the design of AI systems that are more effective at understanding and aligning with user intent. This study aims to advance toward a human-centered AI system by bringing together human communication strategies for the design of AI systems.

This article presents the world's first rapid drone flocking control using natural language through generative AI. The described approach enables the intuitive orchestration of a flock of any size to achieve the desired geometry. The key feature of the method is the development of a new interface based on Large Language Models to communicate with the user and to generate the target geometry descriptions. Users can interactively modify or provide comments during the construction of the flock geometry model. By combining flocking technology and defining the target surface using a signed distance function, smooth and adaptive movement of the drone swarm between target states is achieved. Our user study on FlockGPT confirmed a high level of intuitive control over drone flocking by users. Subjects who had never previously controlled a swarm of drones were able to construct complex figures in just a few iterations and were able to accurately distinguish the formed swarm drone figures. The results revealed a high recognition rate for six different geometric patterns generated through the LLM-based interface and performed by a simulated drone flock (mean of 80% with a maximum of 93\% for cube and tetrahedron patterns). Users commented on low temporal demand (19.2 score in NASA-TLX), high performance (26 score in NASA-TLX), attractiveness (1.94 UEQ score), and hedonic quality (1.81 UEQ score) of the developed system. The FlockGPT demo code repository can be found at: coming soon

Residential Load Profile (RLP) generation and prediction are critical for the operation and planning of distribution networks, especially as diverse low-carbon technologies (e.g., photovoltaic and electric vehicles) are increasingly adopted. This paper introduces a novel flow-based generative model, termed Full Convolutional Profile Flow (FCPFlow), which is uniquely designed for both conditional and unconditional RLP generation, and for probabilistic load forecasting. By introducing two new layers--the invertible linear layer and the invertible normalization layer--the proposed FCPFlow architecture shows three main advantages compared to traditional statistical and contemporary deep generative models: 1) it is well-suited for RLP generation under continuous conditions, such as varying weather and annual electricity consumption, 2) it demonstrates superior scalability in different datasets compared to traditional statistical models, and 3) it also demonstrates better modeling capabilities in capturing the complex correlation of RLPs compared with deep generative models.

Introduction: Recently, the effectiveness of Large Language Models (LLMs) has increased rapidly, allowing them to be used in a great number of applications. However, the risks posed by the generation of false information through LLMs significantly limit their applications in sensitive areas such as healthcare, highlighting the necessity for rigorous validations to determine their utility and reliability. To date, no study has extensively compared the performance of LLMs on Polish medical examinations across a broad spectrum of specialties on a very large dataset. Objectives: This study evaluated the performance of three Generative Pretrained Transformer (GPT) models on the Polish Board Certification Exam (Pa\'nstwowy Egzamin Specjalizacyjny, PES) dataset, which consists of 297 tests. Methods: We developed a software program to download and process PES exams and tested the performance of GPT models using OpenAI Application Programming Interface. Results: Our findings reveal that GPT-3.5 did not pass any of the analyzed exams. In contrast, the GPT-4 models demonstrated the capability to pass the majority of the exams evaluated, with the most recent model, gpt-4-0125, successfully passing 222 (75%) of them. The performance of the GPT models varied significantly, displaying excellence in exams related to certain specialties while completely failing others. Conclusions: The significant progress and impressive performance of LLM models hold great promise for the increased application of AI in the field of medicine in Poland. For instance, this advancement could lead to the development of AI-based medical assistants for healthcare professionals, enhancing the efficiency and accuracy of medical services.

OpenAI released draft documentation Wednesday laying out how it wants ChatGPT and its other AI technology to behave. Part of the lengthy Model Spec document discloses that the company is exploring a leap into porn and other explicit content. OpenAI's usage policies curently prohibit sexually explicit or even suggestive materials, but a "commentary" note on part of the Model Spec related to that rule says the company is considering how to permit such content. "We're exploring whether we can responsibly provide the ability to generate NSFW content in age-appropriate contexts through the API and ChatGPT," the note says, using a colloquial term for content considered "not safe for work" contexts. "We look forward to better understanding user and societal expectations of model behavior in this area."

The California judge presiding over Elon Musk's lawsuit against OpenAI and its CEO, Sam Altman, has removed himself from the case. Judge Ethan Schulman on Monday sustained a challenge from Musk's lawyers, which cited a California state law that allows plaintiffs and defendants to remove a judge they believe cannot grant an impartial trial. The law, known as California Code of Civil Procedure 170.6, does not require the person issuing the challenge to provide any factual basis for their claim that the judge is prejudiced against them. Each side in a case gets one such peremptory challenge, which is granted as long as it is filed with correct language and within a certain time frame. Lawyers for Altman and Musk did not respond to requests for comment.

When it comes to AI at Adobe, my team has taken a comprehensive approach that includes investment in foundational AI, strategic adoption, an AI ethics framework, legal considerations, security, and content authentication. The rollout follows a phased approach, starting with pilot groups and building communities around AI. This approach includes experimenting with and documenting use cases like writing and editing, data analysis, presentations and employee onboarding, corporate training, employee portals, and improved personalization across HR channels. The rollouts are accompanied by training podcasts and other resources to educate and empower employees to use AI in ways that improve their work and keep them more engaged. While there are innumerable ways that CIOs can leverage generative AI to help surface value at scale for knowledge workers, I'd like to focus on digital documents--a space in which Adobe has been a leader for over 30 years.

OpenAI is reportedly working on a search feature for ChatGPT that could make the chatbot a veritable alternative to Google Search. According to Bloomberg, the company is currently developing the capability, which can scour the web for answers to your queries and spit out results complete with citations to their sources. ChatGPT could take information from Wikipedia or blog posts, for instance, and link to their original pages when you ask it questions. Bloomberg says that in one version of the experimental search function, ChatGPT can show you images along with its written responses whenever they're relevant. For example, if the chatbot deems illustrated instructions or diagrams useful for an inquiry, such as "how to change a doorknob" or "how to clean a split-type AC," then it could include them in its responses.