OpenAI outlined limits on using its tools in politics during the run-up to elections in 2024, amid mounting concern that artificial-intelligence systems could mass-produce misinformation and sway voters in high-profile races. OpenAI's ChatGPT and Dall-E are some of the most powerful AI chatbot and image-generation applications available. The growth of such tools has raised worry that software made by OpenAI and its peers could be used to manipulate voters with false news stories and computer-generated images and video.

Get ready to share your custom chatbot with the whole world. OpenAI recently launched its GPT Store, after it delayed the project following the chaos of CEO Sam Altman's firing and reinstatement late in 2023. Similar to OpenAI's GPT-4 model and web browsing capabilities, only those who pay 20 a month for ChatGPT Plus can create and use "GPTs." The GPT acronym in ChatGPT actually stands for "generative pretrained transformers," but in this context, the company is using GPT as a term that refers to a unique version of ChatGPT with additional parameters and a little extra training data. Here's how to make your GPT public and some advice to help you get started with the GPT Store.

Asia is becoming the latest hunting ground for global investors in data centers, as companies from KKR & Co. to Bain Capital bet on the region's growing computing and data storage needs following an artificial intelligence boom. Like in the U.S., Asia is seeing a surge in demand for data centers as giants like Amazon and Alphabet's Google boost cloud services, the recent generative AI wave fuels data and capacity requirements, and the region's growing population spurs storage needs. Demand in Southeast Asia and North Asia is expected to expand about 25% a year through 2028, according to Cushman & Wakefield data. That compares with 14% a year in the U.S.

Thanks to recent advances in generative AI, we are able to prompt large language models (LLMs) to produce texts which are fluent and grammatical. In addition, it has been shown that we can elicit attempts at grammatical error correction (GEC) from LLMs when prompted with ungrammatical input sentences. We evaluate how well LLMs can perform at GEC by measuring their performance on established benchmark datasets. We go beyond previous studies, which only examined GPT* models on a selection of English GEC datasets, by evaluating seven open-source and three commercial LLMs on four established GEC benchmarks. We investigate model performance and report results against individual error types. Our results indicate that LLMs do not always outperform supervised English GEC models except in specific contexts -- namely commercial LLMs on benchmarks annotated with fluency corrections as opposed to minimal edits. We find that several open-source models outperform commercial ones on minimal edit benchmarks, and that in some settings zero-shot prompting is just as competitive as few-shot prompting.

Many companies rely on APIs of managed AI models such as OpenAI's GPT-4 to create AI-enabled experiences in their products. Along with the benefits of ease of use and shortened time to production, this reliance on proprietary APIs has downsides in terms of model control, performance reliability, up-time predictability, and cost. At the same time, there has been a flurry of open source small language models (SLMs) that have been made available for commercial use. However, their readiness to replace existing capabilities remains unclear, and a systematic approach to test these models is not readily available. In this paper, we present a systematic evaluation methodology for, and characterization of, modern open source SLMs and their trade-offs when replacing a proprietary LLM APIs for a real-world product feature. We have designed SLaM, an automated analysis tool that enables the quantitative and qualitative testing of product features utilizing arbitrary SLMs. Using SLaM, we examine both the quality and the performance characteristics of modern SLMs relative to an existing customer-facing OpenAI-based implementation. We find that across 9 SLMs and 29 variants, we observe competitive quality-of-results for our use case, significant performance consistency improvement, and a cost reduction of 5x-29x when compared to OpenAI GPT-4.

Upon its release in late 2022, ChatGPT has brought a seismic shift in the entire landscape of AI, both in research and commerce. Through instruction-tuning a large language model (LLM) with supervised fine-tuning and reinforcement learning from human feedback, it showed that a model could answer human questions and follow instructions on a broad panel of tasks. Following this success, interests in LLMs have intensified, with new LLMs flourishing at frequent interval across academia and industry, including many start-ups focused on LLMs. While closed-source LLMs (e.g., OpenAI's GPT, Anthropic's Claude) generally outperform their open-source counterparts, the progress on the latter has been rapid with claims of achieving parity or even better on certain tasks. This has crucial implications not only on research but also on business. In this work, on the first anniversary of ChatGPT, we provide an exhaustive overview of this success, surveying all tasks where an open-source LLM has claimed to be on par or better than ChatGPT.

As AI systems quickly improve in both breadth and depth of performance, they lend themselves to creating increasingly powerful and realistic agents, including the possibility of agents modeled on specific people. We anticipate that within our lifetimes it may become common practice for people to create a custom AI agent to interact with loved ones and/or the broader world after death. We call these generative ghosts, since such agents will be capable of generating novel content rather than merely parroting content produced by their creator while living. In this paper, we first discuss the design space of potential implementations of generative ghosts. We then discuss the practical and ethical implications of generative ghosts, including potential positive and negative impacts on individuals and society. Based on these considerations, we lay out a research agenda for the AI and HCI research communities to empower people to create and interact with AI afterlives in a safe and beneficial manner.

The advent of Generative AI, particularly through Large Language Models (LLMs) like ChatGPT and its successors, marks a paradigm shift in the AI landscape. Advanced LLMs exhibit multimodality, handling diverse data formats, thereby broadening their application scope. However, the complexity and emergent autonomy of these models introduce challenges in predictability and legal compliance. This paper delves into the legal and regulatory implications of Generative AI and LLMs in the European Union context, analyzing aspects of liability, privacy, intellectual property, and cybersecurity. It critically examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA) draft, in addressing the unique challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential gaps and shortcomings in the legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models, ensuring they align with the EU's evolving digital landscape and legal standards.

Last year, politicians and policymakers around the world also grappled to make sense of how AI will fit into society. President Biden issued an executive order saying AI was the "most consequential technology of our time." The United Kingdom convened a global AI forum where Prime Minister Rishi Sunak warned that "humanity could lose control of AI completely." The concerns include the risk that "generative" AI -- which can create text, video, images and audio -- can be used to create misinformation, displace jobs or even help people create dangerous bioweapons.

It is a truth universally acknowledged, that a single man in possession of a computer must be in want of an AI girlfriend. Certainly a lot of enterprising individuals seem to think there's a lucrative market for digital romance. OpenAI recently launched its GPT Store, where paid ChatGPT users can buy and sell customized chatbots (think Apple's app store, but for chatbots) – and the offerings include a large selection of digital girlfriends. "AI girlfriend bots are already flooding OpenAI's GPT store," a headline from Quartz, who first reported on the issue, blared on Thursday. Quartz went on to note that "the AI girlfriend bots go against OpenAI's usage policy … The company bans GPTs'dedicated to fostering romantic companionship or performing regulated activities'."