OpenAI has rolled out its store for custom GPTs and a new ChatGPT subscription tier for smaller teams. The GPT Store allows developers and users to share and profit from their custom versions of the viral chatbot. The GPT Store allows OpenAI to turn ChatGPT's white-hot prominence in the tech world into a tollkeeper's business model, taking a cut of revenue like in Apple's App Store. Anyone can build and share GPTs -- you don't need coding experience -- but creators must make a Builder Profile that shares their real name or points users to a verified website. OpenAI says a revenue program for GPT creators is coming soon in Q1. "As a first step, US builders will be paid based on user engagement with their GPTs," the company wrote, promising to provide more info as the program's launch approaches.

OpenAI is an unconventional company in many ways, but last November it borrowed a page from the standard tech industry playbook: It held a developer conference where CEO Sam Altman urged software makers to build on top of ChatGPT. The company said it would soon launch a marketplace where developers and non-techies alike could create custom functions for the chatbot and make money by sharing them with the world. The reaction to that news was mixed, with some hailing the birth of a new platform and others turning a laundry app demoed onstage into a meme. But whether meme-worthy or momentous, OpenAI's app store is part of a broader strategy to maintain its edge in the competitive AI landscape. Like Apple and Google's YouTube have done so well, OpenAI now wants to incentivize developers and creators to supply fresh content for its platform, so that it can keep offering new experiences that draw in users.

False and misleading information supercharged with cutting-edge artificial intelligence that threatens to erode democracy and polarize society is the top immediate risk to the global economy, the World Economic Forum said in a report Wednesday. In its latest Global Risks Report, the organization also said an array of environmental risks pose the biggest threats in the longer term. The report was released ahead of the annual elite gathering of CEOs and world leaders in the Swiss ski resort town of Davos and is based on a survey of nearly 1,500 experts, industry leaders and policymakers. The report listed misinformation and disinformation as the most severe risk over the next two years, highlighting how rapid advances in technology also are creating new problems or making existing ones worse. The authors worry that the boom in generative AI chatbots like ChatGPT means that creating sophisticated synthetic content that can be used to manipulate groups of people won't be limited any longer to those with specialized skills. AI is set to be a hot topic next week at the Davos meetings, which are expected to be attended by tech company bosses including OpenAI CEO Sam Altman, Microsoft CEO Satya Nadella and AI industry players like Meta's chief AI scientist, Yann LeCun.

In the decades to come, 2023 may be remembered as the year of generative AI hype, where ChatGPT became arguably the fastest-spreading new technology in human history and expectations of AI-powered riches became commonplace. The year 2024 will be the time for recalibrating expectations. Of course, generative AI is an impressive technology, and it provides tremendous opportunities for improving productivity in a number of tasks. But because the hype has gone so far ahead of reality, the setbacks of the technology in 2024 will be more memorable. More and more evidence will emerge that generative AI and large language models provide false information and are prone to hallucination--where an AI simply makes stuff up, and gets it wrong.

Generative AI has become pervasive in society, witnessing significant advancements in various domains. Particularly in the realm of Text-to-Image (TTI) models, Latent Diffusion Models (LDMs), showcase remarkable capabilities in generating visual content based on textual prompts. This paper addresses the potential of LDMs in representing local cultural concepts, historical figures, and endangered species. In this study, we use the cultural heritage of Rio Grande do Sul (RS), Brazil, as an illustrative case. Our objective is to contribute to the broader understanding of how generative models can help to capture and preserve the cultural and historical identity of regions. The paper outlines the methodology, including subject selection, dataset creation, and the fine-tuning process. The results showcase the images generated, alongside the challenges and feasibility of each concept. In conclusion, this work shows the power of these models to represent and preserve unique aspects of diverse regions and communities.

Is AI set to redefine the legal profession? We argue that this claim is not supported by the current evidence. We dive into AI's increasingly prevalent roles in three types of legal tasks: information processing; tasks involving creativity, reasoning, or judgment; and predictions about the future. We find that the ease of evaluating legal applications varies greatly across legal tasks, based on the ease of identifying correct answers and the observability of information relevant to the task at hand. Tasks that would lead to the most significant changes to the legal profession are also the ones most prone to overoptimism about AI capabilities, as they are harder to evaluate. We make recommendations for better evaluation and deployment of AI in legal contexts.

ChatGPT is a language model based on Generative AI. Existing research work on ChatGPT focused on its use in various domains. However, its potential for Sign Language Translation (SLT) is yet to be explored. This paper addresses this void. Therefore, we present GPT's evolution aiming a retrospective analysis of the improvements to its architecture for SLT. We explore ChatGPT's capabilities in translating different sign languages in paving the way to better accessibility for deaf and hard-of-hearing community. Our experimental results indicate that ChatGPT can accurately translate from English to American (ASL), Australian (AUSLAN), and British (BSL) sign languages and from Arabic Sign Language (ArSL) to English with only one prompt iteration. However, the model failed to translate from Arabic to ArSL and ASL, AUSLAN, and BSL to Arabic. Consequently, we present challenges and derive insights for future research directions.

While deep generative models are showing exciting abilities in computer vision and natural language processing, their adoption in communication frameworks is still far underestimated. These methods are demonstrated to evolve solutions to classic communication problems such as denoising, restoration, or compression. Nevertheless, generative models can unveil their real potential in semantic communication frameworks, in which the receiver is not asked to recover the sequence of bits used to encode the transmitted (semantic) message, but only to regenerate content that is semantically consistent with the transmitted message. Disclosing generative models capabilities in semantic communication paves the way for a paradigm shift with respect to conventional communication systems, which has great potential to reduce the amount of data traffic and offers a revolutionary versatility to novel tasks and applications that were not even conceivable a few years ago. In this paper, we present a unified perspective of deep generative models in semantic communication and we unveil their revolutionary role in future communication frameworks, enabling emerging applications and tasks. Finally, we analyze the challenges and opportunities to face to develop generative models specifically tailored for communication systems.

Since the launch of applications such as DALL-E, Midjourney, and Stable Diffusion, generative artificial intelligence has been controversial as a tool for creating artwork. While some have presented longtermist worries about these technologies as harbingers of fully automated futures to come, more pressing is the impact of generative AI on creative labour in the present. Already, business leaders have begun replacing human artistic labour with AI-generated images. In response, the artistic community has launched a protest movement, which argues that AI image generation is a kind of theft. This paper analyzes, substantiates, and critiques these arguments, concluding that AI image generators involve an unethical kind of labour theft. If correct, many other AI applications also rely upon theft.

The internet has enabled an unprecedented free flow and wide distribution of information on a global scale, which largely accelerated the democratization of information, fueling platforms like Wikipedia, YouTube, and StackOverflow. While this facilitated information democratization, it concurrently lowered barriers against unauthorized data use and piracy. The success of Deep Learning (DL) owes significantly to the availability of large-scale datasets available for training DL models [3], predominantly sourced from the internet [4].