A growing body of literature on generative artificial intelligence reveals a surprisingly consistent stylized fact: when people use generative AI tools, the set of content they produce tends to be more homogeneous than content produced by more traditional means [4, 22, 49, 56, 67, 69, 84, 106, 108]. Across a wide range of domains including peer review [56], writing [67], digital art [108], and survey responses [106], access to generative AI tools (GAITs) leads to less diverse outcomes. Researchers refer to this phenomenon--where the use of similar or identical underlying AI tools lead to convergence in outcomes--as algorithmic monoculture [50] or homogenization [12]. Much of the empirical literature on the subject treats homogenization itself as the primary object of study, seeking to quantify and deeply understand it. Here, we begin our analysis further downstream. We ask: What are the consequences of monoculture in generation? When homogenization has negative consequences, how should we expect content producers to behave in response?

By means of pilot experiments for the language pair German and Galician, this paper examines the concept of efficiency and intelligence in lexicography and artificial intelligence, AI. The aim of the experiments is to gain empirically and statistically based insights into the lexicographical text type,dictionary article, in the responses of ChatGPT 3.5, as well as into the lexicographical data on which this chatbot was trained. Both quantitative and qualitative methods are used for this purpose. The analysis is based on the evaluation of the outputs of several sessions with the same prompt in ChatGPT 3.5. On the one hand, the algorithmic performance of intelligent systems is evaluated in comparison with data from lexicographical works. On the other hand, the ChatGPT data supplied is analysed using specific text passages of the aforementioned lexicographical text type. The results of this study not only help to evaluate the efficiency of this chatbot regarding the creation of dictionary articles, but also to delve deeper into the concept of intelligence, the thought processes and the actions to be carried out in both disciplines.

Artificial Intelligence (AI), like any transformative technology, has the potential to be a double-edged sword, leading either toward significant advancements or detrimental outcomes for society as a whole. As is often the case when it comes to widely-used technologies in market economies (e.g., cars and semiconductor chips), commercial interest tends to be the predominant guiding factor. The AI community is at risk of becoming polarized to either take a laissez-faire attitude toward AI development, or to call for government overregulation. Between these two poles we argue for the community of AI practitioners to consciously and proactively work for the common good. This paper offers a blueprint for a new type of innovation infrastructure including 18 concrete milestones to guide AI research in that direction. Our view is that we are still in the early days of practical AI, and focused efforts by practitioners, policymakers, and other stakeholders can still maximize the upsides of AI and minimize its downsides. We talked to luminaries such as recent Nobelist John Jumper on science, President Barack Obama on governance, former UN Ambassador and former National Security Advisor Susan Rice on security, philanthropist Eric Schmidt on several topics, and science fiction novelist Neal Stephenson on entertainment. This ongoing dialogue and collaborative effort has produced a comprehensive, realistic view of what the actual impact of AI could be, from a diverse assembly of thinkers with deep understanding of this technology and these domains. From these exchanges, five recurring guidelines emerged, which form the cornerstone of a framework for beginning to harness AI in service of the public good. They not only guide our efforts in discovery but also shape our approach to deploying this transformative technology responsibly and ethically.

With the help of these systems, anyone can create something that would previously have been considered a remarkable work of art. In human-AI collaboration, the computer seems to have become more than a tool. Many who have made their first contact with current generative AIs see them as "creativity machines" while for others the term "machine creativity" remains an oxymoron. This article is about (the possibility of) creativity in computers within the current Machine Learning paradigm. It outlines some of the key concepts behind the technologies and the innovations that have contributed to this qualitative shift, with a focus on text-to-image systems. The nature of Artificial Creativity as such is discussed, as well as what this might mean for art. AI may become a responsible collaborator with elements of independent machine authorship in the artistic process.

Laboratory of Algal, Synthetic, and Systems Biology, Division of Science and Math, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE 2. Department of Biology, New York University, New York, NY, USA 3. Biotechnology Research Center, Technology Innovation Institute (TII), PO Box: 9639, Masdar City, Abu Dhabi, UAE Correspondence should be addressed to D.R.N. (drn2@nyu.edu) The models achieved F1 scores up to 95 and operated 16,580x faster and at 2.9x the recall of BLASTP. They effectively classified the algal "dark proteome", (e.g., uncharacterized proteins comprising ~65% of total proteins), validated on new data including a new, complete Hi-C/Pacbio Chlamydomonas genome. SR models reached high accuracy (F1 > 86) when trained on less than 2% of available data, rapidly achieving strong generalization capacity. High accuracy was achieved when training data had intact or scrambled terminal information, demonstrating robust generalization to incomplete sequences.

The rapid adoption of generative artificial intelligence (GenAI) in research presents both opportunities and ethical challenges that should be carefully navigated. Although GenAI tools can enhance research efficiency through automation of tasks such as literature review and data analysis, their use raises concerns about aspects such as data accuracy, privacy, bias, and research integrity. This paper develops the ETHICAL framework, which is a practical guide for responsible GenAI use in research. Employing a constructivist case study examining multiple GenAI tools in real research contexts, the framework consists of seven key principles: 'Examine policies and guidelines', 'Think about social impacts', 'Harness understanding of the technology', 'Indicate use', 'Critically engage with outputs', 'Access secure versions', and'Look at user agreements'. Applying these principles will enable researchers to uphold research integrity while leveraging GenAI's benefits. The framework addresses a critical gap between awareness of ethical issues and practical action steps, providing researchers with concrete guidance for ethical GenAI integration. This work has implications for research practice, institutional policy development, and the broader academic community while adapting to an AI-enhanced research landscape. The ETHICAL framework can serve as a foundation for developing AI literacy in academic settings and promoting responsible innovation in research methodologies.

Large Language Models (LLMs) have demonstrated great potential in various language processing tasks, and recent studies have explored their application in compiler optimizations. However, all these studies focus on the conventional open-source LLMs, such as Llama2, which lack enhanced reasoning mechanisms. In this study, we investigate the errors produced by the fine-tuned 7B-parameter Llama2 model as it attempts to learn and apply a simple peephole optimization for the AArch64 assembly code. We provide an analysis of the errors produced by the LLM and compare it with state-of-the-art OpenAI models which implement advanced reasoning logic, including GPT-4o and GPT-o1 (preview). We demonstrate that OpenAI GPT-o1, despite not being fine-tuned, outperforms the fine-tuned Llama2 and GPT-4o. Our findings indicate that this advantage is largely due to the chain-of-thought reasoning implemented in GPT-o1. We hope our work will inspire further research on using LLMs with enhanced reasoning mechanisms and chain-of-thought for code generation and optimization.

OpenAI has now launched its new AI model called Sora, which can generate realistic videos from text-based prompts. The tool is available to both ChatGPT Plus and ChatGPT Pro subscribers via sora.com. ChatGPT Plus subscribers can generate up to 50 priority videos in 720p resolution up to five seconds long in duration, while ChatGPT Pro subscribers can generate unlimited videos with up to 500 priority videos in 1080p resolution up to 20 seconds long in duration. ChatGPT Pro users can also generate up to five videos simultaneously and download generated videos without watermarks on them. All videos generated via Sora will have C2PA metadata to indicate that they've been created using AI.

For more than two years, every new AI announcement has lived in the shadow of ChatGPT. No model from any company has eclipsed or matched that initial fever. But perhaps the closest any firm has come to replicating the buzz was this past February, when OpenAI first teased its video-generating AI model, Sora. Tantalizing clips--woolly mammoths kicking up clouds of snow, Pixar-esque animations of adorable fluffy critters--promised a stunning future, one in which anyone can whip up high-quality clips by typing simple text prompts into a computer program. But Sora, which was not immediately available to the public, remained just that: a teaser.

We leverage a natural app-update event at Replika AI, a popular US-based AI companion, to shed light on these questions. We find that, after the app removed its erotic role play (ERP) feature, preventing intimate interactions between consumers and chatbots that were previously possible, this event triggered perceptions in customers that their AI companion's identity had discontinued. This in turn predicted negative consumer welfare and marketing outcomes related to loss, including mourning the loss, and devaluing the'new' AI relative to the'original'. Experimental evidence confirms these findings. Further experiments find that AI companions users feel closer to their AI companion than even their best human friend, and mourn a loss of their AI companion more than a loss of various other inanimate products. In short, consumers are forming human-level relationships with AI companions; disruptions to these relationships trigger real patterns of mourning as well as devaluation of the offering; and the degree of mourning and devaluation are explained by perceived discontinuity in the AIs identity. Our results illustrate that relationships with AI are truly personal, creating unique benefits and risks for consumers and firms alike. The development of large language models (LLMs) and generative artificial intelligence (AI) has not only led to many new business applications (e.g., search, education software), but also enabled a new class of chatbots that has the potential to be used for building'synthetic' social relationships, which we refer to as AI companions. An increasing number of consumers use this technology to satisfy social goals (Broadbent et al. 2023; Chaturvedi et al. 2023; De Freitas et al. 2023).