In this research, we explore the efficacy and potential of Generative AI models, specifically focusing on their application in role-playing simulations exemplified through Spyfall, a renowned mafia-style game. By leveraging GPT-4's advanced capabilities, the study aimed to showcase the model's potential in understanding, decision-making, and interaction during game scenarios. Comparative analyses between GPT-4 and its predecessor, GPT-3.5-turbo, demonstrated GPT-4's enhanced adaptability to the game environment, with significant improvements in posing relevant questions and forming human-like responses. However, challenges such as the model;s limitations in bluffing and predicting opponent moves emerged. Reflections on game development, financial constraints, and non-verbal limitations of the study were also discussed. The findings suggest that while GPT-4 exhibits promising advancements over earlier models, there remains potential for further development, especially in instilling more human-like attributes in AI.

Soon after China's artificial intelligence rules came into effect last month, a series of new AI chatbots began trickling onto the market, with government approval. The rules have already been watered down from what was initially proposed, and so far, China hasn't enforced them as strictly as it could, experts say. China's regulatory approach will likely have huge implications for the technological competition between the country and its AI superpower rival the U.S. The Cyberspace Administration of China's (CAC) Generative AI Measures, which came into effect on Aug. 15, are some of the strictest in the world. They state that the generative AI services should not generate content "inciting subversion of national sovereignty or the overturn of the socialist system," or "advocating terrorism or extremism, promoting ethnic hatred and ethnic discrimination, violence and obscenity, as well as fake and harmful information." Preventing AI chatbots from spewing out unwanted or even toxic content has been a challenge for AI developers around the world.

Edwards, who ended up using the real, flesh-and-blood human Hans Zimmer for the soundtrack of his movie, said he played the AI-generated track back to the composer. Zimmer, he said, found it amusing. Edwards's experiment speaks to an issue at the heart of one of the biggest fights facing Hollywood today. Artists and creatives are up in arms over generative AI. Hollywood is currently at a standstill as actors and writers are striking over fairer labor conditions and the use of generative AI in the film industry.

Alison Lomax's presence on the video streaming platform she runs is relatively scant compared with the YouTubers with whom she spends much of her time. But what clips exist succinctly chart the marketing tech revolution she's been navigating: there's a badly framed 12 minutes from 2014 of Lomax lecturing on the rise of influencers working with brands; in another she describes how TV companies woke up to the potential of partnering with YouTube in 2016; and there's her on stage at London's podcast show this year, discussing YouTube's imminent relaunch into the booming audio format. Now, Lomax stands at the "inflection point" of the next hot technology: the generative artificial intelligence behind chatbots such as ChatGPT and image generators such as MidJourney. YouTube, launched in 2005, is no stranger to AI: it is used in its recommendation algorithm; to moderate content; and, latterly, for automatic language translation. "We're committed to embracing AI in a bold way," says Lomax. "But we have to do it really responsibly."

Bard, which competes with OpenAI's ChatGPT and Microsoft's Bing, will now be able to look through and summarize emails from Gmail, search through Google Docs and check flight prices with Google Flights, without users needing to leave the AI tool's main screen. Bard appears as a box where a user enters a question. Until now, responses have been limited to simple text replies or photos from Google Images, but the latest updates mean YouTube videos, links to Google Doc files, and summaries of Gmail emails can appear embedded within Bard's responses.

Kara Frederick, tech director at the Heritage Foundation, discusses the need for regulations on artificial intelligence as lawmakers and tech titans discuss the potential risks. An internet watchdog is sounding the alarm over the growing trend of sex offenders collaborating online to use open source artificial intelligence to generate child sexual abuse material. "There's a technical community within the offender space, particularly dark web forums, where they are discussing this technology," Dan Sexton, the chief technology officer at the Internet Watch Foundation (IWF), told The Guardian in a report last week. "They are sharing imagery, they're sharing [AI] models. Sexton's organization has found that offenders are increasingly turning to open source AI models to create illegal child sexual abuse material (CSAM) and distribute it online. Unlike closed AI models such as OpenAI's Dall-E or Google's Imagen, open source AI technology can be downloaded and adjusted by users, according to the report. Sexton said the ability to use such technology has spread among offenders, who take to the dark web to create and distribute realistic images. An internet watchdog is sounding the alarm over the growing trend of sex offenders collaborating online to use open source artificial intelligence to generate child sexual abuse material. "The content that we've seen, we believe is actually being generated using open source software, which has been downloaded and run locally on people's computers and then modified.

In the last decade, despite rapid advancements in artificial intelligence (AI) transforming many industry practices, construction largely lags in adoption. Recently, the emergence and rapid adoption of advanced large language models (LLM) like OpenAI's GPT, Google's PaLM, and Meta's Llama have shown great potential and sparked considerable global interest. However, the current surge lacks a study investigating the opportunities and challenges of implementing Generative AI (GenAI) in the construction sector, creating a critical knowledge gap for researchers and practitioners. This underlines the necessity to explore the prospects and complexities of GenAI integration. Bridging this gap is fundamental to optimizing GenAI's early-stage adoption within the construction sector. Given GenAI's unprecedented capabilities to generate human-like content based on learning from existing content, we reflect on two guiding questions: What will the future bring for GenAI in the construction industry? What are the potential opportunities and challenges in implementing GenAI in the construction industry? This study delves into reflected perception in literature, analyzes the industry perception using programming-based word cloud and frequency analysis, and integrates authors' opinions to answer these questions. This paper recommends a conceptual GenAI implementation framework, provides practical recommendations, summarizes future research questions, and builds foundational literature to foster subsequent research expansion in GenAI within the construction and its allied architecture & engineering domains.

Although generative AI has been successful in many areas, its ability to model geospatial data is still underexplored. Urban flow, a typical kind of geospatial data, is critical for a wide range of urban applications. Existing studies mostly focus on predictive modeling of urban flow that predicts the future flow based on historical flow data, which may be unavailable in data-sparse areas or newly planned regions. Some other studies aim to predict OD flow among regions but they fail to model dynamic changes of urban flow over time. In this work, we study a new problem of urban flow generation that generates dynamic urban flow for regions without historical flow data. To capture the effect of multiple factors on urban flow, such as region features and urban environment, we employ diffusion model to generate urban flow for regions under different conditions. We first construct an urban knowledge graph (UKG) to model the urban environment and relationships between regions, based on which we design a knowledge-enhanced spatio-temporal diffusion model (KSTDiff) to generate urban flow for each region. Specifically, to accurately generate urban flow for regions with different flow volumes, we design a novel diffusion process guided by a volume estimator, which is learnable and customized for each region. Moreover, we propose a knowledge-enhanced denoising network to capture the spatio-temporal dependencies of urban flow as well as the impact of urban environment in the denoising process. Extensive experiments on four real-world datasets validate the superiority of our model over state-of-the-art baselines in urban flow generation. Further in-depth studies demonstrate the utility of generated urban flow data and the ability of our model for long-term flow generation and urban flow prediction. Our code is released at: https://github.com/tsinghua-fib-lab/KSTDiff-Urban-flow-generation.

With recent advances in generative AI, conversational models like ChatGPT have become feasible candidates for TAs. We investigate the practicality of using generative AI as TAs in introductory programming education by examining novice learners' interaction with TAs in a subgoal learning environment. To compare the learners' interaction and perception of the AI and human TAs, we conducted a between-subject study with 20 novice programming learners. Learners solve programming tasks by producing subgoals and subsolutions with the guidance of a TA. Our study shows that learners can solve tasks faster with comparable scores with AI TAs. Learners' perception of the AI TA is on par with that of human TAs in terms of speed and comprehensiveness of the replies and helpfulness, difficulty, and satisfaction of the conversation. Finally, we suggest guidelines to better design and utilize generative AI as TAs in programming education from the result of our chat log analysis.

A moderately detailed consideration of interactive LLMs as cognitive systems is given, focusing on LLMs circa mid-2023 such as ChatGPT, GPT-4, Bard, Llama, etc.. Cognitive strengths of these systems are reviewed, and then careful attention is paid to the substantial differences between the sort of cognitive system these LLMs are, and the sort of cognitive systems human beings are. It is found that many of the practical weaknesses of these AI systems can be tied specifically to lacks in the basic cognitive architectures according to which these systems are built. It is argued that incremental improvement of such LLMs is not a viable approach to working toward human-level AGI, in practical terms given realizable amounts of compute resources. This does not imply there is nothing to learn about human-level AGI from studying and experimenting with LLMs, nor that LLMs cannot form significant parts of human-level AGI architectures that also incorporate other ideas. Social and ethical matters regarding LLMs are very briefly touched from this perspective, which implies that while care should be taken regarding misinformation and other issues, and economic upheavals will need their own social remedies based on their unpredictable course as with any powerfully impactful technology, overall the sort of policy needed as regards modern LLMs is quite different than would be the case if a more credible approximation to human-level AGI were at hand.