The humanlike responses of large language models (LLMs) have prompted social scientists to investigate whether LLMs can be used to simulate human participants in experiments, opinion polls and surveys. Of central interest in this line of research has been mapping out the psychological profiles of LLMs by prompting them to respond to standardized questionnaires. The conflicting findings of this research are unsurprising given that mapping out underlying, or latent, traits from LLMs' text responses to questionnaires is no easy task. To address this, we use psychometrics, the science of psychological measurement. In this study, we prompt OpenAI's flagship models, GPT-3.5 and GPT-4, to assume different personas and respond to a range of standardized measures of personality constructs. We used two kinds of persona descriptions: either generic (four or five random person descriptions) or specific (mostly demographics of actual humans from a large-scale human dataset). We found that the responses from GPT-4, but not GPT-3.5, using generic persona descriptions show promising, albeit not perfect, psychometric properties, similar to human norms, but the data from both LLMs when using specific demographic profiles, show poor psychometrics properties. We conclude that, currently, when LLMs are asked to simulate silicon personas, their responses are poor signals of potentially underlying latent traits. Thus, our work casts doubt on LLMs' ability to simulate individual-level human behaviour across multiple-choice question answering tasks.

This paper explores the seamless integration of Generative AI (GenAI) and Evolutionary Algorithms (EAs) within the domain of large-scale multi-objective optimization. Focusing on the transformative role of Large Language Models (LLMs), our study investigates the potential of LLM-Assisted Inference to automate and enhance decision-making processes. Specifically, we highlight its effectiveness in illuminating key decision variables in evolutionarily optimized solutions while articulating contextual trade-offs. Tailored to address the challenges inherent in inferring complex multi-objective optimization solutions at scale, our approach emphasizes the adaptive nature of LLMs, allowing them to provide nuanced explanations and align their language with diverse stakeholder expertise levels and domain preferences. Empirical studies underscore the practical applicability and impact of LLM-Assisted Inference in real-world decision-making scenarios.

FOX News' Eben Brown reports on AI going mainstream in healthcare, which doctors say has the potential to create stronger relationships with patients. The use of generative AI may not be helpful in reducing burnout in health care, new research suggests. Previous research indicated that increased time spent using electronic health record (EHR) systems and handling administrative responsibilities has been a burden on doctors. So some people had heralded artificial intelligence as a potential solution -- yet recent investigations by U.S. health systems found that large language models (LLMs) did not simplify clinicians' day-to-day responsibilities. WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

"Very creepy," was April's first thought when she saw her face on a generative AI website. April is one half of the Maddison twins. She and her sister Amelia make content for OnlyFans, Instagram and other platforms, but they also existed as a custom generative AI model – made without their consent. "It was really weird to see our faces, but not really our faces," she says. Deepfakes – the creation of realistic but false imagery, video and audio using artificial intelligence – is on the political agenda after the federal government announced last week it would introduce legislation to ban the creation and sharing of deepfake pornography as part of measures to combat violence against women.

On Monday, Stack Overflow and OpenAI announced a new API partnership that will integrate Stack Overflow's technical content with OpenAI's ChatGPT AI assistant. The deal has sparked controversy among Stack Overflow's user community, with many expressing anger and protest over the use of their contributed content to support and train AI models. I'm just going to delete/deface my answers one by one," wrote one user on sister site Stack Exchange. "I don't care if this is against your silly policies, because as this announcement shows, your policies can change at a whim without prior consultation of your stakeholders. Stack Overflow is a popular question-and-answer site for software developers that allows users to ask and answer technical questions related to coding.

Law enforcement in the United States, United Kingdom, and Australia this week named a Russian national as the person behind LockBitSupp, the pseudonym of the leader of the LockBit ransomware gang that the US says is responsible for extracting 500 million from its victims. Dmitry Yuryevich Khoroshev has been sanctioned and charged with 26 criminal counts in the US, which combined could result in a prison sentence of 185 years. That is, if he's ever arrested and successfully prosecuted--an extremely rare event for suspects who live in Russia. Elsewhere in the world of cybercrime, WIRED's Andy Greenberg interviewed a representative of Cyber Army of Russia, a group of hackers who have targeted water utilities in the US and Europe and are said to have ties to the notorious Russian military hacking unit known as Sandworm. The responses from Cyber Army of Russia were littered with pro-Kremlin talking points--and some curious admissions.

A team of researchers from the Tokyo Institute of Technology, Fujitsu and others have announced the development of a large language model that can serve as a foundation for generative artificial intelligence, using the Japanese supercomputer Fugaku. Trained extensively on data in Japanese, which account for 60% of the total training data, the Fugaku-LLM model revealed Friday is expected to lead to research on generative AI tailored to domestic needs. In May 2023, the researchers -- also including those from Tohoku University, Nagoya University, the government-backed research institute Riken, CyberAgent and Kotoba Technologies -- launched the project employing the supercomputer jointly developed by Fujitsu and Riken.

Long considered a tech hinterland, Southeast Asia is fast emerging as a center of gravity for the industry. The CEOs of Apple, Microsoft and Nvidia are among the industry chieftains who've swung through the region in past months, committing billions of dollars in investment and holding forth with heads of state from Indonesia to Malaysia. Amazon just this week took over a giant conference hall in downtown Singapore to unfurl a 9 billion investment plan before a thousands-strong audience cheering and waving glow sticks. After decades of playing second fiddle to China and Japan, the region of about 675 million people is drawing more tech investment than ever. For data centers alone, the world's biggest companies are set to splurge up to 60 billion over the next few years as Southeast Asia's young populations embrace video streaming, online shopping and generative AI.

Generative Artificial Intelligence (GAI) is taking the world by storm with its unparalleled content creation ability. Large Language Models (LLMs) are at the forefront of this movement. However, the significant resource demands of LLMs often require cloud hosting, which raises issues regarding privacy, latency, and usage limitations. Although edge intelligence has long been utilized to solve these challenges by enabling real-time AI computation on ubiquitous edge resources close to data sources, most research has focused on traditional AI models and has left a gap in addressing the unique characteristics of LLM inference, such as considerable model size, auto-regressive processes, and self-attention mechanisms. In this paper, we present an edge intelligence optimization problem tailored for LLM inference. Specifically, with the deployment of the batching technique and model quantization on resource-limited edge devices, we formulate an inference model for transformer decoder-based LLMs. Furthermore, our approach aims to maximize the inference throughput via batch scheduling and joint allocation of communication and computation resources, while also considering edge resource constraints and varying user requirements of latency and accuracy. To address this NP-hard problem, we develop an optimal Depth-First Tree-Searching algorithm with online tree-Pruning (DFTSP) that operates within a feasible time complexity. Simulation results indicate that DFTSP surpasses other batching benchmarks in throughput across diverse user settings and quantization techniques, and it reduces time complexity by over 45% compared to the brute-force searching method.

Generative AI (GenAI) has spurred the expectation of being creative, due to its ability to generate content, yet so far, its creativity has somewhat disappointed, because it is trained using existing data following human intentions to generate outputs. The purpose of this paper is to explore what is required to evolve AI from generative to creative. Based on a reinforcement learning approach and building upon various research streams of computational creativity, we develop a triple prompt-response-reward engineering framework to develop the creative capability of GenAI. This framework consists of three components: 1) a prompt model for expected creativity by developing discriminative prompts that are objectively, individually, or socially novel, 2) a response model for observed creativity by generating surprising outputs that are incrementally, disruptively, or radically innovative, and 3) a reward model for improving creativity over time by incorporating feedback from the AI, the creator/manager, and/or the customers. This framework enables the application of GenAI for various levels of creativity strategically.