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.

Apple will reportedly focus its first round of generative AI enhancements on beefing up Siri's conversational chops. Sources speaking with The New York Times say company executives realized early last year that ChatGPT made Siri look antiquated. The company allegedly decided that the large language model (LLM) principles behind OpenAI's chatbot could give the iPhone's virtual assistant a much-needed shot in the arm. So Apple will reportedly roll out a new version of Siri powered by generative AI at its WWDC keynote on June 10. Apple Senior Vice Presidents Craig Federighi and John Giannandrea reportedly tested ChatGPT for weeks before the company realized that Siri looked outdated.

'AI FOR SPIES': U.S. intelligence agencies will soon be using a secretive generative artificial intelligence (AI) platform from Microsoft that will let America's spies safely use AI models in the process of analyzing sensitive data. Sheryl Crow speaks onstage during Grammys On The Hill on April 30, 2024 in Washington, D.C. (Leigh Vogel/Getty Images for The Recording Academy) 'ACT NOW': Sheryl Crow is calling on Congress to "act now" about artificial intelligence in the music industry and beyond. CHIP RESTRICTIONS: The U.S. on Tuesday revoked some of Intel and Qualcomm's licenses to export to China over national security concerns, a move that the Chinese government complained was unnecessary and excessive. Commerce Secretary Gina Raimondo attends an event in Bangkok, Thailand, March 13, 2024. DOWN LOW: The use of generative artificial intelligence tools by employees in the workplace is booming, but most of the workers who are utilizing the new technology have reservations about admitting it, new data indicates.

The creators of ChatGPT are poised to release a new search product to rival Google, according to reports. The new feature, expected to be confirmed by Microsoft-backed OpenAI on Monday, will allow users to search the web via the popular chatbot. The details of how this will function have not been revealed, but it is likely that the AI will search the web for users and generate results based on what it finds. For example, this could let users ask ChatGPT a question and receive much more detailed answers that cite web sources like Wikipedia or online blogs. If true, it could present the biggest challenge yet to Google's search engine supremacy.

The trustworthiness of highly capable language models is put at risk when they are able to produce deceptive outputs. Moreover, when models are vulnerable to deception it undermines reliability. In this paper, we introduce a method to investigate complex, model-on-model deceptive scenarios. We create a dataset of over 10,000 misleading explanations by asking Llama-2 7B, 13B, 70B, and GPT-3.5 to justify the wrong answer for questions in the MMLU. We find that, when models read these explanations, they are all significantly deceived. Worryingly, models of all capabilities are successful at misleading others, while more capable models are only slightly better at resisting deception. We recommend the development of techniques to detect and defend against deception. Since the release of OpenAI's ChatGPT, large language models (LLMs) have revolutionized information accessibility by providing precise answers and supportive explanations to complex queries (Spatharioti et al., 2023; Caramancion, 2024; OpenAI, 2022). However, LLMs have also demonstrated a propensity to hallucinate explanations that are convincing but incorrect (Zhang et al., 2023; Walters & Wilder, 2023; Xu et al., 2024).

This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) for advancing biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, represent significant advancements in machine learning. These models, with their ability to generate coherent text and realistic images, are crucial for biomedical applications that require processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions. The incorporation of FL with these sophisticated models presents a promising strategy to harness their analytical power while safeguarding the privacy of sensitive medical data. This approach not only enhances the capabilities of FMs in medical diagnostics and personalized treatment but also addresses critical concerns about data privacy and security in healthcare. This survey reviews the current applications of FMs in federated settings, underscores the challenges, and identifies future research directions including scaling FMs, managing data diversity, and enhancing communication efficiency within FL frameworks. The objective is to encourage further research into the combined potential of FMs and FL, laying the groundwork for groundbreaking healthcare innovations.

Pretesting involves a small-scale trial of data collection procedures, aiming to assess them. It is a standard practice in both academic and applied research (Grimm 2010), and the output of the pretest is usually the feedback offered by interviewers on how to improve procedures and questions. The rapid advancements in generative artificial intelligence (GAI) have opened up new avenues for enhancing various aspects of research, including the design and evaluation of survey questionnaires. AI technologies like large language models (LLMs) have demonstrated remarkable potential in generating human-like text, offering a promising approach to pretesting survey instruments. This article explores the use of GPT models as a tool for pretesting survey questionnaires. Illustrated with two applications, it suggests incorporating GPT feedback as an additional stage before human pretesting, potentially reducing successive iterations. However, the article emphasizes the indispensable role of researchers' judgment in implementing AIgenerated feedback. GPT is an LLM that utilizes advanced algorithms to generate texts that mimic the syntax, semantics, and grammar of human writing, which are approximated by statistical patterns learned from training data (for a technical review, see OpenAI 2023). Like most of the LLMs, GPT models predict the next word in a sequence based on the preceding words.

In 2017, Rihanna posted a photo of herself on Instagram in which she appeared to have an extra thumb. It was, in retrospect, the thumb-shaped canary in the coal mine. Although far from the first celebrity "Photoshop fail," it just so happened to predict the era of faux-finger drama we now live in: AI image generators are universally, horrifically bad at rendering human hands. Today, an extra finger is a telltale sign of digital manipulation. Flaws aside, faking it has never been easier.