Abstract: This study evaluates the efficacy of ChatGPT as an AI teaching and learning support tool in an integrated circuit systems course at a higher education institution in an Asian country. Various question types were completed, and ChatGPT responses were assessed to gain valuable insights for further investigation. The objective is to assess ChatGPT's ability to provide insights, personalized support, and interactive learning experiences in engineering education. The study includes the evaluation and reflection of different stakeholders: students, lecturers, and engineers. The findings of this study shed light on the benefits and limitations of ChatGPT as an AI tool, paving the way for innovative learning approaches in technical disciplines. Furthermore, the study contributes to our understanding of how digital transformation is likely to unfold in the education sector. ChatGPT, Generative AI, Digital transformation, engineering education, tutorial design, peer-assisted learning, AI-assisted learning, integrated circuit education. School of Science, Engineering, and Technology, RMIT University Vietnam e-mail: thanh.pham@rmit.edu.vn 1 BACKGROUND There is a growing interest in using artificial intelligence (AI) to improve teaching and learning [1, 2]. Generative AI tools like ChatGPT understand and generate human-like responses in real-time [3].

With rapid advances in generative artificial intelligence, the text-to-music synthesis task has emerged as a promising direction for music generation from scratch. However, finer-grained control over multi-track generation remains an open challenge. Existing models exhibit strong raw generation capability but lack the flexibility to compose separate tracks and combine them in a controllable manner, differing from typical workflows of human composers. To address this issue, we propose JEN-1 Composer, a unified framework to efficiently model marginal, conditional, and joint distributions over multi-track music via a single model. JEN-1 Composer framework exhibits the capacity to seamlessly incorporate any diffusion-based music generation system, \textit{e.g.} Jen-1, enhancing its capacity for versatile multi-track music generation. We introduce a curriculum training strategy aimed at incrementally instructing the model in the transition from single-track generation to the flexible generation of multi-track combinations. During the inference, users have the ability to iteratively produce and choose music tracks that meet their preferences, subsequently creating an entire musical composition incrementally following the proposed Human-AI co-composition workflow. Quantitative and qualitative assessments demonstrate state-of-the-art performance in controllable and high-fidelity multi-track music synthesis. The proposed JEN-1 Composer represents a significant advance toward interactive AI-facilitated music creation and composition. Demos will be available at https://www.jenmusic.ai/audio-demos.

Generative artificial intelligence holds enormous potential to revolutionize decision-making processes, from everyday to high-stake scenarios. However, as many decisions carry social implications, for AI to be a reliable assistant for decision-making it is crucial that it is able to capture the balance between self-interest and the interest of others. We investigate the ability of three of the most advanced chatbots to predict dictator game decisions across 108 experiments with human participants from 12 countries. We find that only GPT-4 (not Bard nor Bing) correctly captures qualitative behavioral patterns, identifying three major classes of behavior: self-interested, inequity-averse, and fully altruistic. Nonetheless, GPT-4 consistently underestimates self-interest and inequity-aversion, while overestimating altruistic behavior. This bias has significant implications for AI developers and users.

Generative AI is becoming a key component of business operations and customer service interactions today. According to Salesforce research, three out of five workers (61%) either currently use or plan to use generative AI in their roles. A full 68% of these employees are confident that the technology--which can churn out text, video, image, and audio content almost instantaneously--will enable them to provide more enriching customer experiences. Sixty percent of the surveyed employees believe that human oversight is indispensable for effective and trustworthy generative AI. Generative AI has enormous potential to revolutionize business operations, but how companies decide to employ it will make all the difference.

LinkedIn is adding a new, AI-powered perk for its premium subscribers: a built-in job coach that uses AI and LinkedIn data to help job seekers find, research and apply for roles. The new feature arrives as the company announced its user base has grown to 1 billion members as it looks to ramp up its investment in AI-driven features. The Microsoft-owned company has increasingly been experimenting with AI features for its paying members. Earlier this year, it introduced the ability to use generative AI to write better profile descriptions and messages to hiring managers. But the latest AI perks aim to provide an even more personalized experience.

Structure-based drug design (SBDD) stands at the forefront of drug discovery, emphasizing the creation of molecules that target specific binding pockets. Recent advances in this area have witnessed the adoption of deep generative models and geometric deep learning techniques, modeling SBDD as a conditional generation task where the target structure serves as context. Historically, evaluation of these models centered on docking scores, which quantitatively depict the predicted binding affinity between a molecule and its target pocket. Though state-of-the-art models purport that a majority of their generated ligands exceed the docking score of ground truth ligands in test sets, it begs the question: Do these scores align with real-world biological needs? In this paper, we introduce the delta score, a novel evaluation metric grounded in tangible pharmaceutical requisites. Our experiments reveal that molecules produced by current deep generative models significantly lag behind ground truth reference ligands when assessed with the delta score. This novel metric not only complements existing benchmarks but also provides a pivotal direction for subsequent research in the domain.

The research field of end-user programming has largely been concerned with helping non-experts learn to code sufficiently well in order to achieve their tasks. Generative AI stands to obviate this entirely by allowing users to generate code from naturalistic language prompts. In this essay, we explore the extent to which "traditional" programming languages remain relevant for non-expert end-user programmers in a world with generative AI. We posit the "generative shift hypothesis": that generative AI will create qualitative and quantitative expansions in the traditional scope of end-user programming. We outline some reasons that traditional programming languages may still be relevant and useful for end-user programmers. We speculate whether each of these reasons might be fundamental and enduring, or whether they may disappear with further improvements and innovations in generative AI. Finally, we articulate a set of implications for end-user programming research, including the possibility of needing to revisit many well-established core concepts, such as Ko's learning barriers and Blackwell's attention investment model.

The rapid development and adoption of Generative AI (GAI) technology in the form of chatbots such as ChatGPT and Claude has greatly increased interest in agentic machines. This paper introduces the Autonomous Cognitive Entity (ACE) model, a novel framework for a cognitive architecture, enabling machines and software agents to operate more independently. Drawing inspiration from the OSI model, the ACE framework presents layers of abstraction to conceptualize artificial cognitive architectures. The model is designed to harness the capabilities of the latest generative AI technologies, including large language models (LLMs) and multimodal generative models (MMMs), to build autonomous, agentic systems. The ACE framework comprises six layers: the Aspirational Layer, Global Strategy, Agent Model, Executive Function, Cognitive Control, and Task Prosecution. Each layer plays a distinct role, ranging from setting the moral compass and strategic thinking to task selection and execution. The ACE framework also incorporates mechanisms for handling failures and adapting actions, thereby enhancing the robustness and flexibility of autonomous agents. This paper introduces the conceptual framework and proposes implementation strategies that have been tested and observed in industry. The goal of this paper is to formalize this framework so as to be more accessible.

AI expert Marva Bailer explains how, even though there are currently laws in place, the average person has more access than ever to create deepfakes of celebrities. Artificial intelligence (AI) is advancing rapidly, bringing unprecedented benefits to us, yet it also poses serious risks, such as chemical, biological, radiological and nuclear (CBRN) threats, that could have catastrophic consequences for the world. How can we ensure that AI is used for good and not evil? How can we prepare for the worst-case scenarios that might arise from AI? CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER These are some of the questions that OpenAI, a leading AI research lab and the company behind ChatGPT, is trying to answer with its new Preparedness team. Its mission is to track, evaluate, forecast and protect against the frontier risks of AI models.

What if The Atlantic owned a train car? Amtrak, I had just learned on the internet, allows owners of private railcars to lash onto runs along the Northeast Corridor, among other routes. "We should have a train car," I slacked an editor. Moments later, it appeared on my screen, bright red with our magazine's logo emblazoned in white, just like I'd ordered. It's an old logo, and misspelled, but the effect was the same: A momentary notion--one unworthy of relating to someone in private, let alone executing--had been realized, thanks to DALL-E 3, an artificial-intelligence image generator now built into Microsoft Bing's Image Creator website.