The widespread adoption of generative AI in software engineering marks a paradigm shift, offering new opportunities to design and utilize software engineering tools while influencing both developers and the artifacts they create. Traditional empirical methods in software engineering, including quantitative, qualitative, and mixed-method approaches, are well established. However, this paradigm shift introduces novel data types and redefines many concepts in the software engineering process. The roles of developers, users, agents, and researchers increasingly overlap, blurring the distinctions between these social and technical actors within the field. This paper examines how integrating AI into software engineering challenges traditional research paradigms. It focuses on the research phenomena that we investigate, the methods and theories that we employ, the data we analyze, and the threats to validity that emerge in this new context. Through this exploration, our goal is to understand how AI adoption disrupts established software development practices that creates new opportunities for empirical software engineering research.

This research explores the opportunities of Generative AI (GenAI) in the realm of higher education through the design and development of a multimodal chatbot for an undergraduate course. Leveraging the ChatGPT API for nuanced text-based interactions and Google Bard for advanced image analysis and diagram-to-code conversions, we showcase the potential of GenAI in addressing a broad spectrum of educational queries. Additionally, the chatbot presents a file-based analyser designed for educators, offering deep insights into student feedback via sentiment and emotion analysis, and summarising course evaluations with key metrics. These combinations highlight the crucial role of multimodal conversational AI in enhancing teaching and learning processes, promising significant advancements in educational adaptability, engagement, and feedback analysis. By demonstrating a practical web application, this research underlines the imperative for integrating GenAI technologies to foster more dynamic and responsive educational environments, ultimately contributing to improved educational outcomes and pedagogical strategies.

A consortium led by Elon Musk said it has offered 97.4bn to buy the nonprofit that controls OpenAI, months after the billionaire sued the artificial intelligence startup to block it from transitioning to a for-profit firm. Musk's bid, revealed on Monday, could ratchet up longstanding tensions with OpenAI CEO Sam Altman over the future of the startup at the heart of a boom in generative AI technology. Altman promptly posted on X: "No thank you but we will buy twitter for 9.74 billion if you want." The two are already embroiled in an ongoing lawsuit. Musk criticised a 500bn OpenAI-led project called Stargate announced with great fanfare at the White House just after United States President Donald Trump returned to office, suggesting the investors involved lacked the funding for the project.

Elon Musk escalated his feud with OpenAI and its CEO Sam Altman on Monday. The billionaire is leading a consortium of investors that announced it had submitted a bid of 97.4bn for "all assets" of the artificial intelligence company to OpenAI's board of directors. The startup, which operates ChatGPT, has been working to restructure itself away from its original non-profit status. OpenAI also operates a for-profit subsidiary, and Musk's unsolicited offer could complicate the company's plans. The Wall Street Journal first reported the proposed bid. "If Sam Altman and the present OpenAI, Inc. Board of Directors are intent on becoming a fully for-profit corporation, it is vital that the charity be fairly compensated for what its leadership is taking away from it: control over the most transformative technology of our time," said Marc Toberoff, the attorney representing the investors.

OpenAI is widely credited with helping bring artificial intelligence tools into the mainstream and sparking huge investment in the sector. Musk and Altman co-founded the start-up in 2015 as a non-profit company, but the relationship has soured since the Tesla and X boss departed the firm in 2018. Altman is said to be restructuring the company to become a for-profit entity, stripping it of its non-profit board - a move Musk argues means the company has abandoned its founding mission of developing AI for the benefit of humanity. But OpenAI argues its transition into a for-profit firm is required to secure the money needed for developing the best artificial intelligence models. "It's time for OpenAI to return to the open-source, safety-focused force for good it once was. We will make sure that happens," Musk said in a statement.

Elon Musk has launched a 97.4 billion bid to take control of OpenAI. The Wall Street Journal reports a group of investors led by Musk's xAI submitted an unsolicited offer to the company's board of directors on Monday. The group wants to buy the nonprofit that controls OpenAI's for-profit arm. When asked for comment, an OpenAI spokesperson pointed Engadget to an X post from CEO Sam Altman. "No thank you but we will buy twitter for 9.74 billion if you want," Altman wrote on the social media platform Musk owns.

Roblox, Discord, OpenAI and Google are launching a nonprofit organization called ROOST, or Robust Open Online Safety Tools, which hopes "to build scalable, interoperable safety infrastructure suited for the AI era." The organization plans on providing free, open-source safety tools to public and private organizations to use on their own platforms, with a special focus on child safety to start. The press release announcing ROOST specifically calls out plans to offer "tools to detect, review, and report child sexual abuse material (CSAM)." Partner companies are providing funding for these tools, and the technical expertise to build them, too. The operating theory of ROOST is that access to generative AI is rapidly changing the online landscape, making the need for "reliable and accessible safety infrastructure" all the more urgent.

Generative AI (Gen AI) with large language models (LLMs) are being widely adopted across the industry, academia and government. Cybersecurity is one of the key sectors where LLMs can be and/or are already being used. There are a number of problems that inhibit the adoption of trustworthy Gen AI and LLMs in cybersecurity and such other critical areas. One of the key challenge to the trustworthiness and reliability of LLMs is: how consistent an LLM is in its responses? In this paper, we have analyzed and developed a formal definition of consistency of responses of LLMs. We have formally defined what is consistency of responses and then develop a framework for consistency evaluation. The paper proposes two approaches to validate consistency: self-validation, and validation across multiple LLMs. We have carried out extensive experiments for several LLMs such as GPT4oMini, GPT3.5, Gemini, Cohere, and Llama3, on a security benchmark consisting of several cybersecurity questions: informational and situational. Our experiments corroborate the fact that even though these LLMs are being considered and/or already being used for several cybersecurity tasks today, they are often inconsistent in their responses, and thus are untrustworthy and unreliable for cybersecurity.

Recent advancements in diffusion techniques have propelled image and video generation to unprece- dented levels of quality, significantly accelerating the deployment and application of generative AI. However, 3D shape generation technology has so far lagged behind, constrained by limitations in 3D data scale, complexity of 3D data process- ing, and insufficient exploration of advanced tech- niques in the 3D domain. Current approaches to 3D shape generation face substantial challenges in terms of output quality, generalization capa- bility, and alignment with input conditions. We present TripoSG, a new streamlined shape diffu- sion paradigm capable of generating high-fidelity 3D meshes with precise correspondence to input images. Specifically, we propose: 1) A large-scale rectified flow transformer for 3D shape generation, achieving state-of-the-art fidelity through training on extensive, high-quality data. 2) A hybrid supervised training strategy combining SDF, normal, and eikonal losses for 3D VAE, achieving high- quality 3D reconstruction performance. 3) A data processing pipeline to generate 2 million high- quality 3D samples, highlighting the crucial rules for data quality and quantity in training 3D gen- erative models. Through comprehensive experi- ments, we have validated the effectiveness of each component in our new framework. The seamless integration of these parts has enabled TripoSG to achieve state-of-the-art performance in 3D shape generation. The resulting 3D shapes exhibit en- hanced detail due to high-resolution capabilities and demonstrate exceptional fidelity to input im- ages. Moreover, TripoSG demonstrates improved versatility in generating 3D models from diverse image styles and contents, showcasing strong gen- eralization capabilities. To foster progress and innovation in the field of 3D generation, we will make our model publicly available.

While generative robot policies have demonstrated significant potential in learning complex, multimodal behaviors from demonstrations, they still exhibit diverse failures at deployment-time. Policy steering offers an elegant solution to reducing the chance of failure by using an external verifier to select from low-level actions proposed by an imperfect generative policy. Here, one might hope to use a Vision Language Model (VLM) as a verifier, leveraging its open-world reasoning capabilities. However, off-the-shelf VLMs struggle to understand the consequences of low-level robot actions as they are represented fundamentally differently than the text and images the VLM was trained on. In response, we propose FOREWARN, a novel framework to unlock the potential of VLMs as open-vocabulary verifiers for runtime policy steering. Our key idea is to decouple the VLM's burden of predicting action outcomes (foresight) from evaluation (forethought). For foresight, we leverage a latent world model to imagine future latent states given diverse low-level action plans. For forethought, we align the VLM with these predicted latent states to reason about the consequences of actions in its native representation--natural language--and effectively filter proposed plans. We validate our framework across diverse robotic manipulation tasks, demonstrating its ability to bridge representational gaps and provide robust, generalizable policy steering. Videos can be found on the project website: https://yilin-wu98.github.io/forewarn/.