To meet the shifts in post-pandemic learning needs and the demand of artificial intelligence (AI) advancement on workforce development, the education system seeks new instructional and learning strategies that are personalized, effective, safe, and scalable [8]. Throughout the years, richer and more complex educational data have been generated by the advancement of instructional practices, providing vast potential for analyses but at the same time posing challenges to the approaches that process such data. Conventional quantitative methods are limited by the capacity of calculation and the efficiency of models, hence preventing efforts to improve teaching and learning outcomes. AI/ML approaches are able to effectively process the existing and forthcoming complex data with scalability and precision [5], presenting an unprecedented opportunity to promote the research and instructional practices in education. These characteristics of new data and methods provide timely and actionable insights into the dynamics of the instructional environment. Furthermore, in recent years, this trend has been accelerated by the rapid adoption of generative AI tools, such as ChatGPT and Bard, which synergizes the capabilities of both text analysis and generation. A new field of research has emerged, in which researchers integrate the cutting-edge AI/ML techniques with educational domain knowledge of curriculum, teaching, and learning and to explore crucial questions for instructional improvement.

The recent advancements in Generative AI have significantly advanced the field of text-to-image generation. The state-of-the-art text-to-image model, Stable Diffusion, is now capable of synthesizing high-quality images with a strong sense of aesthetics. Crafting text prompts that align with the model's interpretation and the user's intent thus becomes crucial. However, prompting remains challenging for novice users due to the complexity of the stable diffusion model and the non-trivial efforts required for iteratively editing and refining the text prompts. To address these challenges, we propose PromptCharm, a mixed-initiative system that facilitates text-to-image creation through multi-modal prompt engineering and refinement. To assist novice users in prompting, PromptCharm first automatically refines and optimizes the user's initial prompt. Furthermore, PromptCharm supports the user in exploring and selecting different image styles within a large database. To assist users in effectively refining their prompts and images, PromptCharm renders model explanations by visualizing the model's attention values. If the user notices any unsatisfactory areas in the generated images, they can further refine the images through model attention adjustment or image inpainting within the rich feedback loop of PromptCharm. To evaluate the effectiveness and usability of PromptCharm, we conducted a controlled user study with 12 participants and an exploratory user study with another 12 participants. These two studies show that participants using PromptCharm were able to create images with higher quality and better aligned with the user's expectations compared with using two variants of PromptCharm that lacked interaction or visualization support.

As the world grapples with how to regulate artificial intelligence, Washington faces a unique dilemma: how to secure America's position as the global AI leader, while guarding against AI's possible risks? Although any country seeking to regulate AI must balance regulation and innovation, this task is especially hard for the United States because we have more to lose. The United Kingdom, European Union, and China all have formidable AI companies, but U.S. firms dominate the field, propelled by our uniquely open innovation ecosystem. This dominance was on display recently, which saw OpenAI release Sora, a powerful new text-to-video platform, and Google introduce Gemini 1.5, its next-generation AI model that can absorb requests more than 30 times the size of its predecessor. If these trends continue, and AI proves the game-changer that many expect--surrendering U.S. leadership is not an option.

The US Army Research Laboratory is exploring whether OpenAI's technology can improve battle planning – although only in the context of a military video game. The US military has already explored using AI technologies to analyse battlefield images and even identify targets for airstrikes – but it only recently began testing large language models and other types of generative AI that empower commercial AI chatbots.

The letter was signed by experts in AI research, policy, and law, including Stanford University's Percy Liang; Pulitzer Prize-winning journalist Julia Angwin; Renée DiResta from the Stanford Internet Observatory; Mozilla fellow Deb Raji, who has pioneered research into auditing AI models; ex-government official Marietje Schaake, a former member of European Parliament; and Brown University professor Suresh Venkatasubramanian, a former adviser to the White House Office of Science and Technology Policy.

After more than a year in development, Lore Machine is now available to the public for the first time. For 10 a month, you can upload 100,000 words of text (up to 30,000 words at a time) and generate 80 images for short stories, scripts, podcast transcripts, and more. There are price points for power users too, including an enterprise plan costing 160 a month that covers 2.24 million words and 1,792 images. The illustrations come in a range of preset styles, from manga to watercolor to pulp '80s TV show. Zac Ryder, founder of creative agency Modern Arts, has been using an early-access version of the tool since Lore Machine founder Thobey Campion first showed him what it could do.

If you'll recall, The Times sued both companies for using its published articles to train their GPT large language models (LLMs) without permission and compensation. In its filing, the company has accused The Times of pushing "doomsday futurology" by claiming that AI technologies pose a threat to independent journalism. It follows OpenAI's court filing from late February that's also seeking to dismiss some important elements on the case. Like OpenAI before it, Microsoft accused The Times of crafting "unrealistic prompts" in an effort to "coax the GPT-based tools" to spit out responses matching its content. It also compared the media organization's lawsuit to Hollywood studios' efforts to " stop a groundbreaking new technology:" The VCR. Instead of destroying Hollywood, Microsoft explained, the VCR helped the entertainment industry flourish by opening up revenue streams.

Large Language Models (LLMs) are becoming a prominent generative AI tool, where the user enters a query and the LLM generates an answer. To reduce harm and misuse, efforts have been made to align these LLMs to human values using advanced training techniques such as Reinforcement Learning from Human Feedback (RLHF). However, recent studies have highlighted the vulnerability of LLMs to adversarial jailbreak attempts aiming at subverting the embedded safety guardrails. To address this challenge, this paper defines and investigates the Refusal Loss of LLMs and then proposes a method called Gradient Cuff to detect jailbreak attempts. Gradient Cuff exploits the unique properties observed in the refusal loss landscape, including functional values and its smoothness, to design an effective two-step detection strategy. Experimental results on two aligned LLMs (LLaMA-2-7B-Chat and Vicuna-7B-V1.5) and six types of jailbreak attacks (GCG, AutoDAN, PAIR, TAP, Base64, and LRL) show that Gradient Cuff can significantly improve the LLM's rejection capability for malicious jailbreak queries, while maintaining the model's performance for benign user queries by adjusting the detection threshold.

To advance the neural decoding of Portuguese, in this paper we present a fully open Transformer-based, instruction-tuned decoder model that sets a new state of the art in this respect. To develop this decoder, which we named Gerv\'asio PT*, a strong LLaMA~2 7B model was used as a starting point, and its further improvement through additional training was done over language resources that include new instruction data sets of Portuguese prepared for this purpose, which are also contributed in this paper. All versions of Gerv\'asio are open source and distributed for free under an open license, including for either research or commercial usage, and can be run on consumer-grade hardware, thus seeking to contribute to the advancement of research and innovation in language technology for Portuguese.

In the rapidly evolving field of Large Language Models (LLMs), there is a critical need to thoroughly analyze their capabilities and risks. Central to our investigation are two novel elements. Firstly, it is the innovative parallels between the statistical patterns of word relationships within LLMs and Martin Heidegger's concepts of "ready-to-hand" and "present-at-hand," which encapsulate the utilitarian and scientific altitudes humans employ in interacting with the world. This comparison lays the groundwork for positioning LLMs as the digital counterpart to the Faculty of Verbal Knowledge, shedding light on their capacity to emulate certain facets of human reasoning. Secondly, a structural analysis of human reasoning, viewed through Heidegger's notion of truth as "unconcealment" is conducted This foundational principle enables us to map out the inputs and outputs of the reasoning system and divide reasoning into four distinct categories. Respective cognitive faculties are delineated, allowing us to place LLMs within the broader schema of human reasoning, thus clarifying their strengths and inherent limitations. Our findings reveal that while LLMs possess the capability for Direct Explicative Reasoning and Pseudo Rational Reasoning, they fall short in authentic rational reasoning and have no creative reasoning capabilities, due to the current lack of many analogous AI models such as the Faculty of Judgement. The potential and risks of LLMs when they are augmented with other AI technologies are also evaluated. The results indicate that although LLMs have achieved proficiency in some reasoning abilities, the aspiration to match or exceed human intellectual capabilities is yet unattained. This research not only enriches our comprehension of LLMs but also propels forward the discourse on AI's potential and its bounds, paving the way for future explorations into AI's evolving landscape.