The video that OpenAI released to unveil its new text-to-video tool, Sora, has to be seen to be believed. The demonstration reportedly prompted movie producer Tyler Perry to pause an 800m studio investment. Tools like Sora promise to translate a user's vision into realistic moving images with a simple text prompt, the logic goes, making studios obsolete. Others worry that artificial intelligence (AI) like this could be exploited by those with darker imaginations. Malicious actors could use these services to create highly realistic deepfakes, confusing or misleading voters during an election or simply causing chaos by seeding divisive rumours.

Whether through the frowning high-definition face of a chimpanzee or a psychedelic, pink-and-red-hued doppelganger of himself, Reuven Cohen uses AI-generated images to catch people's attention. "I've always been interested in art and design and video and enjoy pushing boundaries," he says--but the Toronto-based consultant, who helps companies develop AI tools, also hopes to raise awareness of the technology's darker uses. "It can also be specifically trained to be quite gruesome and bad in a whole variety of ways," Cohen says. He's a fan of the freewheeling experimentation that has been unleashed by open source image-generation technology. But that same freedom enables the creation of explicit images of women used for harassment.

OpenAI has hit back at Elon Musk's lawsuit accusing it of betraying its altruistic roots, claiming the Tesla chief executive had in fact supported the artificial intelligence company's plans to create a for-profit unit. Executives at the ChatGPT maker released a blogpost containing what it claimed was historical email correspondence with Musk in which the entrepreneur suggested merging the San Francisco-based startup with Tesla and said it should attach to the electric carmaker "as its cash cow". The blog, authored by OpenAI executives including its chief executive, Sam Altman, claims that in 2017 "we and Elon decided the next step for the mission was to create a for-profit entity". Last week Musk filed a lawsuit accusing OpenAI, where he was a founding board member, of deviating from its foundational mission by forming a for-profit unit – and putting making money before its core aim of producing technology for the benefit of humanity. "We're sad that it's come to this with someone whom we've deeply admired – someone who inspired us to aim higher, then told us we would fail, started a competitor, and then sued us when we started making meaningful progress towards OpenAI's mission without him," said OpenAI.

Last week saw the release of Kara Swisher's Burn Book, the highly anticipated career memoir from a titanic, justly celebrated veteran of tech journalism. Considering her unique, outsize stature in Silicon Valley, and her decadeslong record of landing bombshell inside scoops about the single most important industry of the 21st century, Swisher's choice to promote her latest project with the help of famous friends (Don Lemon, Massachusetts Gov. Maura Healey, etc.) certainly makes sense. What makes much less sense, however, is her selection of tech-world executives. The book tour is going to be lit -- with guest moderators like @RobertIger, @laurenepowell, @mcuban, @donlemon, @reidhoffman, @sama and more. Some of the "moderators" on her tour include Laurene Powell Jobs, Disney CEO Bob Iger, OpenAI CEO Sam Altman, LinkedIn co-founder Reid Hoffman, and Lean In board member Adam Grant. Per NPR's Steve Inskeep, she personally requested that these folks "interview her on stage," in a series of conversations she intends to turn into individual podcast episodes.

Elon Musk, who sued OpenAI for violating its non-profit mission and chasing profits, allegedly wanted the organization to merge with Tesla when it was starting to plan its transition into a for-profit entity in order to accomplish its goals. Well, either that or get full control of the company, OpenAI said in a blog post. The organization responded to Musk's lawsuit by publishing old emails from 2015 to 2018 when he was still involved in its operations. When OpenAI introduced itself to the world back in 2015, it announced that it had 1 billion in funding. Apparently, Musk was the one who suggested that figure, even though OpenAI had raised less than 45 million from him and around 90 million from other donors.

This study investigates the generation of synthetic disinformation by OpenAI's Large Language Models (LLMs) through prompt engineering and explores their responsiveness to emotional prompting. Leveraging various LLM iterations using davinci-002, davinci-003, gpt-3.5-turbo and gpt-4, we designed experiments to assess their success in producing disinformation. Our findings, based on a corpus of 19,800 synthetic disinformation social media posts, reveal that all LLMs by OpenAI can successfully produce disinformation, and that they effectively respond to emotional prompting, indicating their nuanced understanding of emotional cues in text generation. When prompted politely, all examined LLMs consistently generate disinformation at a high frequency. Conversely, when prompted impolitely, the frequency of disinformation production diminishes, as the models often refuse to generate disinformation and instead caution users that the tool is not intended for such purposes. This research contributes to the ongoing discourse surrounding responsible development and application of AI technologies, particularly in mitigating the spread of disinformation and promoting transparency in AI-generated content.

The recent surge in research focused on generating synthetic data from large language models (LLMs), especially for scenarios with limited data availability, marks a notable shift in Generative Artificial Intelligence (AI). Their ability to perform comparably to real-world data positions this approach as a compelling solution to low-resource challenges. This paper delves into advanced technologies that leverage these gigantic LLMs for the generation of task-specific training data. We outline methodologies, evaluation techniques, and practical applications, discuss the current limitations, and suggest potential pathways for future research.

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.