Adobe has widely released a new and potentially contentious feature: text-to-image generation for Photoshop powered by Firefly, first teased in April. As with LLMs like Dall-E and Mid Journey, you can use it to create an image from scratch by typing a description into Photoshop's updated generative AI tool. I tried it with the text "Dramatic low angle view of a steamship from the 1800s in a storm with large waves and lightning" in multiple styles (anime, watercolor, sketch, realistic) and got decent results. The usual AI art caveats apply though, particularly with weird details if you look closely. But it certainly created useable results and you have the benefit of already being inside Photoshop to fix any errors.

When Noah saw the email, a wave of anxiety hit. It was spring 2023, and the Activision artist was reading a message from the company's then chief technology officer, Michael Vance, about how artificial intelligence was "top of mind" at the video game publisher. Systems were still being tested, Vance wrote, but "what we have seen thus far holds a ton of promise." There had been a couple emails like this sent to the employees of the studio, which produces the juggernaut Call of Duty series. A previous one had approved the internal use of generative AI tools Midjourney and Stable Diffusion for producing concept art.

The ethical implications and potentials for misuse of Generative Artificial Intelligence are increasingly worrying topics. This paper explores how easily the default ethical guardrails of ChatGPT, using its latest customization features, can be bypassed by simple prompts and fine-tuning, that can be effortlessly accessed by the broad public. This malevolently altered version of ChatGPT, nicknamed "RogueGPT", responded with worrying behaviours, beyond those triggered by jailbreak prompts. We conduct an empirical study of RogueGPT responses, assessing its flexibility in answering questions pertaining to what should be disallowed usage. Our findings raise significant concerns about the model's knowledge about topics like illegal drug production, torture methods and terrorism. The ease of driving ChatGPT astray, coupled with its global accessibility, highlights severe issues regarding the data quality used for training the foundational model and the implementation of ethical safeguards. We thus underline the responsibilities and dangers of user-driven modifications, and the broader effects that these may have on the design of safeguarding and ethical modules implemented by AI programmers.

Artificial intelligence (AI) has become a commodity for people because of the advent of generative AI (GenAI) models that bridge the usability gap of AI by providing a natural language interface to interact with complex models. These GenAI models range from text generation - such as two-way chat systems - to the generation of image or video from textual descriptions input by a user. These advancements in AI have impacted Dentistry in multiple aspects. In dental education, the student now has the opportunity to solve a plethora of questions by only prompting a GenAI model and have the answer in a matter of seconds. GenAI models can help us deliver better patient healthcare by helping practitioners gather knowledge quickly and efficiently. Finally, GenAI can also be used in dental research, where the applications range from new drug discovery to assistance in academic writing. In this review, we first define GenAI models and describe their multiple generation modalities; then, we explain and discuss their current and potential applications in Dentistry; and finally, we describe the challenges these new technologies impose in our area.

Vision language models (VLMs) demonstrate impressive capabilities in visual question answering and image captioning, acting as a crucial link between visual and language models. However, existing open-source VLMs heavily rely on pretrained and frozen vision encoders (such as CLIP). Despite CLIP's robustness across diverse domains, it still exhibits non-negligible image understanding errors. These errors propagate to the VLM responses, resulting in sub-optimal performance. In our work, we propose an efficient and robust method for updating vision encoders within VLMs. Our approach selectively and locally updates encoders, leading to substantial performance improvements on data where previous mistakes occurred, while maintaining overall robustness. Furthermore, we demonstrate the effectiveness of our method during continual few-shot updates. Theoretical grounding, generality, and computational efficiency characterize our approach.

Recently, advanced Large Language Models (LLMs) such as GPT-4 have been integrated into many real-world applications like Code Copilot. These applications have significantly expanded the attack surface of LLMs, exposing them to a variety of threats. Among them, jailbreak attacks that induce toxic responses through jailbreak prompts have raised critical safety concerns. To identify these threats, a growing number of red teaming approaches simulate potential adversarial scenarios by crafting jailbreak prompts to test the target LLM. However, existing red teaming methods do not consider the unique vulnerabilities of LLM in different scenarios, making it difficult to adjust the jailbreak prompts to find context-specific vulnerabilities. Meanwhile, these methods are limited to refining jailbreak templates using a few mutation operations, lacking the automation and scalability to adapt to different scenarios. To enable context-aware and efficient red teaming, we abstract and model existing attacks into a coherent concept called "jailbreak strategy" and propose a multi-agent LLM system named RedAgent that leverages these strategies to generate context-aware jailbreak prompts. By self-reflecting on contextual feedback in an additional memory buffer, RedAgent continuously learns how to leverage these strategies to achieve effective jailbreaks in specific contexts. Extensive experiments demonstrate that our system can jailbreak most black-box LLMs in just five queries, improving the efficiency of existing red teaming methods by two times. Additionally, RedAgent can jailbreak customized LLM applications more efficiently. By generating context-aware jailbreak prompts towards applications on GPTs, we discover 60 severe vulnerabilities of these real-world applications with only two queries per vulnerability. We have reported all found issues and communicated with OpenAI and Meta for bug fixes.

Artificial intelligence (AI), like other technologies in the past, will likely affect the economy in many ways, potentially stimulating growth and changing the way people work.11 The effect of AI on work will be multifaceted and will likely vary across occupations and industries.4 The public release of tools such as Dall-E 2, which generates digital images from natural language prompts, in September 2022 and ChatGPT, which generates text responses to natural language prompts, in November 2022 has drawn the attention of the general public to progress in generative AI technologies, stimulating excitement in the potential of these technologies, but also concern over potential negative effects on employment. The expanded scope of uses presented by generative AI technologies has raised questions regarding whether such technologies may affect a broader range of occupations, including those that are highly creative.16 Recent research, including our own9 and work by the Pew Research Center,12 suggests there is a strong positive correlation between exposure to generative AI and median salaries, the required level of education, and the presence of creative abilities within an occupation, and that occupations with a higher percent of female or Asian workers are more exposed, whereas occupations with a higher percent of Black or Hispanic workers are less exposed to generative AI.

The lawsuits allege Udio produced output with "striking resemblances" to songs including Dancing Queen by ABBA and All I Want For Christmas Is You by Mariah Carey, while Suno allegedly turned out songs similar to I Got You (I Feel Good) by James Brown and Johnny B. Goode by Chuck Berry, among others. Record labels were able to basically recreate versions of very famous songs with highly specific prompts, then linked to them in the lawsuits. I made a short compilation here:https://t.co/9Nu7rW7eqD These lawsuits are not the first to trouble the booming generative AI industry. Visual artists have sued makers of image generating systems, while various newspapers are suing OpenAI, the owner of ChatGPT, for similar allegations.

The first concert I bought tickets to after the pandemic subsided was a performance of the British singer-songwriter Birdy, held last April in Belgium. I've listened to Birdy more than to any other artist; her voice has pulled me through the hardest and happiest stretches of my life. I know every lyric to nearly every song in her discography, but that night Birdy's voice had the same effect as the first time I'd listened to her, through beat-up headphones connected to an iPod over a decade ago--a physical shudder, as if a hand had reached across time and grazed me, somehow, just beneath the skin. Countless people around the world have their own version of this ineffable connection, with Taylor Swift, perhaps, or the Beatles, Bob Marley, or Metallica. My feelings about Birdy's music were powerful enough to propel me across the Atlantic, just as tens of thousands of people flocked to the Sphere to see Phish earlier this year, or some 400,000 went to Woodstock in 1969.

Similarly to the factors of anthropomorphism summarized by [11], we identified the following factors as relevant when Large Language Models (LLMs) are increasingly being used LLM-based chatbots are used in learning scenarios: The learning in learning environments to support teaching--be it as learning agent, i.e., chatbot, the learner itself, and environmental companions or as tutors [1]-[3]. With our contribution, we factors which influence the learner (see Figure 1). According to the media equation [4], people tend to respond to media in the same way as they would respond to another person. A study conducted by the Georgia Institute of Technology showed that chatbots can be successfully implemented in learning environments. As LLM-based chatbots such as OpenAI's GPT Looking at the agent, several factors can contribute to series are increasingly used in educational tools, it is important anthropomorphization. Cognitive intelligence refers to the to understand how the attribution processes to LLM-based ability to perceive, reason, and act on problems; to combine chatbots in terms of anthropomorphization affect learners' efficient, useful, goal-oriented, and autonomous actions with emotions.