AI image generators Midjourney and Stable Diffusion trained their models with the works of countless artists without their permission or compensation, artist says. Pieces of artwork can be created in the blink of an eye with AI technology. Whether it's writing an essay, creating a video or a drawing, if you can think it, AI can probably create it. Text to image generators are an evolving form of AI that are able to create images based off of a few words inputted by users. Depending on the software being used, images can get extremely detailed and complex.

Generative Artificial Intelligence (GenAI) has emerged as a powerful technology capable of autonomously producing highly realistic content in various domains, such as text, images, audio, and videos. With its potential for positive applications in creative arts, content generation, virtual assistants, and data synthesis, GenAI has garnered significant attention and adoption. However, the increasing adoption of GenAI raises concerns about its potential misuse for crafting convincing phishing emails, generating disinformation through deepfake videos, and spreading misinformation via authentic-looking social media posts, posing a new set of challenges and risks in the realm of cybersecurity. To combat the threats posed by GenAI, we propose leveraging the Cyber Kill Chain (CKC) to understand the lifecycle of cyberattacks, as a foundational model for cyber defense. This paper aims to provide a comprehensive analysis of the risk areas introduced by the offensive use of GenAI techniques in each phase of the CKC framework. We also analyze the strategies employed by threat actors and examine their utilization throughout different phases of the CKC, highlighting the implications for cyber defense. Additionally, we propose GenAI-enabled defense strategies that are both attack-aware and adaptive. These strategies encompass various techniques such as detection, deception, and adversarial training, among others, aiming to effectively mitigate the risks posed by GenAI-induced cyber threats.

Multiple solutions mainly originate from the existence of redundant degrees of freedom in the robot arm, which may cause difficulties in inverse model learning but they can also bring many benefits, such as higher flexibility and robustness. Current multi-solution inverse model learning methods rely on conditional deep generative models, yet they often fail to achieve sufficient precision when learning multiple solutions. In this paper, we propose Conditional Embodied Self-Supervised Learning (CEMSSL) for robot arm multi-solution inverse model learning, and present a unified framework for high-precision multi-solution inverse model learning that is applicable to other conditional deep generative models. Our experimental results demonstrate that our framework can achieve a significant improvement in precision (up to 2 orders of magnitude) while preserving the properties of the original method. The related code will be available soon.

In the beginning, the chatbots and their ilk fed on the human-made internet. Various generative-AI models of the sort that power ChatGPT got their start by devouring data from sites including Wikipedia, Getty, and Scribd. They consumed text, images, and other content, learning through algorithmic digestion their flavors and texture, which ingredients go well together and which do not, in order to concoct their own art and writing. Generative AI is utterly reliant on the sustenance it gets from the web: Computers mime intelligence by processing almost unfathomable amounts of data and deriving patterns from them. ChatGPT can write a passable high-school essay because it has read libraries' worth of digitized books and articles, while DALL-E 2 can produce Picasso-esque images because it has analyzed something like the entire trajectory of art history.

For this week's TIME100 Most Influential Companies cover story about OpenAI and its CEO Sam Altman, TIME's former editor-in-chief Edward Felsenthal sat down with a number of company executives in early May, including two sessions with Altman, transcribed below. The conversations have been condensed and edited for clarity. Sam Altman: One thing I use it for every day is help with summarization. I can't really keep up on my inbox anymore, but I made a little thing to help it summarize for me and pull out important stuff from unknown senders, and that's very helpful. I used it to translate an article for someone I'm meeting next week, to prepare for that. This is sort of a funny thing, I used it to help me draft a tweet that I was having a hard time with. Not as much as it might have seemed from the outside.

Many of us dream of being an artist at one point in our lives, but dodgy sketching can often stop us from getting there. Now, these dreams may soon be possible, as a new tool can transform your bad doodles into masterpieces thanks to the power of artificial intelligence (AI). Tech giant Qualcomm unveiled its game-changing ControlNet software earlier this week, which turns image prompts into whatever you like within 12 seconds. Unlike many other models of its kind - such as Adobe AI Firefly - ControlNet surprisingly doesn't need the internet to function and could soon be a major mobile phone app. While it has not yet been released, the firm claims that producing images here will be completely private, with no data backed up to a third-party cloud.

The booming popularity of AI-driven chatbots like OpenAI's ChatGPT and Google's Bard has both captivated and concerned officials, who have said they are worried about again failing to protect consumers from the perils of Silicon Valley's latest craze. It's prompted lawmakers to hold a wave of public hearings and private meetings with industry leaders, researchers and advocates as they look to get their bearings in the quickly changing AI field.

Competition for jobs is fierce in China right now. After he graduated from college with a business major earlier this year, David struggled to find work. There were too many applicants for every position, and, he says, "even if you find a job, the pay is not as great as previous years, and you have to work long hours." After David--who asked for anonymity to talk freely about his business--saw some videos on Weibo and WeChat about ChatGPT, the generative artificial intelligence chatbot released to great fanfare late last year by the US tech company OpenAI, he was struck with an idea. There's a thriving essay-writing business in China, with students asking tutors and experts to help them with their homework.

Code contains security and functional bugs. The process of identifying and localizing them is difficult and relies on human labor. In this work, we present a novel approach (FLAG) to assist human debuggers. FLAG is based on the lexical capabilities of generative AI, specifically, Large Language Models (LLMs). Here, we input a code file then extract and regenerate each line within that file for self-comparison. By comparing the original code with an LLM-generated alternative, we can flag notable differences as anomalies for further inspection, with features such as distance from comments and LLM confidence also aiding this classification. This reduces the inspection search space for the designer. Unlike other automated approaches in this area, FLAG is language-agnostic, can work on incomplete (and even non-compiling) code and requires no creation of security properties, functional tests or definition of rules. In this work, we explore the features that help LLMs in this classification and evaluate the performance of FLAG on known bugs. We use 121 benchmarks across C, Python and Verilog; with each benchmark containing a known security or functional weakness. We conduct the experiments using two state of the art LLMs in OpenAI's code-davinci-002 and gpt-3.5-turbo, but our approach may be used by other models. FLAG can identify 101 of the defects and helps reduce the search space to 12-17% of source code.

Deployed multimodal systems can fail in ways that evaluators did not anticipate. In order to find these failures before deployment, we introduce MultiMon, a system that automatically identifies systematic failures -- generalizable, natural-language descriptions of patterns of model failures. To uncover systematic failures, MultiMon scrapes a corpus for examples of erroneous agreement: inputs that produce the same output, but should not. It then prompts a language model (e.g., GPT-4) to find systematic patterns of failure and describe them in natural language. We use MultiMon to find 14 systematic failures (e.g., "ignores quantifiers") of the CLIP text-encoder, each comprising hundreds of distinct inputs (e.g., "a shelf with a few/many books"). Because CLIP is the backbone for most state-of-the-art multimodal systems, these inputs produce failures in Midjourney 5.1, DALL-E, VideoFusion, and others. MultiMon can also steer towards failures relevant to specific use cases, such as self-driving cars. We see MultiMon as a step towards evaluation that autonomously explores the long tail of potential system failures. Code for MULTIMON is available at https://github.com/tsb0601/MultiMon.