Artists outraged by artificial intelligence that copies in seconds the styles they have sacrificed years to develop are waging battle online and in court. Fury erupted in the art community last year with the release of generative artificial intelligence (AI) programs that can convincingly carry out commands such as drawing a dog like cartoonist Sarah Andersen would, or a nymph the way illustrator Karla Ortiz might do. Such style-swiping AI works are cranked out without the original artist's consent, credit or compensation--the three C's at the heart of a fight to change all that. In January, artists including Andersen and Ortiz filed a class-action lawsuit against DreamUp, Midjourney and Stable Diffusion, three image-generating AI models programmed with art found online. Andersen told AFP she felt "violated" when first she saw an AI drawing that copied the style of her "Fangs" comic book work.

It is an AI-supported chatbot that helps users to generate content. As the world is slowly turning towards artificial intelligence, the ask or demand for quality AI content generators is increasing. For sure, OpenAi's ChatGPT4 has proved to be a great AI asset.

An open letter with signatures from hundreds of the biggest names in tech, including Elon Musk, has urged the world's leading artificial intelligence labs to pause the training of new super-powerful systems for six months, saying that recent advances in AI present "profound risks to society and humanity." The letter comes just two weeks after the public release of OpenAI's GPT-4, the most powerful AI system ever released, which has led researchers to slash their expectations for when AGI--or artificial general intelligence that surpasses human cognitive ability--will arrive. Many experts fear that, as an AI arms race heats up, humanity is sleepwalking into catastrophe. "Advanced AI could represent a profound change in the history of life on Earth, and should be planned for and managed with commensurate care and resources," the letter says. "Unfortunately, this level of planning and management is not happening, even though recent months have seen AI labs locked in an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control."

On Wednesday, March 29 at 19:30 GMT: As powerful artificial intelligence systems improve their ability to create images, video and text, researchers are increasingly worried about the technology's seemingly inevitable role in disinformation and propaganda campaigns. Advancements in generative AI have the potential to radically alter our information environment to the point where humans – and even machines – may not have the ability to distinguish between content that is AI-generated versus human-made. The proliferation of tools powered by generative AI are making disinformation easier to produce, paving the way for a host of new problems with no clear solutions for online content moderators. AI ethicists and others within the industry are calling for more regulatory measures. And while many have hailed generative AI for its ability to provide highly personalised recommendations, the same online personal data that trains AI programmes could potentially be used to manipulate people en masse via chatbots to share conspiracy theories or foreign propaganda.

OpenAI is a research organization focusing on artificial intelligence (AI) development. They create advanced AI technologies using machine learning, deep learning, and natural language processing. OpenAI technology is built on a foundation of neural networks, algorithms modeled after how the human brain processes information. These networks consist of interconnected nodes that simulate neurons, allowing the system to learn from large datasets and make predictions based on that data. GPT (Generative Pre-trained Transformer), a deep learning model capable of creating human-like language, is one of the core technologies developed by OpenAI.

Microsoft on Tuesday announced a chatbot designed to help cybersecurity professionals understand critical issues and find ways to fix them. The company has been busy bolstering its software with artificial intelligence models from startup OpenAI after OpenAI's ChatGPT bot captured the public imagination following its November debut. The resulting generative AI software can at times be "usefully wrong," as Microsoft put it earlier this month when talking up new features in Word and other productivity apps. But Microsoft is proceeding nevertheless, as it seeks to keep growing a cybersecurity business that fetched more than $20 billion in 2022 revenue. The Microsoft Security Copilot draws on GPT-4, the latest large language model from OpenAI -- in which Microsoft has invested billions -- and a security-specific model Microsoft built using daily activity data it gathers.

Artificial intelligence (AI) systems will increasingly be used to cause harm as they grow more capable. In fact, AI systems are already starting to be used to automate fraudulent activities, violate human rights, create harmful fake images, and identify dangerous toxins. To prevent some misuses of AI, we argue that targeted interventions on certain capabilities will be warranted. These restrictions may include controlling who can access certain types of AI models, what they can be used for, whether outputs are filtered or can be traced back to their user, and the resources needed to develop them. We also contend that some restrictions on non-AI capabilities needed to cause harm will be required. Though capability restrictions risk reducing use more than misuse (facing an unfavorable Misuse-Use Tradeoff), we argue that interventions on capabilities are warranted when other interventions are insufficient, the potential harm from misuse is high, and there are targeted ways to intervene on capabilities. We provide a taxonomy of interventions that can reduce AI misuse, focusing on the specific steps required for a misuse to cause harm (the Misuse Chain), and a framework to determine if an intervention is warranted. We apply this reasoning to three examples: predicting novel toxins, creating harmful images, and automating spear phishing campaigns.

With the phenomenal success of diffusion models and ChatGPT, deep generation models (DGMs) have been experiencing explosive growth from 2022. Not limited to content generation, DGMs are also widely adopted in Internet of Things, Metaverse, and digital twin, due to their outstanding ability to represent complex patterns and generate plausible samples. In this article, we explore the applications of DGMs in a crucial task, i.e., improving the efficiency of wireless network management. Specifically, we firstly overview the generative AI, as well as three representative DGMs. Then, a DGM-empowered framework for wireless network management is proposed, in which we elaborate the issues of the conventional network management approaches, why DGMs can address them efficiently, and the step-by-step workflow for applying DGMs in managing wireless networks. Moreover, we conduct a case study on network economics, using the state-of-the-art DGM model, i.e., diffusion model, to generate effective contracts for incentivizing the mobile AI-Generated Content (AIGC) services. Last but not least, we discuss important open directions for the further research.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. This year we will continue to see changes in the tech industry. As we have been preparing for a fundamental in how we work, technology plays a major role in how employees and companies can operate better, smarter, more efficiently and more productively. More than that, tech is a key player in how employees can connect with one another to create a truly collaborative work environment. There are three major areas where we will see a shift across tech.

Is prompt engineering a process that tries to get accurate, logical, and consistent answers from an AI language model? Or is it a way to find the faults in a language model and then fix them to achieve the perfect artificial intelligence model, which kills "prompt engineering?" In this article, we'll concentrate on ChatGPT because it is the most popular model at the moment. But just in case this AI tool is new to you, I suggest you read our "ChatGPT for Beginners" article first. We'll also look at prompts for image generators like DALLE 2. I have written a few articles about this LLM (large language model) and learned that it is not so smart.