Seismic advances in generative AI algorithms for imagery, text, and other data types has led to the temptation to use synthetic data to train next-generation models. Repeating this process creates an autophagous ("self-consuming") loop whose properties are poorly understood. We conduct a thorough analytical and empirical analysis using state-of-the-art generative image models of three families of autophagous loops that differ in how fixed or fresh real training data is available through the generations of training and in whether the samples from previousgeneration models have been biased to trade off data quality versus diversity. Our primary conclusion across all scenarios is that without enough fresh real data in each generation of an autophagous loop, future generative models are doomed to have their quality (precision) or diversity (recall) progressively decrease.

UK universities have drawn up a set of guiding principles to ensure that students and staff are AI literate, as the sector struggles to adapt teaching and assessment methods to deal with the growing use of generative artificial intelligence. Vice-chancellors at the 24 Russell Group research-intensive universities have signed up to the code. They say this will help universities to capitalise on the opportunities of AI while simultaneously protecting academic rigour and integrity in higher education. While once there was talk of banning software like ChatGPT within education to prevent cheating, the guidance says students should be taught to use AI appropriately in their studies, while also making them aware of the risks of plagiarism, bias and inaccuracy in generative AI. Staff will also have to be trained so they are equipped to help students, many of whom are already using ChatGPT in their assignments.

Remember the early days of the AI hype train, when everyone spent their time making stupid images using text prompts? If you want to recapture the nostalgic haze of, uh, late 2022, Picsart has got you covered. The popular image editor just launched an AI-powered animated GIF generator. The major difference between earlier text-to-image platforms like DALL-E and Picsart's new tool is animation. DALL-E is best known for making static images, whereas Picsart's software creates animated GIFs, just like the ones you've been sending in group chats and social media platforms for years.

Schumer's plan is a culmination of many other, smaller policy actions. On June 14, Senators Josh Hawley (a Republican from Missouri) and Richard Blumenthal (a Democrat from Connecticut) introduced a bill that would exclude generative AI from Section 230 (the law that shields online platforms from liability for the content their users create). Last Thursday, the House science committee hosted a handful of AI companies to ask questions about the technology and the various risks and benefits it poses. House Democrats Ted Lieu and Anna Eshoo, with Republican Ken Buck, proposed a National AI Commission to manage AI policy, and a bipartisan group of senators suggested creating a federal office to encourage, among other things, competition with China. Though this flurry of activity is noteworthy, US lawmakers are not actually starting from scratch on AI policy.

The current advances in generative AI for learning large neural network models with the capability to produce essays, images, music and even 3D assets from text prompts create opportunities for a manifold of disciplines. In the present paper, we study the potential of deep text-to-3D models in the engineering domain, with focus on the chances and challenges when integrating and interacting with 3D assets in computational simulation-based design optimization. In contrast to traditional design optimization of 3D geometries that often searches for the optimum designs using numerical representations, such as B-Spline surface or deformation parameters in vehicle aerodynamic optimization, natural language challenges the optimization framework by requiring a different interpretation of variation operators while at the same time may ease and motivate the human user interaction. Here, we propose and realize a fully automated evolutionary design optimization framework using Shap-E, a recently published text-to-3D asset network by OpenAI, in the context of aerodynamic vehicle optimization. For representing text prompts in the evolutionary optimization, we evaluate (a) a bag-of-words approach based on prompt templates and Wordnet samples, and (b) a tokenisation approach based on prompt templates and the byte pair encoding method from GPT4. Our main findings from the optimizations indicate that, first, it is important to ensure that the designs generated from prompts are within the object class of application, i.e. diverse and novel designs need to be realistic, and, second, that more research is required to develop methods where the strength of text prompt variations and the resulting variations of the 3D designs share causal relations to some degree to improve the optimization.

Undoubtedly, the evolution of Generative AI (GenAI) models has been the highlight of digital transformation in the year 2022. As the different GenAI models like ChatGPT and Google Bard continue to foster their complexity and capability, it's critical to understand its consequences from a cybersecurity perspective. Several instances recently have demonstrated the use of GenAI tools in both the defensive and offensive side of cybersecurity, and focusing on the social, ethical and privacy implications this technology possesses. This research paper highlights the limitations, challenges, potential risks, and opportunities of GenAI in the domain of cybersecurity and privacy. The work presents the vulnerabilities of ChatGPT, which can be exploited by malicious users to exfiltrate malicious information bypassing the ethical constraints on the model. This paper demonstrates successful example attacks like Jailbreaks, reverse psychology, and prompt injection attacks on the ChatGPT. The paper also investigates how cyber offenders can use the GenAI tools in developing cyber attacks, and explore the scenarios where ChatGPT can be used by adversaries to create social engineering attacks, phishing attacks, automated hacking, attack payload generation, malware creation, and polymorphic malware. This paper then examines defense techniques and uses GenAI tools to improve security measures, including cyber defense automation, reporting, threat intelligence, secure code generation and detection, attack identification, developing ethical guidelines, incidence response plans, and malware detection. We will also discuss the social, legal, and ethical implications of ChatGPT. In conclusion, the paper highlights open challenges and future directions to make this GenAI secure, safe, trustworthy, and ethical as the community understands its cybersecurity impacts.

In this review, we explore the potential applications of Artificial General Intelligence (AGI) models in healthcare, focusing on foundational Large Language Models (LLMs), Large Vision Models, and Large Multimodal Models. We emphasize the importance of integrating clinical expertise, domain knowledge, and multimodal capabilities into AGI models. In addition, we lay out key roadmaps that guide the development and deployment of healthcare AGI models. Throughout the review, we provide critical perspectives on the potential challenges and pitfalls associated with deploying large-scale AGI models in the medical field. This comprehensive review aims to offer insights into the future implications of AGI in medical imaging, healthcare and beyond.

What Will AI Do to Your Job? Take a Look at What It's Already Doing to Coders Artificial-intelligence software is eating the software industry, as companies turn to generative AI tools to save money on programmers. It's a sign of what's to come for many white-collar workers.

But in practice Generative AI has made significant progress recently, with the actual identifiability of individuals can be highly applications spanning text, code, image, video, speech, and context-specific as different types of information carry different structured data (Sequoia Capital, 2022). Investor interest has levels of identifiability risks depending on the circumstances. However, whether the ChatGPT (Bloomberg, 2023), which has reached 100M resultant synthetic data constitutes personal or anonymous monthly users (Reuters, 2023). This raises the question, as well. Active legal cases against Generative AI companies what constitutes a sufficient level of anonymization.

Artificial intelligence is either going to save humanity or finish it off, depending on who you speak to. Either way, every week there are new developments and breakthroughs. The US company behind the ChatGPT chatbot, OpenAI, has announced that its first international office will be in London. The move is a boost for the UK prime minister, Rishi Sunak, who has described the AI race as one of the "greatest opportunities" for the country's tech industry. OpenAI said it chose the UK capital because of its "rich culture and exceptional talent pool".