During Prime Minister Rishi Sunak's recent visit to Washington D.C., as he announced that the U.K. would host the first global summit on AI regulation later this year, he bristled in response to a reporter's question about whether the "midsize country" could naturally lead the debate, given that the E.U. is close to passing a landmark AI bill. "That midsize country happens to be a global leader in AI," he said. "You would be hard-pressed to find many other countries other than the U.S. in the Western world with more expertise and talent in AI." The Prime Minister's response revealed the dilemma he now faces in positioning the U.K. as a key player in reining in AI's potential negative consequences without stifling innovation, amid growing fears around generative artificial intelligence. Following the U.K.'s departure from the European Union, experts say Sunak is attempting to carve out a pivotal role to help keep the country globally relevant by playing the role of an "honest broker" between the different regulatory approaches of the E.U. and the U.S. when it comes to AI.

Since Google I/O in May, the company has heavily promoted its generative text and image AI tools to help people do everything from draft essays to create art. However, its core business model is selling ads and products. Today the company unveiled a new shopping tool that may help do exactly that. Now, customers in the United States can virtually "try on" women's tops. The company uses images of real models ranging from XXS to 3XL to wear AI-generated versions of clothes from hundreds of brands sold across Google, like Anthropologie, Everlane, and H&M.

An explosion of interest in AI from businesses and consumers has kicked off an arms race between Google and its archrival Microsoft to develop and sell AI tools and put the tech into existing products, from Google Search to Microsoft Word. The boom was triggered by OpenAI, a much smaller company, which released its ChatGPT chatbot in November, astonishing the world with its ability to conduct cogent conversations, pass professional exams and write computer code. Experts attribute OpenAI's success in part to its use of human testers and trainers to refine the bot and teach it to be less offensive and more interesting than previous versions of the technology.

The European Union's flagship artificial intelligence regulation took a major step toward becoming law on Wednesday, after lawmakers voted to approve the text of the law that would ban real-time facial recognition, and place new transparency requirements on generative AI tools like ChatGPT. AI Act--will now progress to the final "trilogue" stage of the E.U.'s regulatory process. There, officials will attempt to reach a compromise between the draft of the law just approved by the E.U. Parliament, a different version preferred by the bloc's executive branch, and the desires of member states. That process will begin on Wednesday night and must be completed by January if the law is to come into force before E.U. elections next year.

Microsoft may own almost half of OpenAI, but a recent expose hints the pair aren't the happiest of bedfellows. The Wall Street Journal claims the AI company warned Microsoft not to incorporate GPT-4 into Bing search without further training, but it did so anyway. It resulted in several high-profile examples of odd behavior, including bots arguing with users, and at least one instance of a user being urged to dissolve their marriage and elope with Bing instead. There's resentment, too, on Microsoft's side, finding its own internal AI projects overlooked in favor of OpenAI. Which, despite the close financial ties, is very much free to work with Microsoft's rivals in plenty of fields.

McKinsey's report is one of the few so far to quantify the long-term impact of generative A.I. on the economy. The report arrives as Silicon Valley has been gripped by a fervor over generative A.I. tools like ChatGPT and Google's Bard, with tech companies and venture capitalists investing billions of dollars in the technology. The tools -- some of which can also generate images and video, and carry on a conversation -- have started a debate over how they will affect jobs and the world economy. Some experts have predicted that the A.I. will displace people from their work, while others have said the tools can augment individual productivity. Last week, Goldman Sachs released a report warning that A.I. could lead to worker disruption and that some companies would benefit more from the technology than others.

The Tokyo Metropolitan Government will begin using the artificial intelligence chatbot ChatGPT for writing texts and carrying out other clerical work in all of its offices from August, Gov. Yuriko Koike said Tuesday. ChatGPT "has the potential to greatly transform the way public administration is conducted," Koike said during a metropolitan assembly session. She added that "better city governance" can be achieved by assessing the positive and negative aspects of the AI service. Koike also said the metropolitan government will use ChatGPT for tasks including preparing documents in question-and-answer format, and seek input from its employees about other practical uses for the generative AI tool. This could be due to a conflict with your ad-blocking or security software.

We introduce GHP-MOFassemble, a Generative artificial intelligence (AI), High Performance framework to accelerate the rational design of metal-organic frameworks (MOFs) with high CO2 capacity and synthesizable linkers. Our framework combines a diffusion model, a class of generative AI, to generate novel linkers that are assembled with one of three pre-selected nodes into MOFs in a primitive cubic (pcu) topology. The CO2 capacities of these AI-generated MOFs are predicted using a modified version of the crystal graph convolutional neural network model. We then use the LAMMPS code to perform molecular dynamics simulations to relax the AI-generated MOF structures, and identify those that converge to stable structures, and maintain their porous properties throughout the simulations. Among 120,000 pcu MOF candidates generated by the GHP-MOFassemble framework, with three distinct metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer), a total of 102 structures completed molecular dynamics simulations at 1 bar with predicted CO2 capacity higher than 2 mmol/g at 0.1 bar, which corresponds to the top 5% of hMOFs in the hypothetical MOF (hMOF) dataset in the MOFX-DB database. Among these candidates, 18 have change in density lower than 1% during molecular dynamics simulations, indicating their stability. We also found that the top five GHP-MOFassemble's MOF structures have CO2 capacities higher than 96.9% of hMOF structures. This new approach combines generative AI, graph modeling, large-scale molecular dynamics simulations, and extreme scale computing to open up new pathways for the accelerated discovery of novel MOF structures at scale.

Text-to-image generation has seen an explosion of interest since 2021. Today, beautiful and intriguing digital images and artworks can be synthesized from textual inputs ("prompts") with deep generative models. Online communities around text-to-image generation and AI generated art have quickly emerged. This paper identifies six types of prompt modifiers used by practitioners in the online community based on a 3-month ethnographic study. The novel taxonomy of prompt modifiers provides researchers a conceptual starting point for investigating the practice of text-to-image generation, but may also help practitioners of AI generated art improve their images. We further outline how prompt modifiers are applied in the practice of "prompt engineering." We discuss research opportunities of this novel creative practice in the field of Human-Computer Interaction (HCI). The paper concludes with a discussion of broader implications of prompt engineering from the perspective of Human-AI Interaction (HAI) in future applications beyond the use case of text-to-image generation and AI generated art.

This paper explores educational interactions involving humans and artificial intelligences not as sequences of prompts and responses, but as a social process of conversation and exploration. In this conception, learners continually converse with AI language models within a dynamic computational medium of internet tools and resources. Learning happens when this distributed system sets goals, builds meaning from data, consolidates understanding, reconciles differences, and transfers knowledge to new domains. Building social generative AI for education will require development of powerful AI systems that can converse with each other as well as humans, construct external representations such as knowledge maps, access and contribute to internet resources, and act as teachers, learners, guides and mentors. This raises fundamental problems of ethics. Such systems should be aware of their limitations, their responsibility to learners and the integrity of the internet, and their respect for human teachers and experts. We need to consider how to design and constrain social generative AI for education.