Danish media outlets have demanded that the nonprofit web archive Common Crawl remove copies of their articles from past data sets and stop crawling their websites immediately. Common Crawl plans to comply with the request, first issued on Monday. Executive director Rich Skrenta says the organization is "not equipped" to fight media companies and publishers in court. It made the request on behalf of four media outlets, including Berlingske Media and the daily newspaper Jyllands-Posten. The New York Times made a similar request of Common Crawl last year, prior to filing a lawsuit against OpenAI for using its work without permission.

Many burned-out workers have likely dreamed of hiring a career coach or résumé writer. Now, LinkedIn is introducing chats with generative AI career experts based on real people. Other new AI tools within the platform will help people write résumés and cover letters or evaluate their qualifications for jobs posted. LinkedIn has ramped up its generative AI tools in the past year and is moving to incorporate the tech into even more of its offerings. On Thursday, the career site announced new features like a pilot for AI-powered expert advice, an interactive chat to break down information in LinkedIn courses, and more AI features that can be used to search for and apply for jobs for its premium users in English.

Apple executives used the keynote address of this week's annual WWDC developers conference to debut all of the artificial intelligence capabilities that are coming to iPhones, iPads, and Macs. The team showed off how generative tools will help users write emails, clean up iPhone photos, illustrate presentations, and make custom emoji characters. Adding AI to everything is par for the course in 2024, as all of the big tech companies have been loading up their software with similar generative features. But Apple is late to this particular party. The company has been perceived as being "behind" in generative AI, since OpenAI, Microsoft, Google, and a whole bunch of startups have already made massive inroads.

At its annual developers conference on Monday, Apple announced its long-awaited artificial intelligence system, Apple Intelligence, which will customize user experiences, automate tasks and – the CEO Tim Cook promised – will usher in a "new standard for privacy in AI". While Apple maintains its in-house AI is made with security in mind, its partnership with OpenAI has sparked plenty of criticism. OpenAI tool ChatGPT has long been the subject of privacy concerns. Launched in November 2022, it collected user data without explicit consent to train its models, and only began to allow users to opt out of such data collection in April 2023. Apple says the ChatGPT partnership will only be used with explicit consent for isolated tasks like email composition and other writing tools. But security professionals will be watching closely to see how this, and other concerns, will play out.

When Apple's Chief Executive Officer Tim Cook and his top deputies this week unveiled a landmark arrangement with OpenAI to integrate ChatGPT into the iPhone, iPad and Mac, they were mum on the financial terms. A question left unanswered on Monday: Which company is paying the other as part of a tight collaboration that has potentially lasting monetary benefits for both. But, according to people briefed on the matter, the partnership isn't expected to generate meaningful revenue for either party -- at least, not at the outset.

At Apple's recently concluded annual conference for developers, the company announced that it teamed up with OpenAI to bring its technology to the iPhone and its other devices. It's easy to imagine a huge amount of money changing hands in a deal between a massive corporation and a fast-rising tech firm. But according to a new Bloomberg report, nobody paid anybody in that partnership. Apple is reportedly not paying OpenAI, because it believes that putting its technology in front of hundreds of millions of users is equal to or even better than any kind of monetary payment. Apple will use OpenAI's GPT-4o model to power AI tasks on iOS 18, iPadOS 18 and macOS Sequoia.

The lure of virtual relationships has not gone unnoticed by the industry. When OpenAI launched its latest version of ChatGPT in May it revealed it had been programmed to sound chatty and respond flirtatiously to certain prompts. The company's CEO, Sam Altman posted a single word – "her" on X, formerly known as Twitter. This was seemingly in reference to the 2013 movie in which a man falls in love with his AI virtual assistant. OpenAI added that it was "exploring whether we can responsibly provide the ability to generate NSFW [not safe for work] content".

To develop rigorous knowledge about ML models -- and the systems in which they are embedded -- we need reliable measurements. But reliable measurement is fundamentally challenging, and touches on issues of reproducibility, scalability, uncertainty quantification, epistemology, and more. This dissertation addresses criteria needed to take reliability seriously: both criteria for designing meaningful metrics, and for methodologies that ensure that we can dependably and efficiently measure these metrics at scale and in practice. In doing so, this dissertation articulates a research vision for a new field of scholarship at the intersection of machine learning, law, and policy. Within this frame, we cover topics that fit under three different themes: (1) quantifying and mitigating sources of arbitrariness in ML, (2) taming randomness in uncertainty estimation and optimization algorithms, in order to achieve scalability without sacrificing reliability, and (3) providing methods for evaluating generative-AI systems, with specific focuses on quantifying memorization in language models and training latent diffusion models on open-licensed data. By making contributions in these three themes, this dissertation serves as an empirical proof by example that research on reliable measurement for machine learning is intimately and inescapably bound up with research in law and policy. These different disciplines pose similar research questions about reliable measurement in machine learning. They are, in fact, two complementary sides of the same research vision, which, broadly construed, aims to construct machine-learning systems that cohere with broader societal values.

As generative AI continues to evolve, educators face the challenge of preparing students for a future where AI-assisted work is integral to professional success. This paper introduces ChatISA, an in-house, multi-model chatbot designed to support students in an Information Systems and Analytics department. ChatISA comprises four primary modules-Coding Companion, Project Coach, Exam Ally, and Interview Mentor-each tailored to enhance different aspects of the educational experience. Through iterative development, student feedback, and leveraging open-source frameworks, we created a robust tool that addresses coding inquiries, project management, exam preparation, and interview readiness. The implementation of ChatISA revealed significant insights and challenges, including the necessity of ethical guidelines and balancing AI usage with maintaining student agency. Our findings underscore the importance of adaptive pedagogy and proactive engagement with AI tools to maximize their educational benefits. To support broader adoption and innovation, all code for ChatISA is made publicly available on GitHub, enabling other institutions to customize and integrate similar AI-driven educational tools within their curricula.

The use of generative artificial intelligence (GenAI) in academia is a subjective and hotly debated topic. Currently, there are no agreed guidelines towards the usage of GenAI systems in higher education (HE) and, thus, it is still unclear how to make effective use of the technology for teaching and learning practice. This paper provides an overview of the current state of research on GenAI for teaching and learning in HE. To this end, this study conducted a systematic review of relevant studies indexed by Scopus, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The search criteria revealed a total of 625 research papers, of which 355 met the final inclusion criteria. The findings from the review showed the current state and the future trends in documents, citations, document sources/authors, keywords, and co-authorship. The research gaps identified suggest that while some authors have looked at understanding the detection of AI-generated text, it may be beneficial to understand how GenAI can be incorporated into supporting the educational curriculum for assessments, teaching, and learning delivery. Furthermore, there is a need for additional interdisciplinary, multidimensional studies in HE through collaboration. This will strengthen the awareness and understanding of students, tutors, and other stakeholders, which will be instrumental in formulating guidelines, frameworks, and policies for GenAI usage.