Apple is apparently looking to take all the help it can get to integrate generative AI into its recently announced Apple Intelligence. According to a report by the Wall Street Journal, citing sources with knowledge of the discussions, Apple has held talks with Meta about the possibility of using the company's generative AI model. It also reportedly had similar discussions with startups Anthropic and Perplexity. As of now, though, nothing has been finalized, WSJ reports. At WWDC earlier this month, Apple officially announced its much-rumored partnership with OpenAI that will bring ChatGPT to newer iPhones, iPads and Macs with the upcoming generation of the devices' OS.

ChatGPT can now easily pass any Turing test, a measure of successful A.I. proposed by a founder of computer science, Alan Turing. But contemporary Turing tests leave out the most interesting part of Turing's original test: the gender-bending. I can usually spot A.I. writing in my students' work by the overuse of words like "delve," but the accuracy of artificial intelligence is impossible to deny. A.I. is being integrated into every aspect of our written culture, from news sources to classrooms to medicine. But in 1950, Turing's ideas about A.I. were prescient, creative, and, when I read them, surprisingly queer.

For a few days, AI chip juggernaut Nvidia sat on the throne as the world's biggest company, but behind the its staggering success are questions on whether new entrants can stake a claim to the artificial intelligence bonanza. Nvidia, which makes the processors that are the only option to train generative AI's large language models, is now Big Tech's newest member and its stock market takeoff has lifted the whole sector. Even tech's second rung on Wall Street has ridden on Nvidia's coattails with Oracle, Broadcom, HP and a spate of others seeing their stock valuations surge, despite sometimes shaky earnings.

Generative artificial intelligence (GenAI) has the potential to improve healthcare through automation that enhances the quality and safety of patient care. Powered by foundation models that have been pretrained and can generate complex content, GenAI represents a paradigm shift away from the more traditional focus on task-specific classifiers that have dominated the AI landscape thus far. We posit that the imminent application of GenAI in healthcare will be through well-defined, low risk, high value, and narrow applications that automate healthcare workflows at the point of care using smaller foundation models. These models will be finetuned for different capabilities and application specific scenarios and will have the ability to provide medical explanations, reference evidence within a retrieval augmented framework and utilizing external tools. We contrast this with a general, all-purpose AI model for end-to-end clinical decision making that improves clinician performance, including safety-critical diagnostic tasks, which will require greater research prior to implementation. We consider areas where 'human in the loop' Generative AI can improve healthcare quality and safety by automating mundane tasks. Using the principles of implementation science will be critical for integrating 'end to end' GenAI systems that will be accepted by healthcare teams.

In an opulent ballroom on a Saturday night, the classic pump-up anthem "Eye of the Tiger" blared as artificial intelligence enthusiasts tapped away on their keyboards. This was a hackathon -- an event where participants have a set amount of time to collaborate on a project they present to the crowd -- at a sprawling mansion about 30 minutes south of San Francisco. As a professional freelance photographer, I've spent the past decade documenting the people and culture of Silicon Valley. Ever since OpenAI's ChatGPT debuted in November 2022, countless entrepreneurs have been inspired to make their own generative AI tools. Now, nearly every new start-up has an AI element -- technology that automates simple tasks, for example, or a chatbot that provides mental health tips.

This study addresses a critical gap by exploring student and academic staff experiences with AI and GenAI tools, focusing on their familiarity and comfort with current and potential future applications in learning and assessment. An online survey collected data from 35 academic staff and 282 students across two universities in Vietnam and one in Singapore, examining GenAI familiarity, perceptions of its use in assessment marking and feedback, knowledge checking and participation, and experiences of GenAI text detection. Descriptive statistics and reflexive thematic analysis revealed a generally low familiarity with GenAI among both groups. GenAI feedback was viewed negatively; however, it was viewed more positively when combined with instructor feedback. Academic staff were more accepting of GenAI text detection tools and grade adjustments based on detection results compared to students. Qualitative analysis identified three themes: unclear understanding of text detection tools, variability in experiences with GenAI detectors, and mixed feelings about GenAI's future impact on educational assessment. These findings have major implications regarding the development of policies and practices for GenAI-enabled assessment and feedback in higher education.

Recent advancements in large language models have revolutionized information access, as these models harness data available on the web to address complex queries, becoming the preferred information source for many users. In certain cases, queries are about publicly available data, which can be effectively answered with data visualizations. In this paper, we investigate the ability of large language models to provide accurate data and relevant visualizations in response to such queries. Specifically, we investigate the ability of GPT-3 and GPT-4 to generate visualizations with dataless prompts, where no data accompanies the query. We evaluate the results of the models by comparing them to visualization cheat sheets created by visualization experts.

Artificial intelligence (AI) is interacting with people at an unprecedented scale, offering new avenues for immense positive impact, but also raising widespread concerns around the potential for individual and societal harm. Today, the predominant paradigm for human--AI safety focuses on fine-tuning the generative model's outputs to better agree with human-provided examples or feedback. In reality, however, the consequences of an AI model's outputs cannot be determined in isolation: they are tightly entangled with the responses and behavior of human users over time. In this paper, we distill key complementary lessons from AI safety and control systems safety, highlighting open challenges as well as key synergies between both fields. We then argue that meaningful safety assurances for advanced AI technologies require reasoning about how the feedback loop formed by AI outputs and human behavior may drive the interaction towards different outcomes. To this end, we introduce a unifying formalism to capture dynamic, safety-critical human--AI interactions and propose a concrete technical roadmap towards next-generation human-centered AI safety.

Deep Reinforcement Learning (DRL) is regarded as a promising tool for optical network optimization. However, the flexibility and efficiency of current DRL-based solutions for optical network optimization require further improvement. Currently, generative models have showcased their significant performance advantages across various domains. In this paper, we introduce OpticGAI, the AI-generated policy design paradigm for optical networks. In detail, it is implemented as a novel DRL framework that utilizes generative models to learn the optimal policy network. Furthermore, we assess the performance of OpticGAI on two NP-hard optical network problems, Routing and Wavelength Assignment (RWA) and dynamic Routing, Modulation, and Spectrum Allocation (RMSA), to show the feasibility of the AI-generated policy paradigm. Simulation results have shown that OpticGAI achieves the highest reward and the lowest blocking rate of both RWA and RMSA problems. OpticGAI poses a promising direction for future research on generative AI-enhanced flexible optical network optimization.

The Swedish buy-now-pay-later company Klarna has become something of a poster child for the potential benefits of generative artificial intelligence. The company relies on AI to create and tailor promotional images and to draft marketing copy, saving millions of dollars. Earlier this year it said an AI chatbot assistant was doing the work of 700 human customer-service agents, which it forecast would boost profits by 40 million this year. Klarna's approach highlights generative AI's promise for powering businesswide systems, like customer service. U.S. businesses are investing in AI, and they're eager to see such gains.