With concerns around generative AI ever-present, Google has announced an expansion of its Vulnerability Rewards Program (VRP) focused on AI-specific attacks and opportunities for malice. As such, the company released updated guidelines detailing which discoveries qualify for rewards and which fall out of scope. For example, discovering training data extraction that leaks private, sensitive information falls in scope, but if it only shows public, nonsensitive data, then it wouldn't qualify for a reward. Last year, Google gave security researchers $12 million for bug discoveries. Google explained that AI presents different security issues than their other technology -- such as model manipulation and unfair bias -- requiring new guidance to mirror this.

As the generative artificial intelligence gold rush intensifies, concerns about the data used to train machine learning tools have grown. Artists and writers are fighting for a say in how AI companies use their work, filing lawsuits and publicly agitating against the way these models scrape the internet and incorporate their art without consent. Some companies have responded to this pushback with "opt-out" programs that give people a choice to remove their work from future models. OpenAI, for example, debuted an opt-out feature with its latest version of the image-to-text generator Dall-E. This August, when Meta began allowing people to submit requests to delete personal data from third parties used to train Meta's generative AI models, many artists and journalists interpreted this new process as Meta's very limited version of an opt-out program.

A handful of tech companies are jeopardising humanity's future through unrestrained AI development and must stop their "race to the bottom", according to the scientist behind an influential letter calling for a pause in building powerful systems. Max Tegmark, a professor of physics and AI researcher at the Massachusetts Institute of Technology, said the world was "witnessing a race to the bottom that must be stopped". Tegmark organised an open letter published in April, signed by thousands of tech industry figures including Elon Musk and the Apple co-founder Steve Wozniak, that called for a six-month hiatus on giant AI experiments. "We're witnessing a race to the bottom that must be stopped," Tegmark told the Guardian. "We urgently need AI safety standards, so that this transforms into a race to the top. AI promises many incredible benefits, but the reckless and unchecked development of increasingly powerful systems, with no oversight, puts our economy, our society, and our lives at risk. Regulation is critical to safe innovation, so that a handful of AI corporations don't jeopardise our shared future."

Zuckerberg said that chief among the company's investment priorities in 2024 will be artificial intelligence, where Meta will hire more engineers and build up its computing resources. Last month, the company launched conversational chatbots that allows users to find information and generate images -- a partial attempt to compete with OpenAI's popular ChatGPT amid an industry-wide boom in generative AI. The company also announced this summer that its new Llama 2 "large language model" -- a highly complex algorithm trained on billions of words scraped from the open internet -- will be available for researchers and companies to use freely.

Academic writing is an indispensable yet laborious part of the research enterprise. This Perspective maps out principles and methods for using generative artificial intelligence (AI), specifically large language models (LLMs), to elevate the quality and efficiency of academic writing. We introduce a human-AI collaborative framework that delineates the rationale (why), process (how), and nature (what) of AI engagement in writing. The framework pinpoints both short-term and long-term reasons for engagement and their underlying mechanisms (e.g., cognitive offloading and imaginative stimulation). It reveals the role of AI throughout the writing process, conceptualized through a two-stage model for human-AI collaborative writing, and the nature of AI assistance in writing, represented through a model of writing-assistance types and levels. Building on this framework, we describe effective prompting techniques for incorporating AI into the writing routine (outlining, drafting, and editing) as well as strategies for maintaining rigorous scholarship, adhering to varied journal policies, and avoiding overreliance on AI. Ultimately, the prudent integration of AI into academic writing can ease the communication burden, empower authors, accelerate discovery, and promote diversity in science.

Recent advances in machine learning have made revolutionary breakthroughs in computer games, image and natural language understanding, and scientific discovery. Foundation models and large-scale language models (LLMs) have recently achieved human-like intelligence thanks to BigData. With the help of self-supervised learning (SSL) and transfer learning, these models may potentially reshape the landscapes of neuroscience research and make a significant impact on the future. Here we present a mini-review on recent advances in foundation models and generative AI models as well as their applications in neuroscience, including natural language and speech, semantic memory, brain-machine interfaces (BMIs), and data augmentation. We argue that this paradigm-shift framework will open new avenues for many neuroscience research directions and discuss the accompanying challenges and opportunities.

We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products and services. Our framework for automatically measuring harms from LLMs builds on existing technical and sociotechnical expertise and leverages the capabilities of state-of-the-art LLMs, such as GPT-4. We use this framework to run through several case studies investigating how different LLMs may violate a range of RAI-related principles. The framework may be employed alongside domain-specific sociotechnical expertise to create measurements for new harm areas in the future. By implementing this framework, we aim to enable more advanced harm measurement efforts and further the responsible use of LLMs.

We explore the value of generative AI tools, such as ChatGPT, in helping investors uncover dimensions of corporate risk. We develop and validate firm-level measures of risk exposure to political, climate, and AI-related risks. Using the GPT 3.5 model to generate risk summaries and assessments from the context provided by earnings call transcripts, we show that GPT-based measures possess significant information content and outperform the existing risk measures in predicting (abnormal) firm-level volatility and firms' choices such as investment and innovation. Importantly, information in risk assessments dominates that in risk summaries, establishing the value of general AI knowledge. We also find that generative AI is effective at detecting emerging risks, such as AI risk, which has soared in recent quarters. Our measures perform well both within and outside the GPT's training window and are priced in equity markets. Taken together, an AI-based approach to risk measurement provides useful insights to users of corporate disclosures at a low cost.

Generative AI applications are recently catering to a vast user base by creating diverse and high-quality AI-generated content (AIGC). With the proliferation of mobile devices and rapid growth of mobile traffic, providing ubiquitous access to high-quality AIGC services via wireless communication networks is becoming the future direction for AIGC products. However, it is challenging to provide optimal AIGC services in wireless networks with unstable channels, limited bandwidth resources, and unevenly distributed computational resources. To tackle these challenges, we propose a semantic communication (SemCom)-empowered AIGC (SemAIGC) generation and transmission framework, where only semantic information of the content rather than all the binary bits should be extracted and transmitted by using SemCom. Specifically, SemAIGC integrates diffusion-based models within the semantic encoder and decoder for efficient content generation and flexible adjustment of the computing workload of both transmitter and receiver. Meanwhile, we devise a resource-aware workload trade-off (ROOT) scheme into the SemAIGC framework to intelligently decide transmitter/receiver workload, thus adjusting the utilization of computational resource according to service requirements. Simulations verify the superiority of our proposed SemAIGC framework in terms of latency and content quality compared to conventional approaches.

Generative Artificial Intelligence (AI) is a cutting-edge technology capable of producing text, images, and various media content leveraging generative models and user prompts. Between 2022 and 2023, generative AI surged in popularity with a plethora of applications spanning from AI-powered movies to chatbots. In this paper, we delve into the potential of generative AI within the realm of the World Wide Web, specifically focusing on image generation. Web developers already harness generative AI to help crafting text and images, while Web browsers might use it in the future to locally generate images for tasks like repairing broken webpages, conserving bandwidth, and enhancing privacy. To explore this research area, we have developed WebDiffusion, a tool that allows to simulate a Web powered by stable diffusion, a popular text-to-image model, from both a client and server perspective. WebDiffusion further supports crowdsourcing of user opinions, which we use to evaluate the quality and accuracy of 409 AI-generated images sourced from 60 webpages. Our findings suggest that generative AI is already capable of producing pertinent and high-quality Web images, even without requiring Web designers to manually input prompts, just by leveraging contextual information available within the webpages. However, we acknowledge that direct in-browser image generation remains a challenge, as only highly powerful GPUs, such as the A40 and A100, can (partially) compete with classic image downloads. Nevertheless, this approach could be valuable for a subset of the images, for example when fixing broken webpages or handling highly private content.