The problem with Bluey is there's not enough of it. Even with 151 seven-minute-long episodes of the popular children's animated show out there, parents of toddlers still desperately wait for Australia's Ludo Studio to release another season. The only way to get more Bluey more quickly is if they create their own stories starring the Brisbane-based family of blue heeler dogs. The London-based developer and father used OpenAI's latest tool, customizable bots called GPTs, to create a story generator for his young daughter. The bot, which he calls Bluey-GPT, begins each session by asking people their name, age, and a bit about their day, then churns out personalized tales starring Bluey and her sister Bingo.

What to do when surrounded by people who are losing their minds about the Newest New Thing? Answer: reach for the Gartner Hype Cycle, an ingenious diagram that maps the progress of an emerging technology through five phases: the "technology trigger", which is followed by a rapid rise to the "peak of inflated expectations"; this is succeeded by a rapid decline into the "trough of disillusionment", after which begins a gentle climb up the "slope of enlightenment" – before eventually (often years or decades later) reaching the "plateau of productivity". Given the current hysteria about AI, I thought I'd check to see where it is on the chart. It shows that generative AI (the polite term for ChatGPT and co) has just reached the peak of inflated expectations. That squares with the fevered predictions of the tech industry (not to mention governments) that AI will be transformative and will soon be ubiquitous.

Apple has been noticeably missing in the list of companies with their own generative AI product, but based on a new report by The New York Times, it's looking to change that real soon. In recent weeks, Apple has reportedly started negotiating with major publishers and news organizations to ask for permission to use their content to train the generative AI system it's developing. The company doesn't expect to get its hands on their content for free, though, and The Times says it's offering them multi-year deals worth at least $50 million for access to their news archives. Apparently, some of the publishers it approached are concerned about the repercussions of letting Apple use their news articles throughout the years. They think a broad licensing deal for their archives could lead to legal issues along the way.

Gif-sur-Yvette 91191 Abstract We suggest the implementation of the Dual Use Research of Concern (DURC) framework, originally designed for life sciences, to the domain of generative AI, with a specific focus on Large Language Models (LLMs). With its demonstrated advantages and drawbacks in biological research, we believe the DURC criteria can be effectively redefined for LLMs, potentially contributing to improved AI governance. Acknowledging the balance that must be struck when employing the DURC framework, we highlight its crucial political role in enhancing societal awareness of the impact of generative AI. As a final point, we offer a series of specific recommendations for applying the DURC approach to LLM research. Keywords: Dual Use Research of Concern (DURC), Generative AI, Large Language Models (LLMs), AI Ethics Conflict of interest No conflict of interest to report. Funding This research was supported through projects TechEthos (grant number 101006249) and MultiRATE (grant number 101073929) funded by the European Commission Horizon program. Ethics approval No human subjects were involved in the study. Consent No data needing consent has been used. Data availability statement In this article, we do not analyze or generate any datasets. Author Contribution All authors contributed to the study conception and design. Sections 1 and 4 were written with equal contribution. Sections 2 and 3 were conceived by Adomaitis and later edited by Grinbaum.

The advent of large language models (LLMs) has heightened interest in their potential for multimodal applications that integrate language and vision. This paper explores the capabilities of GPT-4V in the realms of geography, environmental science, agriculture, and urban planning by evaluating its performance across a variety of tasks. Data sources comprise satellite imagery, aerial photos, ground-level images, field images, and public datasets. The model is evaluated on a series of tasks including geo-localization, textual data extraction from maps, remote sensing image classification, visual question answering, crop type identification, disease/pest/weed recognition, chicken behavior analysis, agricultural object counting, urban planning knowledge question answering, and plan generation. The results indicate the potential of GPT-4V in geo-localization, land cover classification, visual question answering, and basic image understanding. However, there are limitations in several tasks requiring fine-grained recognition and precise counting. While zero-shot learning shows promise, performance varies across problem domains and image complexities. The work provides novel insights into GPT-4V's capabilities and limitations for real-world geospatial, environmental, agricultural, and urban planning challenges. Further research should focus on augmenting the model's knowledge and reasoning for specialized domains through expanded training. Overall, the analysis demonstrates foundational multimodal intelligence, highlighting the potential of multimodal foundation models (FMs) to advance interdisciplinary applications at the nexus of computer vision and language.

User modeling (UM) aims to discover patterns or learn representations from user data about the characteristics of a specific user, such as profile, preference, and personality. The user models enable personalization and suspiciousness detection in many online applications such as recommendation, education, and healthcare. Two common types of user data are text and graph, as the data usually contain a large amount of user-generated content (UGC) and online interactions. The research of text and graph mining is developing rapidly, contributing many notable solutions in the past two decades. Recently, large language models (LLMs) have shown superior performance on generating, understanding, and even reasoning over text data. The approaches of user modeling have been equipped with LLMs and soon become outstanding. This article summarizes existing research about how and why LLMs are great tools of modeling and understanding UGC. Then it reviews a few categories of large language models for user modeling (LLM-UM) approaches that integrate the LLMs with text and graph-based methods in different ways. Then it introduces specific LLM-UM techniques for a variety of UM applications. Finally, it presents remaining challenges and future directions in the LLM-UM research. We maintain the reading list at: https://github.com/TamSiuhin/LLM-UM-Reading

This paper conducts a robustness audit of the safety feedback of PaLM 2 through a novel toxicity rabbit hole framework introduced here. Starting with a stereotype, the framework instructs PaLM 2 to generate more toxic content than the stereotype. Every subsequent iteration it continues instructing PaLM 2 to generate more toxic content than the previous iteration until PaLM 2 safety guardrails throw a safety violation. Our experiments uncover highly disturbing antisemitic, Islamophobic, racist, homophobic, and misogynistic (to list a few) generated content that PaLM 2 safety guardrails do not evaluate as highly unsafe. We briefly discuss the generalizability of this framework across eight other large language models.

This is Atlantic Intelligence, an eight-week series in which The Atlantic's leading thinkers on AI will help you understand the complexity and opportunities of this groundbreaking technology. The bedrock of the AI revolution is the internet, or more specifically, the ever-expanding bounty of data that the web makes available to train algorithms. ChatGPT, Midjourney, and other generative-AI models "learn" by detecting patterns in massive amounts of text, images, and videos scraped from the internet. The process entails hoovering up huge quantities of books, art, memes, and, inevitably, the troves of racist, sexist, and illicit material distributed across the web. Earlier this week, Stanford researchers found a particularly alarming example of that toxicity: The largest publicly available image data set used to train AIs, LAION-5B, reportedly contains more than 1,000 images depicting the sexual abuse of children, out of more than 5 billion in total.

Andrew Perlman, Dean of the Suffolk University School of Law in Boston, is no stranger to examining innovative legal technology, but his recent experiment with generative artificial intelligence (AI)--Open AI's ChatGPT, to be precise--led him to think the technology may create bigger changes to the way law is practiced than the Internet itself. Perlman published one of the legal community's first evaluations of ChatGPT's capabilities in creating convincing arguments and answers to typical questions, in "The Implications of ChatGPT For Legal Services and Society" (https://bit.ly/3NuhFxG),

The growth of low-end hardware has led to a proliferation of machine learning-based services in edge applications. These applications gather contextual information about users and provide some services, such as personalized offers, through a machine learning (ML) model. A growing practice has been to deploy such ML models on the user's device to reduce latency, maintain user privacy, and minimize continuous reliance on a centralized source. However, deploying ML models on the user's edge device can leak proprietary information about the service provider. In this work, we investigate on-device ML models that are used to provide mobile services and demonstrate how simple attacks can leak proprietary information of the service provider. We show that different adversaries can easily exploit such models to maximize their profit and accomplish content theft. Motivated by the need to thwart such attacks, we present an end-to-end framework, SODA, for deploying and serving on edge devices while defending against adversarial usage. Our results demonstrate that SODA can detect adversarial usage with 89% accuracy in less than 50 queries with minimal impact on service performance, latency, and storage.