OpenAI seems to make headlines every day and this time it's for a double dose of security concerns. The first issue centers on the Mac app for ChatGPT, while the second hints at broader concerns about how the company is handling its cybersecurity. Earlier this week, engineer and Swift developer Pedro José Pereira Vieito dug into the Mac ChatGPT app and found that it was storing user conversations locally in plain text rather than encrypting them. The app is only available from OpenAI's website, and since it's not available on the App Store, it doesn't have to follow Apple's sandboxing requirements. Vieito's work was then covered by The Verge, and after the exploit attracted attention, OpenAI released an update that added encryption to locally stored chats.

In this paper, we introduce a comprehensive reformulation of the task known as Circuit Discovery, along with DiscoGP, a novel and effective algorithm based on differentiable masking for discovering circuits. Circuit discovery is the task of interpreting the computational mechanisms of language models (LMs) by dissecting their functions and capabilities into sparse subnetworks (circuits). We identified two major limitations in existing circuit discovery efforts: (1) a dichotomy between weight-based and connection-edge-based approaches forces researchers to choose between pruning connections or weights, thereby limiting the scope of mechanistic interpretation of LMs; (2) algorithms based on activation patching tend to identify circuits that are neither functionally faithful nor complete. The performance of these identified circuits is substantially reduced, often resulting in near-random performance in isolation. Furthermore, the complement of the circuit -- i.e., the original LM with the identified circuit removed -- still retains adequate performance, indicating that essential components of a complete circuits are missed by existing methods. DiscoGP successfully addresses the two aforementioned issues and demonstrates state-of-the-art faithfulness, completeness, and sparsity. The effectiveness of the algorithm and its novel structure open up new avenues of gathering new insights into the internal workings of generative AI.

In this study, we explored the progression trajectories of artificial intelligence (AI) systems through the lens of complexity theory. We challenged the conventional linear and exponential projections of AI advancement toward Artificial General Intelligence (AGI) underpinned by transformer-based architectures, and posited the existence of critical points, akin to phase transitions in complex systems, where AI performance might plateau or regress into instability upon exceeding a critical complexity threshold. We employed agent-based modelling (ABM) to simulate hypothetical scenarios of AI systems' evolution under specific assumptions, using benchmark performance as a proxy for capability and complexity. Our simulations demonstrated how increasing the complexity of the AI system could exceed an upper criticality threshold, leading to unpredictable performance behaviours. Additionally, we developed a practical methodology for detecting these critical thresholds using simulation data and stochastic gradient descent to fine-tune detection thresholds. This research offers a novel perspective on AI advancement that has a particular relevance to Large Language Models (LLMs), emphasising the need for a tempered approach to extrapolating AI's growth potential and underscoring the importance of developing more robust and comprehensive AI performance benchmarks.

Image data augmentation constitutes a critical methodology in modern computer vision tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; thereby, improving the performance and robustness of machine learning models in downstream tasks. In parallel, augmentation approaches can also be used for editing/modifying a given image in a context- and semantics-aware way. Diffusion Models (DMs), which comprise one of the most recent and highly promising classes of methods in the field of generative Artificial Intelligence (AI), have emerged as a powerful tool for image data augmentation, capable of generating realistic and diverse images by learning the underlying data distribution. The current study realizes a systematic, comprehensive and in-depth review of DM-based approaches for image augmentation, covering a wide range of strategies, tasks and applications. In particular, a comprehensive analysis of the fundamental principles, model architectures and training strategies of DMs is initially performed. Subsequently, a taxonomy of the relevant image augmentation methods is introduced, focusing on techniques regarding semantic manipulation, personalization and adaptation, and application-specific augmentation tasks. Then, performance assessment methodologies and respective evaluation metrics are analyzed. Finally, current challenges and future research directions in the field are discussed.

Traditional recommender systems (RS) typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample from complex data distributions, including user-item interactions, text, images, and videos, enabling novel recommendation tasks. This comprehensive, multidisciplinary survey connects key advancements in RS using Generative Models (Gen-RecSys), covering: interaction-driven generative models; the use of large language models (LLM) and textual data for natural language recommendation; and the integration of multimodal models for generating and processing images/videos in RS. Our work highlights necessary paradigms for evaluating the impact and harm of Gen-RecSys and identifies open challenges. This survey accompanies a tutorial presented at ACM KDD'24, with supporting materials provided at: https://encr.pw/vDhLq.

This study investigates whether OpenAI's ChatGPT-3.5 and ChatGPT-4 can forecast future events. To evaluate the accuracy of the predictions, we take advantage of the fact that the training data at the time of our experiments (mid 2023) stopped at September 2021, and ask about events that happened in 2022. We employed two prompting strategies: direct prediction and what we call future narratives which ask ChatGPT to tell fictional stories set in the future with characters retelling events that happened in the past, but after ChatGPT's training data had been collected. We prompted ChatGPT to engage in storytelling, particularly within economic contexts. After analyzing 100 trials, we find that future narrative prompts significantly enhanced ChatGPT-4's forecasting accuracy. This was especially evident in its predictions of major Academy Award winners as well as economic trends, the latter inferred from scenarios where the model impersonated public figures like the Federal Reserve Chair, Jerome Powell. As a falsification exercise, we repeated our experiments in May 2024 at which time the models included more recent training data. ChatGPT-4's accuracy significantly improved when the training window included the events being prompted for, achieving 100% accuracy in many instances. The poorer accuracy for events outside of the training window suggests that in the 2023 prediction experiments, ChatGPT-4 was forming predictions based solely on its training data. Narrative prompting also consistently outperformed direct prompting. These findings indicate that narrative prompts leverage the models' capacity for hallucinatory narrative construction, facilitating more effective data synthesis and extrapolation than straightforward predictions. Our research reveals new aspects of LLMs' predictive capabilities and suggests potential future applications in analytical contexts.

Cloudflare has released a new free tool that prevents AI companies' bots from scraping its clients' websites for content to train large language models. The cloud service provider is making this tool available to its entire customer base, including those on free plans. "This feature will automatically be updated over time as we see new fingerprints of offending bots we identify as widely scraping the web for model training," the company said. In a blog post announcing this update, Cloudflare's team also shared some data about how its clients are responding to the boom of bots that scrape content to train generative AI models. According to the company's internal data, 85.2 percent of customers have chosen to block even the AI bots that properly identify themselves from accessing their sites.

Who is the president of the United States? But one popular artificial intelligence app thinks otherwise. While A.I. models have been known to hallucinate (i.e., make stuff up), they typically don't mess up extremely simple things like name the president. If you ask OpenAI's ChatGPT who the U.S. president is, it'll give you the correct answer, a short bio, and links to the White House website and Biden's Wikipedia article. The same is true for Anthropic's Claude and Meta AI, while Google's Gemini straight-up refuses to answer the question because it's related to politics.

Later this year, millions of Apple devices will begin running Apple Intelligence, Cupertino's take on generative AI that, among other things, lets people create images from text prompts. But some members of the creative community are unhappy about what they say is the company's lack of transparency around the raw information powering the AI model that makes this possible. "I wish Apple would have explained to the public in a more transparent way how they collected their training data," Jon Lam, a video games artist and a creators' rights activist based in Vancouver, told Engadget. "I think their announcement could not have come at a worse time." Creatives have historically been some of the most loyal customers of Apple, a company whose founder famously positioned it at the "intersection of technology and liberal arts."

Multimodal foundation models that can holistically process text alongside images, video, audio, and other sensory modalities are increasingly used in a variety of real-world applications. However, it is challenging to characterize and study progress in multimodal foundation models, given the range of possible modeling decisions, tasks, and domains. In this paper, we introduce Holistic Evaluation of Multimodal Models (HEMM) to systematically evaluate the capabilities of multimodal foundation models across a set of 3 dimensions: basic skills, information flow, and real-world use cases. Basic multimodal skills are internal abilities required to solve problems, such as learning interactions across modalities, fine-grained alignment, multi-step reasoning, and the ability to handle external knowledge. Information flow studies how multimodal content changes during a task through querying, translation, editing, and fusion. Use cases span domain-specific challenges introduced in real-world multimedia, affective computing, natural sciences, healthcare, and human-computer interaction applications. Through comprehensive experiments across the 30 tasks in HEMM, we (1) identify key dataset dimensions (e.g., basic skills, information flows, and use cases) that pose challenges to today's models, and (2) distill performance trends regarding how different modeling dimensions (e.g., scale, pre-training data, multimodal alignment, pre-training, and instruction tuning objectives) influence performance. Our conclusions regarding challenging multimodal interactions, use cases, and tasks requiring reasoning and external knowledge, the benefits of data and model scale, and the impacts of instruction tuning yield actionable insights for future work in multimodal foundation models.