Until late November, when the epic saga of OpenAI's board breakdown unfolded, the casual observer could be forgiven for assuming that the ecosystem around generative AI was vibrant and competitive. But this is not the case--nor has it ever been. And understanding why is fundamental to understanding what AI is, and what threats it poses. Put simply, in the context of the current paradigm of building larger- and larger-scale AI systems, there is no AI without Big Tech. With vanishingly few exceptions, every startup, new entrant, and even AI research lab is dependent on these firms. Those with the money make the rules.

When the board of OpenAI suddenly fired the company's CEO last month, it sparked speculation that board members were rattled by the breakneck pace of progress in artificial intelligence and the possible risks of seeking to commercialize the technology too quickly. Robust Intelligence, a startup founded in 2020 to develop ways to protect AI systems from attack, says that some existing risks need more attention. Working with researchers from Yale University, Robust Intelligence has developed a systematic way to probe large language models (LLMs), including OpenAI's prized GPT-4 asset, using "adversarial" AI models to discover "jailbreak" prompts that cause the language models to misbehave. While the drama at OpenAI was unfolding, the researchers warned OpenAI of the vulnerability. They say they have yet to receive a response.

AI development and concerns about its safety continue to grow at a rapid pace with little regulation in place. The latest industry-based solution to this comes courtesy of IBM and Meta, which have announced the creation of the AI Alliance. Its mission centers on "fostering an open community and enabling developers and researchers to accelerate responsible innovation in AI while ensuring scientific rigor, trust, safety, security, diversity and economic competitiveness." Part of this work will involve efforts to expand the number of open-source AI models -- ones with public source code -- which runs counter to the private models of companies like OpenAI and Google. Open-sourcing is a key pillar of the AI Alliance.

The recent OpenAI saga, in which Microsoft exerted its quiet but firm dominance over the "capped profit" entity, provides a powerful demonstration of what we've been analyzing for the last half-decade. To wit: those with the money make the rules. And right now, they're engaged in a race to the bottom, releasing systems before they're ready in an attempt to retain their dominant position. Relying on a few unaccountable corporate actors for core infrastructure is a problem for democracy, culture, and individual and collective agency. Without significant intervention, the AI market will only end up rewarding and entrenching the very same companies that reaped the profits of the invasive surveillance business model that has powered the commercial internet, often at the expense of the public. The Cambridge Analytica scandal was just one among many that exposed this seedy reality.

While existing image-text alignment models reach high quality binary assessments, they fall short of pinpointing the exact source of misalignment. In this paper, we present a method to provide detailed textual and visual explanation of detected misalignments between text-image pairs. We leverage large language models and visual grounding models to automatically construct a training set that holds plausible misaligned captions for a given image and corresponding textual explanations and visual indicators. We also publish a new human curated test set comprising ground-truth textual and visual misalignment annotations. Empirical results show that fine-tuning vision language models on our training set enables them to articulate misalignments and visually indicate them within images, outperforming strong baselines both on the binary alignment classification and the explanation generation tasks. Our method code and human curated test set are available at: https://mismatch-quest.github.io/

Communicating science and technology is essential for the public to understand and engage in a rapidly changing world. Tweetorials are an emerging phenomenon where experts explain STEM topics on social media in creative and engaging ways. However, STEM experts struggle to write an engaging "hook" in the first tweet that captures the reader's attention. We propose methods to use large language models (LLMs) to help users scaffold their process of writing a relatable hook for complex scientific topics. We demonstrate that LLMs can help writers find everyday experiences that are relatable and interesting to the public, avoid jargon, and spark curiosity. Our evaluation shows that the system reduces cognitive load and helps people write better hooks. Lastly, we discuss the importance of interactivity with LLMs to preserve the correctness, effectiveness, and authenticity of the writing.

The formidable capacity for zero- or few-shot decision-making in language agents encourages us to pose a compelling question: Can language agents be alternatives to PPO agents in traditional sequential decision-making tasks? To investigate this, we first take environments collected in OpenAI Gym as our testbeds and ground them to textual environments that construct the TextGym simulator. This allows for straightforward and efficient comparisons between PPO agents and language agents, given the widespread adoption of OpenAI Gym. To ensure a fair and effective benchmarking, we introduce $5$ levels of scenario for accurate domain-knowledge controlling and a unified RL-inspired framework for language agents. Additionally, we propose an innovative explore-exploit-guided language (EXE) agent to solve tasks within TextGym. Through numerical experiments and ablation studies, we extract valuable insights into the decision-making capabilities of language agents and make a preliminary evaluation of their potential to be alternatives to PPO in classical sequential decision-making problems. This paper sheds light on the performance of language agents and paves the way for future research in this exciting domain. Our code is publicly available at~\url{https://github.com/mail-ecnu/Text-Gym-Agents}.

Private data publishing is a technique that involves releasing a modified dataset to preserve user privacy while enabling downstream machine learning tasks. While many private data publishing algorithms exist, traditional algorithms (e.g., DPPro [1], PrivBayes [2], etc.) based on releasing tabular data are not suitable for modern machine learning tasks involving complex structures such as images, videos, and texts. To tackle this, a series of deep learning algorithms have emerged, such as DP-GAN [3] and PATE-GAN [4], which are based on training a Deep Generative Model (DGM) to generate data with complex structures, such as images, texts, and audios. These methods generate fake data based on the trained DGM and publish it instead of the raw data to respect users' privacy. However, as empirically observed by Takagi et al. [5], these DGM-based methods often suffer from training instability, such as mode collapse and high computational costs and lead to low utility, which is defined as the usefulness of the private data. For example, in the case of classification datasets, utility can be measured by classification accuracy. DPMix -- a new data publishing technique proposed by Lee et al. [6] -- does not rely on training deep generative models and has the potential to improve utility. DPMix, as opposed to DGM-based methods, directly adds noise to the raw dataset -- thereby taking into account users' privacy -- and publishes the noisy version of the dataset. Concretely, inspired by Zhang et al. [7], DPMix first mixes the data points by averaging groups of raw data (with group size m), then adds noise to each individual mixture of data points to respect privacy concerns, and finally publishes the noisy

Last week, a team of researchers published a paper showing that it was able to get ChatGPT to inadvertently reveal bits of data including people's phone numbers, email addresses and dates of birth that it had been trained on by asking it to repeat words "forever". Doing this now is a violation of ChatGPT's terms of service, according to a report in 404 Media and Engadget's own testing. "This content may violate our content policy or terms of use", ChatGPT responded to Engadget's prompt to repeat the word "hello" forever. "If you believe this to be in error, please submit your feedback -- your input will aid our research in this area." There's no language in OpenAI's content policy, however, that prohibits users from asking the service to repeat words forever, something that 404 Media notes.

One of the highlights of the recent Open Data Institute (ODI) Summit 2023 was the showing of a short film by artist and AI collaborator, Alan Warburton. This video essay was commissioned by the ODI's Data as Culture programme and addresses the cultural impacts of generative AI. The ODI Summit 2023 took place on 7 November and featured keynote presentations, lightening talks, and panel discussions. The event brought together representatives from civil society, academia, industry, and government. Find out more on the ODI website.