An enduring problem with today's generative artificial intelligence (AI) tools, like ChatGPT, is that they often confidently assert false information. Computer scientists call this behavior "hallucination," and it's a key barrier to AI's usefulness. Hallucinations have led to some embarrassing public slip-ups. In February, AirCanada was forced by a tribunal to honor a discount that its customer-support chatbot had mistakenly offered to a passenger. In May, Google was forced to make changes to its new "AI overviews" search feature, after the bot told some users that it was safe to eat rocks. And last June, two lawyers were fined 5,000 by a U.S. judge after one of them admitted he had used ChatGPT to help write a court filing.

You may not be familiar with Kuaishou, but this Chinese company just hit a major milestone: It's released the first ever text-to-video generative AI model that's freely available for the public to test. The short-video platform, which has over 600 million active users, announced the new tool, called Kling, on June 6. Like OpenAI's Sora model, Kling is able to generate videos up to two minutes long from prompts. But unlike Sora, which still remains inaccessible to the public four months after OpenAI debuted it, Kling has already started letting people try the model themselves. Zeyi Yang, our China reporter, has been putting it through its paces.

For four years, Jacob Hilton worked for one of the most influential startups in the Bay Area -- OpenAI. His research helped test and improve the truthfulness of AI models such as ChatGPT. He believes artificial intelligence can benefit society, but he also recognizes the serious risks if the technology is left unchecked. Hilton was among 13 current and former OpenAI and Google employees who this month signed an open letter that called for more whistleblower protections, citing broad confidentiality agreements as problematic. "The basic situation is that employees, the people closest to the technology, they're also the ones with the most to lose from being retaliated against for speaking up," says Hilton, 33, now a researcher at the nonprofit Alignment Research Center, who lives in Berkeley.

Last week, Apple held its Worldwide Developers Conference, the annual event that is often used to showcase the company's most significant innovations. Much of the presentation this year was devoted to A.I., or, as the company is branding it, Apple Intelligence. Whereas Google and Microsoft have leaped headlong into A.I. with their Gemini and OpenAI products, respectively, Apple is so far taking a narrower approach. The A.I. model it is unveiling on iPhone hardware is relatively weak. A.I. models are measured on their number of "parameters," or the variables adjusted during the training process; while OpenAI's GPT-4 has more than one and a half trillion parameters, Apple's model has three billion.

The short-video platform, which has over 600 million active users, announced the new tool on June 6. Like OpenAI's Sora model, Kling is able to generate videos "up to two minutes long with a frame rate of 30fps and video resolution up to 1080p," the company says on its website. But unlike Sora, which still remains inaccessible to the public four months after OpenAI trialed it, Kling soon started letting people try the model themselves. I got access to it after downloading Kuaishou's video-editing tool, signing up with a Chinese number, getting on a waitlist, and filling out an additional form through Kuaishou's user feedback groups. The model can't process prompts written entirely in English, but you can get around that by either translating the phrase you want to use into Chinese or including one or two Chinese words.

Recent Large Language Models (LLMs) have demonstrated impressive capabilities at tasks that require human intelligence and are a significant step towards human-like artificial intelligence (AI). Yet the performance of LLMs at reasoning tasks have been subpar and the reasoning capability of LLMs is a matter of significant debate. While it has been shown that the choice of the prompting technique to the LLM can alter its performance on a multitude of tasks, including reasoning, the best performing techniques require human-made prompts with the knowledge of the tasks at hand. We introduce a framework for what we call Combinatorial Reasoning (CR), a fully-automated prompting method, where reasons are sampled from an LLM pipeline and mapped into a Quadratic Unconstrained Binary Optimization (QUBO) problem. The framework investigates whether QUBO solutions can be profitably used to select a useful subset of the reasons to construct a Chain-of-Thought style prompt. We explore the acceleration of CR with specialized solvers. We also investigate the performance of simpler zero-shot strategies such as linear majority rule or random selection of reasons. Our preliminary study indicates that coupling a combinatorial solver to generative AI pipelines is an interesting avenue for AI reasoning and elucidates design principles for future CR methods.

Journalists are among the many users of large language models (LLMs). To better understand the journalist-AI interactions, we conduct a study of LLM usage by two news agencies through browsing the WildChat dataset, identifying candidate interactions, and verifying them by matching to online published articles. Our analysis uncovers instances where journalists provide sensitive material such as confidential correspondence with sources or articles from other agencies to the LLM as stimuli and prompt it to generate articles, and publish these machine-generated articles with limited intervention (median output-publication ROUGE-L of 0.62). Based on our findings, we call for further research into what constitutes responsible use of AI, and the establishment of clear guidelines and best practices on using LLMs in a journalistic context.

Given the rise of multimedia content, human translators increasingly focus on culturally adapting not only words but also other modalities such as images to convey the same meaning. While several applications stand to benefit from this, machine translation systems remain confined to dealing with language in speech and text. In this work, we take a first step towards translating images to make them culturally relevant. First, we build three pipelines comprising state-of-the-art generative models to do the task. Next, we build a two-part evaluation dataset: i) concept: comprising 600 images that are cross-culturally coherent, focusing on a single concept per image, and ii) application: comprising 100 images curated from real-world applications. We conduct a multi-faceted human evaluation of translated images to assess for cultural relevance and meaning preservation. We find that as of today, image-editing models fail at this task, but can be improved by leveraging LLMs and retrievers in the loop. Best pipelines can only translate 5% of images for some countries in the easier concept dataset and no translation is successful for some countries in the application dataset, highlighting the challenging nature of the task. Our code and data is released here: https://github.com/simran-khanuja/image-transcreation.

This study investigates how LLMs, specifically GPT-3.5 and GPT-4, can develop tailored questions for Grade 9 math, aligning with active learning principles. By utilizing an iterative method, these models adjust questions based on difficulty and content, responding to feedback from a simulated 'student' model. A novel aspect of the research involved using GPT-4 as a 'teacher' to create complex questions, with GPT-3.5 as the 'student' responding to these challenges. This setup mirrors active learning, promoting deeper engagement. The findings demonstrate GPT-4's superior ability to generate precise, challenging questions and notable improvements in GPT-3.5's ability to handle more complex problems after receiving instruction from GPT-4. These results underscore the potential of LLMs to mimic and enhance active learning scenarios, offering a promising path for AI in customized education. This research contributes to understanding how AI can support personalized learning experiences, highlighting the need for further exploration in various educational contexts

The advent of generative AI models has revolutionized digital content creation, yet it introduces challenges in maintaining copyright integrity due to generative parroting, where models mimic their training data too closely. Our research presents a novel approach to tackle this issue by employing an overfitted Masked Autoencoder (MAE) to detect such parroted samples effectively. We establish a detection threshold based on the mean loss across the training dataset, allowing for the precise identification of parroted content in modified datasets. Preliminary evaluations demonstrate promising results, suggesting our method's potential to ensure ethical use and enhance the legal compliance of generative models.