Current large language models demonstrate deficiencies in understanding low-resource languages, particularly the minority languages in China. This limitation stems from the scarcity of available pre-training data. To address this accessibility challenge, we present MC$^2$, a Multilingual Corpus of Minority Languages in China, which is the largest open-source corpus of its kind so far. MC$^2$ includes four underrepresented languages: Tibetan, Uyghur, Kazakh, and Mongolian. Notably, we focus on the less common writing systems of Kazakh and Mongolian, i.e., Kazakh Arabic script and traditional Mongolian script, respectively, which have been long neglected in previous corpus construction efforts. Recognizing the prevalence of language contamination within existing corpora, we adopt a quality-centric solution for collecting MC$^2$, prioritizing accuracy while enhancing diversity. Furthermore, we underscore the importance of attending to the multiplicity of writing systems, which is closely related to the cultural awareness of the resulting models. The MC$^2$ corpus and related models are made public to the community.

We present Piva (Preserving Identity with Variational Score Distillation), a novel optimization-based method for editing images and 3D models based on diffusion models. Specifically, our approach is inspired by the recently proposed method for 2D image editing - Delta Denoising Score (DDS). We pinpoint the limitations in DDS for 2D and 3D editing, which causes detail loss and over-saturation. To address this, we propose an additional score distillation term that enforces identity preservation. This results in a more stable editing process, gradually optimizing NeRF models to match target prompts while retaining crucial input characteristics. We demonstrate the effectiveness of our approach in zero-shot image and neural field editing. Our method successfully alters visual attributes, adds both subtle and substantial structural elements, translates shapes, and achieves competitive results on standard 2D and 3D editing benchmarks. Additionally, our method imposes no constraints like masking or pre-training, making it compatible with a wide range of pre-trained diffusion models. This allows for versatile editing without needing neural field-to-mesh conversion, offering a more user-friendly experience.

Large Language Models have taken the cognitive science world by storm. It is perhaps timely now to take stock of the various research paradigms that have been used to make scientific inferences about ``cognition" in these models or about human cognition. We review several emerging research paradigms -- GPT-ology, LLMs-as-computational-models, and ``silicon sampling" -- and review recent papers that have used LLMs under these paradigms. In doing so, we discuss their claims as well as challenges to scientific inference under these various paradigms. We highlight several outstanding issues about LLMs that have to be addressed to push our science forward: closed-source vs open-sourced models; (the lack of visibility of) training data; and reproducibility in LLM research, including forming conventions on new task ``hyperparameters" like instructions and prompts.

With the continuous advancement of large language models (LLMs), it is essential to create new benchmarks to effectively evaluate their expanding capabilities and identify areas for improvement. This work focuses on multi-image reasoning, an emerging capability in state-of-the-art LLMs. We introduce ReMI, a dataset designed to assess LLMs' ability to Reason with Multiple Images. This dataset encompasses a diverse range of tasks, spanning various reasoning domains such as math, physics, logic, code, table/chart understanding, and spatial and temporal reasoning. It also covers a broad spectrum of characteristics found in multi-image reasoning scenarios. We have benchmarked several cutting-edge LLMs using ReMI and found a substantial gap between their performance and human-level proficiency. This highlights the challenges in multi-image reasoning and the need for further research. Our analysis also reveals the strengths and weaknesses of different models, shedding light on the types of reasoning that are currently attainable and areas where future models require improvement. To foster further research in this area, we are releasing ReMI publicly: https://huggingface.co/datasets/mehrankazemi/ReMI.

As soon as Apple announced its plans to inject generative AI into the iPhone, it was as good as official: The technology is now all but unavoidable. AI has already colonized web search, appearing in Google and Bing. OpenAI, the 80 billion start-up that has partnered with Apple and Microsoft, feels ubiquitous; the auto-generated products of its ChatGPTs and DALL-Es are everywhere. Rarely has a technology risen--or been forced--into prominence amid such controversy and consumer anxiety. Certainly, some Americans are excited about AI, though a majority said in a recent survey, for instance, that they are concerned AI will increase unemployment; in another, three out of four said they believe it will be abused to interfere with the upcoming presidential election.

We don't know if OpenAI, the creator of ChatGPT, is actually making any money so far. But thanks to a Wednesday report in The Information, what we do know is that the company doubled its annualized revenue -- a measure of the previous month's revenue multiplied by 12, as the publication helpfully explained -- in the last six months. OpenAI's annualized revenue was 3.4 billion, CEO Sam Altman reportedly told staff. Most of this revenue came from a subscription version of ChatGPT, which offers higher messaging limits to people who pay at least 20 a month, as well as from developers who pay the company to use the company's large language models in their own apps and services. About 200 million on an annualized basis comes from Microsoft, which gives OpenAI a cut of sales of OpenAI's large language models to customers using Azure, Microsoft's cloud computing platform aimed at businesses.

Elon Musk has withdrawn his lawsuit against OpenAI, a day before a judge was set to hear a request for dismissal. Musk sued OpenAI, saying its founders had violated its nonprofit status, to become a de-facto part of Microsoft. OpenAI said there was no such violation, and the lawsuit was likely a way for Musk to gain access to its secrets. Despite ending the suit, Musk might be nursing this grudge, tweeting if Apple integrates OpenAI's tools into its software, he'll ban iPhones from his companies. You can't mirror your iPhone while mirroring your Mac on Apple Vision Pro Netflix drops a proper trailer for Arcane's second (and last) season Apple Intelligence: What devices and features will actually be supported?

Victor Miller is running for mayor of Cheyenne, Wyoming, with an unusual campaign promise: If elected, he will not be calling the shots--an AI bot will. VIC, the Virtual Integrated Citizen, is a ChatGPT-based chatbot that Miller created. And Miller says the bot has better ideas--and a better grasp of the law--than many people currently serving in government. "I realized that this entity is way smarter than me, and more importantly, way better than some of the outward-facing public servants I see," he says. According to Miller, VIC will make the decisions and Miller will be its "meat puppet," attending meetings, signing documents, and otherwise doing the corporeal job of running the city.

AI safety is a growing concern as more companies integrate generative AI into their offerings and use large language models in consumer products. Last month, Google faced sharp criticism after its experimental "AI Overviews" tool, which purports to answer users' questions, suggested dangerous activities such as eating one small rock per day or adding glue to pizza. In February, Air Canada came under fire when its AI-enabled chatbot promised a fake discount to a traveler.

Elon Musk has dropped his lawsuit accusing OpenAI and its co-founders Sam Altman and Greg Brockman of reneging on the startup's pledge to develop artificial intelligence for the benefit of humanity. Lawyers in the United States representing Musk, on Tuesday asked a California judge to dismiss the suit, court filings showed. No reason was provided for the application to dismiss the suit. Musk in February filed a suit claiming that ChatGPT had set "aflame" its founding agreement to put the good of humanity ahead of profit-seeking when it signed an investment deal with Microsoft. "To this day, OpenAI Inc's website continues to profess that its charter is to ensure that AGI'benefits all of humanity'," Musk claimed in the suit.