OpenAI just announced that all users will soon be able to generate images directly inside of ChatGPT. This will be the default image generation tool in 4o, so there will be no need to open Dall-E whenever you want to whip up a picture of a cat in space eating lasagna or whatever. The company says that the platform will "generate high-quality images based on your prompt, conversation and uploaded files." To the latter point, it'll be able to transform pre-existing images based on prompts. OpenAI is also boasting about significant improvements in text rendering and contextual understanding.

The new model makes progress on technical issues that have plagued AI image generators for years. While most have been great at creating fantastical images or realistic deepfakes, they've been terrible at something called binding, which refers to the ability to identify certain objects correctly and put them in their proper place (like a sign that says "hot dogs" properly placed above a food cart, not somewhere else in the image). It was only a few years ago that models started to succeed at things like "Put the red cube on top of the blue cube," a feature that is essential for any creative professional use of AI. Generators also struggle with text generation, typically creating distorted jumbles of letter shapes that look more like captchas than readable text. The model is able to generate 12 discrete graphics within a single image--like a cat emoji or a lightning bolt--and place them in proper order.

Earlier this month, Microsoft revamped the Copilot for Windows app. Now, the AI assistant is getting a different kind of facelift--in the form of a brand-new interface, reports Windows Latest. The new Copilot interface may seem familiar to anyone who's been using OpenAI's ChatGPT, with the chat history now moved to a side panel on the left instead of as a pop-up window alongside the convo. There's also a button to hide the side panel and give more space to the convo itself. Microsoft has also chosen to start using the same icons as ChatGPT for the "expand" and "conversation" buttons.

The most sophisticated AI models in existence today have scored poorly on a new benchmark designed to measure their progress towards artificial general intelligence (AGI) – and brute-force computing power won't be enough to improve, as evaluators are now taking into account the cost of running the model. There are many competing definitions of AGI, but it is generally taken to refer to an AI that can perform any cognitive task that humans can do. To measure this, the ARC Prize Foundation previously launched a test of reasoning abilities called ARC-AGI-1. Last December, OpenAI announced that its o3 model had scored highly on the test, leading some to ask if the company was close to achieving AGI. But now a new test, ARC-AGI-2, has raised the bar.

AI-generated content isn't all it's cracked up to be. Sure, ChatGPT can write an article in seconds, but it usually gets something wrong, lacks SEO optimization, and requires more editing than its worth. It only makes sense that content creators are turning to a new AI article writer. Those needing a tool that actually writes articles instead of just sputtering out nonsense will also want to check out Katteb AI. It creates fact-checked, SEO-optimized content, complete with in-text citations and images.

As a result, some policymakers and business leaders--in Europe, in particular--are reconsidering their reliance on US-based tech and asking whether they can quickly spin up better, homegrown alternatives. This is particularly true for AI. One of the clearest examples of this is in social media. Yasmin Curzi, a Brazilian law professor who researches domestic tech policy, put it to me this way: "Since Trump's second administration, we cannot count on [American social media platforms] to do even the bare minimum anymore." Social media content moderation systems--which already use automation and are also experimenting with deploying large language models to flag problematic posts--are failing to detect gender-based violence in places as varied as India, South Africa, and Brazil.

Disorganized thinking is a key diagnostic indicator of schizophrenia-spectrum disorders. Recently, clinical estimates of the severity of disorganized thinking have been shown to correlate with measures of how difficult speech transcripts would be for large language models (LLMs) to predict. However, LLMs' deployment challenges -- including privacy concerns, computational and financial costs, and lack of transparency of training data -- limit their clinical utility. We investigate whether smaller neural language models can serve as effective alternatives for detecting positive formal thought disorder, using the same sliding window based perplexity measurements that proved effective with larger models. Surprisingly, our results show that smaller models are more sensitive to linguistic differences associated with formal thought disorder than their larger counterparts. Detection capability declines beyond a certain model size and context length, challenging the common assumption of ``bigger is better'' for LLM-based applications. Our findings generalize across audio diaries and clinical interview speech samples from individuals with psychotic symptoms, suggesting a promising direction for developing efficient, cost-effective, and privacy-preserving screening tools that can be deployed in both clinical and naturalistic settings.

Modern LLMs struggle with efficient updates, as each new pretrained model version requires repeating expensive alignment processes. This challenge also applies to domain- or language-specific models, where fine-tuning on specialized data must be redone for every new base model release. In this paper, we explore the transfer of fine-tuning updates between model versions. Specifically, we derive the diff vector from one source model version, which represents the weight changes from fine-tuning, and apply it to the base model of a different target version. Through empirical evaluations on various open-weight model versions, we show that transferring diff vectors can significantly improve the target base model, often achieving performance comparable to its fine-tuned counterpart. For example, reusing the fine-tuning updates from Llama 3.0 8B leads to an absolute accuracy improvement of 10.7% on GPQA over the base Llama 3.1 8B without additional training, surpassing Llama 3.1 8B Instruct. In a multilingual model development setting, we show that this approach can significantly increase performance on target-language tasks without retraining, achieving an absolute improvement of 4.7% and 15.5% on Global MMLU for Malagasy and Turkish, respectively, compared to Llama 3.1 8B Instruct. Our controlled experiments reveal that fine-tuning transfer is most effective when the source and target models are linearly connected in the parameter space. Additionally, we demonstrate that fine-tuning transfer offers a stronger and more computationally efficient starting point for further fine-tuning. Finally, we propose an iterative recycling-then-finetuning approach for continuous model development, which improves both efficiency and effectiveness. Our findings suggest that fine-tuning transfer is a viable strategy to reduce training costs while maintaining model performance.

Recent advances in large language models have highlighted the critical need for precise control over model outputs through predefined constraints. While existing methods attempt to achieve this through either direct instruction-response synthesis or preferential response optimization, they often struggle with constraint understanding and adaptation. This limitation becomes particularly evident when handling fine-grained constraints, leading to either hallucination or brittle performance. We introduce Generative Adversarial Policy Optimization (GAPO), a novel framework that combines GAN-based training dynamics with an encoder-only reward model to progressively learn and adapt to increasingly complex constraints. GAPO leverages adversarial training to automatically generate training samples of varying difficulty while utilizing the encoder-only architecture to better capture prompt-response relationships. Extensive experiments demonstrate GAPO's superior performance across multiple benchmarks, particularly in scenarios requiring fine-grained constraint handling, where it significantly outperforms existing methods like PPO, DPO, and KTO. Our results suggest that GAPO's unique approach to preferential prompt learning offers a more robust and effective solution for controlling LLM outputs. Code is avaliable in https://github.com/MikeGu721/GAPO.

This paper compares large language models (LLMs) and traditional natural language processing (NLP) tools for performing word segmentation, part-of-speech (POS) tagging, and named entity recognition (NER) on Chinese texts from 1900 to 1950. Historical Chinese documents pose challenges for text analysis due to their logographic script, the absence of natural word boundaries, and significant linguistic changes. Using a sample dataset from the Shanghai Library Republican Journal corpus, traditional tools such as Jieba and spaCy are compared to LLMs, including GPT-4o, Claude 3.5, and the GLM series. The results show that LLMs outperform traditional methods in all metrics, albeit at considerably higher computational costs, highlighting a trade-off between accuracy and efficiency. Additionally, LLMs better handle genre-specific challenges such as poetry and temporal variations (i.e., pre-1920 versus post-1920 texts), demonstrating that their contextual learning capabilities can advance NLP approaches to historical texts by reducing the need for domain-specific training data.