It looks like OpenAI is building its own X-like social media network, according to a report by The Verge. We don't have many specifics, but we do know there's an internal prototype that adds a social feed to ChatGPT's image generation tool. It remains unclear if OpenAI will launch this social network as a standalone app or if it will be integrated within the ChatGPT app, which is what the prototype indicates. The report does suggest that OpenAI CEO Sam Altman has been asking for feedback about the social network from people outside of the company. One potential reason for this step is that the app would allow OpenAI to gather real-time data from users to train its AI models.

By the time you read this article, there's a good chance it will have already been scanned by an artificially intelligent machine. If asked about the artist David Salle, large language models such as ChatGPT or Gemini may repurpose some of the words below to come up with their answer. The bigger the data set, the more convincing the response – and Salle has been written about exhaustively since he first rose to art world stardom in the 1980s. The question is whether AI can ever say anything new about the artist and his work, or if it's for ever condemned to generate more of the same. A similar question lingers beneath the surface of the paintings that Salle has been making since 2023, a new series of which he has just unveiled at Thaddaeus Ropac in London.

Just before the weekend, the US Customs and Border Protection published a list of products excluded from Trump's tariffs, including smartphones, PCs, memory chips and let's say 80 percent of everything we write about at Engadget. However, that's more because they'll be siloed into a specific product category. Commerce Secretary Howard Lutnick said in an interview on Sunday: "Those products are going to be part of the semiconductor sectoral tariffs, which are coming." The new exclusions would exempt many devices and parts from both the 10 percent global tariff and the steeper tariff on China. Lutnick told ABC News' Jonathan Karl that, in doing this, the president was "just making sure everyone understood that all of these products are outside the reciprocal tariffs and they are going to have their own separate way of being considered."

While everyone else keeps paying monthly just to use one AI model, you can get lifelong access to the entire AI dream team--ChatGPT, Gemini, Claude, Midjourney, and the other top names--for a single, flat fee. And right now, this lifetime subscription is at an all-time low price of 79.97 for a very limited time (reg. Most AI tools lock you into a single model and a monthly subscription. That means you're either juggling multiple tabs and tools or picking just one and missing out on major capabilities altogether. You can generate AI text, images, code, video, and even audio with just a few clicks.

In their work PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks presented at AAAI 2025, Chen Feng, Ziquan Liu, Zhuo Zhi, Ilija Bogunovic, Carsten Gerner-Beuerle, and Miguel Rodrigues developed a new way to certify the performance of machine learning models in the presence of adversarial attacks with population-level risk guarantees. Here, Chen tells us more about their methodology, the main findings, and some of the implications of this work. This paper focuses on making machine learning models safer against adversarial attacks--those sneaky tweaks to data, like altering an image just enough to trick an AI into misclassifying it. We developed a new approach called PROSAC, which stands for PROvably SAfe Certification. It's a way to test and certify that a model can hold up under any kind of attack, not just a few specific ones.

We investigate reinforcement learning (RL) algorithms in the context of fine-tuning large language models (LLMs) with verifiable rewards. Our focus is on mathematical reasoning tasks, which have recently received significant attention following the release of models such as OpenAI's O1 Model (Jaech et al., 2024) and DeepSeek-R1 (DeepSeek-AI et al., 2025). The dominant approach in LLM post-training has been Proximal Policy Optimization (PPO) (Schulman et al., 2017; Bai et al., 2022; Ouyang et al., 2022). However, PPO requires an additional critic network beyond the vanilla Reinforce algorithm (Williams and Peng, 1991), introducing both computational overhead and algorithmic complexity. Meanwhile, the deterministic transition nature of LLM also simplifies the problem with a relatively lower variance, many of PPO's sophisticated components may be unnecessary in this setting. This observation has inspired growing interest in designing simpler yet effective RL algorithms for post-training LLMs. Several recent works revisit Reinforce-style approaches, including ReMax (Li et al., 2023), RLOO (Ahma-dian et al., 2024; Kool et al., 2019), GRPO (Shao et al., 2024), and Reinforce++ (Hu, 2025). In parallel, other methods explore different directions beyond policy gradients. Reward-ranked fine-tuning (RAFT) (Anthony et al., 2017; Dong et al., 2023) iteratively generates n responses per prompt, filter out those with incorrect answers, and fine-tune the LLM on the remaining accepted samples.

Reinforcement learning from human feedback (RLHF) has emerged as a key technique for aligning the output of large language models (LLMs) with human preferences. To learn the reward function, most existing RLHF algorithms use the Bradley-Terry model, which relies on assumptions about human preferences that may not reflect the complexity and variability of real-world judgments. In this paper, we propose a robust algorithm to enhance the performance of existing approaches under such reward model misspecifications. Theoretically, our algorithm reduces the variance of reward and policy estimators, leading to improved regret bounds. Empirical evaluations on LLM benchmark datasets demonstrate that the proposed algorithm consistently outperforms existing methods, with 77-81% of responses being favored over baselines on the Anthropic Helpful and Harmless dataset.

Large Language Models (LLMs) are indispensable in today's applications, but their inference procedure -- generating responses by processing text in segments and using a memory-heavy Key-Value (KV) cache -- demands significant computational resources, particularly under memory constraints. This paper formulates LLM inference optimization as a multi-stage online scheduling problem where sequential prompt arrivals and KV cache growth render conventional scheduling ineffective. We develop a fluid dynamics approximation to provide a tractable benchmark that guides algorithm design. Building on this, we propose the Waiting for Accumulated Inference Threshold (WAIT) algorithm, which uses multiple thresholds to schedule incoming prompts optimally when output lengths are known, and extend it to Nested WAIT for cases with unknown output lengths. Theoretical analysis shows that both algorithms achieve near-optimal performance against the fluid benchmark in heavy traffic conditions, balancing throughput, latency, and Time to First Token (TTFT). Experiments with the Llama-7B model on an A100 GPU using both synthetic and real-world datasets demonstrate improved throughput and reduced latency relative to established baselines like vLLM and Sarathi. This work bridges operations research and machine learning, offering a rigorous framework for the efficient deployment of LLMs under memory constraints.

As the prevalence of mental health crises increases on social media platforms, identifying and preventing potential harm has become an urgent challenge. This study introduces a large language model (LLM)-based text transfer recognition method for social network crisis intervention, enhanced with domain-specific mental health knowledge. We propose a multi-level framework that incorporates transfer learning using BERT, and integrates mental health knowledge, sentiment analysis, and behavior prediction techniques. The framework includes a crisis annotation tool trained on social media datasets from real-world events, enabling the model to detect nuanced emotional cues and identify psychological crises. Experimental results show that the proposed method outperforms traditional models in crisis detection accuracy and exhibits greater sensitivity to subtle emotional and contextual variations.

Extracting key concepts and their relationships from course information and materials facilitates the provision of visualizations and recommendations for learners who need to select the right courses to take from a large number of courses. However, identifying and extracting themes manually is labor-intensive and time-consuming. Previous machine learning-based methods to extract relevant concepts from courses heavily rely on detailed course materials, which necessitates labor-intensive preparation of course materials. This paper investigates the potential of LLMs such as GPT in automatically generating course concepts and their relations. Specifically, we design a suite of prompts and provide GPT with the course information with different levels of detail, thereby generating high-quality course concepts and identifying their relations. Furthermore, we comprehensively evaluate the quality of the generated concepts and relationships through extensive experiments. Our results demonstrate the viability of LLMs as a tool for supporting educational content selection and delivery.