Google Gemini continues to push the limits of what it knows about you. On Wednesday, Google's big initiative was a way to stop Gemini from learning more about you, while notifying users that content you share with it may be used as a foundation for chats with other users. "In the coming weeks, your'Gemini Apps Activity' setting will be renamed'Keep Activity,'" Google said in a blog post. "When this setting is on, a sample of your future uploads will be used to help improve Google services for everyone." Today, Google is allowing Gemini to remember what it knows about you, and this behavior is on by default. "When this setting is on, Gemini remembers key details and preferences you've shared, leading to more natural and relevant conversations, as if you're collaborating with a partner who's already up to speed," Google said.

A long-running feud between Elon Musk and Sam Altman spilled out into the open this week as the AI billionaire heavyweights publicly fought over their rival companies. The latest round in the battle between the X CEO and the CEO of OpenAI began when Musk claimed that Apple had been favoring Altman's AI app over his own in the Apple Store rankings. "Apple is behaving in a manner that makes it impossible for any AI company besides OpenAI to reach #1 in the App Store, which is an unequivocal antitrust violation," Musk said on X on Monday evening. "xAI will take immediate legal action," he added, referring to the AI company he leads. "Hey @Apple App Store, why do you refuse to put either X or Grok in your'Must Have' section when X is the #1 news app in the world and Grok is #5 among all apps?" he asked.

BubbleML is validated against experimental observations and trends, establishing it as an invaluable resource for ML research.

With more than 39 TB of publicly available engineering data, DrivAerNet++ fills a significant gap in available resources, providing high-quality, diverse data to enhance model training, promote generalization, and accelerate automotive design processes.

Neural Information Processing Systems http://nips.cc/

GPT-5 is the latest version of OpenAI's large language model OpenAI has released its newest AI model, GPT-5, two years after rolling out GPT-4, whose success has driven ChatGPT towards world domination. But despite promises of a similar jump in capability, GPT-5 appears to show little improvement over other leading AI models, hinting that the industry may need a fresh approach to build more intelligent AI systems. OpenAI's own pronouncements hail GPT-5 as a "significant leap in intelligence" from the company's previous models, showing apparent improvements in programming, mathematics, writing, health information and visual understanding. It also promises less frequent hallucinations, which is when an AI presents false information as true. On an internal benchmark measuring "performance on complex, economically valuable knowledge work", OpenAI says GPT‑5 is "comparable to or better than experts in roughly half the cases… across tasks spanning over 40 occupations including law, logistics, sales, and engineering."

The capacity of an LSTM network can be increased by widening and adding layers. However, usually the former introduces additional parameters, while the latter increases the runtime.

ChatGPT's dangerous dietary recommendation led to a man replacing table salt with toxic sodium bromide for three months, causing severe poisoning and hospitalization.

Deep learning-based image stitching pipelines are typically divided into three cascading stages: registration, fusion, and rectangling. Each stage requires its own network training and is tightly coupled to the others, leading to error propagation and posing significant challenges to parameter tuning and system stability. This paper proposes the Simple and Robust Stitcher (SRStitcher), which revolutionizes the image stitching pipeline by simplifying the fusion and rectangling stages into a unified inpainting model, requiring no model training or fine-tuning. We reformulate the problem definitions of the fusion and rectangling stages and demonstrate that they can be effectively integrated into an inpainting task. Furthermore, we design the weighted masks to guide the reverse process in a pre-trained large-scale diffusion model, implementing this integrated inpainting task in a single inference.

Large language models (LLMs) have been observed to exhibit bias towards certain cultures due to the predominance of training data obtained from English corpora. Considering that multilingual cultural data is often expensive to procure, existing methodologies address this challenge through prompt engineering or culture-specific pre-training. In this paper, we propose CultureLLM, a cost-effective solution to integrate cultural differences into LLMs. CultureLLM employs the World Value Survey (WVS) as seed data and generates semantically equivalent training data through the proposed semantic data augmentation. Extensive experiments conducted on 60 culture-related datasets reveal that CultureLLM significantly surpasses various counterparts such as GPT-3.5 (by 8.1 \%) and Gemini Pro (by 9.5 \%), demonstrating performance comparable to or exceeding that of GPT-4.