Existing perception models achieve great success by learning from large amounts of labeled data, but they still struggle with open-world scenarios. To alleviate this issue, researchers introduce open-set perception tasks to detect or segment unseen objects in the training set. However, these models require predefined object categories as inputs during inference, which are not available in real-world scenarios. Recently, researchers pose a new and more practical problem, i.e., open-ended object detection, which discovers unseen objects without any object categories as inputs. In this paper, we present VL-SAM, a training-free framework that combines the generalized object recognition model (i.e., Vision-Language Model) with the generalized object localization model (i.e., Segment-Anything Model), to address the open-ended object detection and segmentation task. Without additional training, we connect these two generalized models with attention maps as the prompts.

The rapid advancement of large language models (LLMs) has increased the need for guardrail models to ensure responsible use, particularly in detecting unsafe and illegal content. While substantial safety data exist in English, multilingual guardrail modeling remains underexplored due to the scarcity of open-source safety data in other languages. To address this gap, we propose a novel two-player Reinforcement Learning (RL) framework, where a generator and a guardrail model co-evolve adversarially to produce high-quality synthetic data for multilingual guardrail training. We theoretically formalize this interaction as a two-player game, proving convergence to a Nash equilibrium. Empirical evaluations show that our model \ours outperforms state-of-the-art models, achieving nearly 10% improvement over LlamaGuard3 (8B) on English benchmarks while being 4.5x faster at inference with a significantly smaller model (0.5B). We achieve substantial advancements in multilingual safety tasks, particularly in addressing the imbalance for lower-resource languages in a collected real dataset. Ablation studies emphasize the critical role of synthetic data generation in bridging the imbalance in open-source data between English and other languages. These findings establish a scalable and efficient approach to synthetic data generation, paving the way for improved multilingual guardrail models to enhance LLM safety. Code, model, and data will be open-sourced at https://github.com/yihedeng9/DuoGuard.

The integration of artificial intelligence into scientific research has reached a new pinnacle with GPT-4V, a large language model featuring enhanced vision capabilities, accessible through ChatGPT or an API. This study demonstrates the remarkable ability of GPT-4V to navigate and obtain complex data for metal-organic frameworks, especially from graphical sources. Our approach involved an automated process of converting 346 scholarly articles into 6240 images, which represents a benchmark dataset in this task, followed by deploying GPT-4V to categorize and analyze these images using natural language prompts. This methodology enabled GPT-4V to accurately identify and interpret key plots integral to MOF characterization, such as nitrogen isotherms, PXRD patterns, and TGA curves, among others, with accuracy and recall above 93%. The model's proficiency in extracting critical information from these plots not only underscores its capability in data mining but also highlights its potential in aiding the creation of comprehensive digital databases for reticular chemistry. In addition, the extracted nitrogen isotherm data from the selected literature allowed for a comparison between theoretical and experimental porosity values for over 200 compounds, highlighting certain discrepancies and underscoring the importance of integrating computational and experimental data. This work highlights the potential of AI in accelerating scientific discovery and innovation, bridging the gap between computational tools and experimental research, and paving the way for more efficient, inclusive, and comprehensive scientific inquiry.

AI-powered platforms offer personalized learning experiences tailored to individual needs, interests, and goals. By employing machine learning algorithms, these platforms can analyze your learning patterns, strengths, and weaknesses to deliver a unique learning plan. AI can be used to develop advanced problem-solving skills and foster critical thinking by offering various interactive tools and resources that promote deeper engagement with learning materials. AI-powered learning platforms can leverage gamification techniques to make learning more engaging and fun. By incorporating game elements into the learning experience, users can stay motivated, retain more information, and develop new skills more effectively.

Welcome to the world of ChatGPT, If you're new to ChatGPT, prompts are your trusty sidekicks, enabling you to communicate with this AI language model. They play a crucial role in getting meaningful responses and unlocking the true potential of ChatGPT. So what are ChatGPT prompts exactly? ChatGPT prompts are a way to interact with the AI model by giving it specific instructions or questions. They come in different classes, including question, statement, and opinion prompts. They are useful for various purposes, such as content creation, customer service, and education. Let's break it down, shall we? Think of prompts as conversation starters or cues.

A great way to get started with AI art is to explore different prompts. Prompts are what you use to describe the image you want to create, and they include lots of different information, from specific artist names and art styles to using pop culture icons and exploring fantasy scenes. Try using the prompt ideas listed below, then experiment with replacing the keywords with different ideas of your own. Soon enough you will get the hang of how to create unique AI art using your own original prompts. Referencing the masters of art history is a great place to begin if you are stuck for ideas.

The weird and wonderful art created when AI and humans unite BBC Will AI kill art? Not likely, says the artist Alexander Reben, who has been working with AI for years. "I knew I had hit upon the right recipe when I got the following output by GPT-3 (which made me laugh a little too hard alone in my studio in lockdown):" "The sculpture contains a plunger, a toilet plunger, a plunger, a plunger, a plunger, and a plunger, each of which has been modified. The first plunger is simply a normal plunger, but the rest represent a series of plungers with more and more of the handle removed until just the rubber cup is left. The title of the artwork is "A Short History of Plungers and Other Things That Go Plunge in the Night" by the artists known as "The Plungers" (whose identity remains unknown). "The Plungers", were a collective of anonymous artists, founded in 1972. They were dedicated to the "conceptualization and promotion of a new art form called Plungism." Plungism was a creative interpretation of the idea of Plungerism, which was defined by The Plungers as "a state of mind wherein the mind of an artist is in a state of flux and able to be influenced by all things, even plungers." The Plungers' works were displayed in New York galleries and included such titles as "Plunger's Progress," "The Plungers," "The Plungers Strike Back," and "Big Plunger 4: The Final Plunger," all of which featured plungers, and "Plungers on Parade," which showed images of plungers in public spaces. The Plungers disappeared and left no trace of their identity."