Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?
Summarized by: Alice Thompson [
arxiv.org]
The paper introduces Commonsense-T2I, a new benchmark to evaluate the ability of text-to-image (T2I) generation models to understand and produce images that align with commonsense. The task involves providing models with two adversarial text prompts that are similar but have minor differences, such as “a lightbulb without electricity” versus “a lightbulb with electricity”. The expected outputs are images that reflect commonsense, like an unlit bulb versus a lit bulb.
The dataset comprises 150 hand-curated examples, each with pairwise prompts and expected outputs, categorized into commonsense types like physical laws, human practices, and biological laws. The research benchmarks various state-of-the-art T2I models, finding that even the best models, like DALL-E 3, achieve only around 48.92% accuracy, indicating a significant gap between current models and human-level commonsense understanding.
The paper also explores using multimodal large language models (LLMs) for automatic evaluation, finding that models like GPT-4V align well with human evaluations. However, the study concludes that current T2I models struggle with commonsense reasoning, highlighting the need for further research and improvement in this area.
Neural Gaffer: Relighting Any Object via Diffusion
Summarized by: Alice Thompson [ arxiv.org]
Neural Gaffer is a novel AI model designed to relight any object in a single image under various lighting conditions. Traditional methods for relighting images often require specific setups or decompose scenes into components like geometry and materials, which can be limiting and inaccurate. Neural Gaffer, however, uses a pre-trained diffusion model fine-tuned on a synthetic dataset to generate high-quality relit images without explicit scene decomposition.
The model works by conditioning an image generator on a target environment map, which is a representation of the desired lighting. This approach allows Neural Gaffer to handle diverse objects and lighting scenarios effectively. The model is evaluated on both synthetic and real-world images, showcasing its ability to generalize well and produce accurate results.
Neural Gaffer also supports various downstream tasks such as text-based relighting, where a text description of the lighting is used, and object insertion, where objects are seamlessly integrated into different scenes. Additionally, it can serve as a strong prior for 3D tasks, enabling the relighting of 3D models with high fidelity.
Overall, Neural Gaffer represents a significant advancement in the field of image relighting, providing a versatile and powerful tool for both 2D and 3D applications.
Unitree Robotics debuts new G1 humanoid with $16K price tag
Summarized by: David Ramirez [www.robotics247.com]
Unitree Robotics has launched its new G1 humanoid robot, priced at $16K. The G1, smaller than its predecessor H1, stands at 127 cm and weighs 35 kg, offering advanced flexibility and movement capabilities. It features up to 43 joints with a maximum torque of 120N.m, enabling high-load dynamic actions. Utilizing deep reinforcement learning, the G1 continuously evolves. The optional Dex3-1 hand allows precise manipulation of various objects. Equipped with Intel RealSense D435 and LIVOX-MID360 3D lidar, it provides 360° environmental perception. The G1 has a two-hour battery life and comes in two versions: the basic G1 and the customizable G1 EDU.
New Amazon AI initiative includes scholarships, free AI courses
Summarized by: David Ramirez [www.aboutamazon.com]
Amazon has launched the “AI Ready” initiative to provide free AI skills training to 2 million people globally by 2025. This includes eight new courses on AI and generative AI, an AWS Generative AI Scholarship for over 50,000 students, and a collaboration with Code.org for an AI-focused Hour of Code. The initiative aims to address the high demand for AI talent, with 73% of employers prioritizing AI skills but struggling to find qualified candidates. The program also offers $12 million in scholarships and AWS Cloud credits to support learning. Amazon continues to invest in upskilling, enhancing career opportunities in AI.
Summarized by: Sophie Zhang [federalnewsnetwork.com]
The Air Force has introduced NIPRGPT, a generative AI tool similar to ChatGPT, for use on unclassified networks. Part of the Dark Saber platform, it aids airmen, Guardians, and civilian employees with tasks like coding and content summarization. Developed by the Air Force Research Laboratory using publicly available AI models, NIPRGPT serves as a testing ground to explore generative AI applications and gather feedback. The Air Force is not committed to any single model or vendor yet, aiming to identify the best-performing models for future use. Expansion to higher classification levels is being considered based on demand.
Indexing the Future: Cloud and Gen AI Advancements that Efficiently Store, Manage and Extract Insights from Data
Summarized by: Sophie Zhang [www.intersystems.com]
InterSystems Global Summit 2024 highlighted significant advancements in generative AI and cloud services integrated into the InterSystems IRIS data platform. Key innovations include vector-enabled SQL databases for efficient high-dimensional data retrieval, AI-driven Data Transformation Language (DTL) generation, and cloud-based offerings like InterSystems Cloud Document Database and Data Fabric Studio. These enhancements aim to streamline data management, improve scalability, and ensure data integrity across industries. The summit emphasized AI’s role in future data management, showcasing tools that integrate AI processes with existing infrastructures to enhance analytics and operational efficiency.
Researchers harness AI for autonomous discovery and optimization of materials
Summarized by: Sophie Zhang [www.sciencedaily.com]
Researchers at Oak Ridge National Laboratory have developed an AI-driven tool that autonomously conducts and optimizes materials synthesis using pulsed laser deposition (PLD). This tool integrates AI, automated experiments, and high-performance computing, enabling it to analyze synthesis conditions and improve material quality without human intervention. The system, led by Sumner Harris, significantly accelerates experimentation by performing tasks 10 times faster and efficiently managing vast parameter spaces. This advancement marks a significant step towards autonomous discovery and optimization in materials science.
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Technical details
Created at: 12 June, 2024, 04:56:45, using gpt-4o.
Processing time: 0:03:00.713238, cost: 1.25$
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