Highlights💡 

OpenAI cybersecurity grant program [Link]

Our goal is to facilitate the development of AI-powered cybersecurity capabilities for defenders through grants and other support. We will grant in increments of $10,000 USD from a fund of $1M USD, in the form of API credits, direct funding and/or equivalents.

Our program seeks to:

1. Empower defenders: We would like to ensure that cutting-edge AI capabilities benefit defenders first and most.

2. Measure capabilities: We are working to develop methods for quantifying the cybersecurity capabilities of AI models, in order to better understand and improve their effectiveness.

3. Elevate discourse: We are dedicated to fostering rigorous discussions at the intersection of AI and cybersecurity, encouraging a comprehensive and nuanced understanding of the challenges and opportunities in this domain.

Below are some generic project ideas that our team has put forward:

• Collect and label data from cyber defenders to train defensive cybersecurity agents

• Detect and mitigate social engineering tactics

• Automate incident triage

• Identify security issues in source code

• Assist network or device forensics

• Automatically patch vulnerabilities

• … read more

NVIDIA Neuralangelo Research Reconstructs 3D Scenes [Link]

NVIDIA Research has developed Neuralangelo, an AI model that utilizes neural networks for 3D reconstruction. It can convert 2D video clips into detailed 3D structures, allowing for the creation of virtual replicas of real-world objects such as buildings and sculptures. Neuralangelo's high fidelity in translating textures of complex materials surpasses previous methods, enabling developers and creative professionals to rapidly create usable virtual objects for various applications. By utilizing instant neural graphics primitives, Neuralangelo captures fine details and optimizes the render to produce realistic 3D objects or scenes. This technology will be showcased at the Conference on Computer Vision and Pattern Recognition (CVPR). Additionally, NVIDIA Research has developed DiffCollage, a diffusion method that creates large-scale content, including panoramic images and looped-motion sequences.

StyleDrop - Impressive new paper from Google. [Link]

TLDR: Generate high-quality images from text prompts in any style described by a single reference image

The research paper introduces StyleDrop, a text-to-image generation system that can produce images in any specified style. It utilizes a generative vision transformer called Muse and is capable of capturing fine details and nuances of a given style, including color schemes, shading, design patterns, and global effects. StyleDrop achieves this by fine-tuning a small number of parameters and improving quality through iterative training with either human or automated feedback. It can even generate impressive results with just a single reference image. The paper compares StyleDrop to other methods like DreamBooth and Textual Inversion on Imagen or Stable Diffusion and demonstrates its superiority in style-tuning text-to-image models. Additionally, StyleDrop can be used to generate stylized characters and collaborate with a personal style assistant, allowing users to train the system with their own brand assets and quickly prototype ideas in their desired style. The paper concludes by highlighting that StyleDrop outperforms existing diffusion models when it comes to style tuning.

Prompt injection explained, with video, slides, and a transcript [Link]

Tools & Links 🛠️

Unclassified 🌀 

• Morning Brew - Get smarter in 5 minutes (it's free) [Link]

• Fiverr - Find the right freelance service, right away [Link]

• WFH.Team - Work from anywhere in the world [Link]

• Add your link here ➜

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