Weekly - 23June2024

Happy Sunday! This is AIPOOOL. The email that tells you what’s going on in Artificial Intelligence space in simple blocks. Get ready to have your mind blown by the sheer power of AI!

In Today’s Email :

• 📺 AI News: AI Drama: Morgan Freeman Slams AI Scams, OpenAI Blocks China, Microsoft's AI Jailbreak, SoftBank's Medical AI Venture!

• ⛏️ Trending Tools: EzNewsletterScan for instant alerts , Epipheo AI for Marketing Videos & many more ..

• 🔰 Quick Grab: Unlocking the Language of Objects: A Journey into NeRFs and Multimodal Understanding

• 🎆Creators Corner: What developers wants ?

• 🅿️Community Poll: What new content do you want from us?

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AI Happenings You Don’t Want To Miss

✨ Morgan Freeman Slams AI Voice Imitations of Himself, Thanks Fans for Calling Out the ‘Scam’.

✨ OpenAI has decisively blocked access to its site from mainland China and Hong Kong, cutting off developers and companies from some of the most advanced AI technologies available today.

✨ Microsoft has disclosed a new type of AI jailbreak attack dubbed “Skeleton Key,” which can bypass responsible AI guardrails in multiple generative AI models. This technique, capable of subverting most safety measures built into AI systems, highlights the critical need for robust security measures across all layers of the AI stack.

✨ SoftBank Group, the Japanese technology investment firm, has announced a strategic joint venture with Tempus AI, a company specializing in AI-driven medical data analysis and treatment recommendations.

Free & Useful AI Tools -

1. EzNewsletterScan - Get instant alerts when your brand is mentioned in newsletters.

2. Epipheo AI - Create your next marketing video in minutes, not weeks.

3. Redactive AI - Designed to facilitate interactions with your permissioned data through an advanced chat interface.

4. DiagramMatic - Transforming textual flow descriptions into Mermaid diagrams.

📜Unlocking the Language of Objects: A Journey into NeRFs and Multimodal Understanding

1. Introduction to NeRFs and Language Models:

   - Imagine a world where computers can understand and describe objects just like humans do. This research explores combining Neural Radiance Fields (NeRFs), which capture detailed 3D information of objects, with Multimodal Large Language Models (MLLMs), which understand and generate human-like language.

2. Creating a NeRF-Language Assistant:

   - The goal is to develop a smart assistant that can look at an object represented by a NeRF and describe it in natural language. This assistant can provide brief captions, detailed descriptions, and even answer questions about the object.

3. Dataset Creation and Annotation:

   - To train the assistant, a dataset called ShapeNeRF–Text was created. This dataset contains 40,000 objects with detailed annotations, including brief captions, detailed descriptions, and Q&A pairs. These annotations help the assistant learn how to describe objects accurately.

4. Comparing Performance with Baselines:

   - The research compares the performance of the NeRF-language assistant with baseline models. Results show that the assistant, named LLaNA, outperforms other methods in tasks like captioning and question answering, showcasing its ability to understand objects better.

   

   Source: LLaNA

5. Advantages of LLaNA:

   - LLaNA stands out because it can extract all necessary information from a single global embedding obtained directly from processing NeRF weights. This means it can provide accurate descriptions without needing to process detailed spatial data, giving it an edge over traditional methods.

6. Implications and Future Directions:

   - This research opens up new possibilities for computers to understand and interact with 3D objects using natural language. By bridging the gap between visual information and textual descriptions, LLaNA paves the way for more advanced applications in fields like computer vision and artificial intelligence.

7. Conclusion:

   - In conclusion, this research demonstrates the power of combining cutting-edge technologies like NeRFs and MLLMs to create a powerful NeRF-language assistant. By unlocking the language of objects, we move closer to a future where machines can communicate with us in a more human-like manner, enhancing our interactions with technology.

In summary, this research paper dives into the exciting realm of NeRFs and Multimodal Language Models, showcasing how these technologies can revolutionize how computers understand and describe objects in the world around us.

🤖 LLM Spotlight of the Week:

🌟 Florence-2-large : An advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation.

🌟 MARS5-TTS : A novel speech model for insane prosody. The model follows a two-stage AR-NAR pipeline with a distinctively novel NAR component

🌟 gemma-2-9b : Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models.

🌟 jina-reranker-v2-base-multilingual : A transformer-based model that has been fine-tuned for text reranking task, which is a crucial component in many information retrieval systems. 

👨‍💻 From Lab to Layman - DoubleTake: Geometry Guided Depth Estimation :

1. Introduction to Depth Estimation: Depth estimation is crucial for various applications like virtual reality and object avoidance. High-quality depth maps are needed for these applications to work effectively.

2. Challenges with Traditional Methods: While offline methods can provide accurate depth maps, they are not suitable for interactive applications. Interactive methods like multi-view stereo (MVS) rely on matching textures in nearby frames, which can be limiting.

3. The DoubleTake Approach: The DoubleTake model introduces a new way of estimating depth by using historical predictions and self-generated geometric hints. This allows for more accurate and detailed depth maps to be generated in real-time.

4. Utilizing Prior Geometry: By incorporating information from previous frames and maintaining a global representation of 3D geometry, the model can provide better depth estimates even in challenging scenarios like occlusions or distant surfaces.

5. The Role of the Hint MLP: The Hint MLP combines cost volume features with hints of prior geometry and confidence measures. This helps the model make more informed depth predictions, leading to improved accuracy.

   

   Source: DoubleTake

6. Real-Time Geometry Updates: The model updates its 3D geometry representation in real-time, ensuring that the depth estimates remain reliable and up-to-date. This process is lightweight and efficient, making it suitable for interactive applications.

7. Experimental Validation: Extensive experiments and evaluations on challenging datasets like ScanNetV2 demonstrate that the DoubleTake model outperforms existing methods in terms of depth estimation and 3D scene reconstruction.

8. Key Contributions: The DoubleTake model introduces a novel approach to depth estimation that leverages historical predictions and geometric hints, leading to state-of-the-art results. It also proposes a new evaluation protocol to assess the performance of depth estimation methods more accurately.

By combining the power of historical data with real-time geometry updates, the DoubleTake model revolutionizes depth estimation, paving the way for more immersive virtual experiences and enhanced augmented reality applications.

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