Welcome to your daily newsletter on AI

What have we got for you today?

• Alibaba Qwen-VQ, a multimodal heavyweight, released

• Devin.ai has version 1.1 released - an AI Agent for hire that codes.

• Supabase tutorial - spin up a javascript chatbot in 5 minutes

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🎯 RELEASES 🎯

Alibaba
Qwen-VQ 72B - a multimodal heavy weight

Synopsis

The Qwen-VQ 72B model represents a significant step forward in multimodal AI, combining powerful language and visual understanding capabilities. Designed for complex real-world applications, this model enhances performance across diverse tasks, from image analysis to advanced language comprehension, setting a new benchmark in AI versatility.

Core Observations

The Qwen-VQ 72B integrates language and vision processing, enabling tasks like image-to-text generation and multimodal Q&A with state-of-the-art accuracy. Built with 72 billion parameters, it one of the largest multimodal models available.

1. Multimodal Capabilities:

   • Demonstrates 85.6% accuracy on multimodal tasks such as visual reasoning and captioning benchmarks, outperforming previous models by over 7%.

2. Scale and Architecture:

   • Trained on a 5 trillion-token dataset combining diverse text and visual inputs, ensuring superior generalization.

3. Benchmark Performance:

   • Achieved a score of 91.4% on Visual Commonsense Reasoning (VCR), surpassing comparable models by 8 points.

   • Outperformed competing systems on text-heavy benchmarks with 92.2% accuracy on SQuAD 2.0 and 89.3% on Natural Questions (NQ).

4. Pretraining and Fine-Tuning:

   • Uses multi-stage pretraining, optimizing for multimodal inputs without sacrificing efficiency.

   • Incorporates adaptive sparse attention mechanisms, reducing computational overhead by up to 30% compared to dense architectures.

5. Efficiency and Scalability:

   • Delivers faster inference speeds, processing multimodal queries at 50 tokens per second, a 25% improvement over previous-generation models.

Broader Context

Qwen-VQ 72B demonstrates Alibaba’s ability to compete with top LLM providers like OpenAI and Google. While OpenAI leads with GPT-4’s general versatility, and Google excels in R&D with models like Gemini, Qwen-VQ 72B’s multimodal focus carves a niche in combining language and vision capabilities.

View here

Devin.ai
AI Coder for Hire - v 1.1 released

Synopsis

Devin is basically an AI agent that contributes to your code base - so think of it as an AI coding agent for hire! Cognition Labs has unveiled Devin 1.1, an upgraded AI model designed for code-editing tasks. The model delivers an increase in performance speed and reduced operational costs, making waves as a tool for developers and businesses optimising their software workflows

Core Observations

1. Performance Improvements:

   • Operates 10% faster than its predecessor

2. Cost Reductions:

   • Operational costs for code-editing tasks have decreased by 12%

3. Enhanced Accuracy:

   • Improvements to language understanding and error correction reduce debugging times by up to 15%

4. Scalable Deployment:

   • Optimized for integration into CI/CD pipelines, enabling large-scale adoption in enterprise environments.

Broader Context

Cognition Labs’ Devin 1.1 positions the company as a competitive player in the AI-driven development tools market, rivaling OpenAI Codex and GitHub Copilot. While top providers focus on feature-rich models, Devin 1.1 emphasizes speed, cost-efficiency, and accuracy, making it a practical choice for industry adoption.

Try it out here

Trending

• Test-driving Google’s Gemini-Exp-1206 model

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⚙️ BUILDERS BYTES ⚙️

What will you learn today?

Learn how to build a AI chatbot using Supabase as the database backend. This is a javascript tutorial that mostly shows how to use the technology (less emphasis on programming).

Key Takeaways

1. Supabase Integration: Use Supabase for secure, scalable data storage and real-time interactions with your chatbot.

2. Vercel Deployment: Deploy a fast, serverless AI chatbot on Vercel with minimal setup and configuration.

3. OpenAI Integration: Integrate OpenAI models to power intelligent, natural-language responses.

4. Code Example Provided: Hands-on example with clear steps for connecting Supabase and Vercel in a chatbot project.

5. Scalable Architecture: Leverage Supabase and Vercel for a cost-effective, production-ready AI chatbot solution.

$ git clone https://github.com/supabase-community/vercel-ai-chatbot
$ npm supabase start
$ npm install
$ npm run dev

We just wanted to show you a snippet for now. The full tutorial is available in our newsletter repo 👉️ code

Do you have a product in AI and would like to contribute?
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Trending

• CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models

• SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory

• DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding (blog)

• Camel-AI Open Sourced OASIS: A Next Generation Simulator for Realistic Social Media Dynamics with One Million Agents

• Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search

🤩 COMMUNITY 🤩

Talks & Events

• Podcast - Imbue CEO Kanjun Qiu on Transforming AI Agents Into Personal Collaborators - Ep. 239

• Video - Flash Attention explained by Andrej Karpathy

THANK YOU

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