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News

Mistral Large 2 Outperforms Leading AI Models

Mistral Large 2, a 123 billion parameter AI model, features a 128k context window and supports 80+ coding languages. Available under Mistral's Research License, it excels in accuracy, reasoning, and instruction-following, setting a new benchmark in open AI models.

Designed for single-node inference, it outperforms its predecessor and rivals top models like GPT-4o and Claude 3 Opus.

Key Takeaways:

1. Multilingual Mastery: Supports dozens of languages, enhancing global applications.

2. Top-Notch Performance: Achieves 84.0% accuracy on MMLU, outperforming leading models.

3. Enhanced Reasoning: Fine-tuned to minimize errors and provide accurate outputs.

4. User-Friendly Licensing: Available for research and non-commercial use with a commercial license option.

5. Broad Ecosystem Support: Partners like Google Cloud and AWS facilitate global accessibility.

Mistral Large 2 uses a vast training dataset and advanced fine-tuning for high accuracy and reliable outputs. Ideal for long-context understanding and multilingual applications, it makes AI development more accessible and efficient.

Try Mistral Large 2 on la Plateforme under the name mistral-large-2407, and test it on le Chat

Meta's AI Manifesto: Embracing Open-Source AI for a Brighter Future

Meta, the parent company of Facebook, has recently made significant strides in the field of artificial intelligence.

Here’s what you need to know:

1. Llama 3.1 Models: Meta has released the Llama 3.1 collection of multilingual large language models (LLMs). These models come in various sizes, including 8B, 70B, and a groundbreaking 405B parameter variant. The Llama 3.1-405B is now one of the largest and most powerful open-source language models available, rivaling proprietary models from companies like OpenAI and Alphabet^.

2. AI Tools Across Platforms: Meta is also launching a suite of AI tools across its platforms, including Facebook, WhatsApp, and Instagram. These tools cover areas such as search, social discovery, advertising, business messaging, and consumer products. Expect enhancements in collaboration, automation, and image generation.

Stable Video 4D: Revolutionizing Multi-Angle Video Generation

Stability AI introduces Stable Video 4D, an innovative AI model that transforms single-object videos into dynamic multi-angle videos from eight distinct views. Users can upload a single video, specify camera angles, and receive multiple novel-view videos in about 40 seconds.

Key Takeaways:

• Multi-Angle Video Generation: Converts a single video into videos from eight different angles.

• Efficiency: Produces 5 frames across 8 views in approximately 40 seconds.

• Customizable: Allows users to specify camera angles for tailored output.

• Future Applications: Potential in game development, video editing, and virtual reality.

• Research Phase: Available on Hugging Face with ongoing improvements.

Users upload a video and specify 3D camera poses. Stable Video 4D generates eight multi-angle videos in about 40 seconds for 5-frame videos, with full 4D optimization taking 20-25 minutes. This model advances from image-based to 3D dynamic video synthesis, enhancing realism and immersion. It offers a lightweight 4D optimization framework, with further refinement and adoption expected.

Canva’s Magic Studio AI Wins Innovation by Design Award

Canva has once again demonstrated its leadership in AI innovation with its Magic Studio AI toolbox, earning Fast Company's 2024 Innovation by Design Award in artificial intelligence.

This achievement follows its previous recognition in 2023. Under CEO Melanie Perkins' direction, Canva has integrated 10 proprietary AI tools and over 50 third-party solutions into a cohesive, user-friendly interface. Magic Studio AI can transform designs, generate images and videos, write copy, and more, all with a single click.

Do It Yourself (DIY)

Kling AI: Global Video Generation Breakthrough

Kling AI has made quite an entrance onto the global stage, and its impressive video model is turning heads. Unlike its initial launch, which required a Chinese phone number, Kling’s global version now grants access to users worldwide. However, the generation length remains capped at five seconds.

What makes Kling noteworthy:

1. Video Length: Kling outpaces OpenAI’s Sora by allowing video generations up to two minutes. This longer duration opens up creative possibilities for users.

2. Detailed Prompts: Users can input prompts of up to 2,000 characters, allowing for nuanced and specific video descriptions.

3. Credit System: Users receive 66 free credits daily, with each video generation costing 10 credits. This pricing model encourages experimentation and creativity.

4. Advanced Technology: Kling leverages 3D reconstruction technology, resulting in more natural and fluid movements within generated videos.

Kling’s democratization of high-quality video creation puts pressure on OpenAI’s Sora to step up its game. As the battle of AI video models intensifies, content creators worldwide stand to benefit from these advancements.

Research

The Looming Threat of AI "Model Collapse"

A recent study published in Nature by researchers led by Ilia Shumailov at Oxford reveals a critical vulnerability in today's machine learning models, termed "model collapse."

This issue is likened to the mythical Ouroboros, symbolizing the self-destructive cycle of AI models consuming their own outputs.

Key Takeaways:

1. Model Collapse: Training AI on AI-generated data can degrade model performance over time by causing a loss of the original data distribution.

2. Pattern Matching: AI models often favor common patterns in their training data, leading to skewed outputs if trained on homogeneous AI-generated content.

3. Data Quality: The rise of AI-generated content online risks worsening the collapse as new models train on this data.

4. Detection and Prevention: Researchers emphasize the need for diverse training data, data sourcing benchmarks, and watermarks to identify AI-generated content.

5. Industry Impact: Companies might hoard valuable human-generated data to maintain an advantage, raising concerns about AI accessibility and equality.

To prevent this, innovative strategies are needed to maintain data diversity and integrity, ensuring AI evolves beneficially without self-referential degradation.

Healthcare

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