GPT 4 Level Open Source in 2024..(Llama 3 Leaks and Mistral 2.0)
TLDRThe transcript discusses the rapid advancements in open-source AI, with a focus on the potential release of an open-source model on par with GPT-4 by Mistral AI in 2024. The company, known for its efficient and ethical AI practices, has already made significant strides with its Mixr model, which is smaller yet competitive with larger models like GPT-3.5 Turbo. The discussion also touches on the challenges faced by open-source models in comparison to proprietary ones like GPT-4, including talent, data, team structure, and infrastructure. Additionally, there's mention of Llama 3, an upcoming open-source model from Meta that aims to compete with GPT-4 while remaining freely available, which could disrupt the market and offer an alternative to OpenAI's dominance.
Takeaways
- 🚀 Open-source AI is rapidly approaching the level of GPT-4, with potential significant developments expected in 2024.
- 🌟 Sam Altman, CEO of OpenAI, has acknowledged the challenge of catching up to GPT-4 but emphasized the importance of developers continuing to try.
- 📚 Popular open-source models like Llama from Meta AI are gaining attention, with Mistral AI emerging as a notable player in the field.
- 📢 Arthur Mench, CEO of Mistral AI, announced plans to release an open-source model comparable to GPT-4 in 2024.
- 🔍 Mistral AI focuses on compute efficiency and powerful AI models, positioning itself as a European alternative to larger AI companies with a focus on ethical practices.
- ⚙️ Mistral's Mixr model is reported to be significantly faster than comparable models, showcasing the company's ability to create efficient AI systems.
- 📈 Mistral AI's small team of 22 employees has made a significant impact in the AI space, rivaling larger companies with more employees.
- 🏆 Mistral's models have been performing well on benchmarks like the Arena ELO, indicating their competitiveness with larger models.
- 💰 Mistral AI recently raised €385 million, which will be used for further development, training, and infrastructure costs.
- 🤖 The cost-effectiveness of Mistral's models could disrupt the industry, especially if they can offer comparable performance to GPT-4 at a fraction of the cost.
- 🔄 The architecture of Mistral's models, which includes a router system for task specialization, is similar to the rumored structure of GPT-4, suggesting a shift towards 'mixture of experts' models.
Q & A
What is the significance of open-source AI models reaching the level of GPT 4 in 2024?
-The significance is that it would democratize access to advanced AI technology, allowing more developers and companies to innovate and create applications that were previously only possible with proprietary, high-level AI models like GPT 4.
Who is Sam Altman, and why is his opinion on catching up to GPT 4 relevant?
-Sam Altman is the CEO of OpenAI, a company at the forefront of AI development. His opinion is relevant because he has insight into the challenges and progress of AI development, and his statement reflects the competitive landscape of AI research and development.
What is Mistral AI, and what are some of its notable achievements?
-Mistral AI is a European AI startup specializing in compute-efficient, powerful, and useful AI models. They focus on making AI models more efficient, helpful, and trustworthy. Notable achievements include the development of the Mixr model, which is reported to be six times faster than comparable models, and their commitment to ethical AI practices and community engagement.
What is the difference between Mistral AI's business model and that of larger companies like OpenAI?
-Mistral AI offers a highly permissive license for their models while maintaining private development and funding. This contrasts with larger companies like OpenAI, which may have more proprietary approaches to model development and access, although OpenAI also provides some level of open-source contribution to the AI community.
How does the size of Mistral AI's team impact its ability to compete with larger companies?
-Despite being a small team of 22 employees, Mistral AI has been able to achieve significant milestones in AI development, indicating that a smaller, focused team with the right expertise can still be highly competitive in the AI space.
What is the ARENA ELO benchmark, and why is it considered important?
-The ARENA ELO benchmark is a system where users interact with AI and rate which of two responses they prefer. The ELO score of the AI system increases based on user preferences. It's important because it provides a user-subjective measure of AI performance, reflecting real-world interactions rather than just objective data.
What are the challenges that open-source AI models face in comparison to proprietary models like GPT 4?
-Open-source AI models face challenges such as limited resources, a lack of centralized structure, and potentially slower iteration speeds due to reliance on public cloud infrastructure. They may also struggle with product development and distribution compared to well-funded proprietary models.
What is the potential impact of an open-source model like Llama 3 reaching the performance level of GPT 4?
-The potential impact is significant, as it could provide a powerful, freely available alternative to proprietary models, fostering innovation and reducing barriers to entry for developers and researchers working with advanced AI technology.
How does the architecture of Mistral's Mixr model contribute to its efficiency and performance?
-Mixr's architecture uses a system of 'experts' where different parts of the model handle different tasks more effectively. A router within the model decides which expert to use for each piece of information, making it more efficient and capable of handling complex tasks.
What are some of the key factors that contribute to the success of AI models like GPT 4?
-Key factors include the talent of the engineering team, access to massive proprietary data sets, a centralized team structure, a product-focused approach rather than just a model, and superior infrastructure for training and development.
What is the potential significance of Mistral AI's funding round, raising €385 million, for the AI industry?
-The significant funding allows Mistral AI to invest heavily in training models, acquiring more GPUs, and covering server costs. This could lead to more advanced and competitive AI models, potentially disrupting the market and challenging the dominance of larger companies.
Outlines
🚀 The Rise of Open Source AI and Mistral's Challenge
The first paragraph discusses the imminent arrival of open source AI models that are approaching the capabilities of GPT 4. It highlights recent developments and statements from CEOs suggesting that 2024 could be a pivotal year for such advancements. The paragraph introduces Mistral, an AI startup that has made significant strides in creating efficient and powerful AI models. Mistral's CEO, Arthur Mench, announced plans to release an open-source model at the level of GPT 4 in 2024. The company's focus on ethical AI practices, transparent access to model weights, and its small but accomplished team are emphasized. Mistral's Mixr model, which is faster and more efficient than comparable models, is also mentioned.
📊 Mistral's ELO Leaderboard Performance and Funding
The second paragraph focuses on Mistral's impressive performance on the ELO leaderboard, where their medium model ranks fourth, outperforming models from larger companies. The discussion highlights the significance of user-rated benchmarks and the company's recent funding round, where they raised €385 million to further develop their models. The paragraph also touches on the cost-effectiveness of Mistral's models compared to GPT 4 and the potential industry disruption this could cause, given the limitations of GPT 4's usage due to rate constraints.
🧠 Mixr's Innovative Architecture and Mistral's Benchmark Success
The third paragraph delves into the innovative architecture of Mistral's Mixr model, which operates like a team of specialists, each handling specific types of problems. The model's efficiency, multilingual capabilities, and suitability for quick thinking tasks are highlighted. The paragraph also discusses the potential for other companies to adopt similar architectures to GPT 4, as suggested by interviews and industry insights, which could lead to the development of competitive AI systems.
🔥 Open Source AI's Rapid Progress and Talent Challenges
The fourth paragraph emphasizes the rapid progress of open source AI, with new models being developed and surpassing previous benchmarks. It mentions a tweet from Elon Musk predicting that GPT 4 level AI will be available on laptops soon. However, it also presents a counterpoint from an AI researcher who argues that open source models may not surpass GPT 4 in the near future, citing factors such as talent, data, team structure, product focus, and infrastructure as advantages for established companies like Open AI.
🌟 Llama's Potential to Compete with GPT 4 and Meta's Strategy
The fifth paragraph discusses the potential of Meta's Llama models, particularly Llama 3 and 4, which are rumored to compete with or surpass GPT 4's capabilities. It mentions a leaked conversation suggesting that Meta has the computing power to train these models and intends to keep them freely available under the Llama license. The paragraph also suggests that Meta aims to establish Llama models as an enabling technology in the LM market, similar to Google's strategy with Android, and that jumping from Llama 2 to Llama 3 may be more challenging due to the potential shift towards a mixture of experts architecture.
Mindmap
Keywords
💡Open Source AI
💡GPT 4
💡Llama
💡Mistral 2.0
💡Compute Efficiency
💡Mixture of Experts
💡Arena ELO
💡Ethical AI Practices
💡Apache 2.0 License
💡Cost-effectiveness
💡Product vs. Model
Highlights
Open source AI is approaching the level of GPT 4, with potential availability in 2024.
Sam Altman stated it's nearly impossible to catch up to GPT 4, but developers should still try.
Mistral AI, a European alternative to larger AI companies, is known for compute-efficient models and ethical AI practices.
Mistral's CEO, Arthur Mench, announced the release of an open-source GPT 4 level model in 2024.
Mistral AI focuses on making AI models more efficient, helpful, and trustworthy.
Their model Mixr is reported to be six times faster than comparable models with 27 billion parameters.
Mistral AI provides access to its models through an API or allows users to deploy the models under an Apache 2.0 license.
Mistral AI's first LLM model, Mrra 7B, is available for free download and use, though not traditionally open source.
Mistral AI has positioned itself as a small but efficient team in the AI space, disrupting the industry with innovative models.
Mistral medium has been performing exceptionally well on benchmarks, such as the Arena ELO.
Mixr's 8-time 7 billion parameters instruct version 0.1 outperforms Google Gemini Pro and GPT 3.5 turbo.
Mistral AI has raised €385 million for model training, GPU costs, and server costs, indicating significant investment in AI development.
Mistral's cost-effectiveness could disrupt the industry, especially if GPT 4's rate limits and costs remain high.
Llama 3 is rumored to compete with GPT 4 and remain freely available under the Llama license.
Meta's Llama models aim to break OpenAI's dominance in the LLM market, potentially offering an enabling technology similar to Android in mobile.
Despite the challenges, open source models like Mistral and Llama are making significant strides in AI, competing with larger companies.
Elon Musk predicts GPT 4 level AI on a laptop in the near future, indicating rapid advancements in AI technology.