Explore AI advancements in models like GPT-4o Mini, Mistral NeMo, and MathΣtral. Discuss coding with Codestral Mamba and LMSYS updates. Touch on AI regulatory concerns and personal poker AI experiences.
GPT-40 Mini offers cost-efficient intelligence with improved performance for broader applications.
Mistral Nemo showcases potential for efficient utilization and development with its large parameter model.
AI models like Mistral Nemo and GPT-40 Mini signify advancements in multilingual capabilities and cost-effective deployment.
Deep dives
Introduction of GPT-40 Mini and Mistral Nemo Models
GPT-40 Mini was introduced featuring cost-efficient intelligence with improved performance at a lower cost. Mistral Nemo, a 12 billion parameter model, with a 128k context window was released under Apache 2.0 license. Despite initial testing limitations, the release showcases potential for efficient utilization and development.
Impact of Cost Reduction on AI Usage
The significant reduction in pricing for GPT-40 Mini enables broader usage across various applications, particularly in decision-making tasks with low costs and latency. Increased affordability allows for efficient model routing, internal organizational tasks, and opens opportunities for fine-tuning to enhance specific functionalities.
Future Advancements and Practical Use of Models
The progression towards versatile AI models like Mistral Nemo and GPT-40 Mini signifies advancements in multilingual capabilities, precise instruction following, and handling multi-turn conversations. These models pave the way for cost-effective deployment, expanded use cases, and potential improvements in various industries through fine-tuning and efficient utilization.
GPT-4 Mini vs. Mistral Nemo
GPT-4 Mini is compared to Mistral Nemo, highlighting the advantages of fine-tuning GPT-4 Mini over the usage of Nemo due to cost considerations.
CodeStrull Mamba and Mathstral Models
CodeStrull Mamba, specialized in code generation, features a 256k token context length and potential productivity benefits. On the other hand, Mathstral, a 7 billion parameter model for math and scientific discovery, receives skepticism for its marketing claims and uncertainties about its practical utility.
Try SimTheory Beta: https://simtheory.ai/chat Show Notes: https://thisdayinai.com/bookmarks/63-ep70 Join our community: https://thisdayinai.com Merch: https://www.thisdayinaimerch.com/ ===== Thanks for listening! ===== CHAPTERS: 00:00 - It's good to be back... 04:12 - Chris's Learnings From Playing Poker Using AI 32:11 - Initial thoughts on GPT-4o Mini from OpenAI 44:15 - Mistral's NeMo 55:01 - Codestral Mamba 1:04:48 - MathΣtral: Scientific Discovery or BS? 1:12:59 - New Models on LMSYS: Column-r Column-u, Eureka by Google 1:09:22 - BOOM FACTOR for new models 1:16:39 - JD Vance Doesn't Want AI Regulatory Capture 1:18:41 - Final thoughts
Get the Snipd podcast app
Unlock the knowledge in podcasts with the podcast player of the future.
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode
Save any moment
Hear something you like? Tap your headphones to save it with AI-generated key takeaways
Share & Export
Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode