Applied aI Tools
AI keeps getting less expensive with every passing day!
Just a couple of weeks back we had the DeepSeek V3 design pressing NVIDIA's stock into a down spiral. Well, today we have this new cost reliable model launched. At this rate of innovation, I am thinking of selling NVIDIA stocks lol.
Developed by researchers at Stanford and the University of Washington, their S1 AI model was trained for mere $50.
Yes - only $50.
This further challenges the dominance of multi-million-dollar designs like OpenAI's o1, DeepSeek's R1, and others.
This breakthrough highlights how development in AI no longer requires massive budget plans, potentially democratizing access to innovative thinking capabilities.
Below, we explore s1's development, advantages, and implications for the AI engineering market.
Here's the initial paper for your reference - s1: Simple test-time scaling
How s1 was developed: Breaking down the methodology
It is very intriguing to discover how scientists across the world are optimizing with limited resources to reduce costs. And these efforts are working too.
I have tried to keep it easy and jargon-free to make it simple to comprehend, keep reading!
Knowledge distillation: The secret sauce
The s1 model utilizes a method called understanding distillation.
Here, a smaller AI design simulates the thinking processes of a bigger, more advanced one.
Researchers trained s1 utilizing outputs from Google's Gemini 2.0 Flash Thinking Experimental, a reasoning-focused design available via Google AI Studio. The group prevented resource-heavy techniques like support learning. They used monitored fine-tuning (SFT) on a dataset of just 1,000 curated concerns. These questions were paired with Gemini's responses and detailed reasoning.
What is monitored fine-tuning (SFT)?
AI keeps getting less expensive with every passing day!
Just a couple of weeks back we had the DeepSeek V3 design pressing NVIDIA's stock into a down spiral. Well, today we have this new cost reliable model launched. At this rate of innovation, I am thinking of selling NVIDIA stocks lol.
Developed by researchers at Stanford and the University of Washington, their S1 AI model was trained for mere $50.
Yes - only $50.
This further challenges the dominance of multi-million-dollar designs like OpenAI's o1, DeepSeek's R1, and others.
This breakthrough highlights how development in AI no longer requires massive budget plans, potentially democratizing access to innovative thinking capabilities.
Below, we explore s1's development, advantages, and implications for the AI engineering market.
Here's the initial paper for your reference - s1: Simple test-time scaling
How s1 was developed: Breaking down the methodology
It is very intriguing to discover how scientists across the world are optimizing with limited resources to reduce costs. And these efforts are working too.
I have tried to keep it easy and jargon-free to make it simple to comprehend, keep reading!
Knowledge distillation: The secret sauce
The s1 model utilizes a method called understanding distillation.
Here, a smaller AI design simulates the thinking processes of a bigger, more advanced one.
Researchers trained s1 utilizing outputs from Google's Gemini 2.0 Flash Thinking Experimental, a reasoning-focused design available via Google AI Studio. The group prevented resource-heavy techniques like support learning. They used monitored fine-tuning (SFT) on a dataset of just 1,000 curated concerns. These questions were paired with Gemini's responses and detailed reasoning.
What is monitored fine-tuning (SFT)?