All About AI - I Did 5 DeepSeek-R1 Experiments | Better Than OpenAI o1?
The video presents a series of experiments using AI models like DeepSeek R1, CLAE 3.5, and OpenAI's 01. The first experiment involves coding a 3D browser simulation using HTML and AI models. DeepSeek R1 successfully completes the task, showcasing its ability to handle complex reasoning and coding tasks. The second experiment combines tool use from Claude with DeepSeek R1's reasoning tokens to analyze weather data and make recommendations, demonstrating how different AI models can be integrated for enhanced functionality. The third experiment tests the models' ability to break free from training data using a variant of the river crossing puzzle. DeepSeek R1 and Claude successfully adapt to the new puzzle setup, while 01 struggles. The final experiment involves chaining reasoning tokens to solve a hypothetical scenario, with DeepSeek R1 eventually reaching a plausible conclusion after multiple iterations. These experiments highlight the potential of AI models to perform complex reasoning and problem-solving tasks, with DeepSeek R1 showing particular promise in handling nuanced challenges.
Key Points:
- DeepSeek R1 excels in creating a 3D browser simulation, highlighting its coding and reasoning capabilities.
- Combining Claude's tool use with DeepSeek R1's reasoning enhances AI functionality, allowing for complex data analysis.
- DeepSeek R1 and Claude adapt well to modified puzzles, showcasing their ability to deviate from training data.
- Chaining reasoning tokens helps AI models reach more accurate conclusions over multiple iterations.
- DeepSeek R1 demonstrates strong potential in handling complex reasoning tasks, outperforming other models in some scenarios.
Details:
1. 🔍 Weekend Experiments with Deep R1
1.1. Experiment Overview
1.2. 3D Browser Simulation Challenge
1.3. AI Agent Tool with Tool Use
1.4. Analyzing Reasoning Tokens
1.5. Alternative River Crossing Puzzle
1.6. Reasoning Test Comparison
2. 👨💻 Coding Challenge: 3D Browser Simulation
- The challenge involved creating a 3D animated browser simulation with interactive features such as wind speed and direction adjustments, transparency settings for particles, and a rotatable wing.
- Three models were evaluated: CLA 3.5 Sonet, 01, and Deep SE R1, focusing on their ability to deliver a functional simulation.
- CLA 3.5 Sonet failed due to technical issues that prevented the simulation from operating correctly, lacking effective handling of wind and transparency features.
- The 01 model also did not succeed, despite providing explanations of the transparency, animation, and rotation processes. The failure was due to incomplete integration of these features in a dynamic environment.
- Deep SE R1 was the only model to achieve a successful simulation, offering a fully functional 3D environment with interactive controls, such as angle and opacity adjustments, and accurately simulating wind effects on particles.
- Issues with control settings were noted in the Deep SE R1 model, but they were minor compared to the overall success in achieving the simulation objectives.
- The successful implementation with Deep SE R1 was attributed to the use of reasoning tokens, which facilitated better insights and execution during development.
3. 🌦️ Combining Models: Weather and Bitcoin Analysis
3.1. Weather Analysis and Recommendations
3.2. Bitcoin Price Analysis and Strategy
4. 🔢 Fun with Reasoning Tokens: Number Guessing Game
- The exercise involves a number guessing game where the AI is prompted to pick a number between 1 and 100, aiming to make it challenging for users to guess.
- The AI utilizes reasoning tokens, demonstrating complex thought processes despite instructions not to overthink, similar to human reasoning.
- To avoid obvious guesses, the AI considers avoiding numbers like 50 or multiples of 5 or 10, opting instead for prime numbers or less common multiples.
- Examples of numbers considered include 67, 73, 37, 53, 17, and 23, with a final choice of 73, highlighting its non-obvious nature.
- The exercise showcases the AI's ability to simulate human-like reasoning, even when directed otherwise, providing an entertaining insight into AI decision-making.