I Did 5 DeepSeek-R1 Experiments | Better Than OpenAI o1?

All About AI
26 Jan 202531:59

TLDRThe creator conducted five experiments with DeepSeek R1 to compare its performance with OpenAI's o1. The first experiment involved coding challenges to create a 3D browser simulation, where DeepSeek R1 was the only model to successfully complete the task. The second experiment combined tool use from Claude with DeepSeek R1's reasoning tokens to analyze weather data and make recommendations. The third experiment explored the reasoning tokens through a number-guessing game, revealing DeepSeek R1's thought process. The fourth experiment tested the models' ability to break free from training data using a variant of the river-crossing puzzle, with DeepSeek R1 and Claude providing correct solutions while o1 struggled. The final experiment involved chaining reasoning tokens to solve a scenario-based puzzle, with DeepSeek R1 arriving at a plausible conclusion. The creator was impressed with DeepSeek R1's performance and looks forward to further exploration of its capabilities.

Takeaways

  • 😀 The creator conducted five experiments to compare DeepSeek R1 with OpenAI's o1 and Claude 3.5.
  • 😎 The first experiment involved coding a 3D browser simulation of a wind tunnel with adjustable parameters; only DeepSeek R1 successfully completed the task.
  • 🤖 The second experiment combined tool use from Claude with reasoning tokens from DeepSeek R1, demonstrating the potential for integrating different AI capabilities.
  • 🔍 The third experiment showcased the entertaining nature of DeepSeek R1's reasoning tokens through a number-guessing prompt, revealing its thoughtful approach.
  • 🧩 The fourth experiment used a variant of the river-crossing puzzle to test the models' ability to deviate from training data; DeepSeek R1 and Claude provided correct solutions, while o1 did not.
  • 🤔 The fifth experiment involved chaining reasoning tokens to solve a scenario-based puzzle, with DeepSeek R1 eventually reaching a plausible conclusion.
  • 📈 The creator expressed excitement about the potential of DeepSeek R1, noting its impressive performance and the interesting reasoning process.
  • 👀 The reasoning tokens of DeepSeek R1 were highlighted as a valuable feature for understanding the model's thought process and for developing more sophisticated AI applications.
  • 🔄 The creator plans to conduct more experiments, including testing local models and exploring the performance graph of DeepSeek R1.
  • 👏 Overall, the experiments demonstrated DeepSeek R1's capabilities in coding, reasoning, and problem-solving, setting it apart from other models like o1 and Claude 3.5.

Q & A

  • What was the first experiment conducted with DeepSeek R1?

    -The first experiment was a coding challenge to create a 3D browser simulation of a wind tunnel with particles, wind speed and direction adjustments, and transparency settings for the particles.

  • How did the models perform in the coding challenge?

    -DeepSeek R1 was the only model that completed the challenge successfully, creating a functional 3D simulation. Claude 3.5 and OpenAI's O1 did not produce a working solution.

  • What was the second experiment about?

    -The second experiment involved combining Claude's tool use with DeepSeek R1's reasoning capabilities to analyze weather data and provide recommendations.

  • What was the result of the second experiment?

    -The combined use of Claude's tool to fetch weather data and DeepSeek R1's reasoning provided a recommendation based on the weather conditions, suggesting it was not ideal for an 84-year-old man with a bad back and knee to go outside.

  • What was the third experiment?

    -The third experiment involved using reasoning tokens to come up with a number between 1 and 100 that would be hard for a user to guess.

  • How did DeepSeek R1 approach the number-guessing challenge?

    -DeepSeek R1 went through a detailed reasoning process, considering prime numbers and avoiding common guesses, eventually choosing the number 73.

  • What was the fourth experiment?

    -The fourth experiment was a variation of the river-crossing puzzle, where a man needs to transport a goat across a river with a wolf and cabbage already on the other side.

  • How did the models perform in the river-crossing puzzle?

    -DeepSeek R1 and Claude correctly identified that the man only needed to take the goat across the river, while OpenAI's O1 did not provide the correct solution.

  • What was the fifth experiment?

    -The fifth experiment involved chaining reasoning tokens to solve a scenario where a person needs to rush to the hospital, with clues hinting at a partner going into labor.

  • What was the outcome of the fifth experiment?

    -DeepSeek R1 correctly deduced that the partner was going into labor, based on the clues provided, such as the blue paint for a nursery and the urgent hospital message.

Outlines

00:00

💻 Experimenting with Deep Seek R1 and Coding Challenges

The narrator describes a series of experiments they conducted over the weekend using Deep Seek R1. The first experiment involved coding challenges where they tasked both CLA 3.5 and Deep Seek R1 to create a 3D browser simulation of a wind tunnel with particles. The narrator explains the setup and the challenges faced, noting that Deep Seek R1 was the only model that successfully completed the task. They also discuss the reasoning tokens used by Deep Seek R1, which they found fascinating and insightful.

05:00

🌐 Combining Models for Weather and Bitcoin Analysis

The narrator explores combining different AI models to perform tasks. They set up a scenario where CLA 3.5 fetches weather data from London and feeds it into Deep Seek R1 for reasoning. They then change the tool to fetch Bitcoin prices and use Deep Seek R1 to analyze the data and provide recommendations. The narrator finds this combination of models and tools intriguing and demonstrates how they can work together to provide insights.

10:00

🔍 Analyzing Reasoning Tokens with Number Guessing

The narrator tests Deep Seek R1 with a prompt to think of a number between 1 and 100, making it tricky for the user to guess. They analyze the reasoning tokens used by Deep Seek R1, which go through a detailed thought process to select a number that is not easily guessable. The narrator finds this process entertaining and shares some of the reasoning steps taken by the model.

15:03

🧩 River Crossing Puzzle with a Twist

The narrator presents a variation of the classic river crossing puzzle involving a man, a goat, a wolf, and cabbage. They run this prompt through Deep Seek R1, CLA 3.5, and Open AI to see how each model handles the problem. Deep Seek R1 and CLA 3.5 correctly identify the solution, while Open AI struggles, highlighting the models' ability to break free from training data and apply reasoning to new scenarios.

20:03

🔗 Chaining Reasoning Tokens for Complex Scenarios

The narrator experiments with chaining reasoning tokens by feeding the output of one reasoning step into the next. They use a scenario involving a person rushing to the hospital and analyze how Deep Seek R1 processes the information. The model provides various interpretations, some humorous and others more accurate, demonstrating the complexity and potential of chained reasoning.

25:04

🎉 Conclusions and Future Experiments

The narrator concludes their experiments with Deep Seek R1, expressing their excitement and satisfaction with the model's capabilities. They mention rumors of promising performance graphs and look forward to further experiments, including testing local models and possibly using the model on Cursor. The narrator thanks the audience for watching and hints at more content to come.

Mindmap

Recommended holding Bitcoin due to consolidating prices
Fetched Bitcoin prices using a tool and reasoned over the trend using DeepSeek R1
Recommended postponing outdoor activities for an 84-year-old man with health issues
Fetched weather data using Claude and reasoned over it using DeepSeek R1
OpenAI o1 also provided a correct conclusion, combining Rangers jersey color with the paint
DeepSeek R1 provided a detailed and correct conclusion
Final conclusion: Partner going into labor, hinted by nursery preparation
Second analysis: Partner's excitement over Rangers win causing labor
First analysis: Partner going into labor, blue paint for nursery
Narrative involving blue paint, renovated room, sunny weather, New York Rangers winning, and an urgent hospital message
Chain reasoning tokens to solve a complex scenario involving multiple hints and distractions
OpenAI o1 failed to provide the correct solution
DeepSeek R1 and Claude correctly reasoned that the man just needs to take the goat across
Man and goat on one side, wolf and cabbage on the other, man has a boat, how does he get to the other side?
Test if models can break free from training data and solve a modified river crossing puzzle
DeepSeek R1 chose 73 as the number
Evaluate numbers like 37, 73, and their properties (e.g., digits being prime)
Consider prime numbers as they are less frequently guessed
Avoid obvious middle numbers like 50
Think of a number between 1 and 100, make it hard for the user to guess
Analyze reasoning tokens for a number-guessing prompt
Bitcoin Price Analysis
Weather in London
Combine tool use from Claude with reasoning tokens from DeepSeek R1
DeepSeek R1 successfully created a detailed and functional simulation
OpenAI o1 produced a simulation but with some control issues
CLA 3.5 Sonet failed to produce a working simulation
DeepSeek R1
OpenAI o1
CLA 3.5 Sonet
Include an adjustable wing that can be rotated and tilted
Create a 3D animated browser wind tunnel simulation with adjustable wind speed, direction, and particle transparency
Results
Reasoning Process
Prompt
Objective
Results
Prompt
Objective
Conclusion
Reasoning Process
Prompt
Objective
Examples
Objective
Results
Models Compared
Objective
Experiment 5: Chained Reasoning Tokens
Experiment 4: River Crossing Puzzle Variation
Experiment 3: Reasoning Tokens Analysis
Experiment 2: Tool Use and Reasoning Integration
Experiment 1: 3D Browser Simulation
DeepSeek-R1 Experiments
Alert

Keywords

💡DeepSeek-R1

DeepSeek-R1 is an advanced AI model that the author experiments with in the video. It is capable of generating reasoning tokens and performing complex tasks such as coding and problem-solving. For example, in the video, the author challenges DeepSeek-R1 to create a 3D browser simulation, demonstrating its ability to generate code and visualize complex scenarios.

💡Reasoning Tokens

Reasoning tokens are the intermediate steps or thought processes that an AI model like DeepSeek-R1 uses to arrive at a conclusion. In the video, the author examines these tokens to understand how the model thinks and solves problems. For instance, when DeepSeek-R1 is tasked with thinking of a number between 1 and 100, the reasoning tokens show its thought process in selecting a number that would be harder to guess.

💡3D Browser Simulation

A 3D browser simulation is a virtual environment created using HTML and JavaScript that allows users to interact with 3D objects and scenes within a web browser. In the video, the author tests the AI models' ability to create such a simulation, specifically a wind tunnel with particles and an adjustable wing, to demonstrate their coding and visualization capabilities.

💡Tool Use

Tool use refers to the ability of an AI model to utilize external tools or APIs to fetch data or perform specific tasks. In the video, the author combines Claude's tool use with DeepSeek-R1's reasoning capabilities to fetch weather data and analyze it, showing how different models can be integrated to achieve more complex functionalities.

💡River Crossing Puzzle

The river crossing puzzle is a classic logic puzzle where a man must transport items across a river using a boat, with certain constraints. In the video, the author modifies this puzzle to test the AI models' ability to deviate from their training data and come up with a solution based on the modified scenario. For example, the man and the goat are already on one side, and the wolf and cabbage are on the other, simplifying the solution.

💡Coding Challenge

A coding challenge is a task that requires writing code to solve a specific problem. In the video, the author sets up a coding challenge for the AI models to create a 3D animated browser simulation. This challenge tests the models' ability to generate functional and visually appealing code.

💡AI Agent

An AI agent is a software entity that can perform tasks autonomously, often using reasoning and decision-making processes. In the video, the author refers to DeepSeek-R1 as an AI agent that can generate reasoning tokens and solve problems, demonstrating its autonomous capabilities.

💡HTML Coding

HTML coding involves writing code in HTML (HyperText Markup Language) to create web pages and web applications. In the video, the author uses HTML coding as part of the challenge to create a 3D browser simulation, showcasing the AI models' ability to generate HTML code for complex visualizations.

💡Reasoning Test

A reasoning test is an assessment designed to evaluate an AI model's ability to think logically and solve problems. In the video, the author conducts a reasoning test by presenting a scenario involving a renovated room, blue paint, and an urgent hospital message, to see if the AI models can correctly deduce the situation.

💡Model Comparison

Model comparison involves evaluating and contrasting the performance of different AI models on the same task. In the video, the author compares the performance of DeepSeek-R1, Claude 3.5, and OpenAI's model on various challenges, such as coding and reasoning tasks, to determine which model performs better in different scenarios.

Highlights

The author conducted five experiments with DeepSeek-R1 to compare its performance with OpenAI's o1 and Claude 3.5.

The first experiment involved coding a 3D browser simulation of a wind tunnel with particles using HTML.

DeepSeek-R1 successfully completed the coding challenge, while Claude 3.5 and o1 did not.

The second experiment combined Claude's tool use with DeepSeek-R1's reasoning tokens to analyze weather data.

The third experiment used reasoning tokens to come up with a number between 1 and 100 that would be hard to guess.

The fourth experiment tested the models' ability to break free from training data using a variant of the river crossing puzzle.

DeepSeek-R1 and Claude correctly solved the river crossing puzzle, while o1 did not.

The fifth experiment involved chaining reasoning tokens to solve a problem involving a hospital message and blue paint.

DeepSeek-R1 provided an interesting but incorrect conclusion about the hospital message scenario.

The author expressed surprise and interest in DeepSeek-R1's performance and reasoning tokens.

The experiments highlighted the potential of combining different models and tools for various tasks.

The author plans to explore more experiments with local models and different APIs in the future.

The reasoning tokens from DeepSeek-R1 showed a detailed thought process in solving problems.

The experiments demonstrated the models' ability to handle complex reasoning and tool use.

The author concluded that DeepSeek-R1 showed impressive performance in the conducted experiments.