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

The video features five experiments with DeepSeek-R1 to assess its capabilities against OpenAI's o1. The first experiment involves coding a 3D browser simulation of a wind tunnel with adjustable parameters. DeepSeek-R1 successfully completes the task, outperforming o1. The second experiment combines Claude's tool use with DeepSeek-R1's reasoning to analyze weather data and provide recommendations. The third experiment uses reasoning tokens to guess a number between 1 and 100, showcasing DeepSeek-R1's thought process. The fourth experiment tests the models' ability to deviate from training data using a river-crossing puzzle variant. The final experiment involves chaining reasoning tokens to solve a scenario-based puzzle. DeepSeek-R1 impresses with its reasoning and ability to escape training data constraints.

Takeaways

• 😀 The author conducted five experiments to compare DeepSeek-R1 with OpenAI's o1 and Claude 3.5.
• 😎 The first experiment involved creating a 3D browser simulation of a wind tunnel using HTML coding, where DeepSeek-R1 outperformed the others.
• 😎 The second experiment combined Claude's tool use with DeepSeek-R1's reasoning tokens to analyze weather data and make recommendations.
• 😎 The third experiment showcased the reasoning tokens of DeepSeek-R1 through a fun number-guessing prompt, revealing its thoughtful decision-making 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 fifth experiment involved chaining reasoning tokens to solve a scenario-based problem, with DeepSeek-R1 providing an interesting and plausible conclusion.
• 😎 The author expressed surprise and impressed with DeepSeek-R1's performance, especially in tasks requiring reasoning and creativity.
• 😎 The experiments highlighted the potential of combining different models and tools to achieve more complex and nuanced results.
• 😎 The author plans to conduct more experiments and explore the capabilities of local models in the future.
• 😎 Overall, the experiments demonstrated the strengths and unique capabilities of DeepSeek-R1 compared to other models, with promising prospects for further development and application.

Q & A

What was the first experiment conducted with DeepSeek-R1?

The first experiment was a coding challenge where DeepSeek-R1, CLA 3.5, and O1 were tasked to create a 3D browser simulation of a wind tunnel with particles, adjustable wind speed and direction, and a rotatable wing.

How did the models perform in the coding challenge?

DeepSeek-R1 was the only model that successfully completed the coding challenge and produced a working 3D simulation. CLA 3.5 and O1 did not produce a working simulation.

What was the second experiment about?

The second experiment involved combining the tool use from Claude with the reasoning tokens from DeepSeek-R1 to analyze and reason over the results of an API call, such as weather data or Bitcoin prices.

Can you give an example of how the models were combined in the second experiment?

In one example, Claude fetched the weather in London, and DeepSeek-R1 used the reasoning tokens to determine if it was a good day for an 84-year-old man with a bad back and knee to go outside.

What was the third experiment?

The third experiment was a reasoning test where DeepSeek-R1 had to think of a number between 1 and 100 and make it difficult for the user to guess, showcasing the reasoning process through the reasoning tokens.

What was the fourth experiment?

The fourth experiment was a variation of the river crossing puzzle, where a man, a goat, a wolf, and cabbage were on different sides of the river, and the model had to determine how the man could get to the other side without any conflicts.

How did the models perform in the river crossing puzzle?

DeepSeek-R1 and Claude correctly concluded that the man simply needed to take the goat across the river since the wolf and cabbage were already on the other side. O1 did not arrive at the correct solution.

What was the fifth experiment?

The fifth experiment involved chaining reasoning tokens to solve a scenario where the user had to guess what was happening based on clues like blue paint, a renovated room, and an urgent hospital message.

What conclusion did DeepSeek-R1 reach in the fifth experiment?

DeepSeek-R1 concluded that the user's partner was going into labor, hinted by the preparation of a nursery, which prompted the urgent hospital message.

How did O1 perform in the fifth experiment?

O1 also correctly concluded that the user's partner was going into labor, combining the clues of the blue paint for a nursery and the urgent hospital message.

Outlines

00:00

💻 Experimenting with DeepSeek R1

The narrator outlines five experiments they plan to conduct using DeepSeek R1, including coding challenges, combining models, analyzing reasoning tokens, and testing the models' ability to break free from training data. The first experiment involves creating a 3D browser simulation of a wind tunnel with particles, challenging both CLA 3.5 and DeepSeek R1 to complete the task using HTML coding and AI tools. The narrator runs the experiment and compares the results, noting that DeepSeek R1 successfully completes the simulation, while CLA 3.5 does not.

05:00

🔍 Combining Models and Tools

The narrator explores combining different models and tools, such as using CLA 3.5 to fetch weather data and feeding it into DeepSeek R1 for reasoning. They demonstrate this by checking the weather in London and then reasoning whether it's suitable for an elderly man with health issues to go outside. The narrator also experiments with fetching Bitcoin prices and using DeepSeek R1 to provide buy, sell, or hold recommendations based on the price trends.

10:00

📊 Analyzing Reasoning Tokens

The narrator delves into the reasoning tokens generated by DeepSeek R1, using a prompt where the model has to think of a number between 1 and 100 that would be hard for the user to guess. They analyze the reasoning process, noting how the model considers various factors like avoiding obvious numbers and choosing prime numbers to make the task more challenging. The narrator finds this analysis entertaining and insightful into the model's thought process.

15:03

🧩 River Crossing Puzzle Variation

The narrator tests the models' ability to break free from training data by presenting a variation of the classic river crossing puzzle. Instead of the usual setup, the puzzle has the man and goat on one side and the wolf and cabbage on the other, with the man needing to get to the other side. The narrator runs the experiment with DeepSeek R1, CLA 3.5, and OpenAI, noting that DeepSeek R1 and CLA 3.5 correctly identify the simple solution of taking the goat across, while OpenAI struggles and fails to provide the correct answer.

20:03

🔗 Chaining Reasoning Tokens

The narrator experiments with chaining reasoning tokens by feeding the output of one reasoning step into the next. They use a prompt involving a person rushing to the hospital after receiving a message, with hints like blue paint and a renovated room suggesting a baby's nursery. The narrator runs the experiment multiple times, noting the model's evolving conclusions and eventual correct identification of the partner going into labor. They also test the prompt with OpenAI, which also arrives at the correct conclusion.

Keywords

DeepSeek-R1

DeepSeek-R1 is an AI model that the speaker experiments with in the video. It is compared to other models like CLA 3.5 and O1 to determine its capabilities. For example, in the coding challenge, DeepSeek-R1 was able to create a 3D animated browser simulation, which neither CLA 3.5 nor O1 could do, showcasing its advanced capabilities in generating complex code.

Coding Challenge

A test where AI models are asked to write code to solve a specific problem. In the video, the challenge was to create a 3D animated browser simulation with adjustable wind speed, direction, and particle transparency. This challenge was used to compare the coding abilities of DeepSeek-R1, CLA 3.5, and O1.

Reasoning Tokens

These are the thought processes or steps an AI model takes to arrive at a conclusion. The speaker finds it interesting to analyze these tokens, as seen when DeepSeek-R1 is thinking about how to create the 3D simulation and when it tries to pick a number between 1 and 100 that would be hard to guess.

Tool Use

The ability of an AI model to use external tools or APIs to fetch information. In the video, CLA 3.5 uses a weather tool to fetch the weather in London, and this information is then fed into DeepSeek-R1 for further reasoning, demonstrating how different models can be combined to achieve a task.

3D Browser Simulation

A virtual environment created using HTML and JavaScript that simulates a 3D space. In the coding challenge, the goal was to create a wind tunnel simulation where particles and a wing could be manipulated, and the effects of wind on the wing could be visualized.

Alternative River Crossing Puzzle

A variation of the classic river crossing puzzle where a man needs to transport a wolf, a goat, and cabbage across a river without any of them being left alone together. The speaker uses an alternative version to test if the AI models can break free from their training data and come up with a solution based on the user's request.

Reasoning Test

A test designed to evaluate the reasoning abilities of AI models. The speaker created their own reasoning test and ran it on DeepSeek-R1 and O1 to compare their performance and extract knowledge from the results.

HTML Coding

The use of HTML (HyperText Markup Language) to write code for web pages. In the video, HTML coding is used to create the 3D browser simulation, and the AI models are evaluated based on their ability to generate the correct HTML code to achieve the desired simulation.

AI Agent

An AI model that can perform tasks autonomously. The speaker mentions using an AI agent tool with tool use, which implies that the AI can interact with external tools and perform tasks without human intervention.

Chain Reasoning

A method where the reasoning process is broken down into a series of steps or chains. The speaker experiments with chaining reasoning tokens in DeepSeek-R1 to see if it can arrive at a more accurate conclusion by building on previous reasoning steps.

Highlights

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

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

DeepSeek-R1 was able to complete the coding challenge, while o1 and Claude 3.5 failed to produce a working solution.

Second experiment combined tool use from Claude with reasoning tokens from DeepSeek-R1 to analyze weather data.

Successfully integrated weather data from London into DeepSeek-R1's reasoning process to provide recommendations.

Third experiment involved analyzing reasoning tokens from DeepSeek-R1 for a number-guessing prompt.

DeepSeek-R1's reasoning tokens showed a detailed thought process in selecting a number between 1 and 100.

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 puzzle, while o1 failed to provide the correct solution.

Fifth experiment involved chaining reasoning tokens from DeepSeek-R1 to solve a scenario-based problem.

DeepSeek-R1's chained reasoning tokens led to a humorous but incorrect conclusion in the first attempt.

Further attempts with chained reasoning tokens resulted in more accurate conclusions.

DeepSeek-R1 demonstrated impressive reasoning capabilities in solving complex problems.

Comparison with o1 showed that DeepSeek-R1 had a more detailed reasoning process in certain tasks.

Overall, DeepSeek-R1 showed potential for advanced reasoning and problem-solving in various experiments.