I tested DeepSeek vs. OpenAI-o1 for data science tasks: Here’s what I found.

Key Concepts:

• DeepSeek R1: An open-access, free AI model focused on reasoning.
• Reinforcement Learning: The primary method used to train DeepSeek R1, without relying on human-labeled datasets.
• Reasoning Tasks: Complex problem-solving that DeepSeek R1 excels at.
• OpenAI Models: Specifically, GPT-4 and GPT-3.5, used as benchmarks for comparison.
• Parameter Size: The number of parameters (e.g., 7B, 14B, 67B) indicating model size and computational requirements.
• AMA (Apple Machine learning framework): A tool for running open-source LLMs locally.
• Open WebUI: An offline AI platform that supports AMA and other LLM runners, providing a user-friendly interface.
• Logarithmic Scale: A non-linear scale used in data visualization that can distort perceptions.

DeepSeek R1 Overview:

DeepSeek R1 is presented as a groundbreaking open-access AI model that rivals OpenAI's models in reasoning capabilities. Unlike OpenAI's models, DeepSeek R1 is free to use and allows for further fine-tuning. The model is trained using reinforcement learning, enabling it to solve reasoning tasks by refining its thought process over time.

Performance Benchmarks:

• Mathematical Reasoning: DeepSeek R1 performs on par with or slightly better than OpenAI's models.
• Coding and Software Engineering: Matches OpenAI's models in performance.
• Knowledge Tests (MMLU, GPQA Diamonds): Shows strong performance, though OpenAI retains a slight edge.

Accessing and Running DeepSeek R1:

• Web Interface: Accessible via chat.deepseek.com after creating an account.
• API: Available at a lower cost than OpenAI's API.
• Local Execution: Possible using tools like AMA, but the full 67B parameter model requires significant computational resources (400GB+ disk space, high memory). Distilled models (e.g., 7B, 14B parameters) can run on more modest hardware.

Step-by-Step Process for Local Setup (using AMA and Open WebUI):

1. Download a Distilled Model: Choose a smaller parameter model (e.g., 14B) suitable for your hardware.
2. Install AMA: Set up the AMA framework for running the LLM locally.
3. Install Docker: Install Docker to run Open WebUI as a container.
4. Run Open WebUI: Download and run Open WebUI as a Docker container.
5. Connect to Local Models: Open WebUI automatically connects to models managed by AMA.
6. Experiment: Use the Open WebUI interface to interact with the DeepSeek R1 model.

Comparative Analysis with OpenAI Models:

The video presents a side-by-side comparison of DeepSeek R1 and OpenAI's GPT-3.5 and GPT-4 on real-world data science questions.

• Data Cleaning and Pre-processing: Both models provide systematic approaches, but GPT-4 offers a more detailed and complete answer, including steps like understanding the data and business context. DeepSeek R1's answers are more direct.
• Coding Task: GPT-4 generated code to visualize customer risk data with two graphs (bar chart and box plot) without errors, while DeepSeek R1's code had a minor error and produced a single, potentially harder-to-read graph.
• Data Misrepresentation Detection: GPT-4 successfully identified the use of a logarithmic scale in a graph designed to downplay the addictive properties of Oxycontin. DeepSeek R1's response was less accurate, failing to pinpoint the logarithmic scale issue.

Notable Quotes:

• "This kind of destroys the AI industry. It's now one of the most advanced free AI models we can use."
• "...the model can naturally learn to solve reasoning tasks with more thinking time, refining its thought process carefully before moving on to the next steps. This way the model can discover the AHA moments on its own which is pretty remarkable."
• "I was not much worried about AI being so smart and taking over the world. I was more worried about big tech companies making a fortune by charging arms and legs to get access to smart AI. So this really brings me a lot of hope."

Technical Terms and Concepts:

• Parameters: Variables that a machine learning model learns during training. More parameters generally mean a more complex and capable model, but also higher computational requirements.
• Reinforcement Learning: A type of machine learning where an agent learns to make decisions by receiving rewards or penalties for its actions.
• Fine-tuning: The process of taking a pre-trained model and further training it on a specific dataset to improve its performance on a particular task.
• Logarithmic Scale: A scale in which equal distances represent equal ratios, rather than equal values. It is used to display data with a wide range of values, but can also be used to distort perceptions.

Logical Connections:

The video progresses logically from introducing DeepSeek R1 to demonstrating its capabilities and comparing it to established models. It starts with an overview, moves to performance benchmarks, details access methods, provides a step-by-step guide for local setup, and concludes with a comparative analysis and final thoughts.

Synthesis/Conclusion:

DeepSeek R1 is a promising open-access AI model that offers strong reasoning capabilities, rivaling OpenAI's models in many areas. While it may not surpass GPT-4 in all tasks (particularly coding and vision-related tasks), its free availability and open nature make it a valuable tool for data scientists, researchers, and anyone interested in exploring AI. The video highlights the potential of reinforcement learning and the importance of open-source AI in democratizing access to advanced technology.