DeepSeek Explained: AI is Changing EVERYTHING!

Summary

This video provides an in-depth yet simplified explanation of the DeepPsych model, focusing on its training techniques. The presenter, Sandeco, compares the training methods of DeepPsych with OpenAI's models. He introduces key concepts such as reinforcement learning, model distillation, and the chain of thought technique. The video uses an analogy of chess to explain the training process, showing how the model learns through trial and error, progressively improving. The importance of smaller models and how they can be fine-tuned with private data is also discussed, along with practical applications of the technology.

Takeaways

• 😀 Deep Psych's training involves three key steps: reinforcement learning, model distillation, and chain of thought.
• 😀 The first step, reinforcement learning, is explained using the analogy of a neural network learning to play chess, receiving feedback from an expert system.
• 😀 OpenAI uses a supervised learning model, where data is labeled and the system is trained using vast amounts of information to improve its accuracy.
• 😀 Deep Psych, on the other hand, learns by playing against itself, progressively improving through trial and error in a self-reinforcing manner.
• 😀 In reinforcement learning, the system is rewarded for correct actions and penalized for mistakes, similar to how pets are trained.
• 😀 The model distillation process involves training a smaller, less powerful network by transferring knowledge from a more advanced model (the teacher network).
• 😀 Deep Psych uses a model distillation technique to create smaller networks that can run on local machines while still maintaining high performance.
• 😀 After training, Deep Psych's smaller models, such as the 8B parameter network, can be used on local machines for tasks like document processing and data privacy.
• 😀 Using a local Deep Psych model ensures data privacy because the model operates without needing to be connected to the internet.
• 😀 The 'chain of thought' technique helps Deep Psych provide better answers by outlining the reasoning process step by step before arriving at a conclusion.
• 😀 The 'chain of thought' method helps resolve complex problems, as the model breaks down the task into manageable steps to find the best solution.

Q & A

What is the primary focus of the video script?

The primary focus of the video script is to explain how the 'Deep Psyche' (DeePsyc) system works, with an emphasis on its training process and the techniques used, such as reinforcement learning, model distillation, and chain-of-thought reasoning.

What is reinforcement learning, and how does it apply to DeePsyc?

Reinforcement learning is a technique where an agent learns by playing games or taking actions, receiving rewards or punishments based on its performance. In the case of DeePsyc, it plays games like chess against itself, learning from mistakes and improving over time through feedback from a reward system.

How does DeePsyc's approach to reinforcement learning differ from OpenAI's approach?

DeePsyc uses a self-play mechanism where it plays against itself, whereas OpenAI employs external experts (like Stockfish in chess) to provide feedback on the best moves. DeePsyc learns autonomously, improving from its own mistakes rather than relying on an external source for evaluation.

What is 'model distillation' and how is it used in DeePsyc?

Model distillation is a technique where a smaller, less powerful model is trained by learning from a larger, more powerful model. In DeePsyc, a smaller model is trained to imitate the behavior of a larger model (R1 671B), improving its performance and efficiency while retaining much of the original model's capabilities.

What role does 'chain-of-thought reasoning' play in DeePsyc?

Chain-of-thought reasoning involves breaking down complex problems into smaller, sequential steps of reasoning, which allows DeePsyc to tackle more complex tasks. It helps the system think through multiple stages before arriving at an answer, improving the quality of its responses.

What is an example of chain-of-thought reasoning in action?

An example is when DeePsyc solves a math problem, such as calculating how many tennis balls Roger has. DeePsyc first breaks down the problem step-by-step (e.g., starting with 5 balls, adding 6 more, and arriving at the final total), showing each part of its thought process before concluding the answer.

Why is it beneficial to use a smaller model like DeePsyc 8B on local machines?

Using a smaller model like DeePsyc 8B on local machines provides privacy, as the data remains secure on the local device, without needing to send it over the internet. Additionally, it allows users to retrain the model with their private data, making it more customized and relevant to specific needs.

How does DeePsyc compare in terms of parameters to other models like OpenAI's?

DeePsyc R1 8B has about 8 billion parameters, which is significantly fewer than larger models like OpenAI's, which can have nearly a trillion parameters. However, DeePsyc achieves a similar intellectual capacity despite being smaller in size.

What is the advantage of training DeePsyc on a user's private data?

Training DeePsyc on private data allows the model to better understand specific contexts, such as a user's documents, company data, or industry-specific knowledge. This creates a more personalized and effective system, capable of responding accurately to questions based on the user's unique data.

How does DeePsyc ensure data privacy while training on local machines?

DeePsyc ensures data privacy by keeping all data on local machines and not uploading it to the cloud. This way, users can securely train the model with sensitive information, such as private documents or industry secrets, without exposing them to external servers or the internet.