Shake the world Deepseek - Tech Explanation

Deepseek: The AI That's Shaking Things Up (And Why You Should Care)

Introduction:

The world of Artificial Intelligence is constantly evolving, but every now and then, something truly groundbreaking comes along. Deepseek is one of those moments. Its arrival has caused ripples, even impacting the stock market. Why? Because it's not just another AI model; it's a significant leap forward in how AI learns and operates. In this blog, we'll break down the magic behind Deepseek, even if you're new to the world of AI. We'll explore:

1. What makes Deepseek fundamentally different from other AI models?
2. How does Deepseek's innovative approach actually work?
3. What are the potential implications of Deepseek for the future of AI?

Let's demystify Deepseek and understand why it's making such a splash.

1. What Makes Deepseek Fundamentally Different?

To understand Deepseek, we need to talk about how AI models typically learn. There are three primary methods:

• Supervised Learning: Think of this as learning with a teacher. You're given labeled examples—pictures of cats labeled "cat," pictures of dogs labeled "dog." The AI learns to recognize these patterns. Most current AI models rely heavily on this.
• Unsupervised Learning: This is like learning by exploring. You give the AI a bunch of data without labels, and it finds patterns on its own. Imagine sorting a box of mixed LEGO bricks without instructions.
• Reinforcement Learning: This is learning through trial and error, like training a pet. The AI takes actions and receives rewards or punishments based on the outcomes. AlphaGo, which mastered the game of Go, is a prime example.

Most large language models (LLMs) rely heavily on supervised learning. This means they need massive datasets, which are expensive and time-consuming to collect and curate. Moreover, if the data contains errors or biases, the AI will learn and perpetuate them.

Imagine this: You have 10 news outlets. Three are known for accurate, high-quality reporting, while the other seven simply copy whatever gets the most views, regardless of accuracy. This results in an 8:1:1 ratio. If a traditional AI is trained on this data, it will likely consider the copied, less accurate information as "truth" due to its higher frequency.

Here's where Deepseek diverges:

• Cost-Effective Data Use: Deepseek aims to reduce the reliance on massive, costly datasets. This means they can build powerful models with fewer resources.
• Optimized Model Size: Instead of simply scaling up model size, Deepseek focuses on creating models that are "just right." This means smaller, more efficient models that can still deliver excellent performance.
• Reinforcement Learning for Verification: Deepseek uses reinforcement learning to verify the accuracy of its responses. This allows the model to learn from its own mistakes and improve over time, filtering out the "copycat" noise.

2. How Does Deepseek's Innovative Approach Actually Work?

Retrieved from: https://arxiv.org/pdf/2501.12948

Deepseek's approach is detailed in their paper, "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning." Here's a simplified breakdown:

• Deepseek starts with a base model "Deepseek-V3",  with similar in architecture to models used in ChatGPT or Google's Gemini, which are based on transformer networks. [DeepseekV3 Paper]
• Then, they apply reinforcement learning. Essentially, another model acts as a "judge," evaluating the base model's responses.
• If the response is accurate, the base model receives a "reward." If it's inaccurate, it receives a "punishment."
• This feedback loop allows the model to refine its reasoning and improve its accuracy over time.
• Imagine someone giving a speech, and another person giving feedback. The speaker gets better. Now, imagine lots of people giving feedback, and even feedback on the feedback. That's how Deepseek gets better.
• This system is similar to having a coach provide constant feedback, allowing the AI to learn and improve.
• This relates to the concept of "Chain of Thought" reasoning, where the AI breaks down complex problems into smaller, manageable steps.

This process allows Deepseek to learn and improve without needing vast amounts of external data, significantly reducing costs and increasing efficiency.

3. What Are the Potential Implications for the Future of AI?

Deepseek's way of doing things could change AI a lot:

• AI for Everyone: By needing less data, Deepseek could make AI easier for smaller groups to build.
• More Reliable AI: Checking its own answers makes Deepseek more accurate.
• Old Ideas, New Tricks: Deepseek shows that old methods, like learning by trying, can be powerful. If you still remember, AlphaGo, mastered the ancient game of Go, defeated a Go world champion, is actually using the knowledge of reinforcement learning. When it lose the game, it got punish; if it wins, it got reward. By training a million to trillion times, all potential situation is seen by the computer, and therefore, there is no chance for a human to win the AI due to the memory limit of human brain.
• Focus on Being Efficient: Deepseek shows that smaller can be better.
• Tech Keeps Changing: Old tech can be useful again.

Deepseek reminds us that new ideas often come from old ones. Engineering does not have an absolute answer, it only got better in same sense, but worse in another sense. Some old technology might not be used before for some reason, but it could be really helpful in the future. Every methodology is a tool for fixing a specific problem, but if that tech is born at the right time. Never think a technology is old school, or outdated, in some days in the future, it may be helpful.