DeepSeek is a new development in artificial intelligence (AI) that has brought some exciting changes to how large AI models are trained and how they "think" or reason. Let’s break it down step by step so anyone, including college students without a deep background in AI, can understand it.
What is DeepSeek, and Why Does It Matter?
DeepSeek is a project focused on building large AI models, similar to systems like ChatGPT, but with some key differences. Its contributions fall into two main areas:
- How the model is trained (pretraining): This is like teaching the model the basics.
- How the model reasons (enhancing reasoning capabilities): This is about improving how well the model can "think" and solve problems.
The cool part is that DeepSeek makes these processes cheaper and more efficient, meaning smaller companies or even researchers could use similar techniques without spending millions of dollars.
1. Pretraining the Model: Building the Foundation
Before an AI model can do anything, it needs to be trained on massive amounts of data—like giving it an education. DeepSeek made some clever changes to how this training works to make it faster and cheaper. Let’s look at what they did:
Training Stability
Training large AI models is tricky. Imagine trying to fill a huge jar with water—if you pour too fast, it overflows (this is like a "gradient explosion"), and if you pour too slow, nothing happens (this is like a "gradient vanishing"). DeepSeek found ways to keep the process balanced so the training works smoothly, even for gigantic models.
FB8 Mixed Precision
AI models do a lot of math, and they don’t always need super-precise numbers to get good results. Think of it like rounding $5.998 to $6. By using smaller numbers (like 8-bit floating point instead of 32-bit), DeepSeek reduced the amount of memory and computing power needed, making training faster and cheaper without losing accuracy.
Multi-word Prediction
Most AI models learn by predicting one word at a time, like filling in the blank: "The cat is on the ____." DeepSeek instead predicts multiple words at once, which is faster—kind of like skipping ahead in a sentence instead of going word by word.
Multi-attention Heads
AI models "pay attention" to different parts of the data they learn from. Imagine you’re studying for an exam and focusing on multiple sections of a book at the same time. DeepSeek improved this process, making the model better at focusing on important details.
MOE (Mixture of Experts)
This idea is like having a team of specialists who only work when needed. Instead of using the whole AI model for every task, MOE activates only the parts (or "experts") that are most useful, saving energy and time.
Why This Matters:
With these tricks, DeepSeek trained a massive AI model (600 billion parameters—this is like the "size" of its brain) for just $6 million. Normally, this would cost much more, so it’s a big deal for making AI more affordable.
2. Enhancing Reasoning: Teaching the Model to "Think"
AI models aren’t just about answering questions—they need to "reason" or make logical connections between ideas. DeepSeek made progress in this area too.
What Is Reasoning?
Think of reasoning like solving a puzzle. For example, if you know "All dogs bark" and "Max is a dog," you can figure out that "Max barks." AI models need to do this kind of logical thinking to answer complex questions.
Unsupervised Learning
Most AI models learn reasoning skills by using labeled data (like flashcards where the answer is written on the back). But labeling data takes a lot of time and money. DeepSeek showed that it’s possible to teach reasoning skills using unlabeled data—just giving the model a huge amount of text and letting it figure things out on its own.
Reinforcement Learning
This is like a reward system for the AI. Imagine you’re training a dog: when it does something right, you give it a treat. Reinforcement learning works the same way—when the AI makes a good prediction, it gets a "reward" that helps it learn to do better next time.
Why This Matters:
DeepSeek didn’t need expensive, labeled data to teach reasoning, which makes the process much easier and cheaper. Even though the reasoning level is still not as advanced as some cutting-edge models, it’s a big step forward for open-source AI.
How Will DeepSeek Change the AI Industry?
DeepSeek’s breakthroughs could have two major effects on the AI world:
1. Making AI More Accessible
- Right now, big companies like OpenAI (the makers of ChatGPT) have a huge advantage because they can spend millions of dollars building powerful AI models. But DeepSeek’s methods show that smaller teams or even individuals can build competitive models at a fraction of the cost.
- Why This Is Important: More people building AI means more innovation, and it could lead to cheaper tools for everyone.
2. Impact on Hardware
- Advanced GPUs (like Nvidia’s H100 or GB200) are currently essential for training large AI models. DeepSeek’s approach raises questions about whether we really need such expensive hardware. But in the long term, powerful GPUs will likely still be necessary for faster training and reasoning.
- Why This Is Important: It challenges the idea that only the most expensive hardware is worth investing in, which could change how AI research is funded.
Let’s Recap
DeepSeek is exciting because it:
- Made training AI models much cheaper and faster through smart engineering (like mixed precision and MOE).
- Showed that reasoning skills can emerge from unsupervised data, without the need for expensive manual labeling.
These changes could make AI more accessible to smaller companies and researchers while shaking up the hardware market. For college students, this is a great example of how innovations in both science (e.g., reasoning) and engineering (e.g., training efficiency) work together to push the boundaries of technology.