DeepSeek: What Happened, What Matters, and Why It’s Interesting

First

• Apologies for the audio! We had a production error…

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What’s new

• DeepSeek has created breakthroughs in both: How AI systems are trained (making it much more affordable) and how they run in real-world use (making them faster and more efficient)

Details

• FP8 Training: Working With Less Precise Numbers
  • Traditional AI training requires extremely precise numbers
  • DeepSeek found you can use less precise numbers (like rounding $10.857643 to $10.86)
  • Cut memory and computation needs significantly with minimal impact
  • Like teaching someone math using rounded numbers instead of carrying every decimal place
• Learning from Other AIs (Distillation)
  • Traditional approach: AI learns everything from scratch by studying massive amounts of data
  • DeepSeek's approach: Use existing AI models as teachers
  • Like having experienced programmers mentor new developers:
• Trial & Error Learning (for their R1 model)
  • Started with some basic "tutoring" from advanced models
  • Then let it practice solving problems on its own
  • When it found good solutions, these were fed back into training
  • Led to "Aha moments" where R1 discovered better ways to solve problems
  • Finally, polished its ability to explain its thinking clearly to humans
• Smart Team Management (Mixture of Experts)
  • Instead of one massive system that does everything, built a team of specialists
  • Like running a software company with:
    • 256 specialists who focus on different areas
    • 1 generalist who helps with everything
    • Smart project manager who assigns work efficiently
  • For each task, only need 8 specialists plus the generalist
  • More efficient than having everyone work on everything
• Efficient Memory Management (Multi-head Latent Attention)
  • Traditional AI is like keeping complete transcripts of every conversation
  • DeepSeek's approach is like taking smart meeting minutes
  • Captures key information in compressed format
  • Similar to how JPEG compresses images
• Looking Ahead (Multi-Token Prediction)
  • Traditional AI reads one word at a time
  • DeepSeek looks ahead and predicts two words at once
  • Like a skilled reader who can read ahead while maintaining comprehension

Why This Matters

• Cost Revolution: Training costs of $5.6M (vs hundreds of millions) suggests a future where AI development isn't limited to tech giants.
• Working Around Constraints: Shows how limitations can drive innovation—DeepSeek achieved state-of-the-art results without access to the most powerful chips (at least that’s the best conclusion at the moment).

What’s Interesting

• Efficiency vs Power: Challenges the assumption that advancing AI requires ever-increasing computing power - sometimes smarter engineering beats raw force.
• Self-Teaching AI: R1's ability to develop reasoning capabilities through pure reinforcement learning suggests AIs can discover problem-solving methods on their own.
• AI Teaching AI: The success of distillation shows how knowledge can be transferred between AI models, potentially leading to compounding improvements over time.
• IP for Free: If DeepSeek can be such a fast follower through distillation, what’s the advantage of OpenAI, Google, or another company to release a novel model?