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Deep-dive into DeepSeek (Practical AI #302)
Jan 31, 2025
The podcast dives into the buzz around DeepSeek's new AI model, DeepSeek R1, unpacking its rising popularity amid privacy and geopolitical concerns. It addresses how this model contrasts with open science principles and sheds light on evolving perceptions of AI accessibility and security. The hosts explore the implications of AI model biases and prompt injection attacks, while also examining the training architecture of DeepSeek. Lastly, they discuss the shifting landscape of enterprise budgets and the necessity for businesses to adapt their AI investments.
50:49
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Quick takeaways
- DeepSeek R1's cost-effective training raises questions about the sustainability of current AI models' operational expenses and future competition.
- The open release of DeepSeek's model encourages innovation but poses significant privacy and security concerns due to its Chinese origins.
Deep dives
Significance of DeepSeek R1 Launch
The launch of DeepSeek R1, developed by a Chinese startup, has generated significant buzz in the AI community due to its competitive performance compared to leading models like OpenAI's GPT series. Remarkably, DeepSeek achieved similar results while incurring a fraction of the costs—between five to six million dollars for the final training phase. This efficiency raises questions about the sustainability of existing models’ operational costs and the resources required for their development. As a result, DeepSeek R1's entry has sparked a broader discussion about the future dynamics of AI model development, especially concerning competition and economic feasibility.
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