DeepSeek, a suite of AI models developed by a Chinese startup, has recently become a hot topic in the AI community. Boasting performance on par with, and sometimes exceeding, that of OpenAI's latest models – all while claiming significantly lower training and operational costs – DeepSeek is sparking both excitement and scrutiny. This article breaks down what DeepSeek is, its potential impact, and considerations for its safe use.
DeepSeek refers to a collection of cutting-edge AI models developed by a Chinese startup named DeepSeek. These models have garnered attention for their impressive capabilities and alleged cost-efficiency compared to established players like OpenAI. Distinguishing itself further, DeepSeek has openly shared its methodologies and made its models accessible to researchers globally.
Given the buzz surrounding DeepSeek, many are eager to explore its capabilities. However, prioritizing security and data privacy is crucial.
Here's a breakdown of safe and unsafe ways to interact with DeepSeek:
Important Note: Currently, there are NO approved methods for using DeepSeek with non-public or sensitive data outside of secure environments like AWS Bedrock.
DeepSeek's most compelling claim is its efficiency. The company reports training costs of under $6 million, a fraction of the $100 million reportedly spent on training ChatGPT's 4o model. Inference costs (the cost of interacting with the model) are also significantly lower, around 1/50th of the cost of Anthropic's Claude 3.5 Sonnet.
Factors contributing to this efficiency:
Independent verification confirms that DeepSeek requires less power to run compared to similar models. Further reading on this topic is available at VentureBeat and CNBC.
DeepSeek has openly acknowledged using data from OpenAI's o1 "reasoning" model for training. This challenges the conventional wisdom that AI models require vast amounts of human-created data. By using ChatGPT's "thinking" scripts as training data, DeepSeek essentially distilled existing knowledge into its model. Whether this approach is sustainable long-term remains to be seen. For detailed insights, watch this YouTube explainer.
DeepSeek stands out by publishing its methodology and making its models openly available. This fosters innovation and allows researchers worldwide to incorporate DeepSeek's breakthroughs, inspect its workings, and create derivative models. This open-source approach is already inspiring others to replicate DeepSeek's efficiency, as seen in projects like the Hong Kong team working on GitHub refining Alibaba's Qwen model.
DeepSeek's emergence has significant implications for the AI industry.
While DeepSeek offers impressive capabilities, it's crucial to acknowledge potential biases. Intentional "guardrails" within the model reflect limitations.
DeepSeek's advancements highlight that AI model development is only one part of the equation. The future lies in how we leverage these models to create innovative and impactful AI-powered applications.
DeepSeek represents a significant leap forward in AI efficiency and accessibility. While safety and ethical considerations must be carefully addressed, its open-source nature and impressive performance promise to drive further innovation and unlock new possibilities within the AI landscape.
