David Andre showcases Deep Seek R1, an open-source AI model that challenges OpenAI's O1 by offering similar performance at a significantly lower cost.
In this video, David Andre introduces Deep Seek R1, an open-source AI model claimed to be as powerful as OpenAI's latest model, O1, but at a substantially lower cost. He emphasizes the rapid advancement in AI, showcased by the quick release of Deep Seek R1 only 46 days after the O1. The company Deep Seek has also released several smaller models, making AI more accessible. Additionally, he discusses how Deep Seek's focus on reinforcement learning rather than extensive supervised training allows it to autonomously enhance its problem-solving capability, claiming it utilizes thinking time effectively, which he likens to the evolution of problem-solving found in advanced AI models like AlphaZero. Andre encourages viewers to leverage this technology, as it empowers smaller developers to take on larger tech firms like OpenAI by integrating these cost-effective models into their applications, further democratizing AI development.
Content rate: B
The content is informative and presents intriguing claims about the capabilities and cost-effectiveness of Deep Seek R1 compared to OpenAI's offerings. However, while many points are backed by evidence, the analysis lacks some critical explorations of the claims' limitations and the implications of using such technology. There is a positive bias that might skew the audience's perception without presenting possible counterarguments, which detracts slightly from the overall educational value.
AI OpenSource Technology Development
Claims:
Claim: Deep Seek R1 is an open-source AI model that performs comparably to OpenAI's O1.
Evidence: Deep Seek R1 is said to be as powerful as O1, and community evaluations reportedly support this claim.
Counter evidence: Comparative benchmarks may vary based on specific use cases and tasks, and more rigorous testing could yield different results.
Claim rating: 8 / 10
Claim: Deep Seek R1 is 27 times cheaper than O1 for token usage.
Evidence: The cost for Deep Seek R1 is stated to be $0.55 per million input tokens and $2.2 for output compared to O1's $15 and $60 respectively.
Counter evidence: While pricing is considerably cheaper as stated, the actual performance in all contexts may not be uniformly better, which can affect overall cost-effectiveness in certain applications.
Claim rating: 9 / 10
Claim: Deep Seek used a reinforcement learning approach without human-labeled examples to train R1.
Evidence: The model is described as starting from zero and enhancing its reasoning abilities through self-exploration, akin to advances seen with AlphaZero.
Counter evidence: The absence of supervised training can also potentially lead to gaps in knowledge or reasoning that might be covered with human guidance.
Claim rating: 7 / 10
Model version: 0.25 ,chatGPT:gpt-4o-mini-2024-07-18