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OpenAI says DeepSeek stole ChatGPT data sets to train its AI Model, claims to have 'solid evidence'
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  • OpenAI says DeepSeek stole ChatGPT data sets to train its AI Model, claims to have 'solid evidence'

OpenAI says DeepSeek stole ChatGPT data sets to train its AI Model, claims to have 'solid evidence'

FP Staff • January 30, 2025, 16:51:47 IST
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OpenAI has claimed it found evidence suggesting that DeepSeek used distillation, a technique that extracts data from larger models to train smaller ones. OpenAI’s GPT-4 model, which cost over $100 million to train, is an example of a large and complex AI system

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OpenAI says DeepSeek stole ChatGPT data sets to train its AI Model, claims to have 'solid evidence'
The situation takes on an ironic tone, as OpenAI itself made substantial advancements by scraping data from the internet without explicit consent, a practice that has sparked criticism in the past. This has led to some questioning the ethics of how AI companies gather and use data. Image Credit: Reuters

OpenAI has raised serious concerns about Chinese AI startup DeepSeek, suspecting the company of using its data to train its own models. DeepSeek has gained significant attention for its cost-effective AI solutions, which are seen as strong competitors to OpenAI’s offerings. Following this, OpenAI and its partner Microsoft are now investigating whether DeepSeek used OpenAI’s API to integrate its models into their own systems.

According to sources cited by Bloomberg, Microsoft’s security researchers discovered large amounts of data being exfiltrated from OpenAI developer accounts in late 2024, which they believe are linked to DeepSeek.

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OpenAI has claimed it found evidence suggesting that DeepSeek used distillation, a technique that extracts data from larger models to train smaller ones. This method is efficient, but OpenAI argues that using it to create competing models is a violation of its terms of service.

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The distillation technique: A common practice or IP theft?

Distillation is a well-known technique in AI development, allowing smaller models to replicate the performance of more powerful ones at a fraction of the cost. OpenAI’s GPT-4 model, which cost over $100 million to train, is an example of a large and complex AI system.

However, OpenAI claims that DeepSeek has used its models to train its own system through distillation, which it argues is a violation of its terms of service. The company has not disclosed specifics of the evidence it has gathered but says it is confident that DeepSeek has used its data without permission.

Oh, the irony…

The situation takes on an ironic tone, as OpenAI itself made substantial advancements by scraping data from the internet without explicit consent, a practice that has sparked criticism in the past. This has led to some questioning the ethics of how AI companies gather and use data.

Despite OpenAI’s own history of data scraping, the company is now taking action to protect its intellectual property, especially as it faces competition from companies like DeepSeek that seem to be rapidly catching up with its capabilities.

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DeepSeek’s meteoric rise could be short-lived because of China’s data policies, history of lies
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Growing concerns over IP theft

The allegations against DeepSeek have sparked reactions from various figures in the tech world. David Sacks, former AI czar under President Trump, speculated that DeepSeek’s actions might constitute intellectual property theft, noting that there is “substantial evidence” of distillation being used to extract knowledge from OpenAI’s models.

OpenAI has responded by asserting that Chinese companies, among others, are frequently attempting to reverse-engineer leading US AI models. To protect its intellectual property, OpenAI stated that it is working closely with the US government and taking countermeasures to safeguard its technology from being exploited by competitors.

As investigations continue, the incident has brought the debate around intellectual property rights in AI development to the forefront, highlighting the growing tensions between global competitors and the ethical implications of using proprietary data to create rival models.

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