- Google Bard and ChatGPT are two popular large language models (LLMs) capable of generating human-quality text, with some technical and legal differences. While Bard claims to be more accurate and readable, ChatGPT offers more accessibility with its free and paid tiers.
Both Google Bard and ChatGPT are suitable for rough drafts, structured data, and validated objective facts, but they should not be used without human oversight for decisions with legal or significant consequences. Reliability and accuracy should be considered experimental, as acknowledged by Google Bard’s onboarding experience.
In terms of privacy protection, neither tool is particularly great. ChatGPT is relatively more privacy-protecting, but both tools have access to sensitive information and limited control over user data. Google Bard’s claim of not collecting personal information for advertising purposes is misleading, and both systems can retain and use data in ways that don’t meet privacy-protecting requirements defined by law.
Supplemental Information ℹ️
The article highlights the technical and legal differences between Google Bard and ChatGPT, emphasizing the need for human oversight when using these tools for important decisions. It also delves into the privacy practices of both systems, revealing concerns about data retention and usage. The author suggests exploring alternative tools that prioritize privacy and discusses Private AI’s Private GPT as a potential solution. The article concludes by emphasizing the importance of taking privacy seriously while developing the future of large language models.
The article discusses the similarities and differences between Google Bard and ChatGPT, two popular language models. It points out that both tools are good for rough drafts and providing information, but human oversight is necessary for important decisions. The article raises concerns about the privacy practices of both systems and suggests exploring alternative tools. Overall, it emphasizes the need to prioritize privacy in the development of large language models.
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