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The emergence of generative AI models, particularly following the release of ChatGPT, has sparked a debate on the implications of open versus closed models. Open models are characterized by their transparency and accessibility, allowing users to modify and share the systems, while closed models maintain proprietary control over their architecture and data. This article explores the benefits and drawbacks of both approaches, particularly in terms of legal risks and operational implications.
The discussion highlights the complexities surrounding licensing restrictions, copyright risks, and privacy concerns associated with open datasets. Companies like OpenAI and Google exemplify the contrasting approaches, with their closed models offering indemnification against intellectual property claims, while open models may expose users to greater legal liabilities. Ultimately, the choice between open and closed models involves weighing trade-offs related to innovation, control, and compliance.
• Open models promote transparency but come with legal and operational risks.
• Closed models provide better control and indemnification against copyright claims.
The article discusses how generative AI models can be categorized as open or closed, impacting their usability and legal implications.
The article references OSAID to highlight the ongoing debate about the openness of generative AI models.
The article explains how closed models often provide access through APIs, maintaining proprietary control over their underlying technology.
The company is highlighted in the article for its role in the generative AI landscape and its approach to model openness.
The article discusses Google's approach to closed models and the implications for users regarding intellectual property and safety.
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