Copyright Challenges Slide in using genertaive AI
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Generative AI models that can produce creative works like images, text, music, etc. raise interesting copyright questions. Some of the key issues include: Who owns the copyright of the AI's creations? This is a tricky question. Some options could be: The AI system itself: But currently AI systems are not legal persons and cannot own copyright. The AI's developers: But they did not actually create the work, the AI did. Giving copyright to developers may discourage innovation. No one: The works could enter the public domain. But this may discourage investment in developing generative AI. A hybrid model where both the developers and public domain are considered. Infringement issues: There is a possibility that the AI's creations may incorporate elements from existing copyrighted works. This could put the AI developers at risk of copyright infringement claims. They need to be very careful about what data they train the AI on. Fair use doctrine: In some cases, the AI's works may qualify as fair use of copyrighted material for purposes like education, commentary, criticism, etc. But determining fair use can be complex and subjective. Attribution and anonymization: If the AI's works are based on a large corpus, it may be difficult to attribute elements of the work to specific human creators. Anonymization techniques may help but also raise copyright issues. Impact on human creators: Widespread use of generative AIs could significantly impact human artists, musicians, writers, etc. by reducing demand for their works. This could discourage human creativity and arts. There are no easy answers here but it is an important issue as generative AI becomes more sophisticated and widely used. Policymakers and AI developers need to think through these questions carefully while designing policies and systems. Balancing the interests of all stakeholders will be key. |
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Blog 5 |
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From the blog |
How Dataknobs help in building data productsEnterprises are most successful when they treat data like a product. It enable to use data in multiple use cases. However data product should be designed differently compared to software product. Generative AI is one of approach to build data productGenerative AI has enabled many transformative scenarios. We combine generative AI, AI, automation, web scraping, ingesting dataset to build new data products. We have expertise in generative AI, but for business benefit we define our goal to build data product in data centric manner. |
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Spotlight |
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Generative AI slides |
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Learn more about challenges: |
Link |
| Challengs-overview |
| Type-of-challenges |
| Copyright-challenges |
| Ethical-issues |
| Threats |
| Trust-issues |
| Uncontrolled-behavior |
From the Slides blog
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