Intellectual Property in the Age of AI
Ari Massoudi
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Welcome to the confusing world of artificial intelligence (AI) and intellectual property.
The Different Typologies of AI and Their Legal Challenges
1. Traditional AI: The Good Old Algorithms
Traditional AI are those good old algorithms that take input data and produce results based on predefined rules. Think of movie recommendation systems that push the latest blockbuster to you to watch.
Challenges: The main problem with intellectual property here is determining what is actually patentable. An algorithm itself is often considered a mathematical formula, and mathematical formulas cannot be patented. So what to do?
Solutions: Companies can opt for trade secret protection, which consists of keeping the inner workings of the algorithm secret (much like the secret recipe for Coca-Cola). They can also patent the specific application of the algorithm, that is, how it is used in a specific context.
2. Machine Learning AI: When Machines Learn
Machine learning is a method where machines teach themselves by analyzing large amounts of data. Imagine a program that learns to recognize cats in photos after seeing millions of images of cats and dogs.
Challenges: When an AI learns and evolves, who owns the final model? Is it the one who created the original algorithm, or the one who provided the data? And what about potential biases in the data?
Solutions: Here, copyright can come to the rescue, as it protects original works. Companies can protect the source code of their models. In addition, to avoid bias, they can ensure that the data used is varied and representative. As for the question of ownership, clear contracts when collaborating can clarify who owns what.
3. Generative AIs: When Machines Create
These creative AIs, such as those that generate works of art or articles, raise unique questions. Who owns the rights to a work created by a machine?
Challenges: Current copyright laws are based on the idea that only a human can be a creator. So, who is the author? The programmer? The user who requested the creation?
Solutions: One solution could be to change laws to include AI-generated creations under “machine copyright.” In the meantime, companies can ensure that users agree to clear terms of use regarding ownership of the generated works.
LLMs and API-Connected Services: Maintaining Control
LLMs, or large language models, like GPT-3 or GPT-4, are those artificial intelligences that can understand and generate text. Companies often use them through APIs (application programming interfaces), which allow different software to talk to each other.
Challenges: The challenge here is to protect the interface and the generated results. If an API provides access to an AI model, how do you prevent someone from reproducing the results and claiming authorship?
Solutions: Protecting the API itself can be done through copyright for the code, and patents for the unique methods of interacting with the model. API usage agreements should be clear about what is allowed and how the data can be used.
Conclusion: Navigating the Intellectual Property Maze
IP in AI is a constantly evolving field and presents unique challenges. But with creative strategies and a clear understanding of current laws, tech companies can protect their innovations while continuing to innovate.
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ITW Kamyar Maserrat - Patent Attorney Expert in Deeptech
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Co-hosts:
Gerald helps early-stage health tech companies achieve rapid growth and global impact. Leveraging his deep industry experience and extensive network, he guides teams in areas like gene therapy, medical devices, and diagnostics to navigate complex regulatory landscapes and secure strategic partnerships.
Dr. Benjamin DELSOL (PhD, LL.M) , Quantum Physicist, European Patent Attorney, Expert Deep Tech Innovation / DELSOL
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1 个月Ari Massoudi valuable read, rightly put from traditional algorithms to generative AI, protecting innovations requires a mix of trade secrets, copyrights, patents, and clear agreements. The intersection of AI and intellectual property is a challenging yet fascinating space.