AI/ML platforms, trade secrets can include the structure of the AI/ML model, proprietary training data, a particular method of using the AI/ML model, any output calculated by the AI/ML model that is converted into an end product for a customer, and aspects of the platform.

We previously discussed which portions of an artificial intelligence/machine-learning ("AI/ML") platform can be patented. Under what circumstances, however, is it best to keep at least a portion of the platform a trade secret? And what are some best practices for protecting trade secrets? In this post, we explore important considerations and essential business practices to keep in mind when working to protect the value of trade secrets specific to AI/ML platforms, as well as the pros and cons of trade secret versus patent protection. What qualifies as a "trade secret" can be extraordinarily broad, depending on the relevant jurisdiction, as, generally speaking, a trade secret is information that is kept confidential and derives value from being kept confidential. This can potentially include anything from customer lists to algorithms.

We previously discussed which portions of an artificial intelligence/machine-learning ("AI/ML") platform can be patented. Under what circumstances, however, is it best to keep at least a portion of the platform a trade secret? And what are some best practices for protecting trade secrets? In this post, we explore important considerations and essential business practices to keep in mind when working to protect the value of trade secrets specific to AI/ML platforms, as well as the pros and cons of trade secret versus patent protection. What qualifies as a "trade secret" can be extraordinarily broad, depending on the relevant jurisdiction, as, generally speaking, a trade secret is information that is kept confidential and derives value from being kept confidential.

These advances will create a network where almost every device can be simultaneously connected, enabling technologies not possible today. Governments and private entities are just beginning to invest in the technology, and projections suggest commercial availability around 2030. But given 6G's anticipated ubiquity and potential to change the landscape, we would be wise to begin learning about it now. Artificial intelligence ("AI") represents a new frontier in the global economy: Some estimates say it could contribute up to $15.7 trillion worldwide by 2030. Increases in computing power and innovations in computer science have fueled AI innovation.

From self-driving vehicles and autonomous drones, to virtual doctors and automated personal assistants, AI is expected to fundamentally disrupt the way that people live, work and interact with each other. AI is increasingly the key to significant innovations across almost all segments of society, manifesting itself in vastly different applications. There are vast opportunities for businesses operating in industries where AI has become more prevalent. However, with these opportunities come significant challenges. Ashurst's series of articles on AI will consider these issues from an IP perspective.

The Situation: Artificial intelligence ("AI") technology is exploding across virtually all industries. Technology companies are innovating at warp speed, and even companies that do not principally identify as "technology companies" are becoming increasingly "high tech" in how they deliver goods and services. Innovation is outpacing the law--many aspects of AI legal protection are still open questions. The Result: With innovation comes imitation … and infringement and misappropriation. Companies must be vigilant in protecting and enforcing their intellectual property ("IP") rights in AI.

Artificial intelligence ("AI") is a technical field of computer science that includes machine learning, natural language processing, speech processing, expert systems, robotics and machine vision. The term "artificial intelligence" is sometimes challenged in favor of machine intelligence or machine learning. Machine learning automates decision making using programming rules and in some cases training data sets. Human subject matter experts can provide feedback on results as part of a training process. Machine learning can adapt its programming based on the training process and feedback.