Artificial intelligence (AI) tools have become a widespread part of the creative process, whether to generate or refine content, or even manufacture related products.
An American Chamber of Commerce study indicated that the use of generative-AI tools by small businesses, like chatbots and image creation, nearly doubled to 40% from 2023 to 2024[1], with that number continuing to rise.
This raises some interesting intellectual property (IP) questions around ownership and usage rights
In the first part of our two-part series, we explore generative-AI and the various types of AI tools it encompasses, as well as the types of IP issues that arise.
What is AI?
Let's start with the basics: AI is a field of computer science that focuses on enabling machines to perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, and decision-making. AI involves creating systems that can understand, process, and learn from data, enabling them to make predictions and identify patterns.
As AI has evolved, it has become differentiated into two main types at a high level: traditional and generative.
- Traditional or predictive AI systems are primarily designed to respond to specific inputs, analyse data, and make predictions, but they don't create anything new. These include predictive text on phones, voice assistants like Siri or Alexa, as well as recommendation engines used by platforms such as Netflix or Amazon.
- Generative-AI is more complex and can be thought of as the next generation of AI. It's a form of AI that can create something new, such as text, images, designs, videos and music, all from the piece of information you gave it or derived from elsewhere.
Generative-AI has opened new doors for creators, allowing them to work at a faster pace. However, it comes with risks, which we explore from an IP perspective.
How does generative-AI work?
You've likely heard the reference "machine learning", which works by gathering information or datasets from various sources to learn and perform tasks. Generative-AI requires a massive amount of data to function effectively, and machine learning provides the training that fuels the AI to produce its results.
Datasets can be sourced either internally - like customer data held by organisations, or externally - like publicly available data on the internet, licensed data from third parties and open datasets provided by (for example), governments and research institutions.
Here are some examples of current generative-AI tools you may have used:
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Text generation |
Image generation |
Music generation |
Video generation |
Is IP in generative-AI important?
For IP lawyers, identifying the IP issues arising from the use of generative-AI use comes naturally. IP issues touch all stages of the generative-AI process from how and by what generative-AI is trained, what it generates, how it's used, and who owns the output.
So, can content created by AI tools be protected by IP rights? And if so, who owns those rights? In part two, we dive into this in more detail.
[1] https://www.uschamber.com/technology/artificial-intelligence/new-study-reveals-nearly-all-u-s-small-businesses-leverage-ai-enabled-tools-warns-proposed-regulations-could-hinder-growth
