Disclaimer: The following information is research curated from publicly available sources. The information in this post is not intended to advise on the use of AI in intellectual property and should not be used as policy or guidelines for use of AI in research or IP management. Please consult the UNHInnovation Technology Transfer team for specific guidance.
In a world increasingly driven by technology, Artificial Intelligence (AI) has emerged as a transformative force, revolutionizing various aspects of our lives. One domain where AI has made a significant impact is Intellectual Property (IP). In this blog post, I will delve into the concept of AI, its applications in IP, common terminology, types of AI, and the positive and negative impacts it has on this field.
What is AI?
Before we explore the impact of AI on IP, it's crucial to grasp what AI is. In essence, AI, short for Artificial Intelligence, refers to the capability of machines to perform cognitive functions typically associated with human minds. In simpler terms, it's the science of creating machines that can think and make decisions like humans. AI systems can engage in natural language conversations, answer questions, provide information, and generate text-based responses based on the input they receive.
Learn more about the basics of AI: https://www.mygreatlearning.com/blog/what-is-artificial-intelligence/
Examples of AI in Action
- ChatGPT - A Narrow AI: ChatGPT is an advanced AI language model developed by Open AI. It excels in engaging in natural language conversations, answering questions, and generating text-based responses based on the input it receives.
- Self-Driving Cars - Limited Memory AI: These vehicles utilize AI for navigation and decision-making processes, making them more autonomous.
- Netflix Recommendations - Reactive Machines: AI algorithms analyze your viewing habits to recommend content that aligns with your preferences.
- Siri - Another example of Narrow AI: Siri is Apple's virtual assistant that uses AI to understand and respond to user commands and questions.
Learn more about the application of AI in everyday life. https://cointelegraph.com/news/7-artificial-intelligence-examples-in-everyday-life
Common AI Terminology
To navigate the world of AI and IP, it's essential to understand some common terms:
- Data Set: A collection of data used to train, validate, or test AI and machine learning models.
- Hallucination Rate: The frequency with which an AI system provides incorrect information.
- Predictive AI: Focuses on making predictions or forecasts based on existing data.
- Generative AI: Concentrates on creating new data in various forms, such as text, media, or images (think deep fakes).
Learn more about hallucination rates: https://www.marktechpost.com/2023/06/27/what-is-ai-hallucination-what-goes-wrong-with-ai-chatbots-how-to-spot-a-hallucinating-artificial-intelligence/
Types of AI
AI systems can be categorized in various ways:
Open vs. Closed AI:
- Open AI System: Allows external access, modification, and integration with other software and services.
- Closed AI System: Operates independently and does not readily allow external access or modifications.
Narrow vs. Strong AI:
- Narrow/Weak AI: Specializes in performing specific tasks, like answering questions based on user input.
- Strong AI: Possesses a level of general intelligence, allowing it to perform a wide range of tasks.
Reactive Machines vs. Limited Memory:
- Reactive Machines: Lack memory and provide consistent output for a given input.
- Limited Memory: Can learn from past experiences and adapt its actions based on both historical and current data.
Learn more about the four types of AI: https://www.coursera.org/articles/types-of-ai
Impacts of AI in Intellectual Property
Negative Impacts and Concerns
While AI offers immense potential, it also brings challenges to the realm of Intellectual Property:
- Confidentiality: AI systems should not be entrusted with confidential information, as there's a risk of data breaches or misuse.
- Patent Applications: Disclosing an invention to a third party before filing a patent application can pose challenges for IP protection.
- Legal Framework: The legal framework has not kept pace with the rapid advancements in AI, creating uncertainties in IP regulation.
- Trustworthiness: AI, like ChatGPT, can sometimes provide inaccurate information, leading to output trust issues.
Positive Impacts
AI can also be a valuable asset in the IP landscape:
- Efficiency: AI can streamline the process of grant proposal and business plan preparation.
- Reading Comprehension: AI systems excel in understanding complex prompts, which is particularly useful in IP research.
- Tech Transfer Offices: AI can aid in market research, product-market fit identification, and drafting material transfer agreements.
- Healthcare: AI is employed in laboratories, enhancing diagnostic accuracy and saving time in IP-intensive tasks.
An external webinar that delves into the impacts of AI in IP: https://youtu.be/YzwoO2PZ58M?si=Akzobvvo9rz4Cq4Q
Artificial Intelligence is a potent force shaping the landscape of Intellectual Property. While it presents challenges, it also offers exciting opportunities for efficiency and innovation. As the legal and ethical frameworks surrounding AI continue to evolve, understanding its role in IP is essential for anyone engaged in innovation, research, or business in this digital age.
Meet Kayla Bondra
My name is Kayla Bondra, I am interning at UNHInnovation for the fall semester of my senior year here at UNH. I am dual majoring in political science and justice studies and minoring in forensics and security studies. Once I graduate in 2024 I am hoping to take a couple of years off to travel and then continue my education at UNH Law studying IP law. As an intern here my responsibilities include helping in any way that I can from updating confidential briefs to research projects. I am currently working on a project about the effects of AI on IP and have learned so much. Artificial Intelligence is rising in the modern world and something that will have great influence in the future. I am very excited about this project because it has merged two things I am passionate about into one.
