Posts Tagged: "machine learning"

Accelerated Innovation: In Less Than a Year, We’ve Seen a Decade’s Worth of AI and IP Developments

The past year has provided decades’ worth of developments across law and policy in the areas of artificial intelligence (AI) and machine learning (ML) technologies. If 2022 was the breakthrough year for accessible AI, then 2023 can so far be deemed as the first year of likely many more to come in the era of an AI inquisition. “After years of somewhat academic discourse,” reflects Dr. Ryan Abbott, “AI and copyright law have finally burst into the public consciousness—from contributing to the writer’s strike to a wave of high-profile cases alleging copyright infringement from machine learning to global public hearings on the protectability of AI-generated works.” Both the U.S. Copyright Office (USCO) and the U.S. Patent and Trademark Office (USPTO) are in active litigation over the eligibility of generative AI outputs for statutory protection. Additionally, both offices have held numerous webinars and listening sessions and conducted other methods of collecting feedback from the public as they work through policy considerations surrounding AI.

AI Year in Review: A Busy 2022 for AI and IP Promises Even More in 2023

In general, the adoption of artificial intelligence (AI) and machine learning technologies has the potential to impact society in many ways. These technologies can automate tasks and make them more efficient, which can lead to job displacement and other economic impacts. They can also be used to make decisions that affect people’s lives, such as in the criminal justice system or in hiring, which raises ethical concerns. Additionally, the development and use of AI and machine learning technologies can raise issues related to privacy and security. What could be a more fitting way to open a 2022 year-in-review article on AI and machine learning than by asking OpenAI’s newly beta-released ChatGPT tool to contribute? The above paragraph was generated using ChatGPT’s conversational, chat-based dialog input. The initial request of ChatGPT was the prompt: “Explain the social impacts of artificial intelligence and machine learning technologies over the past year.”

Machine Learning Models and the Legal Need for Editability: Surveying the Pitfalls (Part II)

In Part I of this series, we discussed the Federal Trade Commission’s (FTC’s) case against Everalbum as just one example where companies may be required to remove data from their machine learning models (or shut down if unable to do so). Following are some additional pitfalls to note. A. Evolving privacy and data usage restrictions Legislators at the international, federal,…

Machine Learning Models: The Legal Need for Editability (Part I)

A widespread concern with many machine learning models is the inability to remove the traces of training data that are legally tainted. That is, after training a machine learning model, it may be determined that some of the underlying data that was used to develop the model may have been wrongfully obtained or processed. The ingested data may include files that an employee took from a former company, thus tainted with misappropriated trade secrets. Or the data may have been lawfully obtained, but without the adequate permissions to process the data. With the constantly and rapidly evolving landscape of data usage restrictions at the international, federal, state, and even municipal levels, companies having troves of lawfully-obtained data may find that the usage of that data in their machine learning models becomes illegal.

Why the Patent Classification System Needs an Update

Patent categories were established more than 100 years ago. There are dozens of categories that reflect industry at the time: gears, sewing machines, and bicycles, to name a few. While these are certainly useful categories, the patent classification system has not kept up with the times. It leaves out many modern technologies, like inventions that are based on machine learning or blockchain. There are no categories for these innovations, which are reshaping our world in real-time. The problem? When patent classifications don’t actually classify inventions, we have no way of knowing how many inventions in these categories are being registered.