Copyright Licensing Agency

Responsible AI starts with licensing. AI outcomes are strengthened by reliance on responsibly sourced, high-quality copyrighted works. Consent and human centricity are hallmarks of human advancement, and also will enable better AI. The default position of our copyright system is that the person who wishes to use copyright protected works must seek out and obtain a license before engaging in conduct that would implicate any rights protected by copyright law to avoid an infringement claim. This will only seem fair to most observers, given the intellectual and economic labor involved in creating original works, and also in light of the natural and economic justice of granting the creator the right to determine how her works will be used. Of course, the copyright system contains exceptions in certain cases where authorization may not be needed at all (e.g., fair use in the U.S.) or where rights are limited to non-negotiated licenses (e.g., non-voluntary licenses and/or levies). But these exceptions or limitations to rights, in order to serve the public interest of promoting the creation and distribution of original creative works (as well as to comply with international law), must be carefully circumscribed to avoid unfairly prejudicing the legitimate interests of the author. To be acceptable under the Berne 3-Step test, they exist in areas of market failure. AI training generally requires the express and voluntarily granted consent of the author. Any other approach threatens fundamental values underlying our copyright system. This observation is grounded in a number of core truths: that AI training requires the reproduction of protected works; that copies of such works are not (only) ephemeral or transitory and are stored in a manner that permits their retrieval for the purpose of producing expressive output that derives from the training data; and that such AI output do, and are likely to, directly compete in the marketplace for expressive works with the works on which the AI was trained, as well as to unfairly displace licensing opportunities that would otherwise exist for authors of the original works. As we approach how to balance competing interests around AI technologies, we are faced with a litany of arguments that copyright is somehow not fit-for-purpose for this AI. These are the same arguments that were raised with respect to the sound recordings, cable, the photocopier, the internet, and every new technology where one party wanted to make money through uncompensated and unconsented to use of another’s creative works. AI is not as different as the advocates for unfettered, uncompensated reuse pretend.

A rapid growth in the application of embedded programmable processors in systems from simple household machines (e.g., washers and dryers) to complex real-time control in automobiles has been seen over last three decades. Microprocessors, microcontrollers, and digital signal processors (DSPs) have been at the forefront such development. This has warranted training electrical and computer engineers in this important area of embedded system design using a multi-disciplinary approach. Electrical and computer engineering undergraduate programs require at least one embedded system design or programming course to train the future workforce in this important field.

As Associate Register of Copyrights and Director of Registration Policy and Practice at the United States Copyright Office, a significant part of my role is to be an advocate for formalities within the limits of the Berne Convention. I realize that many view registration, recordation, and other formalities as obstacles to authors. However, such formalities need not be impediments, but rather may offer valuable benefits to authors, owners, and the overall copyright ecosystem without unduly burdening creators.

With the recent proliferation of unmanned aerial vehicles (UAV) for geophysical surveying a novel opportunity exists to develop unmanned ground vehicles (UGV) in parallel. This research presents a pilot study to integrate two UGVs, the Kapvik planetary micro-rover and a Husky A200 robotic development platform, with a GSMP 35U magnetometer that has recently been developed for the UAV market. Magnetic noise levels generated by the UGVs in laboratory and field conditions are estimated using the fourth difference method and, at a magnetometer-UGV separation distance of 121 cm, the Kapvik micro-rover was found to generate a noise envelope 0.04 nT whereas the noisier Husky UGV generated an envelope of 3.94 nT. The UGVs were assessed over a series of successful robotic mapping missions which demonstrated their capability for magnetic mapping, and their productivity and versatility in field conditions.

The transition from the printed page to the digital packet has been problematic for both creators of proprietary materials and users of those materials. Legal and economic models for traditional physical objects, such as books and journals, have been taxed by new intellectual property unbounded by physical constraints. Concepts of licensing of copyrighted materials and issues of pricing have taken on new meaning within this world. This paper will discuss the new technologies and their influence upon evolving legal and economic models in the digital domains