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A personalized learning system that adapts to learners' interests, needs, prior knowledge, and available resources is possible with artificial intelligence (AI) that utilizes natural language processing in neural networks. These deep learning neural networks can run on high performance computers (HPC) or on quantum computers (QC). Both HPC and QC are emergent technologies. Understanding both systems well enough to select which is more effective for a deep learning AI program, and show that understanding through example, is the ultimate goal of this project. The entry to learning technologies such as HPC and QC is narrow at present because it relies on classical education methods and mentoring. The gap between the knowledge workers needed, which is in high demand, and those with the expertise to teach, which is being achieved at a much slower rate, is widening. Here, an AI cognitive agent, trained via deep learning neural networks, can help in emergent technology subjects by assisting the instructor-learner pair with adaptive wisdom. We are building the foundations for this AI cognitive agent in this project.

The role of the student facilitator will involve optimizing a deep learning neural network, comparing and contrasting with the newest technologies, such as a quantum computer (and/or a quantum computer simulator) and a high performance computer and showing the efficiency of the different computing approaches. The student facilitator will perform these tasks at the rate described in the proposal. Milestone work will be displayed and shared publicly via posting to the Jupyter Notebooks on Google Colab and linked to regular Github uploads.

Out of all the upper level chemistry courses, physical chemistry is the only course that provides an in-depth insight into the fundamental principles underpinning the concepts taught in various sub-disciplines of chemistry. Further, physical chemistry provides a connection between microscopic and macroscopic worlds of chemistry through mathematical models and experimental methods to test the validity of those models. Therefore, computational techniques are a perfect vehicle to teach content of physical chemistry course to undergraduate students. Additionally, American Chemical Society recommends computational chemistry to be incorporated into undergraduate chemistry curriculum. At Bridgewater State University (BSU) physical chemistry is a two-semester course referred to as 'physical chemistry I' and 'physical chemistry II'. While the overarching goal is to develop computational experiments (referred to as 'dry-labs'), project proposed here focuses on designing and developing dry labs for 'Physical Chemistry II' course at BSU. The inherently theoretical nature of this course along with its connection to wide range of spectroscopic techniques commonly used by chemists and physicists makes this course a perfect choice for assessing BSU students' reception to the idea of dry labs. It should be noted that there are no computational experiments in the current physical chemistry curriculum (both I and II) at BSU. The proposed project focuses on developing 4 - 6 computational experiments to be introduced (in spring 2018) as either stand-alone dry-lab experiments or accompany currently existing experiments. These dry labs will be developed on Gaussian 09 platform, which is currently installed on C3DDB server at MGHPCC. Finally, I also expect to make these experiments available to other New England instructors teaching physical chemistry II or equivalent course interested in incorporating computational chemistry into their curriculum.

The UVM Art and AI Initiative is exploring approaches to artistic image production, comparing the results of StyleGAN and Genetic Algorithms*. More broadly, the project explores emerging artistic practices with Machine Learning and AI while referencing an artistic lineage to the artists Wassily Kandinsky, Jonn Cage and Yoko Ono; these artists employ(ed) instructions and systems in their non-digital artworks. Kandinsky distinguished systems and developed a science of aesthetics with the basic elements of point, line and plane; Cage used the oracle 'I Ching' like a computer to inform his compositional decisions; Ono writes poetic scores that turn her audience into active participants when they follow a series of imaginative instructions. Through this ongoing research and practice, we intend to join the larger conversation about art and A.I and design new curriculum for UVM undergraduate students.

This work began in February 2020 and is led by Jennifer Karson of UVM’s Department of Art and Art History and the CEMS UVM FabLab. The team has included three UVM students: two graduate students in data science and one undergraduate mechanical engineering student. The team currently uses RunwayML for the StyleGAN experiments and Processing, an open-source language and development environment built on top of the Java programming language, for Genetic Algorithms.

Additional summer funding ($2,000) is sought for one of the UVM Art and A.I. Initiative student coders. The funding will assist the team in reaching a short-term goal to present initial findings this July at Alife 2020 Montreal; a longer-term goal is to create an art installation for the UVM Fleming Museum of Art in the spring of 2021. This is a unique opportunity to exhibit as part of the statewide project 2020 Vision: Seeing the World through Technology and alongside the work of internationally renowned computer artist and co-founder of the Processing programming language Casey Reas.

Milestone 1:

Genetic Algorithms: Develop successful genetic algorithm code that meets compositional standard (color, architecture, appropriate datasets) while creating new compositions from the elements of existing hand-drawn compositions. The program should output image files that can be stored and printed at high resolutions on paper.

StyleGAN: Transition from RunwayML to coding in Python and employing VACC computer cluster. The process should output image files that can be stored and printed at high resolutions and on paper to be exhibited.

Milestone 2:

Genetic Algorithms: Create an interactive version of the program that allows for audience participation; can be exhibited in a museum gallery and online.

StyleGAN: Develop video that can be exhibited in museum gallery and online.

*Our Genetic Algorithm base code was developed by Daniel Shiffman