At Google, we believe the arts & social sciences can inform new ways of thinking about and working with artificial intelligence. Our Artists + Machine Intelligence (AMI) Research Awards support higher education faculty pursuing arts and social science research related to machine learning (ML) and its implications on society with funding up to $20,000 USD. Starting August 1, we will welcome proposals across three priority research areas:

1. Futures Writing
2. Arts Writing + Criticism
3. Public understanding of AI through literary & visual culture

Futures Writing
Generative AI represents a generational change in how we create, and who can create. We’re looking to support faculty developing imaginative reflections or philosophical meditations on what possible futures with AI might hold socially, ethically, and aesthetically for the arts, archival sciences, or the open web. Faculty are encouraged to submit writing projects in the following formats: articles, books, or short-form writing. Discipline-specific reflections will be prioritized over broader, more generalized approaches.

Arts Writing + Criticism
Arts writing is essential to the development of a dynamic and vibrant art scene and tradition. We’re looking for faculty developing programs and/or curricula to support arts writers and critics in developing technical or historical know-how of generative AI practices to offer new ways to view, appreciate, understand, and enjoy contemporary art made with machine intelligence. Critical approaches to writing about film and new media are eligible.

Public understanding of AI through literary & visual culture
Media vitally shapes public engagement and understanding around AI technologies. We seek proposals from faculty leading science writing, communication, media production or other programs who are investigating the framing of AI in factual and entertainment media, and its impact on the public understanding of AI technologies. Proposals that study how such representations function across media landscapes to create or support mental models of AI (and the underlying conceptual frameworks that drive such representations) are especially welcome, including those that propose new mental models or explore novel conceptual frameworks and representations of AI.

Please note: Outside of the above priority research areas, we continue to welcome proposals on applications and/or implications of AI for the arts and social sciences, including but not limited to music, film, and writing. Arts-based or participatory research methods that envision possibilities of new technologies together with impacted communities are encouraged. Proposals exclusively for the making of ML-focused artworks are not eligible for funding.

AMI Research Awards 2024 Application Preview

The application is structured in three parts: Applicant information, Proposal Body, and Project Budget.

Applicant information

1. Name of principal investigator
2. Principal Investigator’s CV (.pdf, .doc files)
3. University Name / Department / Website
4. Principal investigator’s academic email / physical mailing address
5. Principal investigator’s Google scholar profile, if applicable
6. Contributing investigator(s) & institutions (if any)

Proposal Body

1. Proposal summary, in 150 words or less
2. Research objective(s). What do you hope to learn through this project? How will this project positively impact the arts, social sciences and/or machine learning?
3. Methodology. How will you achieve your research objective(s)?
4. Expected results. How will this work benefit the public and/or other researchers? Please describe any data sets, models, output and/or tools you plan to share, and where / how you’ll do so.

Project Budget

The project budget for this RFP is $20,000 USD. Please share a budget breakdown, detailing expenses. Indirect, administrative, and overhead costs should not be included in the budget. If you’ve already received or expect to receive financial or in-kind support from other organizations, kindly make note in the budget breakdown.

Artists + Machine Intelligence (AMI) is a forward-looking research program at Google dedicated to building and sustaining a field of creative practitioners working with machine learning. We develop visionary artistic voices, push the boundaries of art and code and grow creative industries impacted by AI technologies through funding, mentorship, and collaborative research. Questions? Email artwithmi@google.com

AMI Research Awards 2024 was originally published in Artists + Machine Intelligence on Medium, where people are continuing the conversation by highlighting and responding to this story.

In 2019, Printed Matter’s LA Art Book Fair returned to the Geffen Contemporary building at MOCA in downtown Los Angeles. Together with Anteism Books, AMI organized an exhibition focusing on works made from 2015 to 2018 to trace the emergent practices of artists creating with machine intelligence.

About the Exhibition

Developments in technologies have long influenced image-making. From photography to aerial and satellite imagery to digital printing techniques, the possibilities for visualization afforded by new technologies have always spawned new forms of expression.

In recent years, advances in machine learning are similarly revolutionizing how artists make pictures. Algorithms inspired by the structure of the human brain, known as deep neural networks, enable features like image search and language translation. When run in reverse, these networks can generate novel images and text.

Focusing on works made from 2015 to 2018, New Technologies, New Visions traces the emergent practices of artists creating with machine intelligence. Drawn primarily from works produced in residency at, or in collaboration with, Google’s Artists + Machine Intelligence program, the show begins with early experiments in understanding and giving form to the algorithmic gaze. It showcases artists who use generative techniques to create portraits and landscapes that come from our world, but are, nonetheless, otherworldly. The show includes a text essay by Hito Steyerl that considers the implications of early AI technologies, setting the scene for new technologies we may want to build.

New Technologies, New Visions is organized by Eva Kozanecka, Kenric Allado-McDowell and Anteism Books.

Works Shown List

Refik Anadol

Archive Dreaming, 2016 — Architectural installation with 4-channel video, 8-channel audio, custom software

Refik Anadol calls himself a data artist, meaning that data is his medium. To visualize more than 1,700,000 digitized documents in the SALT Research Collections, Anadol collaborated with artist and engineer Mike Tyka to employ a technique known as t-distributed Stochastic Neighbor Embedding (t-SNE). Digital files were organized by attributes, and then projected onto a six-dimensional space, and represented in three dimensions. Developed in 2008 by Laurens van der Maaten and Geoffrey Hinton, the t-SNE algorithm is unique in its ability to capture the local structure of the high-dimensional data very well, while also revealing global structure such as the presence of clusters at several scales. The resulting set of data points in space is called a point cloud, or “data universe.” When presented in its native digital form, it allows one to explore new connections between objects in the archive. The final work (not shown) is an immersive architectural space that is a means, Anadol explains, to “reframe memory, history and culture” through the lens of machine intelligence.

Mike Tyka

Portraits of Imaginary People, 2017 — Chromogenic color-prints

In recent years, algorithms have become proficient at producing and manipulating images, video, and sound to a degree that makes it difficult to distinguish generated media from real media. In this series, Tyka creates a novel type of photographic portraiture, namely, photos of people who don’t exist. To produce this work, Tyka sourced thousands of photographs of faces from Flickr to train a machine-learning program called a generative adversarial network (GAN). The images shown here are a result of a simulated “arms race,” as Tyka describes it, between two neural networks: the first network (the “Generator”) tries to generate increasingly convincing output, while a second network (the “Discriminator”) tries to learn to distinguish real photos from the artificially generated ones. At first, both networks are poor at their respective tasks, but with time, the images generated become increasingly realistic. The result is an algorithm that can create new faces based purely on emergent rules about what constitutes a face.

Tom White

Perception Engines, 2018 — Two-color risograph prints

To “see the world through the eyes of a machine” is how Tom White describes his intention for this series. To make Perception Engines, White trained a neural network using ImageNet, a database of images organized by Stanford Vision Lab and used by the industry to train and test machine vision algorithms. The outputs are what a computer believes to be a pair of binoculars, an iron, etc. The artist’s agency lies in selecting a number of parameters for the network, like the colors and thickness of lines, and refining the output by rejecting prints that he doesn’t find aesthetically pleasing. “I am trying to free the algorithm so it can express itself, so people can relate to what it’s saying,” explains White.

Ross Goodwin

WordCar, 2017 — A Cadillac XTS mounted with an Axis M3007 surveillance camera; GPS unit; custom sofware, and a laptop

Ross Goodwin describes himself as “not a poet,” but “a writer of a writer.” In 2017, he trained a character-level text prediction neural network model, known as a long short-term memory recurrent neural network (LSTM RNN), on an undisclosed corpus of text, and set off on a road-trip to generate machine poetry. He outfitted a Cadillac car with a surveillance camera, a GPS unit, a microphone and a clock, and connected it to a portable AI-writing-machine that fed from these input data in real time. As he drove, a manuscript emerged line by line from the machine’s printer on long scrolls of receipt paper that filled the car’s rear seats over the course of their journey. The resulting text was published as a book of poetry in 2017, titled “1 the Road.”

Casey Reas

Untitled Film Stills, Series 2, 2018 — Chromogenic color-prints

For Casey Reas, software is more than a tool: it is an extension of his internal abilities to think and explore. Inspired by the photographs of Cindy Sherman and the films of Stan Brakhage, Reas uses generative adversarial networks to make pictures. To make a single picture, Reas inputs a list of 100 numbers between -1 and 1 into a trained model. Each new value generates a new picture. When Reas moves across this latent space of all possible pictures in an orderly manner, he can generate a series of related stills. In this way, each image in Reas’ Untitled Film Stills is a frame from an imagined film. The result is “ambiguous, dreamlike images that oscillate between clarity and the ephemeral.”

Anna Ridler

Untitled (Tulips), 2018

Named after a virus that causes a stripe pattern variation in tulip petals, “Mosaic Virus” is a film by Ridler that shows tulips blooming, whereby the appearance of each tulip is influenced by the price of bitcoin. To create the film, Anna Ridler took ten thousand photographs of tulips over the course of tulip season, and categorized each by hand to form the training data set for a generative adversarial network. The output are novel flower paintings, or “botanical impossibilities,” as described by Ridler. For Printed Matter’s LA Art Book Fair, Ridler reimagines the film as a print work, displaying 52 imagined tulips as playing cards. “I wanted [the print work] to echo ideas around speculation, risk and chance,” says Ridler.

About LA Art Book Fair — Printed Matter, Inc., is the world’s leading non-profit organization dedicated to the dissemination, understanding and appreciation of artists’ books. The LA Art Book Fair, initiated in 2013, is Printed Matter’s companion fair to the NY Art Book Fair. Held at the Geffen Contemporary building at MOCA in downtown Los Angeles, the LA fair features over 300 exhibitors from the west coast and some 20 countries, and is attended by more than 35,000 visitors annually. The Geffen Contemporary at MOCA is located at 152 North Central Avenue, Los Angeles, CA 90012.

New Technologies, New Visions was originally published in Artists + Machine Intelligence on Medium, where people are continuing the conversation by highlighting and responding to this story.

Update 9/5: Applications are now closed. Decisions for the 2023 application will be announced via email in September 2023. Please check back in 2024 for details on future application cycles.

At Google, we believe the arts & social sciences can inform new ways of thinking about and working with artificial intelligence. Our Artists + Machine Intelligence (AMI) Research Awards support higher education faculty pursuing arts and social science research related to machine learning (ML) and its implications on society with funding up to $20,000 USD.

Starting today, we welcome proposals across three priority research areas:

1. Visual culture and media literacy
2. Methods and strategies for foresighting
3. Cultures shaping agency and AI

Visual culture and media literacy

Visual media vitally connects with audiences and shapes public engagement around AI technologies. We seek proposals that explore visual framing of AI in media (and how these representations function across media landscapes) and that develop new visual metaphors and libraries for use by media, non-profits, and educators. Proposals that consider non-visual strategies for productive public discourse on AI may also be considered.

Methods and strategies for foresighting

Known also as speculative design, scenario planning, or simply, imagining the future, foresighting allows individuals, companies and organizations to envision and explore alternative socio-technical futures, identify potential risks and opportunities, and build consensus around the future of emerging technologies. We’re interested in proposals from the humanities and social sciences who are advancing this method that might inspire new applications in technology research organizations.

Cultures shaping agency and AI

Agency, defined broadly as the ability to form intentions, to act on those intentions, and to monitor one’s own actions, is one of multiple factors important to the responsible and ethical deployment of AI technologies. We’re interested in proposals that seek to understand how culture influences assumptions around agency and how such definitions impact the design and development of AI. What harms occur when the concept of agency is defined through specific cultural lenses, and then deployed and experienced within different global cultural contexts? What benefits might emerge from more expansive cultural definitions? What new or expanded definitions of (or relationships with) agency might facilitate solving the world’s humanitarian and environmental challenges?

Questions? Email questions to artwithmi@google.com. We will provide you with a timely answer or set up a time to talk, if necessary.

Request for Proposals: AMI Research Awards 2023 was originally published in Artists + Machine Intelligence on Medium, where people are continuing the conversation by highlighting and responding to this story.

Getting Started

What is AMI? Artists + Machine Intelligence was founded in 2016 by Blaise Aguera y Arcas at Google Research. Our mission is to build and sustain a field of creative practitioners working with machine learning. We work closely with teams across Google to support the research, development and production of ambitious ML-focused artworks. The program is co-led by Kenric Allado-McDowell (since 2016) and Eva Kozanecka (since 2018).

What are AMI Grants? In 2019, AMI, together with Google Arts & Culture, launched an open call to support six artists working with machine learning with technical mentorship, core Google research, and funding. Artists worked with Google creative technologists to develop and produce artworks over the course of a five-month period. Learn more about the 2020–2021 residency at g.co/artistsmeetai. This program is currently on pause.

What types of projects do you support? In 2023, we are supporting higher education faculty pursuing arts and social science research related to machine learning (ML) and its implications on society with unrestricted funding as part of Google’s Research Awards. A new cycle of funding will re-open in June 2023. Learn more about AMI Research Awards.

Artist Programs

I am an artist developing a project with AI technologies. How do I apply for funding? At the moment, we are not accepting unsolicited applications for artist project funding. Artists who are higher education faculty at a nonprofit institution may apply for AMI Research Awards.

Do you accept applications from outside of the U.S.? We develop projects with artists and faculty around the world. At the moment, we are not accepting unsolicited applications for artist project funding. In general, artists and faculty must be over 18 years old. Google employees and their family members, and residents of OFAC embargoed countries are not eligible to apply.

Do artists retain all rights in their artwork? Yes, as part of AMI’s sponsorship agreement, artists retain all rights to their artwork. An artwork may include the output of a machine learning model, but does not include the software or machine learning model used to create the art.

May I apply with collaborators? Yes, artists and higher education faculty are eligible to apply with collaborators or co-investigators.

Can I submit multiple projects? No, we ask artists and faculty to only submit one application per open call and/or application cycle.

Can I reapply if not chosen for funding? You may apply with the same project from a previous round if your project has progressed. Please reference the current application and update your submission accordingly.

Can I discuss my project with AMI before applying? We are happy to answer any specific questions you have via email. We encourage you to apply if you believe you meet the criteria.

Tools and Resources

I’m an artist starting to experiment with machine learning. How do I start? AI Experiments is a showcase for simple experiments that make it easier for anyone to start exploring machine learning, through pictures, drawings, language, music, and more. You can also check out AI Test Kitchen, an app where people can learn about, experience, and give feedback on Google’s emerging AI technology, or read the latest research from Google on the Google AI blog.

I’m an educator and want to use AI tools in my classroom, where do I start? There are a few curriculums that teachers have made, like Blakeley Payne’s AI Ethics curriculum for middle schoolers, plus tools like Teachable Machine, which lets you train a computer to recognize your own images, sounds, & poses, and Mood Board Search, which lets you train a computer to recognize a visual concept.

Where can I learn more about art and engineering collaborations? AMI launched in 2016 on the 50th anniversary of Experiments in Art and Technology (E.A.T.)’s 9 Evenings: Theater and Engineering event, a historic collaboration between artists and engineers from Bell Labs. The interdisciplinary collective was set up by engineers Billy Klüver and Fred Waldhauer and artists Robert Rauschenberg and Robert Whitman. Learn more about E.A.T.

What’s the best way to stay in touch? Follow us on Medium or Twitter for the latest news, announcements and open-source releases.

More

Have a question that’s not answered in the FAQ? Our team is made up of thought leaders, UX strategists, researchers, and technologists who deliver strategic insights, advice, ideas, and prototypes. We’re excited to help answer your questions, simply email us at artwithmi@google.com

FAQ: Frequent Artist Questions was originally published in Artists + Machine Intelligence on Medium, where people are continuing the conversation by highlighting and responding to this story.

We’re happy to announce a new cohort for AMI Research Awards, our annual open call for proposals on creative applications of machine learning (ML) and cultural research related to ML.

Awards provide faculty with $20,000 USD in unrestricted project funding and the opportunity to partner with Google researchers on their research initiatives to build new and constructive long-term relationships.

The following projects and faculty will receive 2021 AMI Research Awards:

Rethinking AI and Automation in Architecture — Daniel Cardoso Llach, Ph.D. Carnegie Mellon University, in collaboration with Dr. Jean Oh, Carnegie Mellon University

This project brings architecture, AI, and sociotechnical research methods together to imagine and realize humane scenarios for robotically-supported cooperative construction. We’re interested in “robot in the loop” systems that adaptively support — rather than automate, replace, or surveil — the work of construction workers on building sites. Our reflective technology design process comprises ethnographic research and qualitative engagements with construction actors and sites, simulations, technical research combining robotics and reinforcement learning, and the development of a proof of concept system to be demonstrated on site. Striving for dynamic and safe robotically-supported construction environments, our project will help foster humane and sustainable practices in the architecture, engineering, and construction (AEC) industry, and foster new forms of expertise at the intersection of AI, robotics, the building trades, and architecture.

Daniel Cardoso Llach, Ph.D. is an architect and interdisciplinary scholar interested in issues of automation in design, interdisciplinary creativity, human-machine interaction, and critical technical practices in architecture and design. He is Associate Professor at the School of Architecture at Carnegie Mellon University, where he also chairs the Master of Science in Computational Design and co-directs CodeLab, an interdisciplinary graduate research and learning laboratory re-thinking the role of computation in design and the built environment.

Jean Oh, Ph.D. is Associate Research Professor at the Robotics Institute at Carnegie Mellon University, and the director of the interdisciplinary Bot Intelligence Group. Her research focuses on persisting robots that can coexist with humans in shared environments, and she has led several intelligence tasks in government and industry in various problem domains including self-driving cars, disaster response, eldercare, and the arts.

Views of Planet City: Pale Blue Dot Mk2 — Jennifer Chen, Southern California Institute of Architecture (SCI-Arc)

Views of Planet City is a multi-year research project underway at SCI-Arc that critically investigates the possible implications of E.O. Wilson’s “Half Earth” proposition to confine and concentrate human inhabitation of the planet to heal the global ecosystem. Pale Blue Dot Mk2 is one of five segments of Views of Planet City, exploring what the Earth would look like from space in the epoch of Planet City. The project’s objective is to simulate the passage of time represented in satellite images through predictive networks. Notably, the project considers remote sensing technology as a speculative medium, one that not only shows us direct correlations between the causes and effects of our collapsing climate, but that can be reinterpreted and synthesized using deep learning to imagine the reversal of planetary sprawl.

Jennifer Chen is an architect and designer working across buildings, installation, film, and performance. At SCI-Arc she leads Design Studio, Liberal Arts, Visual Studies. She is currently developing her research interests as a designer and educator focusing on speculative futures and post climate environments.

Movement-centric calligraphy and graffiti generation — Frederic Fol Leymarie, Goldsmiths, University of London, in collaboration with Dr. Daniel Berio and Xiaobo Fu, Goldsmiths, University of London

The aim of this research is to computationally generate handwritten art forms such as calligraphy and graffiti through the combination of sequence modeling methods and primitive-based representations of movement. We plan to go beyond the state of the art for generating handwriting, including calligraphy, which is largely based on inputs and outputs consisting of dense point sequences, by building up from our previous research across the fields of machine learning, motor control, visual perception, graphonomics (the experimental study of handwriting and related skills), as well as art practice. We hope to demonstrate that movement primitives can form a fruitful basis as a data representation to significantly improve the performance and robustness of today’s sequence-based deep learning approaches to such generative tasks.

Frederic Fol Leymarie works on creativity and AI systems, with a focus on computer vision and graphics, including robots, which can perform with artistic skills similar to expert humans. He joined Goldsmiths in 2004, where he launched a Master of Science in Arts Computing and a Master of Science in Games Programming. He is also an entrepreneur, launching London Geometry (2011) and DynAikon (2015). Understanding human creative activities, such as in the visual arts, directly feeds into his AI research agenda.

Daniel Berio is a researcher and artist working between computer graphics, robotics, and graffiti art. He recently completed a PhD (Computing) at Goldsmiths, where he developed methods for the computer aided design and procedural generation of graffiti and calligraphy, with applications in robotics. As an artist with a background in graffiti writing, Daniel explores the intersections of this art form with techniques from computer graphics and robotics.

Xiaobo Fu is a visual artist and PhD student in Computer Art at Goldsmiths. Xiaobo’s research focuses on computationally generating Chinese calligraphy which is a challenging task due to its enormous character count and variety of artistic styles. As a calligraphy art lover and practitioner, Xiaobo explores new forms of expression of this thousand-year-old art with cutting-edge machine learning technology in order to understand and expand the application of AI in creative tasks.

Explorisk: Visualizing Risk-Mitigation Scenarios — Tegan Maharaj, University of Toronto, in collaboration with the Cambridge Centre for the Study of Existential Risk

Predictive risk models can help us understand how population-level risks translate to individual-level risks, and how different risks can interact, in order to examine different actions and strategies for mitigating those risks. This project proposes a collaboration with artists, graphic designers, and user-experience experts to develop an intuitive visualization tool for exploring different risk mitigation scenarios. The goal of this tool is to empower policy-makers, researchers, and other individuals to better understand and act upon risks. The core of the proposed research is to develop an intuitive visual language for expressing complex predicted scenarios — potentially short subtitled movies, augmented-reality ‘pictures’, flow-chart-type diagrams, or something else entirely — and an interface which allows researchers to choose between different visualization options.

Tegan Maharaj is Assistant Professor in the Faculty of Information at the University of Toronto, and an affiliate of both the Schwartz-Reisman and Vector institutes. Her current research interests include using deep models for policy analysis and risk mitigation, and designing data or unit test environments to empirically evaluate learning behavior or simulate deployment of an AI system.

Digital Performers Using AI — Michael Rau, Stanford University

This project applies computer vision and machine learning technologies to a 2,500 year old art form — theater — to improve virtual live performances and to discover new aesthetics of performance. The project approaches working with these systems of technology from a poetic and creative standpoint. What are the creative affordances that appear when we apply artificial intelligence to the representations of characters within a live performance? Can we improve theatrical streamed performances using machine learning? The project will undertake research in a creative field by exploring the aesthetics of a “digital performer” alongside a rigorous scientific analysis of the tools and technology to create new software tools, new dramaturgical constructions, and new performance techniques.

Michael Rau is a live performance director specializing in new plays, opera, and digital media projects. He has created work in New York City at Lincoln Center, The Public Theater, PS122, HERE Arts Center, Ars Nova, The Bushwick Starr, in Boston at ART, and recently made his debut at the Humana Festival, as well internationally in the UK, Germany, Brazil, and Czech Republic. He is a New York Theater Workshop Usual Suspect and a professor of directing and devising at Stanford University.

Artists + Machine Intelligence (AMI) is a program at Google that invites artists to work with engineers and researchers together in the design of intelligent systems.

AMI Research Award applications are currently closed. Please check back in February 2022 for details on future application cycles, or follow us on Twitter for updates.

Announcing the 2021 AMI Research Award Recipients was originally published in Artists + Machine Intelligence on Medium, where people are continuing the conversation by highlighting and responding to this story.

Applications for the 2021 funding cycle are now closed.

As part of Google’s ongoing commitment to support faculty working on positive societal initiatives, Artists + Machine Intelligence (AMI) supports artists and academics pursuing social science research that looks at technology’s implications and impacts on individuals and society.

Through the current program, AMI provides $20,000 USD fellowships to permanent faculty at degree-granting institutions. Cash awards in this program are structured as unrestricted gifts to universities. Faculty members may apply with supporting collaborators. Both new and ongoing research are eligible. Google does not retain any intellectual property from the research. See the full list of past recipients

2021 Theme

The field of ML research currently confronts challenges that are as much cultural as they are technical. From dataset to deployment, cultural assumptions and processes influence every aspect of ML, yet cultural disciplines aren’t always consulted when research strategies are defined. To this end, we seek to support researchers pursuing cultural research relevant to ML technologies. We encourage proposals from across social science disciplines, including but not limited to: anthropology, architecture, design, history and linguistics with implications for the development of AI technology, especially generative ML, computer vision, and natural language processing. Learn about previously funded research

Eligibility

• This opportunity is open to permanent faculty members at any accredited university around the world.
• A principal investigator must apply in his or her capacity as a university professor and must be able to accept an award through that university.
• Faculty members may apply with supporting collaborators.
• Funding is focused on supporting the faculty’s research. Both new and ongoing research are eligible.
• Cash awards in this program are structured as unrestricted gifts to universities.
• Our funding is structured as unrestricted gifts to degree-granting universities. We cannot process awards to other institutions (e.g. not-for-profit institutions, hospitals, non-degree-granting research institutes, etc.) even if they are affiliated with a university. We do not allow applications from non-degree-granting research institutions.
• Google does not retain any intellectual property from the research.

Upon completion of the grant, we encourage grantees to share results publicly (in the form of open events, online publications, teaching tools, exhibition, etc) and, where appropriate, to make their software, utilities, data sets, or similar artifacts freely available for others to use via open source licenses, publication, etc.

Selection Criteria

Candidates will be selected based on the strength of their proposal; namely:

• Proposal’s ability to uniquely benefit from (and influence) Google’s Research in generative machine learning, computer vision, and natural language processing.
• Candidate’s commitment to share results of research publicly
• Match between grant funding and the applicant’s needs

Timeline

Application closes on Monday, June 28, 2021 at 11:59 p.m. PT. Successful applicants will be notified in August 2021.

Application Process

Applications are submitted via a Google Form and contain three parts: (1) project overview; (2) proposal body, and (3) project budget.

1. The project overview requests contact information and a CV for the principal investigator.

2. The proposal body requests an abstract, research objectives, a work description, and references. Please also outline a plan to share the output of your project with the broader research communities (e.g. open sourcing code, making data sets public, etc). Please note that this award is structured as an unrestricted gift, so there are no legal requirements once a project is selected for funding.

3. The project budget for this RFP is up to 20,000 USD. Please indicate if you’ve already received or expect to receive financial or in-kind support from other organizations. Indirect, administrative, and overhead costs should not be included in the budget.

Important Information

Educational institutions may have ethics rules that restrict the ability of employees to participate in certain events. By submitting a proposal, the applicant certifies that the entry complies with the institution’s rules and regulations. Successful applicants must also confirm that they can accept the award described in this RFP. By participating in this program, you agree that all award decisions are solely within Google’s discretion. In addition, you agree not to challenge or dispute Google’s decision to make or not make awards.

Please note that Google cannot accept any proposal containing confidential or proprietary information, as such all information included in the proposal will be deemed non-confidential. Google may independently conduct similar development and will own the intellectual property rights in the application(s) or content Google creates.

By submitting this application, you acknowledge that we can use the information you provide in accordance with Google’s Privacy Policy (found at http://www.google.com/privacy.html). In addition, it is our hope that any researcher(s) who benefits from this gift will consider abiding by Google’s AI Principles (http://ai.google/principles).

By accepting an award, entrant agrees and consents to Google and its agencies’ use of entrant’s name and/or likeness for a reasonable time after award of the Research Award in promotional and advertising material of Google (or its agents) as an award recipient.

Applications for the 2021 funding cycle are now closed.

Artists & Machine Intelligence (AMI) is a program at Google that brings together artists, academics and engineers to realize research and projects with machine intelligence. Questions? Feedback? Tweet us or email: artwithmi@google.com

Request for Proposals: AMI Research Awards was originally published in Artists + Machine Intelligence on Medium, where people are continuing the conversation by highlighting and responding to this story.

Editor’s note: A version of this article appeared on The Keyword.

From January to May 2020, six international artists collaborated with Google creative technologists to explore machine learning techniques in film, poetry, sound art, and interactive storytelling, as part of an inaugural grant-making collaboration between Google Research and Google Arts & Culture. Today, we are publishing the outcomes of the grants.

EXPLORE THE ARTISTS AND ARTWORKS

Hip Hop Poetry Bot by Alex Fefegha

Can AI rap? Alex explores speech generation trained on rap and hip hop lyrics by Black artists. For the moment it exists as a proof of concept, as building the experiment in full requires a large, public dataset of rap and hip hop lyrics on which an algorithm can be trained, and such a public archive doesn’t currently exist. The project is therefore launching with an invitation from Alex to rap and hip hop artists to become creative collaborators and contribute their lyrics to create a new, public dataset of lyrics by Black artists.

The Nonsense Laboratory by Allison Parrish

Allison invites you to adjust, poke at, mangle, curate and compress words with a series of playful tools in her Nonsense Laboratory. Powered by a bespoke code library and machine learning model developed by Allison Parrish you can mix and respell words, sequence mouth movements to create new words, rewrite a text so that the words feel different in your mouth, or go on a journey through a field of nonsense.

Let Me Dream Again by Anna Ridler

Anna uses machine learning to try to recreate lost films from fragments of early Hollywood and European cinema that still exist. The outcome? An endlessly evolving, algorithmically generated film and soundtrack. The film will continually play, never repeating itself, over a period of one month.

Dhvāni by Budhaditya Chattopadhyay

Budhaditya brings a lifelong interest in the materiality, phenomenology, political-cultural associations, and the sociability of sound to Dhvāni, a responsive sound installation, comprising 51 temple bells and conducted with the help of machine learning. An early iteration of Dhvāni was installed at EXPERIMENTA Arts & Sciences Biennale 2020 in Grenoble, France. Read more about the project.

Neural Swamp by Martine Syms

Martine uses video and performance to examine representations of blackness across generations, geographies, mediums, and traditions. For this residency, Martine developed Neural Swamp, a play staged across five screens, starring five entities who talk and sing alongside and over each other. Two of the five voices are trained on Martine’s voice and generated using machine learning speech models. The project will premiere at The Philadelphia Museum of Art and Fondazione Sandretto Re Rebaudengo in Fall 2021. Read more about the project.

Knots of Code by Paola Torres Núñez del Prado

Paola studies the history of quipus, a pre-Columbian notation system that is based on the tying of knots in ropes, as part of a new research project, Knots of Code. The project’s first work is a Spanish language poetry-album from Paola and AIELSON, an artificial intelligence system that composes and recites poetry inspired by quipus and emulating the voice of the late Peruvian poet J.E. Eielson. Read more about the project.

Artists & Machine Intelligence (AMI) is a program at Google that brings together artists, academics and engineers to realize research and projects with machine intelligence. Questions? Feedback? Tweet us or email: artwithmi@google.com

Artists meet machine learning was originally published in Artists + Machine Intelligence on Medium, where people are continuing the conversation by highlighting and responding to this story.

As part of Google’s ongoing commitment to support faculty working on positive societal initiatives, Artists + Machine Intelligence (AMI) supports artists and academics pursuing social science research relevant to machine learning technologies.

In the summer of 2020, we invited faculty across the arts & humanities disciplines to submit proposals exploring implications of AI technologies on society, including, but not limited to generative machine learning, computer vision, and natural language processing. Awards provide faculty and Google researchers an opportunity to partner on their research initiatives and build new and constructive long-term relationships.

This year’s selected proposals examine how technology mediates our knowledge and experience of the arts, music, policy, and urban design. Congratulations to this year’s recipients:

• Tools That Make Meaning – Mercedes Bunz, King’s College London, and Eva Jäger, Serpentine Galleries
• Speculative AI: Octavia Butler and Other Possible Worlds – Beth Coleman, University of Toronto
• Collaborative World Building with AI – Anab Jain, Design Investigations, University of Applied Arts Vienna, with Matthew Plummer-Fernández
• Network Bending Differentiable Digital Signal Processing (DDSP) – Matthew Yee-King, Goldsmiths, University of London, and Louis McCallum, University of the Arts, London
• Backyard Home Data Explorer: AI and The Future of Housing – Mimi Zeiger, Southern California Institute of Architecture (SCI-Arc), and Casey Rehm, SCI-Arc

Artists & Machine Intelligence (AMI) is a program at Google that brings together artists, academics and engineers to realize research and projects with machine intelligence. Learn how to apply for the next cycle of research awards, or meet award recipients from previous years here.

Recipients announced for AMI Research Awards 2021 was originally published in Artists + Machine Intelligence on Medium, where people are continuing the conversation by highlighting and responding to this story.

Status: Applications are now closed. Decisions for the 2023 application will be announced via email in September 2023. Please check back in 2024 for details on future application cycles.

As part of Google’s ongoing commitment to support faculty working on positive societal initiatives, Artists + Machine Intelligence (AMI) supports academics pursuing arts and humanities-centered research that looks at technology’s implications and impacts on individuals and society.

Through the current program, AMI provides $20,000 USD fellowships to permanent, full-time faculty at degree-granting institutions. Cash awards in this program are structured as unrestricted gifts to universities. Faculty members may apply with supporting collaborators. Both new and ongoing research are eligible. Google does not retain any intellectual property from the research.

We accept applications from permanent faculty at universities around the world. Funding is focused on supporting the faculty’s research. We do not allow applications from non-degree-granting research institutions. Since our funding is structured as unrestricted gifts to degree-granting universities, we cannot process awards to other institutions (e.g. not-for-profits institutions, hospitals, non-degree-granting research institutes, etc.) even if they are affiliated with a university. A principal investigator must apply in his or her capacity as a university professor and must be able to accept an award through that university.

Meet the 2021 AMI Research Award recipients

See Award Recipients (in alphabetical order)

• Rebecca Allen, University of California, Los Angeles: Re-emergence
• Benjamin Bratton and Casey Rehm, Southern California Institute of Architecture (SCI-Arc): How Cities See: Machine Sensing and Cognition at an Urban Scale
• Mercedes Bunz, King’s College London and Eva Jäger, Serpentine Galleries: Tools That Make Meaning
• Jennifer Chen, Southern California Institute of Architecture (SCI-Arc): Views of Planet City: Pale Blue Dot Mk2
• Beth Coleman, University of Toronto: Speculative AI: Octavia Butler and Other Possible Worlds
• Rebecca Fiebrink and Phoenix Perry, Goldsmiths, University of London: Building interactive machine learning tools for game developers
• Anab Jain, Design Investigations, University of Applied Arts Vienna, in collaboration with Matthew Plummer-Fernández: Collaborative World Building with AI
• Frederic Fol Leymarie, Goldsmiths, University of London, in collaboration with Dr. Daniel Berio and Xiaobo Fu, Goldsmiths, University of London: Movement-centric calligraphy and graffiti generation
• Daniel Cardoso Llach, Ph.D. Carnegie Mellon University, in collaboration with Dr. Jean Oh, Carnegie Mellon University: Rethinking AI and Automation in Architecture
• Golan Levin, Carnegie Mellon University
• Tegan Maharaj, University of Toronto, in collaboration with the Cambridge Centre for the Study of Existential Risk: Explorisk: Visualizing Risk-Mitigation Scenarios
• Ciira Maina, Dedan Kimathi University of Technology: Acoustic monitoring of ecosystems in Kenya
• Lauren McCarthy, University of California, Los Angeles: SOMEONE
• Michael Rau, Stanford University: Digital Performers Using AI
• Casey Reas, University of California, Los Angeles: Expanded Cinema
• Tivon Rice, University of Washington: Models for Environmental Literacy
• Joshua Trees, Royal College of Art, in collaboration with Yvan Martinez and Krister Olsson: Public Foundry
• Matthew Yee-King, Goldsmiths, University of London and Louis McCallum, University of the Arts, London: Network-bending Differentiable Digital Signal Processing (DDSP)
• Mimi Zeiger and Casey Rehm, Southern California Institute of Architecture (SCI-Arc): Backyard Home Data Explorer: AI and the Future of Housing

Artists & Machine Intelligence (AMI) is a program at Google that brings together artists, academics and engineers to realize research and projects with machine intelligence. Questions? Feedback? Tweet us or email: artwithmi@google.com

About AMI Research Awards was originally published in Artists + Machine Intelligence on Medium, where people are continuing the conversation by highlighting and responding to this story.

Every week, we feature collaborators working at the intersection of art and technology. Follow us on Medium for new posts weekly, or explore the archive of featured guests here.

Allison Parrish is a computer programmer, poet, educator, and game designer. Her teaching and practice address the unusual phenomena that blossom when language and computers meet, with a focus on artificial intelligence and computational creativity. She holds a master’s degree from NYU’s Interactive Telecommunications Program (where she is currently a member of the full-time faculty), and her computer-generated poetry has recently been published in Ninth Letter and Vetch. She is also a recipient of AMI’s inaugural artist grants, where she’s exploring the relationships between spelling, pronunciation, and feeling (we won’t say more!) This week, Allison writes in to share what’s top of mind. You can follow Allison on twitter, or right here, on Medium under Allison Parrish.

What is your current state of mind? Exhausted, anxious, angry. Everything is distended. In the park near my apartment that I never used to visit, someone planted bright red tulips beneath the rose bushes. From a few steps away, it looks like the rose blossoms have already blown even though it’s only April. I opened my calendar the other day and it was like looking at hieroglyphics.

Describe a typical Tuesday: On the first Tuesday of the semester, a department administrator came into my office and asked if I didn’t have class right then, and said that in any case my students had been waiting for me in the classroom for twenty minutes and were wondering where I was. Panicked, I checked the registrar website and saw that yes, I did have class then — a class that I had thought was on Thursday. At some point when you’re a professor, you stop having nightmares about classes you’d forgotten you were enrolled in, and start having nightmares about classes you’d forgotten you were teaching. So this was a literal nightmare scenario, and I thought it would be the worst thing to happen this semester, and I was wrong!

Currently reading: Recently I mentioned to a friend that I was interested in writing “lyric essays” and as instruction they recommended Anne Carson’s “Short Talks” in Plainwater. As a computer programmer with (a) very little formal education in literature; (b) quirky aesthetic preferences; and (c) pretty much no interest in lyricism, it’s often difficult for me to connect with a lot of mainstream poetry. But I really love this book. It’s both very clever and very intimate, which is a difficult balance to maintain. I imagine it’s a difficult balance, I mean. I’ve only ever tried to be very clever.

Just started watching: Jon Bois and Alex Rubenstein’s YouTube series on the history of the Seattle Mariners. Can data journalism about sports be both hilarious and deeply moving? Yes, and its name is Dorktown.

Your favorite place on ‘the Internet’: I have an account on friend.camp (Darius Kazemi’s Mastodon-based small-scale social network), which is like a cross between a group chat and pre-hellmouth Twitter. Camp is very good and I don’t know how I’d survive without it!

Where do you find inspiration? Maybe this isn’t fashionable to say — and I’m certainly trying to fuel the argument that you have to love programming in order to be a “real” programmer — but computer programming makes me happy. It makes me especially happy when it is aimless, and useless.

I think artists from all backgrounds depend on constraints, rules and formalisms to produce artworks that surprise even the person who made it. (Think of LeWitt’s wall drawings, or free writing exercises in creative writing classes.) It so happens that formulating constraints, writing rules and honing formalisms in order to produce surprise is the entire art of programming. For me, there is nothing more joyful than the moment where a computer program I wrote produces output that is both clearly the inescapable outcome of my instructions and charming in its unpredictability. The grin you get on your face when you look at your screen and say to yourself “I can’t believe that worked” — that’s the feeling I’m after.

Which came first for you: art or tech? I really need to learn why other people consider these to be separate things.

What are you working on? I read a Johanna Drucker book a while back so I’ve been trying to teach myself more about “material” aspects of language. Most of my experiments in the last few months have been concrete and/or asemic poetry, made with algorithmic drawing techniques or machine learning (thank you, DCGAN, for being so simple and effective). Not unrelated: I took a workshop earlier this year on how to do letterpress printing at The Arm in Brooklyn.

Artwork you’ve been thinking about lately: All of Mirtha Dermisache’s work. Thinking through the analogy between legible/illegible and speakable/unspeakable.

Artist not working with AI that should be: Oh. I don’t really know enough about the art world to not look foolish if I were to answer this. Whoops!

Most exciting development in AI that can apply to artists: Anything that makes it easier, faster, and cheaper to train models from scratch.

Advice you wish you heard 10 years ago: Math is fun and useful and you should learn a lot of it.

In 2020, I want to see more: Funding for the arts.

Tell us about a poem that speaks to you: I can’t pick one, so I’ll just talk about the whole book: Zukofsky’s 80 Flowers, which I adore. Part of what I want to do as a poet is invent forms of language so new that even GPT-2 can’t predict them (yes, I realize there’s an irony in trying to use machine learning to achieve that goal). I find myself constantly coming back to 80 Flowers for guidance on how to do this.

Artists + Machine Intelligence (AMI) is a program at Google that invites artists to work with engineers and researchers together in the design of intelligent systems. Questions? Feedback? Tweet us or email: artwithmi@google.com

Q&A with Allison Parrish was originally published in Artists + Machine Intelligence on Medium, where people are continuing the conversation by highlighting and responding to this story.