A model of virtuosity

A crowd gathered at the MIT Media Lab in September for a concert by musician Jordan Rudess and two collaborators. One of them, violinist and vocalist Camilla Bäckman, has performed with Rudess before. The other — an artificial intelligence model informally dubbed the jam_bot, which Rudess developed with an MIT team over the preceding several months — was making its public debut as a work in progress.

Throughout the show, Rudess and Bäckman exchanged the signals and smiles of experienced musicians finding a groove together. Rudess’ interactions with the jam_bot suggested a different and unfamiliar kind of exchange. During one duet inspired by Bach, Rudess alternated between playing a few measures and allowing the AI to continue the music in a similar baroque style. Each time the model took its turn, a range of expressions moved across Rudess’ face: bemusement, concentration, curiosity. At the end of the piece, Rudess admitted to the audience, “That is a combination of a whole lot of fun and really, really challenging.”

Rudess is an acclaimed keyboardist — the best of all time, according to one Music Radar magazine poll — known for his work with the platinum-selling, Grammy-winning progressive metal band Dream Theater, which embarks this fall on a 40th anniversary tour. He is also a solo artist whose latest album, “Permission to Fly,” was released on Sept. 6; an educator who shares his skills through detailed online tutorials; and the founder of software company Wizdom Music. His work combines a rigorous classical foundation (he began his piano studies at The Juilliard School at age 9) with a genius for improvisation and an appetite for experimentation.

Last spring, Rudess became a visiting artist with the MIT Center for Art, Science and Technology (CAST), collaborating with the MIT Media Lab’s Responsive Environments research group on the creation of new AI-powered music technology. Rudess’ main collaborators in the enterprise are Media Lab graduate students Lancelot Blanchard, who researches musical applications of generative AI (informed by his own studies in classical piano), and Perry Naseck, an artist and engineer specializing in interactive, kinetic, light- and time-based media. Overseeing the project is Professor Joseph Paradiso, head of the Responsive Environments group and a longtime Rudess fan. Paradiso arrived at the Media Lab in 1994 with a CV in physics and engineering and a sideline designing and building synthesizers to explore his avant-garde musical tastes. His group has a tradition of investigating musical frontiers through novel user interfaces, sensor networks, and unconventional datasets.

The researchers set out to develop a machine learning model channeling Rudess’ distinctive musical style and technique. In a paper published online by MIT Press in September, co-authored with MIT music technology professor Eran Egozy, they articulate their vision for what they call “symbiotic virtuosity:” for human and computer to duet in real-time, learning from each duet they perform together, and making performance-worthy new music in front of a live audience.

Rudess contributed the data on which Blanchard trained the AI model. Rudess also provided continuous testing and feedback, while Naseck experimented with ways of visualizing the technology for the audience.

“Audiences are used to seeing lighting, graphics, and scenic elements at many concerts, so we needed a platform to allow the AI to build its own relationship with the audience,” Naseck says. In early demos, this took the form of a sculptural installation with illumination that shifted each time the AI changed chords. During the concert on Sept. 21, a grid of petal-shaped panels mounted behind Rudess came to life through choreography based on the activity and future generation of the AI model.

“If you see jazz musicians make eye contact and nod at each other, that gives anticipation to the audience of what’s going to happen,” says Naseck. “The AI is effectively generating sheet music and then playing it. How do we show what’s coming next and communicate that?”

Naseck designed and programmed the structure from scratch at the Media Lab with assistance from Brian Mayton (mechanical design) and Carlo Mandolini (fabrication), drawing some of its movements from an experimental machine learning model developed by visiting student Madhav Lavakare that maps music to points moving in space. With the ability to spin and tilt its petals at speeds ranging from subtle to dramatic, the kinetic sculpture distinguished the AI’s contributions during the concert from those of the human performers, while conveying the emotion and energy of its output: swaying gently when Rudess took the lead, for example, or furling and unfurling like a blossom as the AI model generated stately chords for an improvised adagio. The latter was one of Naseck’s favorite moments of the show.

“At the end, Jordan and Camilla left the stage and allowed the AI to fully explore its own direction,” he recalls. “The sculpture made this moment very powerful — it allowed the stage to remain animated and intensified the grandiose nature of the chords the AI played. The audience was clearly captivated by this part, sitting at the edges of their seats.”

“The goal is to create a musical visual experience,” says Rudess, “to show what’s possible and to up the game.”

Musical futures

As the starting point for his model, Blanchard used a music transformer, an open-source neural network architecture developed by MIT Assistant Professor Anna Huang SM ’08, who joined the MIT faculty in September.

“Music transformers work in a similar way as large language models,” Blanchard explains. “The same way that ChatGPT would generate the most probable next word, the model we have would predict the most probable next notes.”

Blanchard fine-tuned the model using Rudess’ own playing of elements from bass lines to chords to melodies, variations of which Rudess recorded in his New York studio. Along the way, Blanchard ensured the AI would be nimble enough to respond in real-time to Rudess’ improvisations.

“We reframed the project,” says Blanchard, “in terms of musical futures that were hypothesized by the model and that were only being realized at the moment based on what Jordan was deciding.”

As Rudess puts it: “How can the AI respond — how can I have a dialogue with it? That’s the cutting-edge part of what we’re doing.”

Another priority emerged: “In the field of generative AI and music, you hear about startups like Suno or Udio that are able to generate music based on text prompts. Those are very interesting, but they lack controllability,” says Blanchard. “It was important for Jordan to be able to anticipate what was going to happen. If he could see the AI was going to make a decision he didn’t want, he could restart the generation or have a kill switch so that he can take control again.”

In addition to giving Rudess a screen previewing the musical decisions of the model, Blanchard built in different modalities the musician could activate as he plays — prompting the AI to generate chords or lead melodies, for example, or initiating a call-and-response pattern.

“Jordan is the mastermind of everything that’s happening,” he says.

What would Jordan do

Though the residency has wrapped up, the collaborators see many paths for continuing the research. For example, Naseck would like to experiment with more ways Rudess could interact directly with his installation, through features like capacitive sensing. “We hope in the future we’ll be able to work with more of his subtle motions and posture,” Naseck says.

While the MIT collaboration focused on how Rudess can use the tool to augment his own performances, it’s easy to imagine other applications. Paradiso recalls an early encounter with the tech: “I played a chord sequence, and Jordan’s model was generating the leads. It was like having a musical ‘bee’ of Jordan Rudess buzzing around the melodic foundation I was laying down, doing something like Jordan would do, but subject to the simple progression I was playing,” he recalls, his face echoing the delight he felt at the time. “You’re going to see AI plugins for your favorite musician that you can bring into your own compositions, with some knobs that let you control the particulars,” he posits. “It’s that kind of world we’re opening up with this.”

Rudess is also keen to explore educational uses. Because the samples he recorded to train the model were similar to ear-training exercises he’s used with students, he thinks the model itself could someday be used for teaching. “This work has legs beyond just entertainment value,” he says.

The foray into artificial intelligence is a natural progression for Rudess’ interest in music technology. “This is the next step,” he believes. When he discusses the work with fellow musicians, however, his enthusiasm for AI often meets with resistance. “I can have sympathy or compassion for a musician who feels threatened, I totally get that,” he allows. “But my mission is to be one of the people who moves this technology toward positive things.”

“At the Media Lab, it’s so important to think about how AI and humans come together for the benefit of all,” says Paradiso. “How is AI going to lift us all up? Ideally it will do what so many technologies have done — bring us into another vista where we’re more enabled.”

“Jordan is ahead of the pack,” Paradiso adds. “Once it’s established with him, people will follow.”

Jamming with MIT

The Media Lab first landed on Rudess’ radar before his residency because he wanted to try out the Knitted Keyboard created by another member of Responsive Environments, textile researcher Irmandy Wickasono PhD ’24. From that moment on, “It’s been a discovery for me, learning about the cool things that are going on at MIT in the music world,” Rudess says.

During two visits to Cambridge last spring (assisted by his wife, theater and music producer Danielle Rudess), Rudess reviewed final projects in Paradiso’s course on electronic music controllers, the syllabus for which included videos of his own past performances. He brought a new gesture-driven synthesizer called Osmose to a class on interactive music systems taught by Egozy, whose credits include the co-creation of the video game “Guitar Hero.” Rudess also provided tips on improvisation to a composition class; played GeoShred, a touchscreen musical instrument he co-created with Stanford University researchers, with student musicians in the MIT Laptop Ensemble and Arts Scholars program; and experienced immersive audio in the MIT Spatial Sound Lab. During his most recent trip to campus in September, he taught a masterclass for pianists in MIT’s Emerson/Harris Program, which provides a total of 67 scholars and fellows with support for conservatory-level musical instruction.

“I get a kind of rush whenever I come to the university,” Rudess says. “I feel the sense that, wow, all of my musical ideas and inspiration and interests have come together in this really cool way.”