Scanning the Horizon: The State of Ray Kurzweil’s Music & Sound Predictions

Alison embarks on a journey into the future with a critical look at Ray Kurzweil’s 1999 predictions. In 2024, we’re witnessing the emergence of spatial audio, algorithmically-generated music, and virtual celebrities. While not a perfect prophecy, Kurzweil’s foresight sparks fascination, offering a glimpse into the intersection of AI, music, and the evolving landscape of machine-generated creativity.

Music as Data: Understanding & Analyzing Our Sonic World

Dr. Wilder explores the history of music as data and the limitations of traditional notation when attempting to capture the nuances of performance. He then discusses current LLM approaches, related brain research, and explores proven music-centric approaches to produce useful and meaningful results. 🎸 🎹 🎺 #MusicAI #LanguageAI #MusicNotation #SoundScience

Ask an AI: Why are some musical ideas better than others?

This interview explores the factors that determine the quality and longevity of musical ideas. Greg and his new AI friend discuss the subjective nature of musical judgment, objective measures, success factors, and the evolution of musical ideas, ultimately emphasizing the importance of studying enduring musical ideas to enhance AI-generated music quality. 🤖👂🎶🎧 #AskAnAI #MusicalIdeas

How to Define Musical Identity for Humans & AI Systems

How do we understand, and define the identity of a great performer or an entire piece of music? Looking at research from some of the great musical thinkers of the 20th and 21st centuries, the discussion moves to the specifics of which musical parameters contribute to identity, and how we can best define and codify those parameters so that they’re available to existing AI systems. 🎸 🎹 🦻 #Identity #AI

Examples vs. Ideas: Creative Challenge Facing LLMs

Dr. Greg Wilder dives into the distinction between LLMs trained on past examples and the human creation of inspired ideas, emphasizing various challenges ahead for current language models to generate truly creative and inspired content. He suggests that future creative AI will need to discover ways to identify and apply human-like inspiration to generate lasting art. #CreativeAI #ArtGeneration 🎨 🎶 💃

Lessons from Musical Evolution for Smarter Music Algorithms

How is musical quality shaped by our brains and culture? Can we draw parallels between musical evolution and Darwinian natural selection? The discussion highlights the role of cultural evolution in music and identifies certain musical elements that consistently drive value. Perhaps AI music systems should incorporate these principles to enhance musical value. 🎶🎻🥁 #MusicEvolution #MusicAI

Clio Music: How Do Computers Match Musical Sounds?

Too Much Music takes a look back at the music technology underpinning a startup co-founded by our hosts in the early 2010s. With the motto “Use Music to Find Music” , Clio Music was perhaps the first commercially available search engine to speak the language of music and has been a part of TiVo since 2012. Our hosts share insights from that experience as well as the roles they see music AI technology playing in the future. 🤖 👂

When Language Becomes Sound: How Voices Connect Us

Speech carries more than just words; it conveys emotional content through attributes like pitch and rhythm. In fact, people subconsciously adjust their speech to match others to create deeper connections. Understanding the sonic patterns of speech could enhance AI’s ability to mirror human emotions and preferences, potentially influencing attraction and interactions, even in dating apps. 💭 📣 👂 #SonicAI

Music as Information: What Gets Lost? What Do We Gain?

We love music for how it makes us feel. But the two founders of PatternSonix want to discuss why we might also want to turn music into data. What do we lose? What might we gain? Our fearless hosts begin with music notation and dive into performance nuances, related brain research, and ultimately zero in on some music-centric approaches that work well for intelligent music systems. 🎸 🎹 🎺 #MusicData

Teaching Computers the Pleasure of Musical Anticipation

Teaching intelligent systems to understand sonic surprise is essential to useful AI behavior. Computer analysis of musical expectation codifies the emotional journeys we experience when listening to music. Understanding these emotional arcs with analysis algorithms is an important step in aligning technology and artistry for future innovation. 👂🎶🎧 #EmotionalAI #MusicalAlgorithms