Artificial intelligence’s transformative promise arrived again this week in a cluster of AI-based start-up announcements spanning music discovery, song selection and songwriting.
Singapore-based start-up Musiio launched Musiio Tag, an AI-enabled music discovery tagging tool that makes playlisting and music discovery easier for talent scouts, music libraries and streaming services.
German tech start-up Groovecat launched Cyanite, which quantifies the emotional nature of a song to give music services more rational selection of music content and playlists for objective-driven user experiences.
And, Amadeus Code, the AI-powered songwriting assistant, announce it raised $1.8 million in a Series A round to help it develop the platform and release new features later this year.
Already, an entire industry built around artificial intelligence for creating music is gaining stride.
Large technology companies also offer AI-powered tools and services for music making. Among them: IBM Watson Beat, Google Magenta’s NSynth, Sony’s Flow Machines, and Spotify’s Creator Technology Research Lab.
Table: Google’s Majenta Project
|What||Started by the Google Brain team, Majenta is an open source research project exploring the role of machine learning as a tool in the music creation process|
|When||Launched May 2015 at Moogfest|
|How||By using Google’s TensorFlow open source software and releasing the code on GitHub, Majenta allows developers and musicians to create new compositions with the algorithms|
|Projects||Majenta Studio (beta), a suite of free music-making plugins built on Magenta’s open source tools and models |
ML-Jam, a generative AI model that encourages musicians to tap into uniquely human music through improvisation using existing applications
Musical Transformer, a machine learning model that’s capable of generating relatively coherent tunes with a recognizable repetition.
NSynth, an assistant that uses neural networks to experiment with more than 300,000 instrument sounds created with machine learning
Piano Genie, an AI program that lets untrained piano players improvise fluently on the instrument by using just eight buttons.
Mainstream CEOs Take a Measured View on AI Investment
But despite a steady stream of artificial intelligence innovation in music, and a lot of fanfare, most CEOs in corporate America take a measured view on the technology. They expect that it will take years before the cutting-edge technology gives their businesses a financial lift.
Their long-term view was laid out by consulting firm KPMG in a recent survey of 400 executives in the United States, all of whom had artificial intelligence projects in progress within their companies. The executives generally said that it would take some time before artificial intelligence pays out, a cautiously optimistic position that the technology won’t have an immediate large-scale impact.
Just over half of the executives surveyed, 51%, said it will take three-to-five years before their A.I. projects create a “significant return on investment.” That’s in sharp contrast to last year’s survey, in which only 28% said it would take that long—highlighting how much executives have reconsidered their initial rosy expectations.
Meanwhile, 47% of respondents said they expect significant results in three years or less. That marks a major decline from last year, when 62% expected short-term results.
Machine Learning: What to Know
- The algorithms that computer systems use to perform a specific task effectively without using explicit instructions, relying on patterns and inference
- A subset of artificial intelligence that builds a mathematical model based on sample data, known as “training data,” to make predictions or decisions
- Pioneered in the 1950s, machine learning computers don’t have to be explicitly programmed but can change and improve their algorithms by themselves.