The birthplace of the beloved McIntosh red apple — its farmhouse abandoned, its trees overgrown and its outbuildings fallen into disrepair — is up for sale in Dundela, Ont., about 75 kilometres southeast of Ottawa. It needs work, but for the right lover of apples and/or history, this could make a perfect home.
‘I pray for friends I’ve lost, family, like my uncle who passed away,’ Bruno told me. We were chatting in the nave of the Church of the Santa Cruz of the Souls of the Hanged, a small Catholic church in central São Paulo. Built near the old city gallows, regulars go there to pray to the dead. ‘Whe...
Last December, the world ushered in a new era of popular music: human and artificial intelligence (AI) collaboration.
Musical eras are often defined by their dominant modes of production — analog, electronic, digital — each bringing about new styles and ways of listening. This era is marked by the release of the first AI-human collaborated album, Hello World, by the music collaborative Skygge. Skygge, led by composer and producer Benoît Carré and musician and tech researcher François Pachet, translates to “shadow” in Danish and was inspired by the Hans Christian Andersen story of the same name.
We now know that algorithms can learn human bias, but can they also create highly creative and emotionally engaging music?
Although AI algorithms lack back stories and a creative process — the very things that often draw us into a piece of pop music — they make up for it with their ability to generate the unfamiliar and novel.
Instead of finding inspiration in the social and musical experiences of one person’s life, AI draws on the outputs of thousands of lives. AI interprets these outputs as data, and can offer new melodies, instrumentations and other musical elements, based on statistical probabilities in a data-set.
In 1993, Cope released the Bach-inspired Bach by Design album using EMI. EMI’s music has mostly been appreciated on technical instead of artistic merit. At that point, it seemed AI music functioned best with predictable parameters, like the predominantly rules-based music format of Bach’s fugues.
Because of Cope’s pioneering experiments, AI has had success producing fugues that can pass as human-created, but that could partly be explained by a lack of familiarity with Bach’s music by those who are tasked with identifying the human vs. computer creator.
But Skygge is the first pop music collaboration between human and AI producers.
Challenges exist when using AI technologies to create both classical and pop music styles. The mainstream familiarity of pop music, however, means that it is more difficult to “fool” listeners. The success of most pop artists relies not only on their musical talents but also their ability to craft stories and make connections with their listeners on a personal level.
Listeners become invested in the storytelling, and the extra musical elements that make pop music “pop.” Statistical models generally lack these features, even though the music itself is created from preexisting, human-created works.
For Hello World, each contributing Skygge artist and producer interpreted the Hans Christian Andersen fable within a chosen genre and worked in conjunction with the AI technology. Skygge was funded by a European Research Council grant to explore AI in pop music production. To do so, they used Sony’s Flow-Machines tools.
Instead of using neural networks, as done in Google DeepMind’s Deep Dream Generator, Flow Machines uses a probability equation, known as Markov chains to create catchy tracks. Neural networks require a substantial amount of information to produce an outcome, while Markov chains have the advantage of being able to produce statistical models from much smaller databases.
Based on the information imputed and based on previously recorded music, Flow-Machines suggests melodies, accompaniments and instrumentation. Producers can accept, reject and alter these suggestions to create their AI-human collaboration.
‘Different than anything I’ve ever heard’
Using AI as a pop music collaborator has the potential to push the boundaries of familiarity into new territories. Novelty is often what shifts a song from being merely popular to genre-defining.
The unfamiliar is easy to find on Skygge. Pop-singer Kiesza, one of the contributors to Hello World, created the melody for her track “Hello Shadow” using Flow-Machines. Kiesza said: “This melody sounded different from anything that I’d actually ever heard…I loved it from the beginning….Even though it’s still really haunting…it’s still really catchy.”
Similarly, the eeriness of “In the House of Poetry” is undeniable, and enhanced by the ethereal voice of Kyrie Kristmanson. Flow-Machines took the familiar and translated it into something on the edges of the uncanny. Yet, at the same time, it is catchy. Skygge says they specialize in earworms —songs that stick in your head, becoming undeniably familiar, in spite of their initial unfamiliarity.
A new era of music production
As AI-collaborated pop music becomes more commonplace, it will challenge us as producers and listeners. The question will become much less about whether AI will take the jobs of musicians but more about how, or if, our tastes will evolve as quickly as the production technologies develop.
AI will create a new era of music production, or at the very least, new musical styles. Skygge co-producer Carré said: “At the beginning, a lot of people were afraid that the pianist and the drummers will be replaced, but it never happens this way…It’s humans that find the ways to use [tech] to make interesting things.”
We live in a culture of storytelling, not just in the lyrics and music, but also through the artists themselves. The production of these stories may change, but our engagement with them will not.
Melissa Avdeeff does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
Attempts to replicate classical scientific studies have been failing. These alarming failures have hit psychology, the life sciences and other fields, calling major findings into question. Scientists agree: questionable research practices are rife in many disciplines.
As science is only trustworthy when consistent, we need to make sure future work can be replicated. As such, we have decided to spread the word about proper open scientific practice. This is especially important in the nascent interdisciplinary field of psychedelic science, in which we are now conducting research into the practise of “microdosing” substances like LSD (lysergic acid diethylamide) and “magic” mushrooms (psilocybin).
This phenomenon isn’t limited to psychology: findings from disciplines such as biology, medicine and chemistry can be hard to believe. For example, almost 500 authors were found guilty of misconduct by the Chinese government last year, several cancer research papers have been retracted recently and a recent report indicated that as much as 80 per cent of chemists have trouble replicating findings from the literature.
Several great pieces on The Conversation have tackled this issue so there is lots to review if replicability is new to you.
Instead of hiding inconvenient results or adding unplanned research conditions, scientists can use open science to demonstrate their integrity. Open science involves pre-registering hypotheses before doing research, and publishing the entire data set once the research is done.
Pre-registration happens online. The content of the registration is locked and time stamped, then kept confidential until a set date, when it is released for the public to see. This is done so that the researcher can show they did exactly what they planned to do, which is how we all learned we are supposed to do science. Pre-registration is not even difficult, but researchers need to learn how to do it and adjust.
Once the study has been published, the data set can be made public. This way, the entire scientific community can examine the data, serving at least two purposes. First, the scientific community can verify that the data supports the conclusions made in the study, ensuring no mistakes were made. Second, other scientists can explore for new patterns in the data to create new hypotheses for new studies, moving science forward faster.
Making the data public makes scientists publicly accountable, and is good for the scientific community at large.
Co-operation over competition
So far, most psychedelic research has not been pre-registered, which means it should be considered exploratory and, unfortunately, inconclusive. Some findings may have been by chance rather than clearly caused by the substances used, and these findings need to be replicated by independent labs to ensure they hold up.
A recent call for “Cooperation Over Competition” has been made, but its impact remains to be seen. For now, we take the results on psychedelics that scientists have found on faith.
Pre-registration is the only way to ensure psychedelic science is conducted with a high level of integrity. Psychedelic science is in its infancy, much as mindfulness research was some few decades ago. We must learn from past mistakes if we do not wish to see the same harsh criticisms levelled upon this field in the future.
This will improve and maintain public trust in the scientific endeavour, especially important for these storied substances. As public consumers of science, we should all be critical of new research and remember the Sagan Standard: “Extraordinary claims require extraordinary evidence.”
The authors do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.
Heard of Jenny Everywhere? Me neither, until I was looking for media to use for an open source character drawing contest I was involved in. As I Googled my way around the internet, I happened upon Jenny Everywhere.