The future is now. Once out of reach, parts of our world are now awaiting exploration through an interdisciplinary approach in AI development.
For example, researchers at the University of Jyväskylä are creating an interdisciplinary bridge between dance movements and machine learning technologies. It appears that dancing might be on its way to guide the IT world towards a better understanding of communication, leaving a noticeable mark on the future of AI algorithm development.
How do Argentine Tango’s interactive characteristics contribute to machine learning development? Could it help IT professionals transcribe dancing movements into more efficient lines of code? Learning how to reflect on tango-based enactivism can be a helpful step in humanizing robotics.
In conducted research, a hypothesis stated that an algorithm can categorize different dancing genres (such as jazz, hip-hop, or blues) based on dancers’ movements. As it turns out, the machines were “thinking” differently to humans, surprising researchers with unexpected outcomes.
The research’s primary goal was to use machine learning and computational algorithms to distinguish and adequately categorize eight dancing genres based on motion capture data from participants dancing freely.
The outcome was that the machines recognized personal dancing styles as an individual fingerprint, instead of specifying dance genres based on previously provided data.
The researchers assumed that the machine learning algorithm would easily recognize different dancing styles, as each of them has its own unique characteristics, such as tempo, steps, and overall choreography.
In order to verify the hypothesis, researchers assembled a group of participants whose movements were recorded using an optical motion capture system. It followed the three-dimensional positions of the dancers based on 21 reactive markers attached to each participant’s body.
The data was then transcripted into a set of 20 secondary markers for further accuracy, and marker locations were adjusted accordingly (except the S, and T locations, which remained the same).
Primary markers were placed as shown in picture A.
Secondary markers can be viewed in picture B.
Photo Credit: Emily Carlson, University of Jyväskylä | JYU · Department of Music
Unlike researchers expected, the output they received identified the more personal ID of dancing rather than previously defined genres. Although their initial assumptions were different, these results can provide a whole new world of perspectives for research regarding unique dancing styles and machine learning.
Nowadays, interdisciplinary connections are necessary to create a progress-oriented environment with an awareness of how interaction is crucial for societal development. Machine learning is well on its way to recognizing members of society through their personal movements.
So, how can we connect those research results with people’s daily lives and Argentinian tango? Without further ado, the answer lies under the definition of enactivism. We can understand it as evoking cognitive functions through sensomotoric activity. In other words, it is based on interacting with an environment through receiving information and incorporating it into sense-making processes.
What’s most important about this type of cognitive system is that it doesn’t just absorb specific details from the surroundings and incorporate them into a brain representation without any further analysis. It focuses on integrating both passive and active signals from the environment.
How does this all align with Argentinian Tango? Tango can be defined as consciously coordinated interaction with a partner. Connections in Tango are all about determining how to create specific energy between tangueros and tangueras. This is followed by understanding the partner’s mood and being reactive to or foreseeing movements in the environment, unifying the whole experience into one gracious flow of interaction.
Argentine Tango, in its enactive interpretation, means an improvised but also synchronized structure of movements. It involves the engagement of two individuals on their path to learning coordinated interactions through mutual understanding.
As part of a complex cognitive system for years, the embodiment is being used to develop machine learning algorithms and being steered towards an enactive approach.
Now, it is also interpreted as an inseparable part of Tango in terms of participatory sense-making, mutual incorporation, and consensually coordinated action. Enactivism combines both Tango and machine learning to progress on the study of the complexity of human-robot interaction.
IT professionals should not reduce machines to receivers; they have to interact with their environments to develop algorithms. The attempts to map human cognition should incorporate the enactive approach, developing machines into sensitive and receptive algorithms.
When designing an AI algorithm, it’s essential to set up what kind of outcome you expect primarily. Suppose the robotics industry aims to teach machines how to interact with their environment. In that case, it might be a valuable experience for IT professionals to explore enactivism through different sources, such as Argentinian Tango.
How can IT professionals transfer Tango to their work?
Through exploring the real-world experiences that they wish to include in machine learning development. The question is how adequately scientists and engineers can imitate people’s emotions, improvisational abilities, level of coordination, and sense-making processes.
Researchers combine mental and physical involvement with the environment to understand the body’s movement through dance. Robotics is taking a new direction, in which machines are designed to interact with the environment through developing sense-making processes based on enactivism.
I suppose the question remains: are robots and machines meant to be rigid and emotionless or are they possible to develop new interactions through incorporating dancing techniques? The future will tell.