Could you expand on the relation between machine learning and multidimensionality? Do you see neural nets as a way of overcoming the binaristic basis of computing? Also how do you see multidimensionality as a form of cognition?
The underlying geometries of our technologies become subconscious influences on our lives. In the 90s Marshall McLuhan's axiom "the medium is the message" was a mantra for evangelists of the network. Now networks profoundly influence our economy, elections, global political movements, aesthetic trends, etc. providing a metastructure that conditions everything. I see high-dimensional machine learning models poised to perform a similar transformation on culture.
Works generated with ML hallucination can be described as movements through the latent or high-dimensional space of a neural net. Where a 3D spatial system has dimensions for x, y, and z, neural nets have much higher degrees of dimensionality, which can be imagined as a type of space. For example, an image recognition system might have hundreds of thousands of “axes”, and an address could be generated with a location on each one.
I began to develop a felt sense of these high-dimensional spaces when viewing visual artworks made with AI. But the same sense can be generated around non-visual data. When I saw a mapping of high-dimensional survey data about gender identification and expression created by my director at Google AI, Blaise Aguera y Arcas, it became clear to me that high-dimensional systems could help us experience ourselves outside of restrictive binaristic categories of identity. A binaristic model allows two choices and a spectral model allows for a continuum. But models that take into account many aspects of gender expression (sartorial, behavioral, linguistic, physiological, etc.) can be mapped in high-dimensional space then visualized in two and three dimensions using algorithms like UMAP or t-SNE, to reveal a complex space of possible gender expression that doesn’t conform to a simple binary or even a continuum.
In general, I would characterize multidimensional cognition as thinking with patterns rather than data points. This pattern-based cognition scales to very high levels through organic methods like meditation or co-cognition with other living intelligences like plants, animals, ecosystems, and planetary and star systems. Ideally, can reflect this to ourselves through machine intelligence.