Call for proposals – Finalists


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Proposal title

Artificial Neurodivergence

Applicant(s)
Gabriel Bianconi

Occupation/Organisation
Founder, Scalar Research

Location
NYC

Format
Presentation


Short description

Biological influences on artificial intelligence manifest in both algorithms and data. Algorithms are often biologically inspired: Consider such examples as neural networks and reinforcement learning. Data, quite simply, is the product or byproduct of societal action. The ways in which hegemonic identities skew data have already been widely discussed, but what about their impact on the algorithm itself? In contrast to the neurotypical approach in discussions of artificial intelligence, we investigate the question: β€œWhat could a neurodivergent neural network look like?”

Extended description

In the context of socio-digital networks, artificial intelligence can lead to the emergence of autonomous and semi-autonomous agents. Unlike their biological counterparts, their nativeness to the socio-digital medium magnifies the reach of their latent knowledge, behaviors, and identities. These are imbued onto them, purposefully or not, by their creators in a coevolutionary, possibly stochastic, process. The coevolution between participants, and between participants and the networks, makes for an phenomenon that, disconcertingly, could be both colonized and colonizing.

Biological influences on artificial intelligence manifest in both algorithms and data. Algorithms are often biologically inspired: Consider such examples as neural networks and reinforcement learning. Data, quite simply, is the product or byproduct of societal action. The ways in which hegemonic identities skew data have already been widely discussed, but what about their impact on the algorithm itself? In contrast to the neurotypical approach in discussions of artificial intelligence, we investigate the question: β€œWhat could a neurodivergent neural network look like?”

Related media/links

- Scalar Research

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