Anatomies of Intelligence

Following from a shared interest in livecoding and real-time algorithmic performance, Joana Chicau and Jonathan Reus begin a research project into techniques for in-situ dissections of machine learning algorithms. We seek to better understand the habitual and fixed objects of machine learning as well as their terminologies, and provide counter-techniques for conditions of emergence and movement. In our processual approach, we aim to develop an online repository of terminology and techniques for a critical examination of the “anatomy” of learning and prediction processes, data corpus and models of machine learning algorithms. And explore, through performance practice, how such a toolkit can confront the idealized bodies of artificial intelligence.

theatre
corpus
gesture
modelling
systems-of-knowing


LSTM | PGM | RNN | actors | aesthesis | agency | algorithm | anatomical collections | anatomical demonstrations | anatomical theatre | anatomy | archive | artificialia | brain | categories | classification | collections | colonialism | commodification | cutting | disgust | disgusting | dissection | domestication | elegance | epistemology | error | evaluation | forecasting | freakish | graphs | grouping | hacking | hands-on | history | human body | imagination | imperfect | inscription | knowledge | labelling | materiality | meaning | memory | model | models | monsters | naturalia | neural networks | ontologies | optimization | perfection | poetic | preparation | preparations | python | representations | rhetoric | selection | sensory experience | sensory perception | sensory perceptions | skull | spectacle | speech | tacit | taxonomy | time-series | tools | training | trial | unknown | word2vec |

Catalog