Currently, the preeminent paradigm for building artificial intelligence is the development of large, general-purpose models that aim to be able to perform all tasks at (super)human level. In this talk, I will argue that an ecosystem of specialist models would likely be dramatically more efficient and could be significantly more effective. Such an ecosystem could be built collaboratively by a distributed community and be continually expanded and improved. In this talk, I will outline some of the technical challenges involved in creating model ecosystems, including automatically selecting which models to use for a particular task, merging models to combine their capabilities, and efficiently communicating changes to a model.
* This event is open to the public with emphasis on graduate students in machine learning, computer science, ECE, statistics, mathematics, linguistics, medicine, as well as PhD-level data scientists in the GTA.