Back
Efstratios Gavves
Author/s
About the resource/s
Uploaded by AUTH
Toward A(G)I, whatever definition one chooses for AGI, the main effort is finding the best possible self-supervised, unsupervised, and supervised objectives that scale to the foundation level. Taking a step back, some broader questions arise naturally. Are these objectives teaching our machines how the world looks or how we humans look at the world? Would it make sense to design machines and robots that progressively learn to interact with the world “scientifically”: from turning pixels to symbols, to posing scientific hypotheses for how the world works, then learning better scientific representations (that allow for experimenting with the world), and in the end defining autonomous ways for experimenting with the world and validating the hypotheses? In this lecture, I will present my work and vision for “Robot AI Scientists” that learn autonomously about the world and how to interact “scientifically” using AI that bridges data-based learning with physical understanding and cause-and-effect experimentation.
Documents
Media
Other Sources
