Historical research themes
Open-Endedness
One single run of the algorithm of life was enough to invent all of the complexity found in nature. By using open-ended techniques, we may construct machines that keep learning forever. This may even be possible in the absence of any new data, like theorems are demonstrated from posing new axioms.
Theory of Agency
Moving on from deep learning, designing the next AI paradigms will require better mathematics for learning in itself. Research at Cross Labs started from understanding the emergence of goals in agents, aiming to build a global framework for the problem of credit assignment.
Collective Learning
Animal societies find value in continually transferring knowledge between individuals. Cross Labs’ research in societies of AI agents showed new ways to parallelize computation, learn collectively from each other, and invent new mechanisms that solve problems on a collective scale.
Neuroscience of Intelligence
By observing the processes in diverse forms of intelligence, from human brains to many other living systems, Cross Labs’ researchers were able to translate biological processes into fundamental mathematical principles of intelligence.