Research
BIGGER IS
BETTER?
Huge models and transformers change how we use AI.
But they shouldn’t change how we study intelligence both human,and artificial.
But they shouldn’t change how we study intelligence both human,and artificial.
// Generalization is a bare necessity of life. No animal (humans included) can thrive without it. While partly inspired by our cognitive system, today’s AI is still task-biased.
Hierarchical representations and efficient computations are the matchmakers of nature and nurture.
With that in mind we cannot see a way for AI to achieve such abilities by choosing between rule-based and end-to-end learning. Instead, we strive to find the unified approach that combines their strengths.// We explore the use of inductive biases within deep learning architectures to continue the evolution of AI from models that learn how to solve a specific task (“learning”), to a model-hub that learns how to represent the world through language (“acquisition”).
To achieve this, we develop fundamental building blocks, each with its own inductive bias, that together form a hub - a platform that helps to manipulate the blocks and the relations between them, allowing the freedom and flexibility to model and form almost anything.
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