Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding catastrophic forgetting--remains a key obstacle to achieving human-level intelligence. The progressive networks approach represents a step forward in this direction: they are immune to forgetting and can leverage prior knowledge via lateral connections to previously learned features. We evaluate this architecture extensively on a wide variety of reinforcement learning tasks (Atari and 3D maze games), and show that it outperforms common baselines based on pretraining and finetuning. Using a novel sensitivity measure, we demonstrate that transfer occurs at both low-level sensory and high-level control layers of the learned policy.

TEACH ME AND I REMEMBER. I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Life is a journey through learning experiences.

The intersection between artificial intelligence(AI) and human cognition is one of the most fascinating areas of research in the modern technology space. Deep learning is constantly trying to emulate mechanisms of the human brain in order to improve the capabilities of AI agents. Many of those mechanisms are centered around how humans learn and build knowledge. A recent research paper from DeepMind is proposing a method that emulates the progressive nature of human learning in deep learning model. DeepMind calls this technique progressive neural networks.

TEACH ME AND I REMEMBER. Life is a journey through learning experiences. As, we are continuously learning new tasks and acquiring new knowledge and we have a magical, and purely understood ability to leverage previous experiences to optimize how we build new knowledge. Learning never stops and it shapes as intellectual and social beings. Could we recreate the continuity of learning in artificial intelligence(AI) models.

If you've seen one Atari game you've seen them all, or at least once you've seen enough of them anyway. When we (humans) learn, we don't start from scratch with every new task or experience, instead we're able to build on what we already know. And not just for one new task, but the accumulated knowledge across a whole series of experiences is applied to each new task. Nor do we suddenly forget everything we knew before – just because you learn to drive (for example), that doesn't mean you suddenly become worse at playing chess. But neural networks don't work like we do.