> The Continuous Thought Machine (CTM) is a neural network architecture that enables a novel approach to thinking about data. It departs from conventional feed-forward models by explicitly incorporating the concept of Neural Dynamics as the central component to its functionality.
Still going through the paper, But this looks very exciting to actually see, the internal visual recurrence in action when confronting a task (such as the 2D Puzzle) - making it easier to interpret neural networks over several tasks involving 'time'.
(This internal recurrence may not be new, but applying neural synchronization as described in this paper is).
> Indeed, we observe the emergence of interpretable and intuitive problem-solving strategies, suggesting that leveraging neural timing can lead to more emergent benefits and potentially more effective AI systems
Exactly. Would like to see more applications of this in existing or new architectures that can also give us additional transparency into the thought process on many tasks.
They admitted, apologized, and are in the process of revising the paper.
Mistakes always happen whether small or big. What is more important is to be transparent, learn from it, and make sure the same mistake doesn't happen again.
Still going through the paper, But this looks very exciting to actually see, the internal visual recurrence in action when confronting a task (such as the 2D Puzzle) - making it easier to interpret neural networks over several tasks involving 'time'.
(This internal recurrence may not be new, but applying neural synchronization as described in this paper is).
> Indeed, we observe the emergence of interpretable and intuitive problem-solving strategies, suggesting that leveraging neural timing can lead to more emergent benefits and potentially more effective AI systems
Exactly. Would like to see more applications of this in existing or new architectures that can also give us additional transparency into the thought process on many tasks.
Another great paper from Sakana.