subho406

The model was trained on video classification, image qa and image captioning. Video captioning and video qa is not trained, yet the model shows results on those tasks.

As an author of this paper, I feel such neural networks are indeed a small step towards what we call AGI. By learning shared representations across a variety of tasks makes an AI system more robust to real-world datasets and makes it easy to adapt to new tasks without having to learn everything from scratch (which we humans are naturally capable of doing)

That's a really interesting idea! Giving such neural networks access to more and more modalities helps in useful transfer of information across the various nodes of the network, making it capable of performing zero-shot learning on never before trained tasks. It also makes it easy to add new tasks to the AI system even when data availability is a problem.

Thank you for the review! We will surely correct these mistakes before submitting for final publication.

The memory requirements of DNC is quite high. We used GTX 1060 for training. Increasing the context window anything more than 3 increases the sequence length by a huge amount, causing memory problems. However, we also found that DNC works quite well even on small batch size. We used a batch size of 16 for all our experiments. The training time for a batch size of 16, context window of size 3 and 200k steps is 48h on a GTX 1060 system.