AI is better than humans in many ways such as disease diagnosis, board game fraud detection and etc. However, when it comes to question that required a lot of thinking like this:
What size is the cylinder that is left of the brown metal thing that is left of the big sphere?
A child could answer this question easily. However, the traditional deep learning models just wont show you a reliable result.
Why deep learning isn’t enough
Deep learning models always showing fantastic result at understanding relationships between inputs and outputs. This is pretty enough for problems like classification and perception. However, we want AI to be able to make decision using human reasoning, what we call “common sense”.
Deep reasoning is the field where machines were unable to understand complex relationship with different idea. For example: “all animals eat”. “Dogs are animal”.
Here, human can quickly find out this implicit relationship that all Dogs eat. However, it is not so easy for machine to understand how different things relate to one another. So, how to teach AI with the ability to reason?
Implementing Deep Reasoning
To handle this type of question, DeepMind researchers have come out with a solution in the 3 steps:
LSTMs network are pretty good at understanding the sequences due to their ability to remember the previous part of the sequence. It works well when dealing with question and language. This is because the beginning of the question or language always have great impact on the meaning or influence the at the end. Besides that, the LSTM also creates an embedding block that’s easier for the RN to work with.
Since CNNs are good at identifying the features in the image, it was used to used to extract the objects from the image in the form of feature-map vectors. Just like the embeddings LSTMs produce, feature-map vectors are just a more efficient representation of the objects than pixels, making it easier for the RN to work with.
Next, it can start to understand the relationship between the object in the image once the model has processed the question and the image. Now RN will learn how to use the relationships to answer the question. These outputs are then feed into a multilayer perceptron (MLP), a kind of feedforward neural network, and then those outputs are summed and feed through the final MLP which produce the output.
Deep reasoning allows AI to understand the complication relationship between different “things”. A “relation network” module can easily be combined into a deep learning model to empower the reasoning capabilities. Deep reasoning is the next step for AI. It closes the gap between AI and human and brain.