Limitations of Deep Learning in AI Research

Deep learning a subset of machine learning, has delivered super-human accuracy in a variety of practical uses in the past decade. In contrast to machine learning where an AI agent learns from data based on machine learning algorithms, deep learning is based on a neural network architecture which acts similarly to the human brain, and allows the AI agent to analyze data fed in -- in a structure similar to the way humans do. Deep learning models do not require algorithms to specify what to do with the data, which is made possible thanks to the extraordinary amount of data we as humans, collect and consume -- which in turn is fed to deep learning models [3]. The "traditional" types of deep learning incorporates a different mix of feed-forward modules (frequently convolutional neural networks) and recurrent neural networks (now and then with memory units, such as LSTM [4] or MemNN [5]). These deep learning models are restricted in their capacity to "reason", for example to do long chains of deductions, or streamlining a method to land at an answer.