The Monthly Wrap

Catastrophic interference, also known as catastrophic forgetting, is the tendency of an  artificial neural network  to completely and abruptly forget previously learned information upon learning new information. Neural networks are an important part of the  network approach and connectionist approach  to  cognitive science . With these networks, human capabilities such as memory and learning can be modeled using computer simulations. Catastrophic interference is an important issue to consider when creating connectionist models of memory. It was originally brought to the attention of the scientific community by research from McCloskey and Cohen (1989), and Ratcliff (1990). It is a radical manifestation of the 'sensitivity-stability' dilemma or the 'stability-plasticity' dilemma. Specifically, these problems refer to the challenge of making an artificial neural network that is sensitive to, but not disrupted by, new information(see the diagram).  Lookup tables  and connec

  Machine Learning (ML) Machine learning is a branch of AI, used to process and analyse the data using various algorithms that mimics the learning behaviour of humans for analysing the data. These algorithms predict the outcomes without intervention of human. It provides the systems that automatically learn and improve based on their experience.  Fig 1 depicts the process of machine learning. It consists of five steps starting with collection of data and ends at model deployment. These are 5 basic steps: Collection of Data from various data source Data cleaning and Feature Engineering Model building and Selection of ML Algorithm Model Evaluation Model Deployment Fig. 1 Basic Process of Machine Learning Generally, Data collection is the key process in ML. Based on the addressed problem, we have to obtain the data from different sources. Data can be collected from social media, public opinion poll, or survey of the product success or failure.  Next step in ML is data cleaning.  Before st