Intelligent Process Automation

Knowledge Integration in AI

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So let’s think about how humans learn, we humans are very good at continuously enriching and refining our knowledge and skills by seamlessly combining existing knowledge with new experiences. We exhibit a wide spectrum of learning abilities in various fields. We can be lawyers during the day and go play tennis or go for a run in the evening and make dinner at night. We are fairly adept at doing multiple tasks. When you think about AI systems, that is usually not the case. AI systems are very good at doing a specific task through machine learning alternatively called Narrow Intelligence.

Despite recent breakthroughs and advances, machine learning has a number of shortcomings when it comes to obtaining knowledge in various fields and in developing methods to identify how new and prior knowledge interact to gain more insights. Knowledge integration is the process of synthesizing multiple knowledge representations into a common model. It represents the process of how new information and existing information interact, what effects will the new information will have on existing knowledge and if existing information needs to be modified to accommodate new information.

Why is this concept important? It is important for building a better machine learning model for enterprise knowledge insights.  Not all knowledge will be readily available or can be fed into the machine learning model at once. Substantial knowledge bases are developed incrementally and a growing body of knowledge will need to be added separately. By identifying subtle conflicts and gaps in knowledge, KI facilitates better learning models. Large firms like Google are using a combination of Symbolic AI, Deep learning and Supervised learning to create better knowledge understanding and knowledge reasoning.

If you are an organization looking to extract valuable information and identify patterns within your data to create efficiency, these concepts are critical and I highly recommend doing further research around these to achieving success.

Deep Learning

What is Deep Learning?

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Deep Learning is a subset of machine learning that allows machines to do tasks that typically require human like intelligence. The inspiration for deep learning comes from neuroscience, if you look at the architecture of Deep Learning Neural Networks, they are connected in a fundamental way that mirrors the brain. Deep-learning networks are distinguished from the more commonplace neural networks by their depth; that is, the number of node layers through which data passes in a multistep process.

Earlier versions of neural networks were shallow, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as “deep” learning. So deep as strictly defined means more than one hidden layer.

Neural Network

Deep learning Neural network

In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer’s output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.

Let’s take a simple example of recognizing hand written numbers from 1 – 10. If 10 people wrote the numbers, the numbers will look very different from each person. For a human brain, it is fairly easy to identify these numbers. For a traditional machine it is impossible to detect and hence Neural Networks are used to mimic the way, neurons in the brain interact. These multiple hidden layers allow a computer to determine the nature of a handwritten digit by providing a way for the neural network to build a rough hierarchy of different features that make up the handwritten digit.

For instance, if the input is an array of values representing the individual pixels in the image of the handwritten figure, the next layer might combine these pixels into lines and shapes, the next layer combines those shapes into distinct features like the loops in an 8 or upper triangle in a 4, and so on. By building a picture of these features, neural networks can determine with a very high level of accuracy the number that corresponds to a handwritten digit. Additionally, the model will learn which links between neurons are critical in making successful predictions during training. Over the course of several training cycles, and with the help of occasional manual tuning, the network will continue to learn and generate better predictions until it reaches desired accuracy.

Thus, Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected. Deep learning networks excel at dealing with vast amount of disparate data. In fact, the larger the amount of data the more efficient Deep learning becomes and the more deep learning algorithms learn, the better they perform.

Few additional links on this topic:
MIT Technology Review: https://www.technologyreview.com/s/513696/deep-learning/
Cambridge Univerisity paper: https://bit.ly/2Fbbrlr