The Working Components Of AI
Artificial Intelligence (AI) is the science of creating intelligent machines and algorithms capable of learning and solving tasks that usually require human intelligence. AI is a major driving force towards digital transformation. The applications of AI keeps expanding; for example, AI is being used to predict the risk of chronic disease, inventory optimisation, fraud detection, to name a few.
Machine learning is a subset of AI, a class of algorithm that learns from examples and experiences rather than relying on predefined rules that characterise traditional algorithms. An algorithm is simply a sequence of instructions that a computer carries out to transform input data to output data. A recipe is an excellent example of an algorithm because it states what must be done, step by step. It takes ingredients as input and produces an output, the completed meal.
An algorithm is simply a sequence of instructions that a computer carries out to transform input data to output data.
Machine learning algorithms have been used to analyse a wide variety of both structured and unstructured data (images, text, sounds), found patterns and relationships in these datasets and generated meaningful insight. A famous example is an algorithm in self-driving cars that analyses images and classifies it as “pedestrian” or “no pedestrian.” The algorithm is trained by giving it millions of images labelled as “pedestrian” or “no pedestrian”. When properly trained, the algorithm can analyse an unlabeled image and infer with a high degree of precision, whether it is a pedestrian.
Traditionally machine learning has also required extensive feature engineering, an approach that merges subject matter experts with data science tools to identify the relevant data representations or features that influence an outcome. We’ll learn shortly of modern machine learning algorithms that can determine the right features from the data.
Machine learning is grouped into “supervised” and “unsupervised” methods. In supervised learning, the algorithm is trained using labelled training data like in the example of the self-driving car described above, whereas, in unsupervised learning, the algorithm is trained on data without labels, the algorithm finds meaningful patterns or clusters within the dataset. In retail, for example, an unsupervised machine learning can be used to segment customers for marketing purposes.
Deep Learning is a subset of machine learning and has contributed a significant growth to the advancement of AI in recent times. For example, we mentioned feature engineering as a traditional approach employed by a data scientist to build solid models, in deep learning, however, the relevant features are not predefined by data scientist but instead learned by the algorithm.
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This saves the data scientist a significant amount of time as a huge chunk of their task is devoted to this procedure. Also, for many tasks, it is tough for data scientists to determine features on their own, take image recognition as an example, it’s nearly impossible for a data scientist to extract all the features to train an algorithm in this domain. For such problems, deep learning employs a neural network technology — described below:
Neural network, inspired by the human brain’s network of neurons is a series of algorithms that endeavours to recognise underlying relationships in a set of data through a process that mimics the way the human brain works. Neural networks can adapt to new data; so the network generates the best possible result without having to redesign the output criteria. Think of neural network as a series of chained algorithms with organised hierarchy
In the case of the car recognition task, for instance, the neural network is trained by feeding it a large number of images(with and without cars in them). Each layer of the neural network analyses the various components of the data progressively, identifying edges, corners, contours, rectangles that represent a car’s body and eventually develops the concept of a car. Once appropriately trained, the neural network can be given an image it has not seen before and determines with high precision whether it is a car
Todays CEOs and senior executives should be actively thinking about how AI would affect the landscape in which they function and initiate strategies to innovate and reinvent their businesses processes before a competitor does.
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