Artificial Intelligence (AI), Machine Learning (ML) and Data Science (DS) have become hot topics these days. While there is a lot of excitement around these terms, people use them frequently and loosely, without a clear understanding of their meanings and scopes. In this article, we try to simplify these technologies and explain the difference between them using examples of their real-life applications.
Artificial Intelligence, as the name suggests, is (human) intelligence created artificially through machines and computer systems. The goal of AI is to create computers, software and machines that can learn and reason based on their environment and previous knowledge, and perform the specified task(s). The process of making decisions by reasoning with knowledge is central to AI. The field of AI research is based on computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields.
How does Amazon decide which products to show to you (and are more likely to buy) on top of your search?
How does Netflix decide which shows to recommend to you (& it’s unique for everyone)?
AI is the answer. AI helps drones fly, it helps self-driving cars navigate better, it powers the functionalities Siri and Alexa bring to you and a lot more.
Recent advances in AI have been boosted by the availability of a huge amount of data, development of powerful computers with high computing powers and improvement in machine learning algorithms. Machine Learning, Deep Learning, Natural Language Processing, Speech Recognition, computer vision are among the hottest AI technologies.
Machine Learning (ML) is a subset of AI. It is essentially a combination of mathematical algorithms & statistical models that power AI. ML algorithms enable computers to learn from the available data to make predictions and inferences without requiring explicit programming instructions to perform the required tasks. These ML algorithms can be broadly categorised as under.
In Supervised learning, the algorithms build a mathematical model from the sample or training data which has both the inputs and output labels. Data Classification and Regression are types of supervised learning.
In Unsupervised learning, the algorithms build a model on the data which has only input features and no output labels. The models are trained to find some structure in the data. Examples of unsupervised learning include Clustering and Segmentation.
In Reinforcement learning, an agent learns to perform a task by performing certain actions and improvises itself by learning from the feedback of its own actions.
A few examples of Machine learning algorithms are Logistic Regression, Naïve Bayes, Support Vector Machines, Decision Trees, KNN, K-means clustering. You can learn more about these here.
Data Science is an emerging field that is helping organisations in decision making and improving customer experience by utilising the customers’ data. Customer-facing Organisations are leveraging the data-based insights to refine the target audience segments and provide a personalised experience to users. Logistics companies use data science to find the best routes to deliver products efficiently and timely. Financial institutions are utilising data science to improve fraud detection and revenue generation and so on. The benefits of the application of data science are huge and varied and businesses are just starting to dig into this enormous opportunity to empowerment and profitability.
Artificial Intelligence, Machine Learning and Data Science are different but complementary:
Artificial Intelligence is a broad field of research and study with the aim to create intelligent machines to help human beings solve many challenging problems in computer science, software engineering and operations research. It is still evolving. Machine Learning is a subset of AI and focuses on a narrow range of applications. ML algorithms try to fit a function to the data that best explains the relationship between the input and the output variables. The wide range of ML algorithms allow for finding simple (linear) or complex (non-linear, rule-based etc.) interaction of input and output variables, which could be used for predicting future patterns using new data.
The machine learning techniques comprise an essential part of the data science toolbox. The field of data science combines machine learning with big data, distributed computing capabilities and programming skills along with expertise in statistical models and quantitative techniques. Data science, in all practicalities, aims to understand and analyse actual phenomena with data.
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