Data Science, Machine Learning, Deep Learning and Artificial Intelligence
Distinguishing Data Science, Machine Learning, Deep Learning, and Artificial Intelligence
In our world today, the rise of data-centric solutions/products has brought about the rise of modern technologies like Artificial Intelligence, Machine Learning, Deep Learning, and Data Science. These technologies have also become buzz words. These words are common in the vocabulary of many, but also very few seem to have a proper understanding of what it really entails.
A lot of people seem to get confused and even mix up these technologies. This is seen even among the techies and the practitioners.
In this article, I will explain these technologies basing it on what each entails. There will be an emphasis on how they differ from each other while also working together to achieve the ultimate goal.
Artificial Intelligence (AI) is basically the ability to make machines mimic the cognitive functions of the human brain. This actually means making machines to operate at the level of human intelligence.
AI has different stages which are:
1. Artificial Narrow Intelligence (ANI): This is the use of AI for specific tasks. Example: A machine with the ability to play chess.
2. Artificial General Intelligence (AGI): This is an advanced stage where AI covers more than one specific task. It is more like the power of reason, problem-solving and abstract thinking.
3. Artificial Super Intelligence (ASI): This is the final stage of the intelligence explosion, in which AI surpasses human intelligence across all fields.
Today, we are currently in the final stages of ANI, in which the intelligence of machines and humans are equally based on specific tasks.
Machine Learning (ML) is a subset of Artificial Intelligence. It is a method of training an algorithm with data (input) in order to get the desired output without explicitly programming it to do so. ML is simply a technique for realizing AI.
There are different aspects of ML which are;
1. Supervised Learning: This is a type of machine learning where the machine is trained using already labeled data. This is classified into two categories; classification and regression.
2. Unsupervised Learning: This machine learning type actually works with unlabeled data. In this learning, the algorithm is allowed to act on data without any form of guidance.
Here the machine groups the data based on similarities, patterns, differences, etc. This is classified into two categories; clustering and association.
3. Reinforcement Learning: This area of machine learning is concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.
In this approach, the machines learn by through a form of reward system based on actions taken. This aspect is widely used in robotics.
Deep Learning (DL) is considered a subset of Machine Learning. This has to do with multi-layered training of algorithm with data. It is stated multi-layered as it contains a hidden layer between the input layer and the output layer. DL is also referred to as Deep Neural Network.
Based on the multilayered feature of the Deep Neural Network, the output of one layer is received as input into the next layer before finally getting to the output layer. At each layer, each neuron triggers a particular feature in the input data and learns from it. This process is continued till the learning cycle is complete and the output is gotten.
This is basically used for complex scenarios like image recognition, natural language processing, machine translation, speech recognition, etc.
Data Science (DS) is an interdisciplinary field that focuses on data extraction and processing to get meaningful insights, trends and patterns in order to take decisive steps in business, healthcare and the world at large.
All the mentioned technologies are contained in this field of Data Science and they all basically work with data, thereby making Data Science cut across all of these technologies.