Understanding Artificial Intelligence, Machine Learning and Deep Learning

Artificial Intelligence (AI) and its subsets Machine Learning (ML) and Deep Learning (DL) are playing a major role in Data Science. Data Science is a comprehensive process that involves pre-processing, analysis, visualization and prediction. Lets deep dive into AI and its subsets.

Artificial Intelligence (AI) is a branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is mainly divided into three categories as below

• Artificial Narrow Intelligence (ANI)
• Artificial General Intelligence (AGI)
• Artificial Super Intelligence (ASI).

Narrow AI sometimes referred as ‘Weak AI’, performs a single task in a particular way at its best. For example, an automated coffee machine robs which performs a well-defined sequence of actions to make coffee. Whereas AGI, which is also referred as ‘Strong AI’ performs a wide range of tasks that involve thinking and reasoning like a human. Some example is Google Assist, Alexa, Chatbots which uses Natural Language Processing (NPL). Artificial Super Intelligence (ASI) is the advanced version which out performs human capabilities. It can perform creative activities like art, decision making and emotional relationships.

Now let’s look at Machine Learning (ML). It is a subset of AI that involves modeling of algorithms which helps to make predictions based on the recognition of complex data patterns and sets. Machine learning focuses on enabling algorithms to learn from the data provided, gather insights and make predictions on previously unanalyzed data using the information gathered. Different methods of machine learning are

• supervised learning (Weak AI – Task driven)
• non-supervised learning (Strong AI – Data Driven)
• semi-supervised learning (Strong AI -cost effective)
• reinforced machine learning. (Strong AI – learn from mistakes)

Supervised machine learning uses historical data to understand behavior and formulate future forecasts. Here the system consists of a designated dataset. It is labeled with parameters for the input and the output. And as the new data comes the ML algorithm analysis the new data and gives the exact output on the basis of the fixed parameters. Supervised learning can perform classification or regression tasks. Examples of classification tasks are image classification, face recognition, email spam classification, identify fraud detection, etc. and for regression tasks are weather forecasting, population growth prediction, etc.

Unsupervised machine learning does not use any classified or labelled parameters. It focuses on discovering hidden structures from unlabeled data to help systems infer a function properly. They use techniques such as clustering or dimensionality reduction. Clustering involves grouping data points with similar metric. It is data driven and some examples for clustering are movie recommendation for user in Netflix, customer segmentation, buying habits, etc. Some of dimensionality reduction examples are feature elicitation, big data visualization.

Semi-supervised machine learning works by using both labelled and unlabeled data to improve learning accuracy. Semi-supervised learning can be a cost-effective solution when labelling data turns out to be expensive.

Reinforcement learning is fairly different when compared to supervised and unsupervised learning. It can be defined as a process of trial and error finally delivering results. t is achieved by the principle of iterative improvement cycle (to learn by past mistakes). Reinforcement learning has also been used to teach agents autonomous driving within simulated environments. Q-learning is an example of reinforcement learning algorithms.

Moving ahead to Deep Learning (DL), it is a subset of machine learning where you build algorithms that follow a layered architecture. DL uses multiple layers to progressively extract higher level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces. DL is generally referred to a deep artificial neural network and these are the algorithm sets which are extremely accurate for the problems like sound recognition, image recognition, natural language processing, etc.

To summarize Data Science covers AI, which includes machine learning. However, machine learning itself covers another sub-technology, which is deep learning. Thanks to AI as it is capable of solving harder and harder problems (like detecting cancer better than oncologists) better than humans can.