The difference between Artificial Intelligence, Machine Learning and Deep Learning

Artificial intelligence, machine learning and deep learning have so many published definitions. As buzzwords, their involvement in human activities is inevitable and has continued to raise concerns about its super benefits to man and society vs taking over human jobs. By the end of this post you will understand better the definition, relationship concepts, and how interoperable they can be.

These technologies have improved a lot of processes ranging from finance, healthcare, ecommerce, engineering services and so on.

Artificial Intelligence, Machine Learning and Deep Learning.
Source: Argility

Artificial Intelligence (AI)

Artificial intelligence is a branch of computer science that deals with simulation of human intelligence by machines processes and computational rationality.

Other definitions of artificial intelligence:

  1. The term artificial intelligence is also used to describe a property of machines or programs, meaning the intelligence that the system demonstrates.
  2. A computer system able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision making, and translation between languages.

This technology has advanced at a fast pace and shown capability to handle difficult tasks and ability to process large data sets with core database infrastructure that drive enterprise level software.

AI research uses tools and insights from many fields, including computer science, psychology, probability, neuro-science, cognitive science, linguistics, operations research, economics, control theory, philosophy, optimization and logic. AI research also overlaps with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others. One of the essential purposes of AI is to automate tasks by cutting down labor resources needed by an organization to complete a project.

Types of Artificial Intelligence (AI)

AI can be classified in a number of ways:

  1. Self-aware AI
  2. Reactiveness machines AI
  3. Limited theory AI
  4. Theory of mind AI
  5. Artificial narrow intelligence
  6. Artificial general intelligence
  7. Artificial superhuman intelligence

Machine Learning (ML)

Machine learning is a subset of AI. Machine learning enhance machines with the ability to learn autonomously based on observation and analysis within a given data set without specific programming.

A written program or code for specific purpose, actually defines a set of instructions which the machine will follow, whereas in ML, data inputted helps the machine identify and analyze patterns in a data set by taking decision autonomously based on its observations and learnings from the data set. The “learning” part of machine learning means that ML algorithms attempt to optimize along a certain dimension; meaning they usually try to minimize error or maximize the likelihood of their predictions being true.

This has three names: an error function, a loss function, or an objective function.

Machine learning methods
Source: Exnaxis

Some machine learning methods can dramatically reduce the cost of developing knowledge-based software by extracting knowledge directly from existing databases.

Other machine learning methods enable software systems to improve their performance overtime with minimal human intervention. These approaches are expected to enable the development of effective software for autonomous systems that must operate in poorly understood environments.

Types of ML

  1. Supervised learning
  2. Unsupervised learning
  3. Reinforcement learning

Deep Learning (DL)

Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by human brain, learn from amounts of data. Similarly, to how we learn from experience, the deep learning algorithm performs tasks repeatedly, each time tweaking it a little to improve the outcome. DL can also be thought as the automation of predictive analytics. Deep learning deals with more accuracy, more math and more computation.

This technology has set records for solving problems in areas such as sound recognition, image recognition, recommender systems, natural language processing and bioinformatics.

Deep Learning architectures

  1. Convolutional neural networks
  2. Unsupervised pretrained networks (UPN’s)
  3. Recursive networks
  4. Recurrent neural networks

In summary the relationship between Artificial intelligence (AI), Machine learning (ML), Deep learning (DL) as related to data science with example:

Austin is the capital of Texas. Texas is a state in USA. USA is a country.

AI is the USA. So, AI is the discipline that encompasses ML and DL.

ML is Texas, a sub-field of Ai that deals with teaching programs on how to formulate rules based on data given to them.

DL is Austin, a sub-field of ML that uses neural networks to formulate rules.

Artificial intelligence today is actually Machine learning. ML is a foundational pillar of data science today with learning algorithms like linear regression and deep learning used to solve human problems.

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