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An overview of – Artificial Intelligence vs. Machine Learning vs. Deep Learning vs. Neural Networks


“Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise, you’re going to be a dinosaur within 3 years.”

Artificial Intelligence is the process of transmitting data, information, and human intelligence into machines. Its core objective is to develop self-reliant machines, which can think and act like humans. These machines can imitate human behavior and perform tasks by learning and problem-solving. Most of the AI Business Strategy systems simulate natural intelligence to solve complex issues.

AI focuses on performing 3 cognitive skills just like a human – learning, reasoning, and self-correction. It can be classified into 2 broad categories. They are:

v  Type-1: Based on Capabilities

There are 3-types of artificial intelligence based on the capabilities.

  • Artificial narrow intelligence: This is also called ‘Weak AI’ that can program the machines to perform specific tasks, but in a much better way than a human.
  • Artificial general intelligence: The AI that can perform an array of intellectual/intelligent tasks with the same accuracy level as a human.
  • Artificial superintelligence: This is the most advanced form, also called ‘Active AI’. It can outperform humans in specific tasks with better accuracy and speed in very little time.

v  Type-2: Based on functionality

These are of 4-types that are based on the working principle of machines.

  • Reactive machines: These are the systems that solely react. These systems don’t form memories, and they don’t use any past experiences for making new decisions.
  • Limited memory: These systems reference the past, and information is added over a period of time. The referenced information is short-lived.
  • Theory of mind – This covers systems that can understand human emotions and how they affect decision-making. They are trained to adjust their behavior accordingly.
  • Self-awareness – These systems are designed and created to be aware of themselves. They have the ability to understand their own internal states, predict other people’s feelings, as well as act appropriately.

v  Applications of artificial intelligence

Currently, AI is been used in various ways. A few of them include:

  • Chatbots which answer questions based on user input
  • Machine translation such as Google Translate
  • Self-driving vehicles such as Google’s Waymo
  • AI Robots such as Sophia and Aibo
  • Speech Recognition applications like Apple’s Siri, Google Assistant, Alexa, and Cortana
  • Various facial recognition systems

Machine Learning

The AI and ML are very closely related to each other, as the former is a subset of the latter. ML is a discipline of computer science, which uses computer algorithms and analytics to build predictive models or take decisions from past data or experiences without being explicitly programmed, and is helpful for solving business problems. ML uses a huge amount of structured and semi-structured data so that the ML model can generate appropriate results or allow predictions based on the data.

v  Applications of Machine Learning

The ML is highly used in the following places:

  • Sales forecasting for different products
  • Fraud analysis in banking
  • Product recommendations
  • Stock price prediction

Deep Learning

Deep Learning is a subset of ML, which deals with algorithms inspired by the structure and function of the human brain. The deep learning algorithms can work with a huge amount of both structured and unstructured data. Its core concept lies in Artificial Neural Networks (ANN) that enables machines to make decisions.

The major difference between deep learning and ML is the way data is presented to the machine. ML algorithms need structured data, whereas deep learning networks work on multiple layers of ANN.

v  Applications of deep learning

The concept of deep learning is mainly used in the following places:

  • Captionbot for captioning an image
  • Cancer tumor detection
  • Music generation
  • Image coloring
  • Object detection

Neural Networks

A neural network is a system (software or hardware) that works like a human brain. Based on the neural functionality of the human brain, the concept of artificial neural networks is developed. It not just replicates the human understanding, but also leverages tasks that are far beyond the capabilities of humans.

o Natural Language Processing (NLP) libraries

NLP is about combining computer science, information engineering, linguistics, and AI into one, and programming the system to process and analyze massive datasets. The various NLP libraries are as follows:

  • Natural Language Toolkit (NTLK)
  • Gensim
  • CoreNLP
  • PyNLPI
  • TextBlob
  • spaCy