Difference Between AI and Neural Network (With Table)

Taking inspiration from the human’s structure of mind and implementing it to advance technology results in the creation of two terms- Artificial Intelligence and Neural Networks. You may already be familiar with Amazon Echo, or robot café in Tokyo, all stated here are the implementations. Both AI and Neural networks are interrelated work on getting human capabilities into machines.

AI vs Neural Network

The main difference between AI and Neural networks is that AI is the superset of neural network where neural network is a subset of machine learning that includes artificial neurons whose work is inspired by the brain structure of the human. AI includes a vast range of technology that may be smarter than the human but neural network is interconnected nodes used to create machine algorithms.

AI in older times was a dream but as time advanced so does technology. Siri, Google device, and many more implementations are proof of how machines can mimic the cognitive features of humans. AI comes in variant sizes and algorithms where programs are created that can perform tasks equivalent to humans or sometimes better than humans.

Neural Network or Artificial neural network is a small but integral part of AI. That branch of AI is curated with the purpose to solve complex algorithms. It is inspired by the human brain structure where neuron-like interconnected structures or nodes curate a simple framework to respond to a specific problem. Data validation, customer research, risk management, and many more implementations can curate complex algorithms to respond to bigger problems.

Comparison Table Between AI and Neural Network

Parameters of ComparisonArtificial Intelligence(AI)Neural Networks
DefinitionThe branch of computer science that can mimic human cognitive skills.Stepping stone for AI that is inspired from human brain’s interconnected
neuron structure.
GoalsThe goal is to create super AI that
means making machines smarter than
humans.
To goal is to recognize patterns in data
to make decision.
Nature of fieldThe vast field has subsets like machine learning and deep learning.It is only computational field of
Artificial Intelligence.
InspirationCognitive Skills
Human’s neuron structure inside
brain.
ApplicationSiri, Echo, Weather forecasting, risk
management, customer research.
Paraphrase detection, pattern recognition.
Natural Language processing.

What is AI?

AI is the answer to Turing’s paper question. According to Turing’s paper, computing machinery and intelligence and its fellow Turing’s Test later become the foundation for the establishment of AI. The Turing Test is a query that determines whether a machine specifically a computer can think like humans. That gives the definition for AI that states that ” Artificial Intelligence” or AI is that computer science branch that deals with machines that has human’s cognitive skills.

Several approaches and AI along with Machine Learning and Deep Learning created a paradigm shift in every sector of the technology virtually. The AI is defined under three major categories:

  • Narrow AI: In this type, the machine can perform limited functions and tasks. It is a simulation of human intelligence. The machine with narrow AI generally focuses on a single task and works efficiently on it. Google Assistant, Google Translate are examples of narrow AI.
  • General AI: In this type, the machine can apply human-like intelligence. You can see the instances in movies. Chess-playing computers, self-driving cars are some examples of general AI.
  • Super AI: In this type, the machine will be smarter than humans. Siri, Alexa, Cortona are a few instances of super AI. They can get smarter with time.

LinearFold AI algorithm has been released by Baidu in 2020 to predict the RNA sequence of SARS-Cov-2 Virus in order to develop the vaccination. The procedure is 120 times faster than the regular approaches.

What is Neural Network?

Neural Network, also referred to as Artificial Neural Network, is a sub-field of Deep Learning(sub-field of AI), is a stepping stone in the field of AI that is inspired from the neuron structure of the brain where neurons are closely connected. In ANN, the artificial neurons known as nodes are connected in various network layers.

Neural Network is a network of functions to understand and translate the input data from form to the desired output form. It is a computational learning technique that is designed to mimic the behavior of human brain structure. A large number of neurons are arranged in different layers forming an architecture. These layers are described as follows:

  • Input Layer: The layer is meant to accept inputs in different forms provided by the programmer.
  • Hidden Layer: Two hidden layers between the input and output layer is the processing unit of the architecture. It performs calculations to find hidden patterns and features.
  • Output Layer: The final layers that convey, the processed inputs from the programmer, to the programmer.

The ANN takes inputs from the programmer and calculates the weighted sum of inputs and a bias is included. To determine whether an artificial neuron should fire or not, the activation function exists to derive the output. Fired nodes are derived from output layers and several activation functions exist for every type of type being performed. Data Mining, Paraphrase Detection, Pattern recognition are few examples of Artificial Neuron Networks.

Main Differences Between AI and Neural Network

  1. The main difference between AI and Neural networks is that AI can embed intelligence to mimic human’s cognitive skills whereas Neural Network is the computation sub-branch of machine learning that uses automation functions to derive the hidden patterns and features.
  2. The goal behind AI is to create machines that can be smarter than humans whereas the goal behind the artificial neural network is to solve complex algorithms and find hidden patterns and features.
  3. Unlike AI that is vast, ANN is just a computational field of AI.
  4. Unlike AI that mimics the cognitive skills of a human, a Neural network mimics the neuron structure of the human’s brain.
  5. Applications of AI are machine learning, Siri, Alexa and paraphrase rephrasing and data mining are implementations of neural networks.

Conclusion

With AI, the world is developing and can automate most of the tasks that are performed by humans. Both AI and Artificial Neural networks are interrelated to each other. A neural network turns out to be a stepping stone of AI that is inspired by the neuron structure of the human brain. Siri, Alexa, paraphrase recognition, and many more are the implementations of both.

Reference

  1. https://link.springer.com/article/10.1007/s10120-018-0793-2
  2. https://www.sciencedirect.com/science/article/pii/S0720048X20302722