Table of Contents
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.
Difference Between AI and Neural Network
- 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.
- 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.
- Unlike AI that is vast, ANN is just a computational field of AI.
- Unlike AI that mimics the cognitive skills of a human, a Neural network mimics the neuron structure of the human’s brain.
- Applications of AI are machine learning, Siri, Alexa and paraphrase rephrasing and data mining are implementations of neural networks.
Comparison Between AI and Neural Network
|Parameters of Comparison||Artificial Intelligence(AI)||Neural Networks|
|Definition||The branch of computer science that can mimic human cognitive skills.||Stepping stone for AI that is inspired from human brain’s interconnected|
|Goals||The goal is to create super AI that|
means making machines smarter than
|To goal is to recognize patterns in data |
to make decision.
|Nature of field||The vast field has subsets like machine learning and deep learning.||It is only computational field of|
|Inspiration||Cognitive Skills||Human’s neuron structure inside|
|Application||Siri, Echo, Weather forecasting, risk|
management, customer research.
|Paraphrase detection, pattern recognition.|
Natural Language processing.