What is Artificial Intelligence ?

Answer 1

The term Artificial Intelligence was coined by its creator John McCarthy (an American scientist). He defined it as the science and engineering of making intelligent machines. AI is when a computer makes decisions in a way similar to how humans do. The computer will somehow take into account its environment and take steps to help it reach predetermined goals. It is the theory that computers and mechanical objects could be made, that would be able to carry out actions that would usually require human brain power. While general artificial intelligence is the goal, researchers and programmers have achieved artificial intelligence for specific tasks in areas such as gaming, finance, medical care, robotics, transportation, telecommunications, and more.

"The aim of AI is to develop machines that behave as if they were intelligent." - John McCarthy

Now this raises questions about what is intelligence, so the accepted definition for Artificial Intelligence is perhaps by Elaine Rich.

"Artificial Intelligence is the study of making computers do things at which, at the moment, people are better." - Elaine Rich

Alan Mathison Turing developed the Turing test which is still used to test if a machine is intelligent or not.

Artificial Intelligence has many fields taking its foundation from AI. They include:

1. Machine Learning
2. Neural Networks
3. Natural Language Processing
4. Automated Planning, Scheduling and Reasoning
5. Knowledge Representation
6. Voice, Face, Object, and Character Recognition
7. Neural Networks
8. And a lot more.

Many Programming languages having been developed since the day the term AI was defined. These include languages like IPL(Information Processing Language), Lisp (List Processing - Developed by the Founder himself), Prolog (PROgramming in LOGic) and Haskel.

Today, AI is being used in everyday life and many people are even unaware of its usage, whether it be information retrieval by search engines, GPS in Vehicles, expert systems developed for symptom analysis, chatbots, weather prediction systems, high end games, and virtual assistants like Siri, Google Now, or S-Voice.

Here are some specific applications for artificial intelligence:

Banking - A computer may monitor stocks and trading, virtually trying various moves and activities that humans would be likely to do. Then any activity observed that does not match the rules for expected human activity gets flagged for observation and/or intervention by real humans. Banking computers may actually invest money for real as well.

Customer support - Humans may speak to computers on the telephone and give it information they may otherwise give a human.

Gaming - Computer characters in Video Games use AI logic, and computer versions of board games use AI logic as well.

Mathematics - Mathematical calculations would usually require human brain power to carry out, but the calculator can do it by itself. This is not as exciting as a lot of other applications, but it is a very common one that many don't even think of as artificial intelligence.

Music - Computers can programmed with Music Theory to help compose pieces similar to how humans compose music.

Robotics - Modern robots can be made to be able to play chess and interact with customers.

Answer 2

Intelligence is the ability of a system to adapt and improvise in a new environment, as well as being able to generalize its knowledge and apply it to unfamiliar scenarios. Francois Collet, creator of the machine-learning software library Koras, has said intelligence is tied to a system's ability to adapt.

Answer 3

Artificial Intelligence (AI) is a field of computer science that focuses on creating intelligent machines that can think and learn like humans. AI works by creating algorithms and models that can be trained to perform tasks and make decisions, often based on large amounts of data.

The basic building blocks of AI are algorithms, which are sets of instructions that tell a computer what to do. These algorithms can be simple or complex, depending on the task at hand. For example, a simple algorithm might tell a computer to add two numbers together, while a more complex algorithm might be used to analyze an image and recognize objects within it.

1 - Machine Learning

One of the most common approaches to AI is machine learning, which involves training a machine to recognize patterns in data. Machine learning algorithms are fed large amounts of data, and they use statistical methods to identify patterns and correlations in that data. Once the algorithm has identified these patterns, it can use them to make predictions or decisions based on new data.

There are three main types of machine learning:

A) Supervised learning: This involves training an algorithm on a labeled dataset. The algorithm is given inputs and outputs, and it learns to map inputs to outputs by finding patterns in the data. Once the algorithm has been trained, it can be used to make predictions on new data.

B) Unsupervised learning: This involves training an algorithm on an unlabeled dataset. The algorithm is given inputs but not outputs, and it learns to find patterns in the data without any guidance. Unsupervised learning is often used for clustering or anomaly detection.

C) Reinforcement learning: This involves training an algorithm to make decisions in a specific environment. The algorithm receives feedback in the form of rewards or punishments based on its actions, and it learns to make better decisions over time.

2 - Deep Learning

Another approach to AI is deep learning, which involves training neural networks to perform tasks. Neural networks are computer systems that are modeled on the structure and function of the human brain. They consist of interconnected nodes or neurons that process information and communicate with each other.

In deep learning, neural networks are trained on large datasets using backpropagation, which is a mathematical technique for adjusting the weights of the neurons. This allows the network to learn complex patterns and relationships in the data, and it can be used for tasks such as image and speech recognition, natural language processing, and decision-making.

Neural networks can be trained using supervised or unsupervised learning, and they can be made up of many layers (hence the name "deep" learning). The number of layers in a neural network is known as its depth, and deeper networks are often better at learning complex patterns in data.

3 - Natural Language Processing

Natural language processing (NLP) is another area of AI that involves teaching machines to understand and process human language. NLP algorithms can be used for tasks such as language translation, sentiment analysis, and chatbots.

One common approach to NLP is to use a technique called deep learning, specifically a type of neural network called a recurrent neural network (RNN). RNNs are designed to work with sequences of data, such as words in a sentence, and they can learn to predict the next word in a sequence based on the previous words.

Another approach to NLP is to use a technique called transformer models, which are a type of neural network that can process text in parallel. Transformer models are capable of understanding the context and meaning of words, and they can be used for tasks such as language translation and text summarization.

Answer 4

Artificial intelligence, or AI, is technology that enables computers and machines to simulate human intelligence and problem-solving capabilities.