AI (Artificial Intelligence)

Artificial Intelligence (AI) is the ability of a machine or a programmed computer to learn and think. It is a field of study where it tries to make computer smart. It is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include:

  • Speech recognition
  • Learning
  • Planning
  • Problem-solving

Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties, and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious task.
Machine learning is also a core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions.

Here below are a few areas where AI is mostly used.

  • Natural Language Processing
  • Speech Recognition
  • Virtual Agents
  • Machine Learning
  • Image Recognition and Image Processing:
  • Computer vision system
  • Robotic Process Automation
  • Biometrics
  • Text Analysis and Text processing

Advantages of AI

  • More powerful and more useful computers
  • New and improved interfaces
  • Better handling of information
  • Solving new problems
  • Relieves information overload
  • Conversion of information into knowledge

Disadvantages of AI

  • Cost gets increased
  • Difficulty with software development – slow and expensive
  • Few experienced programmers
  • Few practical products have reached the market as yet

How does AI work?

Artificial intelligence uses machine learning to mimic human intelligence. The computer has to learn how to respond to certain actions, so it uses algorithms and historical data to create something called a propensity model.
Propensity models will then start making predictions.
AI can do much more than this, but those are common uses and functionality for marketing. And while it might seem like the machines are ready to rise and take over, humans are still needed to do much of the work.
Mainly, we use AI to save us time — adding people to email automation and allowing AI to do much of the work while we work on other tasks.

Why is AI important?

  • AI automates repetitive learning and discovery through data.
  • AI analyzes more and deeper data
  • AI adds intelligence
  • AI achieves incredible accuracy
  • AI gets the most out of data.
  • AI adapts through progressive learning algorithms

AI is at the center of a new enterprise to build computational models of intelligence. The main assumption is that intelligence (human or otherwise) can be represented in terms of symbol structures and symbolic operations which can be programmed in a digital computer. There is much debate as to whether such an appropriately programmed computer would be a mind, or would merely simulate one, but AI researchers need not wait for the conclusion to that debate, nor for the hypothetical computer that could model all of the human intelligence. Aspects of intelligent behavior, such as solving problems, making inferences, learning, and understanding language, have already been coded as computer programs, and within very limited domains, such as identifying diseases of soybean plants, AI programs can outperform human experts. Now the great challenge of AI is to find ways of representing the commonsense knowledge and experience that enable people to carry out everyday activities such as holding a wide-ranging conversation or finding their way along a busy street. Conventional digital computers may be capable of running such programs, or we may need to develop new machines that can support the complexity of human thought.