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Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs.
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Artificial intelligence is the most feverish term everywhere. What exactly is Artificial Intelligence? Let us jump to the content with excitement and acceptance towards learning about Artificial Intelligence.
What is Artificial Intelligence?
The term Artificial Intelligence was first introduced decades ago in 1956 by “John McCarthy” at the Dartmouth conference.
The basic meaning of Artificial intelligence is exchange of information or knowledge in a computed and The basic meaning of Artificial intelligence is the exchange of information or knowledge in a computed and calculated way, whether a decision or any sort of action. This is possible only if the machine is seeded with a large amount of data so that it would be able to take action on its own.
The need for Artificial Intelligence is everywhere around us. It’s just that the working behind is not visible to us but a large number of data exchanges, data extraction, data modeling, and data distribution is going on. This leads the way for the further development of Artificial intelligence so that there is no human intervention and all the processes would be automated by Artificial Intelligence.
Types of Artificial Intelligence
Artificial Intelligence is Classified in further four types
- Reactive
- Limited Memory
- Theory of mind
- Self – Aware
Reactive Artificial Intelligence
Reactive Artificial Intelligence includes machines that operates based on the present data or which is taken into the account on the basis of current situation. They are not capable for evaluation of future actions.
Limited Memory
Limited Memory Artificial Intelligence can make improved and more efficient decisions by studying the past data from its memory. The memory in this type of Artificial Intelligence is present as temporary memory that can study past actions and hence predict the future outcome.
Theory of mind
The theory of mind Artificial Intelligence focuses on emotional intelligence so that human beliefs thoughts and emotions are comprehended and the results are given with more accuracy. It is useful in hospitals for predicting the patient’s mental health.
Self Aware
Self-aware Artificial Intelligence is the type of intelligence in which the computer computes itself and has the ability to think and analyze the situations from experience and at the same time learn from those situations. This is not discovered yet.
Artificial Intelligence? But Why?
- Artificial Intelligence is capable of performing fully automated computation and analyzing situations.
- Artificial Intelligence is capable of creating devices that can solve real-time problems with accuracy, especially in the healthcare domain, and can also be used for marketing, traffic issues, and web service management.
- Personal assistants can also be made for physically challenged people.
- Artificial Intelligence opens the paths for development of new and developed technologies.
Applications of Artificial Intelligence
1.Artificial Intelligence in E-Commerce:
Recommendation engines can enable you to engage with your customers, by taking into consideration their usage history, preferences, and interests.
This can lead to improved brand loyalty and an increase in the number of conversions
2. Powered Assistants
Artificial Intelligence powered assistants like virtual shopping assistants and chatbots help improve user experience while shopping online.
3. Speech Recognition
Speech recognition is one such technology that is empowered by AI to add convenience to its users. This new technology has the power to convert voice messages to text. And it also can recognize an individual based on their voice command.
4. Natural Language Processing
Natural language processing is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data.
Applications Of Artificial intelligence:- Top 10 Applications Of Artificial Intelligence in 2021 | Artificial Intelligence Training | Edureka – YouTube
Intelligent Agents
Intelligent Agents is one which can take input from the environment through its sensors and act upon the environment through its actuators. Its actions are always directed to achieved a goal.
Four major rules that are followed are:-
•Rule 1: An AI agent must have the ability to perceive the environment.
•Rule 2: The observation must be used to make decisions.
•Rule 3: Decision should result in an action.
•Rule 4: The action taken by an AI agent must be a rational action.
Characteristics of Intelligent Agents
->They have a learning ability that enables them to learn even as tasks are carried out.
->They have some level of autonomy that allows them to perform certain tasks on their own.
->They can interact with other entities such as agents, humans, and systems.
->New rules can be accommodated by intelligent agents incrementally.
->They exhibit goal-oriented habits.
->They are knowledge-based. They use knowledge regarding communications, processes, and entities.
Sensors:
Sensor is a device which detects the change in the environment and sends the information to other electronic devices. An agent observes its environment through sensors. Sensors can extract the information through touch and store in its database. Capacitors are used in touch phones and the working is based on the vibration.
Actuators:
Actuators are the component of machines that converts energy into motion. The actuators are only responsible for moving and controlling a system. An actuator can be an electric motor, gears, rails, etc.
Categories of Intelligent Agents
Agents can be grouped into four classes based on their degree of perceived intelligence and capability :
1.Simple Reflex Agents
2.Model-Based Reflex Agents
3.Goal-Based Agents
4.Utility-Based Agents Learning Agent
Simple Reflex Agents:
->The Simple reflex agents are the simplest agents. These agents take decisions on the basis of the current percepts and ignore the rest of the percept history.
->These agents only succeed in the fully observable environment.
Problems for the simple reflex agent design approach:
->They have very limited intelligence
->They do not have knowledge of non-perceptual parts of the current state
->Mostly too big to generate and to store. Not adaptive to changes in the environment
Model-Based Reflex Agents:
The Model-based agent can work in a partially observable environment, and track the situation.
->A model-based agent has two important factors:
•Model: It is knowledge about “how things happen in the world,” so it is called a Model-based agent.
•Internal State: It is a representation of the current state based on percept history.
•No Learning
Goal-Based Agents:
The knowledge of the current state environment is not always sufficient to decide for an agent to what to do.
->The agent needs to know its goal which describes desirable situations.
->Goal-based agents expand the capabilities of the model-based agent by having the “goal” information.
->They choose an action, so that they can achieve the goal.
->These agents may have to consider a long sequence of possible actions before deciding whether the goal is achieved or not. Such considerations of different scenario are called searching and planning, which makes an agent proactive.
Utility-Based Agents:
->These agents are similar to the goal-based agent but provide an extra component of utility measurement which makes them different by providing a measure of success at a given state.
->Utility-based agent act based not only goals but also the best way to achieve the goal.
->The Utility-based agent is useful when there are multiple possible alternatives, and an agent has to choose in order to perform the best action.
->The utility function maps each state to a real number to check how efficiently each action achieves the goals.
Learning Agents:
A learning agent in AI is the type of agent which can learn from its past experiences, or it has learning capabilities.
->It starts to act with basic knowledge and then able to act and adapt automatically through learning.
->A learning agent has mainly four conceptual components, which are:
•Learning element: It is responsible for making improvements by learning from environment •Critic: Learning element takes feedback from critic which describes that how well the agent is doing with respect to a fixed performance standard.
•Performance element: It is responsible for selecting external action
•Problem generator: This component is responsible for suggesting actions that will lead to new and informative experiences.
Hence, learning agents are able to learn, analyze performance, and look for new ways to improve the performance.
Links:-
Sofia: A beginning of smart AI:- We Talked To Sophia — The AI Robot That Once Said It Would ‘Destroy Humans’ – YouTube
Is AI destructive:- The Impacts Of Tesla’s Automation & Artificial Intelligence: A Destructive Decade Of Innovation – YouTube