What is artificial intelligence everything in one place?
Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks
Artificial intelligence definition
● Artificial intelligence is a branch of engineering in which we develop an intelligent computer program. In other words, AI is a way of changing computer behavior to think and act like a human.
● In Artificial intelligence we study human behavior on different problems, how humans learn, and plan to solve a problem in his way. Translating human thought and developing intelligent software is the final goal in Artificial intelligence. Can a machine think and behave as humans do? The development of Artificial intelligence started to find a solution. People started to develop an intelligent program that can act like a human.
One or combination of multiple branches can be used to make an intelligent system that can act like a human.
What is artificial intelligence approach?
In the real world, knowledge has few challenges -
● The volume of knowledge is huge, and difficult to manage and store in computer.
● Knowledge is not organized and well-formatted. For example, doctors write a prescription on sample paper which is difficult to store in a computer. That’s why we need an intelligent system to handle it.
● The nature of knowledge is changing constantly. Artificial intelligence approach is a way to organize and use the knowledge smartly in such a way that -
● It should be understood by the people who provide it. It should be editable and useful for the future.
Artificial intelligence Applications and Artificial intelligence examples
Gaming - Look at computer games such as chess where machines think many possible combinations/patterns in playing chess move.
Natural language processing - Google translator is an example of natural language processing where we can interact with the computer and change the human spoken natural language in different human spoken language.
Vision systems - Face recognition is an example of a vision system where computers identify different visual inputs put on the computer. The office attendance system is widely used to recognize different staff in the office to register in and out time.
Speech Recognition - Google audio translator is an example of an intelligent system which are capable to transform audio track in form sentence. Nowadays expert speech recognition systems are capable to handle change in a human tone, background noise, etc.
Handwriting Recognition - There is much software which can read and understand human written text written by a human on paper and able to convert into editable text which computer can understand.
Top 5 artificial intelligence movie
Films educate people and spread ideas in ways a paperback book does today, and they serve as a perfect medium to learn about an idea or dive into a topic with your head first.
Artificial intelligence course
The main goal of this course is to familiarize you with all aspects of AI so that you can start your career as an artificial intelligence engineer. A few of the many topics/modules that you will learn in the program are:
Artificial intelligence in medicine
Artificial Intelligence Medical looks for something new in the content of the method and / or theory of submitted papers. Such a kind of innovation should be embraced especially in the area of AI and Computer Science. Methodology papers deal with the suggestion of specific strategies and related approaches to solving specific scientific problems in specific fields. They should demonstrate, usually through experimental tests, how the proposed method can be used medically, human-centered medical biology, and health care, respectively. They should also provide comparisons with other suggestions, and discuss openly new things. Theater papers focus on basic, common and formal AI topics and should reflect the expected results of the novel solution proposed in a particular medical or health field.
Artificial intelligence vs machine learning
Artificial Intelligence (AI) and machine learning (ML) words have created a lot of talk in the technological world, and with good reason. They help organizations streamline processes and retrieve data to make better business decisions. They improve almost every industry by helping them to become more efficient, and they become an important technology for businesses to maintain competitiveness.
These technologies are committed to skills such as facial recognition features on smartphones, personalized online shopping, visual assistants at home, and health diagnostics.
The need for these technologies — and the professionals who have them — is growing. According to research firm Gartner, the average number of AI projects in the organization is expected to triple over the next two years.
This strong growth creates problems for organizations. They report that their top challenges with this technology include a lack of skills, difficulty understanding AI application situations, and concerns about data scope or quality.
AI and ML, which have been the subject of science fiction for decades, are commonplace in business today. And while these technologies are closely related, the differences between them are significant. Here's a look at AI and ML, high performance and skills, and how you can get into this thriving industry.
Artificial intelligence agents and Environment
An AI system is composed of
What is Agent?
An agent is anything that can perceive its environment through sensors and acts upon that environment through effectors.
Three types of agents:
Human Agents
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Robotic Agents
Software Agents
Agent Terminology
Rationality
Ideal Rational Agent
An ideal rational agent is the one, which is capable of doing expected actions to maximize its performance measure, on the basis of -
Rationality of an agent depends on following factors -
A rational agent always performs right action, where the right action means the action that causes the agent to be most successful in the given percept sequence.
The problem the agent solves is characterized by
Performance Measure,
To design a rational agent we need to specify a task environment
Intelligent agent
An intelligent agent refers to an autonomous entity which acts, directing its activity towards achieving goals, upon an environment using observation through sensors and consequent actuators. Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex.
A reflex machine, such as a thermostat, is considered an example of an intelligent agent.
Intelligent agents are applied as automated online assistants, where they function to perceive the needs of customers in order to perform.
Intelligent agents are also closely related to software agents. In computer science, an intelligent agent is a software agent that has some intelligence, for example, autonomous programs used for operator assistance or data mining are also called "intelligent agents"
Characteristics of Intelligent Agent-
Classes of agent
Simple reflex agents
Simple reflex agents act only on the basis of the current percept, ignoring the rest of the percept history. The agent function is based on the condition-action rule: "if condition, then action".
This agent function only succeeds when the environment is fully observable. Some reflex agents can also contain information on their current state which allows them to disregard conditions whose actuators are already triggered.
Example - The vacuum promises to sense dirt and debris on your floors and clean those areas accordingly. This is an example of a simple reflex agent that operates on the condition (dirty floors) to initiate an action (vacuuming).
Model-based reflex agents
They use a model of the world to choose their actions. They maintain an internal state.
Example - Some examples of items with model-based agents aboard include the Roomba vacuum cleaner and the autonomous car known as Waymo. Both interact with their environments by using what they know - an internal model of the world - and their on-board sensors as well, to make moment-to-moment decisions about their actions.
Goal-based agents
Example - Google's Waymo driverless cars are good examples of a goal-based agent when they are programmed with an end destination, or goal, in mind. The car will then ''think'' and make the right decisions in order to deliver the passenger where they intended to go.
Utility-based agents
They choose actions based on a preference (utility) for each state.
Goals are inadequate when -
Utility-based agents
Think about it this way: A goal-based agent makes decisions based simply on achieving a set goal. You want to travel from Los Angeles to San Diego, the goal-based agent will get you there. San Diego is the goal and this agent will map the right path to get you there. But, if you're traveling from Los Angeles to San Diego and encounter a closed road, the utility-based agent will kick into gear and analyze other routes to get you there, selecting the best option for maximum utility.
In this regard, the utility-based agent is a step above the goal-based agent. Prior to utility based agents, intelligent have had only one goal: clean the floor, turn the air conditioner on, route to the destination, and so forth. With utility based agents, the intelligent agent is presented with multiple good options it must choose from. This makes the utility-based agent more agile and complex since it has some decision making capability.
You've been planning a road trip for a few weeks and the big day is finally here. You get everyone loaded into the car, fire up the GPS and set off in the direction of your weekend adventure. Two hours into your travel, you approach bumper to bumper traffic. It appears as though there might be a car accident up ahead. Automatically, your GPS starts to look for a new route around the hazard, and you are able to exit the highway and navigate around the traffic pile-up.
UBER Re-Routing is the best example of utility based agents.
Learning agents
Learning has the advantage that it allows the agents to initially operate in unknown environments and to become more competent than its initial knowledge alone might allow. The most important distinction is between the "learning element", which is responsible for making improvements, and the "performance element", which is responsible for selecting external actions.
The learning element uses feedback from the "critic" on how the agent is doing and determines how the performance element should be modified to do better in the future. The performance element is what we have previously considered to be the entire agent: it takes in precepts and decides on actions.
The last component of the learning agent is the "problem generator". It is responsible for suggesting actions that will lead to new and informative experiences.
The human is an example of a learning agent. For example, a human can learn to ride a bicycle, even though, at birth, no human possesses this skill.
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