Artificial intelligence (ai) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks
Artificial intelligence(ai) definition
Artificial intelligence def:
● Artificial intelligence(ai) 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(ai), 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 a combination of multiple branches can be used to make an intelligent system that can act like a human.
What is the artificial intelligence(ai) approach?
In the real world, knowledge has few challenges –
● The volume of knowledge is huge, and difficult to manage and store on a computer.
● Knowledge is not organized and well-formatted. For example, doctors write a prescription on sample paper which is difficult to store on a computer. That’s why we need an intelligent system to handle it.
● The nature of knowledge is changing constantly. The artificial intelligence (ai) 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(ai) 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 p
ut 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.
Artificial intelligence(ai) movie –
The top 20 artificial intelligence(ai) films – in pictures
Top 5 artificial intelligence(ai) 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.
- Ex Machina
- 1968 sci-fi classic 2001: A space Odyssey
- WAL -E
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:
- Basics of Deep Learning techniques
- Understanding artificial neural networks
- Training a neural network using the training data
- Convolutional neural networks and its applications
- TensorFlow and Tensor processing units
- Supervised and unsupervised learning methods
- Machine Learning using Python
- Applications of Deep Learning in image recognition, NLP, etc.
- Real-world projects in recommended systems, etc.
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.
- AI-based clinical decision-making;
- Medical information engineering;
- Information-based systems and agents;
- Computational intelligence in bio- and clinical medicine;
- Intelligent and process information systems in health care and medicine;
- Considering natural language in medicine;
- Data analysis and biomedical decision support mines;
- New computer platforms and biomedicine models;
- Intelligent exploitation of various data sources aimed at supporting decision-based clinical activities and in-depth data;
- Smart tools and tools;
- Automated thinking and meta-reasoning in medicine;
- Mechanical studies, human-centered medical biology, and health care;
- AI and medical data science, human-centered medical biology, and health care;
- AI-based modeling and management of health care methods and clinical guidelines;
- AI-based human models and programs;
- AI in medical and health education;
- Methodological, philosophical, ethical, and social problems of AI in health care, human-centered medical biology, and medicine.
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
- an agent
- and its environment
- The agents act in their environment.
- The environment may contain other agents.
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:
- A human agent has sensory organs such as eyes, ears, nose, tongue and skin parallel to the sensors, and other organs such as hands, legs, mouth, for effectors.
- A robotic agent replaces cameras and infrared range finders for the sensors, and various motors and actuators for effectors.
- A software agent has encoded bit strings as its programs and actions.
- Performance measure of agent – It is the criteria, which determines how sucessful an agent is.
- Behaviour of agent – It is the action that agent performs after any given sequence of percepts.
- Percepts – It is agent’s perceptual inputs at a given instance.
- Percept sequence – It is the history of all that an agent has perceived till date.
- Agent function – It is a map from the percept sequence to an action.
- Rationality is nothing but status of being reasonable, sensible, and having good sense of judgment.
- Rationality is concerned with expected actions and results depending upon what the agent has perceived.
- Performing actions with the aim of obtaining useful information is an important part of 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 –
- Its percept sequence
- Its built-in knowledge base
Rationality of an agent depends on following factors –
- The performance measures, which determine the degree of success.
- Agent’s percept sequence till now.
- The agent’s prior knowledge about the environment
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
- and Sensor (PEAS) PEAS P-Performance Measure, E-Environment, A-Actuators, S-Sensor
To design a rational agent we need to specify a task environment
- A problem specification for which the agent is a solution
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-
- Accommodate new problem solving rules incrementally
- Adapt online and in real time
- Are able to analyze themselves in terms of behavior, error and success.
- Learn and improve through interaction with the environment
- Learn quickly from large amounts of data
- Have memory-based exemplar storage and retrieval capacities
- Have parameters to represent short and long term memory, age, forgetting, etc.
Classes of agent
- Simple reflex agents
- Model-based reflex agents
- Goal-based agents
- Utility-based agents
- Learning agents
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 all
ows 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.
- Model, the knowledge about how the things happen in the world.
- Internal State, It is a representation of unobserved aspects of current state depending on percept history.
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.
- They choose their actions in order to achieve goals. Goal based approach is more flexible than reflex agent since the knowledge supporting a decision is explicitly modeled, therefore allowing for changes.
- It is the description of desirable situations.
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.
They choose actions based on a preference (utility) for each state.
Goals are inadequate when –
- There are conflicting goals, out of which only few can
- Goals have some uncertainty of being achieved and you need to weight likelihood of success against the importance of a goal
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 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 percepts 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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