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Artificial intelligence (AI) is an area of computer science that emphasizes on the creation of intelligent machines that work and react like humans. Some of the activities that computers with artificial intelligence are designed for include:

1-Speech recognition
4-Problem solving

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

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:
Problem solving
Ability to manipulate and move objects
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 approach.

Machine learning is another 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. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.

Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with few sub-problems such as facial, object and speech recognition.

Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.

AI continues to become increasingly a part of our daily lives. Voice recognition is is a key enabler of human-machine interface.
Although we often talk (or curse) at our computers, the thought of them talking back to us is somewhat disturbing. The ultimate computer with artificial intelligence was the HAL computer in the science fiction classic, Space Odyessey. HAL not only could understand and respond to human speech, but also determined what was best for the future of mankind, even if that meant sacrificing a few people along the way.

While the computers of today do not have the capabilities of HAL, there is a new era of computers incorporating the use of artificial intelligence. Humans can now communicate with computers through common everyday speech. The start of robotic sounding voices has evolved to a highly sophisticated voice technology system that had sales of over $1.2 billion in 2004. Voice technology systems, powered by artificial intelligence, are no longer just an emerging technology, but are being used by companies from BWM to Dell to Frigidaire to Wal-Mart.

Want a dog, but dont want to feed or walk it? Poo Chi is an interactive dog made by Tiger Electronics. The dog responds to commands through voice recognition. The company says that Poo Chi will grow and mature as you train him. Like a real dog, this one can learn tricks like lie down, sit and shake. However, unlike a real dog, these mechanical pets can also learn to sing songs. And, of course, they dont need to be taken outside, fed, or taken to the vet.

Want to talk to your car? Ford Motor Company has developed an advanced voice technology system so you can communicate with your car. New vehicles can be equipped with a conversational speech interface technology. The system uses a text-to-speech technology that sounds like you are talking to another person, and not a robot. What can you talk about with your car? Want to play music? The system asks what type of music and then will list what artists are available. Need to make a call? Tell your care to call Steven Smith and if there is more than one Steven Smith listed, the car will even ask you which one should be called. Also controlled through voice recognition is the navigation system, climate control, retractable roof and personalization preferences. Fords conversational speech technology currently has a vocabulary of over 50,000 words and unlike kids and pets, it speaks only when spoken to!

Speech recognition technology is also on the rise in the field of customer service call centers. Instead of pushing "1" for service or "2" for complaints; you now talk directly with the computer and learn your bank balance, when your last car payment was received and receiving answers to a wide variety of frequent customer service questions. Some of the companies now using speech recognition technology to answer customers questions are: Bank of America, Sprint, United Airlines, Sony, Sears, Ticketmaster and Nike.

The evolving hand of voice recognition technology is a blessing for those with disabilities. Various recognition software programs take the spoken word and translate it to the written word. Two such programs are IBM ViaVoice and Dragon Naturally Speaking,

As artificial intelligence keeps expanding its scope, voice recognition programs will become more prevalent throughout our everyday lives. Already standard in Microsoft's Widows XP is a voice recognition program that allows users to speak and Microsoft Word will type for you. It is not perfect, but it is quite amazing to experience. Many users do not even know the program is right there on their PC's.

As users do begin to take advantge of the technology and demand grows for better software, typing will become a thing of the past. Manual interfaces of all kinds will become a thing of the past. Viewers of Star Trek the next generation and Voyager are well aware of voice command for everything from making soup to controlling the Holo Deck. This method of interaction is well on its way to becoming reality.
But even this is a temporary stage in the evolution of man-machine interaction. One day, there will be a symbiosis between the two. Implanted chips will eliminate even the need for voice command.

Soon, even HAL may be a distant memory. A representation of the good old days when you actually had to talk to get anything done.


Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

The process of machine learning is similar to that of data mining. Both systems search through data to look for patterns. However, instead of extracting data for human comprehension -- as is the case in data mining applications -- machine learning uses that data to detect patterns in data and adjust program actions accordingly.  Machine learning algorithms are often categorized as being supervised or unsupervized. Supervised algorithms can apply what has been learned in the past to new data. Unsupervised algorithms can draw inferences from datasets.

Facebook's News Feed uses machine learning to personalize each member's feed. If a member frequently stops scrolling in order to read or "like" a particular friend's posts, the News Feed will start to show more of that friend's activity earlier in the feed. Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in the user's data and use to patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend's posts, that new data will be included in the data set and the News Feed will adjust accordingly.


Automated planning and scheduling, in the relevant literature often denoted as simply planning, is a branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehiclesThe planning problem in Artificial Intelligence is about the decision making performed by intelligent creatures like robots, humans, or computer programs when trying to achieve some goal. It involves choosing a sequence of actions that will (with a high likelihood) transform the state of the world, step by step, so that it will satisfy the goal. The world is typically viewed to consist of atomic facts (state variables), and actions make some facts true and some facts false. In the following we discuss a number of ways of formalizing planning, and show how the planning problem can be solved automatically.
We will only focus on the simplest AI planning problem, characterized by the restriction to one agent in a deterministic environment that can be fully observed. More complex forms of planning can be formalized e.g. in the framework of Marvov decision processes, with uncertainty about the effects of actions and therefore without the possibility to predict the results of a plan with certainty.
The most basic planning problem is one instance of the general s-t reachability problem for succinctly represented transition graphs, which has other important applications in Computer Aided Verification (reachability analysis, model-checking), Intelligent Control, discrete event-systems diagnosis, and so on. All of the methods described below are equally applicable to all of these other problems as well, and many of these methods were initially developed and applied in the context of these other problems.
Further, more realistic planning and other problems can use the basic problem as a subprocedure, or more general problems can be reduced to it. For example, temporal planning can often be reduced to the base case directly (Cushing et al. 2007) or the base case can be used as a subprocedure (Rankooh & Ghassem-Sani, 2013)
See notes on temporal planning.
The methods discussed in this tutorial have strengths in different types of problems.
Symbolic methods based on BDDs excel in problems with a relatively small number of state variables (up to one or two hundred), with a complex but regular state space.
Explicit state-space search is generally limited to small state spaces, but the AI planning community has been successfully applying explicit state-space search also to very large state-spaces, when their structure is simple enough to allow useful heuristic distance estimates, and when there are plenty of plans to choose from.
Methods based on logic and constraints (SAT, constraint programming) are strong on problems with relatively high numbers of state variables, especially when constraints about the structure of the solution plans and the reachable state-space are available.


Various techniques are used in order for Ai to solve problems. Some are as follows:

Problem Space − It is the environment in which the search takes place. (A set of states and set of operators to change those states)

Problem Instance − It is Initial state + Goal state.

Problem Space Graph − It represents problem state. States are shown by nodes and operators are shown by edges.

Depth of a problem − Length of a shortest path or shortest sequence of operators from Initial State to goal state.

Space Complexity − The maximum number of nodes that are stored in memory.

Time Complexity − The maximum number of nodes that are created.

Admissibility − A property of an algorithm to always find an optimal solution.

Branching Factor − The average number of child nodes in the problem space graph.

Depth − Length of the shortest path from initial state to goal state.