Artificial Intelligence

You must use Lisp or Problog

Or, for non computer scientists, an "expert system generator" who allow you to program in everyday language (T.Rex for example, a reasoning tool).

The major computer language is lisp and prolog.

to spy on other countries e.g phoning to the other country the satellite of your phone goes up to one satellite ans to another and then reaches your destination.

Human brain, computer can only be programmed to perform the brain can think for itself and be trained to be faster than any computer. It was somebodys brain that invented the computer so it's quite obvious really, not to mention the only reason numbers exist is because we thought them up and the computer cannot do the things that human can do!!!

True

Artificial Intelligence (AI), or machine intelligence, is the field creating PCs and robots equipped for parsing information logically to give mentioned data, gracefully examination, or trigger occasions dependent on discoveries. Through methods like AI and neural organizations, organizations around the world are putting resources into instructing machines to 'think' more like people.

Artificial intelligence can be applied to many areas of medicine. Most notably, it is applied to medical imaging, where artificial intelligence (especially pattern recognition) is used to diagnose images.

yes

speech recognition for text-to-speech and face recognition are two examples

Artificial Intelligence is a company that studies and develops intelligent machines. Some research goals that Artificial Intelligence that have set are to extend the frontiers of what they program and to program machines that could model human behaviour.

The IT industry in India is the subject of much optimism given its past performance. That said, there is a huge potential for growth due to the huge population and IT sector employment in India.

in Canada

To my understanding, theoretically, the knowledge of elasticity can help in the revenue of the firm. However, it can be limited in practical terms as many other factors can affect revenue for the firm. In theory, if we are considering elasticity, what we are considering is the price of the good. You might need a graph to better understand the concepts of elasticity and revenue. Lets say the good has an elastic demand curve. This means that the demand curve is relatively flat, and the goods have a wide variety of substitutes. If this is the case, a good strategy will be to decrease the price of the good. If the price is decreased, and if the demand curve is elastic, theoretically the revenue of the firm will increase. From my interpretation, if the good has got a lot of substitutes, the firm can increase it's revenue by decreasing the price of it's good. This is because consumers are very sensitive to price changes, this means a small decrease in price, will mean a lot of customers will come and purchase the good. This can increase the revenue for the firm. On the other hand, if the demand curve is inelastic, an increase in the price of the good will increase the revenue for the firm. A demand curve that is inelastic is rather steep, and have few substitutes for the consumers. This means that consumers have little choice over product variety. An increase in the price of the product by the firm, will have few customers respond to it. What this means essentially is that the current customers will not repsond to changes in the price, hence they will not reduce the quantity demanded a lot, and they will not mind paying the high price. This can thus increase the revenue for the firm. In summary, if the product that a firm faces is elastic it's wise to decrease the price, as it will increase the revenue for the firm. On the other hand, if the product that a firm faces is inelastic, it's wise to increase the price of the good, as it will increase the revenue for the firm. Hope this helps.

MDI (Multiple Document Interface) is a Microsoft Windows programming interface for creating an application that enables users to work with multiple documents at the same time. Each document is in a separate space with its own controls for scrolling. The user can see and work with different documents such as a spreadsheet, a text document, or a drawing space by simply moving the cursor from one space to another. An MDI application is something like the Windows desktop interface since both include multiple viewing spaces. However, the MDI viewing spaces are confined to the application's window or client area . Within the client area, each document is displayed within a separate child window . MDI applications can be used for a variety of purposes - for example, working on one document while referring to another document, viewing different presentations of the same information, viewing multiple Web sites at the same time, and any task that requires multiple reference points and work areas at the same time

the things which are created by humans is called artificial things, example robots

Artificial Intelligence (AI) is branch of computer science. It is concerned with building smart machines capable of performing tasks that typically require human intelligence. Artificial intelligence Training in Noida is an associative science with multiple approaches, but update in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry. Best IT Training institute in Noida Delhi NCR for Mean offer job based training offered by expert team of trainers having more years of project based experience. Trainers are real time professional and subject matter experts so that they train students to work on projects and real applications. Ducat is the Best Artificial intelligence Coaching Institute. At Ducat, Artificial intelligence Training Course is conduct during day time classes, weekend classes, evening group classes and fast track training classes.

The main difference is that depth-first uses a stack while breadth-first uses a queue.

To illustrate, imagine a binary tree where every node has up to two child nodes and some data. We begin at the root in both cases. With breadth-first, we enqueue the root. We then begin an iterative process. First, we dequeue a node. If the node contains the data being sought then we're done. Otherwise we enqueue the node's immediate children. If the queue is empty, the data being sought does not exist and we're done. Otherwise we begin a new iteration.

With depth first we do the same thing except we stack the nodes (push and pop rather than enqueue and dequeue). Queues are a FIFO structure (first in, first out) while stacks are LIFO (last in, first out). This dramatically alters the sequence in which nodes are examined. Breadth-first examines nodes in sequence, row by row, whereas depth-first examines the depths of the left hand side of each node before examining the depths of the right hand side of each node.

Depth-first is ideally suited to brute force backtracking algorithms (particularly NP-complete problems) as well as for rapidly building sorted lists from unsorted sequential data. Breadth-first search is better suited to creating diagrams from binary trees because a single pass can determine the number of levels and the maximum width required to display the tree, while a second pass can build the diagram one row at a time (typical breadth-first implementations will maintain the width and height as internal members to avoid recalculating them).

Because depth-first employs a stack, implementations often make use of recursion rather than iteration, thus taking advantage of the call stack to provide the necessary backtracking. However, an iterative approach is usually more efficient, particularly if the tree depth exceeds the compiler's ability to inline expand the recursions.

If I said Google is an example of AI there might be some people who will directly disagree but it depends at what degree of relation with AI an example is put forward.

Any software taking its foundations and having techniques of solving issues that are considered to related with AI can be an example of AI.

For instance:

Search Engines ( Google , Bing , Yahoo Search ) all use NLP ( Natural Language Processing ) - Hence are examples of AI

Virtual Assistants - Siri, Google Now , S-Voice - Are developed by their respective companies for assisting users in their day to day task

Chatbots ( ALICE ) - And many others are examples of AI which are enabled to carry on with a conversation started by a user by retrieving answers from static database of pre-stored answers.

It must be noted, however, statistical applications such as Google Translate or simple NLP-like applications such as those used by popular search engines, chat bots or virtual assistance are not commonly regarded as tools or evidence for artificial intelligence. These tools are based on (highly intelligent application of) statistical methods and complex sets of rules, not based on systems which take free decisions based on prior learning.

Learning, reasoning, problem-solving, perception, and language understanding are the five fundamental components of artificial intelligence. All products and services that employ artificial intelligence solutions consider these five fundamental components such as Apple's Siri, Amazon's Alexa, and more. Visit us online for further related info!

expert system architecture need to act like the process expert attempt to do for reasoning based on the given facts. architecture comprise of interface, explanation system, inference engine and knowledge editor as a shell,which can be configured on different knowledge area without changing the whole structure of expert system, and the removable part constitute of domain-specific and general knowledge section. in general knowledge section refer to those knowledge representation(usually in the form of ruled-based) symbols which is needed for expert system to running. domain-specific refer to those facts that entered by user or is the conclusion of the last operation in inference part. resemble it to individual experts, general knowledge section have a role of long-terms memory and domain specific have role of working memory which is manipulated or updated by inference engine. in fact inference engine use both of this storage for reasoning and making inference about the problem. generally inference engines use recognize-act chain in order to solve the problem. process of problem solving in forward chaining method for inference start with typing of problem from GUI by user, the facts go into domain specific storage,in this part the first action on recognition-action cycle of inference engine take place. all of the facts in domain-specific storage being compared with rules in general knowledge storage. this part is called matching, after that inference engine need choose the most appropriate one from conflict set(set of rules in matched in first step). for this purpose it may use conflict resolution strategy which is the job of interpreter. interpreter is part of the inference engine that execute selected agenda based on the given criteria. what will happen then is selection of rule based on namely three main category of strategy:1-in the case of two or more condition use the one that are more specific 2-if two or more rule have appropriate condition for selection use the one used in facts 3-if system match the same condition for two different role the latest rule need to be eliminated because iteration may take place. after selection of rule execution part start if executed result is the one desired problem is solved if not, result store in domain-specific section and cycle renewed. the process of inference engine is the same for back-ward and forward chaining what make difference is the load and reload of working memory and other things.(and this is the thing that u need to find out!). explanation system which is part of the shell(you can see more about shell by searching on wikianswer ;)). explanation system provide description about why expert system decide on particular course of action and how it conclude to this result. mechanism counterintutivelly simple. explanation system just exploit the knowledge pattern it followed during the work of inference engine. it track back use storage to find out what have been decided and how inference engine imply based on th rules.(just like it's human counter part) i don't know much about the detailed flow of work in explanation systems but i really love to. knowledge editor as its name imply manipulate the general knowledge storage, adding , deleting and changing undertake by knowledge engineer but knowledge editor written by professional programmer. hope this would help but again check other places because there s lot more u need to know.good luck have fun!