Artificial Intelligence

The first actual robot was created in 1961 by Joseph F. Engelberger. It was called the Ternstedt Die-caster and basically dropped car parts made from molten steel into a cooling liquid. The word "robot" is first credited to the acclaimed Czech playwright Karel Capek (1890-1938), from the Czech word for forced labor or serf, in his writings in 1920. However, the concept of "mechanical men" has been around since the mid 1800s. The word "robotics" was first used in Runaround, a short story published in 1942, by Isaac Asimov (born Jan. 2, 1920, died Apr. 6, 1992). I, Robot, a collection of several of these stories, was published in 1950.

The short answer is yes. Any full language is capable of this.

You might want to look at the book "Illustrating Evolutionary Computation with Mathematica", for some examples.

The below method is written in the assumption that, the n numbers are sent as an ArrayList of Integer values.

public void computeSumAndAvg(ArrayList lst){ Iterator itr = lst.iterator();

int sum = 0;

float average = 0;

int count = 0;

while(itr.hasNext(){ Integer val = (Integer) itr.next();

sum = sum + val.intValue();

count++; }

average = sum/count;

System.out.println("Sum is: " + sum) ;

System.out.println("Average is: " + average) ; }

Many search algorithms are possible.

Tree-based methods, in which all paths to all solutions are produced, is one option. Each node in the tree would represent a "state" or "configuration" of the problem, while an edge from one node to the next represents the "move" you make. Consequently, finding a solution to this problem is equivalent to building the tree while checking if each node is a valid solution.

Another method, such the A* algorithm is a heuristic search algorithm. You would use a heuristic function that estimates the optimal path to the solution from the current node. It is the quickest, but since it is a heuristic algorithm, it is not guaranteed to always return the correct answer, since this is dependent on the heuristic function you use in your algorithm.

It has two goals

scientific goal and engineering goal

Scientific goal

1. Its aim is to explain various sorts of intelligence. like cognitive science and philosophy.

In Cognitive Science : As a way to understand how natural minds and mental phenomena works e.g visual perception , memory, learning, language.

In Philosophy: to explore some basic and important questions like mind-body relationship and what really consciousness is?.

Engineering goal

1. to get machines do a variety of useful tasks.

2. purpose of artificial intelligence is to design computer programs which somehow mimics human behavior and expertise so that we can utilize human expertise even if expert is unavailable.

3. its aim is to solve real world problems which can only be solved by human intelligence.

The jury is still out about this question. It has been for the last fifty years or so. The proponents of "Strong AI" say that Artificial Intelligence should be a model of human intelligence. The camp of "Weak AI" says that any computer program doing a reasonable job at a task that should require intelligence is intelligent. The Turing test goes with the latter. Many successes of computer applications - e.g. computer chess machines, OCR, speech recognition, web search - use methods which are probably very different than what the human brain uses.

== ==

includes robotics, vision systems, natural language processing, learmning systems, neural networks and expert systems.

Fictional.

Stegnography is defined as the art of hiding information, data or messages in an image. Even the different file formats can be used for the purpose of hiding the information like for example the video or audio etc. The purpose is to pass on the information with out any regard or knowledge of others safely to the destination. The advantage of stegnography is that those who are outside the party even do not realize that some sort of communication is being done.

You will either get a mine, number or it will stay blank. GOOD LUCK

systems that act humanly

systems that think humanly

systems that act rationally

systems that think rationally

.... in that. point many cientist are antropocentric specist and don't value the inteligence of animales and habilites.. animals talks first later machines.. he need se animas to evolve the artificial inteligence

No. The teacher still has a voice, and the student still has an ear. Both, also, have a brain.

The speed at which the progress is being made - its self evident that YES man has already been replaced in my fields by machines and this will continue to happen.

Neo

That depends on who programmed it and what heuristics were used.

Some people call an application "artificial intelligence" and it really isn't very intelligent.

Also, AI works for some things and it's completely useless for other applications.

If you can clearly define the decision making parameters, AI works great. Once it starts getting into the "grey" areas of decision making, AI starts falling apart.

A neural network is designed to simulate a set of neurons, usually connected by synapses. Each neuron makes a simple decision based on its other input synapses, and places the decision on its output synapses. This model mimics the behavior of a brain, and is considered vital to create a true learning system, though modern computers (barring super-computers) do not have the computational resources to execute a neural network with a sufficient number of nodes to be useful (you would need at least a few million neurons firing in unison to be useful).

Artificial intelligence, of course, is software that is designed to pretend like its a living, thinking creature. Older implementations were not learning systems, but rather would take input and offer a conditioned response provided by the programmer ahead of time. These systems seemed to be highly intelligent, so long as you did not leave its realm of preplanned responses. Newer AI systems learn by interacting with the user (for example, remembering their favorite color or music artist), and can sometimes even figure out correlated data based on this information.

However, current AI systems tend to still have limited spheres of knowledge, and without external learning sources, can not make any intelligent responses or decisions outside this realm of information. The missing component, of course, is a system that is capable of learning information and incorporating what it learns into its current knowledge base. Neural networks hold the promise of bridging this gap in the "learning curve" that AI systems have by allowing the AI to actually learn topics that were not covered during its original "training" or "programming."

The relationship between these two technologies could be said to be symbiotic in nature; both of these can be implemented without the other (i.e. a NN could be used inside a coffee maker for some advanced coffee-making logic, and an AI can certainly use other sources of information to make valid responses), but the combination of the two would allow for a more realistic AI that would be capable of learning data by making correlations between seemingly unrelated data (which is how humans learn, coincidentally).

Though this question cannot be answered in full for two reasons -- confidentiality and the nature of an always-changing market and technology landscape that drive these decisions -- I can tell you in broad strokes that we will continue to emphasize tools and features that help create high-quality answers, rather than simply piling up high volume of mediocre or low-quality ones. This includes automatic algorithms to filter out vandalism, powerful tools to help Supervisors patrol the site to clean it up and organize its content, as well as community-centric features that will help contributors work together. Many of these aren't technology as much as well-thought-out feature design and improvement of the user experience. But various type of "artificial intelligence" certainly do come into play as the design the system to automatically help users along the process, using their own actions as indicators of what they need.

Given that the problem of artificial intelligence is most definitely not solved at all, the question cannot be answered. Marvin Minsky himself, who is one of the key figures in AI research, gave an amusing lecture about that very fact not too long ago; the lecture, entitled "Where's HAL," is available as a free podcast on the Internet somewhere. Neural networks are often thought to be the answer to AI problems, so maybe this is the answer to this question, too. Neural networks are a category of algorithms that feature learning, allowing increased precision of answers as the network learns. Today, neural networks are used all over the place. One of the classic examples is software that determines the human language of any written text used as input to the algorithm, but even more everyday devices such as digital cameras probably use neural networks for auto-focus or face recognition algorithms, and the like. Having said that, there is no doubt that Neuron Networks -or any other competing technology- has yet to show an artificial construct that can compete with the natural intelligence of an ant or a fruit fly.

UniversityofCAMBRIDGE









University of OXFORD









IMPERIAL College London









UCL (University College London)









KING'S College London









University of EDINBURGH









University of MANCHESTER









University of BRISTOL









LONDON School of Economics and Political...









University of WARWICK









University of GLASGOW









University of BIRMINGHAM









University of SHEFFIELD









University of YORK









University of ST ANDREWS









University of NOTTINGHAM









University of SOUTHAMPTON









University of LEEDS









DURHAM University









University of SUSSEX









University of LIVERPOOL









CARDIFF University









University of BATH









University of ABERDEEN









QUEEN MARY, University of London









NEWCASTLE University, NEWCASTLE Upon Tyn...









University of LANCASTER









University of LEICESTER









University of READING









QUEEN'S University of Belfast









University of DUNDEE









University of SURREY









LOUGHBOROUGH University









University of EXETER









School of Oriental and African Studies, ...









University of ESSEX









University of STRATHCLYDE









BRUNEL University









University of EAST Anglia (UEA)









ROYAL HOLLOWAY University of London









ASTON University









SWANSEA University









University of BRADFORD









GOLDSMITHS, University of London









BANGOR University









HERIOT-WATT University









ABERYSTWYTH University









University of KENT









The OPEN University









University of HULL