Forex Artificial Intelligence

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Aug 022013

The actual name is termed Forex artificial intelligence and years ago made its introduction, but promptly disappeared off the market, because giant financial conglomerates saw it as a tool that they could dominate for cashing in. During that time a good number of the predicative capacity of Forex artificial intelligence yielded results to the level of 75% accurate Forex signals. The reality of the matter is, any time you are working with any kind of investment and have that kind of accurateness you are doing superb! That specific Forex AI program was bought from a minor computer developer by chief players on Wall Street who used it for years to clear major windfall profits!

The secret to getting large profits is to learn the use, over a regular steady basis, of the technologies of Foreign exchange artificial intelligence. With Forex AI Robots (Artificial Intelligence) you would only need to make some slight adjustments to your applications routine and you’re off towards success. The Foreign Exchange or “FOREX” market industry is one of the most top yielding business industries in the world market nowadays. This particular industry earns millions to billions of dollars in just a one day.

With a huge profusion of scientific and digital age innovations that turn canadian drug stores online up day by day, a number of money making options are rising well beyond several expectations for the market. In the small known location of money trading Artificial intelligence has developed exceedingly fast in contrast to other fields of business and economics. Experts have reviewed and tested a small amount of the artificial intelligence trading systems that are readily available in the marketplace right now and have meticulously witnessed how they carry out in the broad range of marketplace troubles. Although it is accurate that some of them can give you immense earnings; however, one needs to be aware that there is no get rich in a minute kind of deal available, especially when the market goes down too fast to monitor.

With the computer market being so aggressively competitive these days and so many bright programmers out there, improved more steadfast programs of this quality have come about. At present this is not to say thatall Forex AI programs are worth it, but throughout the world there are a couple that are pretty much an automatic-money-machine because the accuracy rate is so spot on that it will most likely leave you shaking your head in disbelief. I actually hate to say it this way, however it is so very true. You can literally sleep your way to earning proceeds with this kind of technology at work for you, because once you are started, the program for the most part is hands-off and has stunning no hassle profit making capability.

The Artificial Intelligence in Big Data

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Jul 192013

Artificial Intelligence (AI) facilitates the efficient and effective supply of information to enterprises for optimized business decision-making. Major IT and software vendor companies are investing billions to generate revenue from AI based commercial solutions in various areas including robotics, machine translators, chat bots, voice recognizers, business intelligence systems, mobility control systems, intelligent search, and more.

The field was founded on the claim that a central property of humans, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine. This raises philosophical issues about the nature of the mind and limits of scientific hubris, issues which have been addressed by myth, fiction and philosophy since antiquity. Artificial intelligence has been the subject of breathtaking optimism, has suffered stunning setbacks and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.

Artificial Intelligence (AI)  research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other. Subfields have grown up around particular institutions, the work of individual researchers, the solution of specific problems, longstanding differences of opinion about how AI should be done and the application of widely differing tools. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence (or “strong AI”) is still a long-term goal of (some) research.

The AI based solution market is valued at US$ 900 million globally by year end 2013 and is expected to grow exponentially over the next five years. Some of the biggest opportunity areas are commercial applications, search in the Big Data environment, and mobility control for generation of actionable business intelligence. The entire mobile/wireless ecosystem is well-positioned for AI via the growing adoption and expanded usage of consumer and enterprise electronics devices including smartphone, tablet, portable devices and wearable technologies.

  • The current mainstream business analytics research and solution development along with the convergence of AI with machine learning techniques will continue to underpin high-value, human decision support solutions.
  • Cognitive systems can combine natural language processing, hypothesis generation and evaluation, and dynamic learning for a powerful, fast, and intelligent problem solving.
  • To effectively apply intelligent problem solving using AI solutions, you can extend the capabilities of IBM Watson and its DeepQA technology architecture to other domains.

The Social Intelligence

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Jun 252013

Social Intelligence (SI) is the ability to get along well with others, and to get them to cooperate with you. Sometimes referred to simplistically as “people skills,” SI includes an awareness of situations and the social dynamics that govern them, and a knowledge of interaction styles and strategies that can help a person achieve his or her objectives in dealing with others. It also involves a certain amount of self-insight and a consciousness of one’s own perceptions and reaction patterns.

From the standpoint of interpersonal skills, Karl Albrecht classifies behavior toward others as falling somewhere on a spectrum between “toxic” effect and “nourishing” effect. Toxic behavior makes people feel devalued, angry, frustrated, guilty or otherwise inadequate. Nourishing behavior makes people feel valued, respected, affirmed, encouraged or competent. A continued pattern of toxic behavior indicates a low level of social intelligence – the inability to connect with people and influence them effectively. A continued pattern of nourishing behavior tends to make a person much more effective in dealing with others; nourishing behaviors are the indicators of high social intelligence.

Social media interaction and networking sans social intelligence does not yield the best that social media has too offer. Most brands today are present in social media sphere in order to stay connected with their audience and also generate mass awareness about their products, services and initiatives. The social intelligence that is gathered through initiated discussion, blogs and articles offers a brand various perspectives about their marketing and branding strategies. If a brand is able to garner greater social intelligence and make the most of it then it can attain much success in the forthcoming days.

However, improving social intelligence too is an essential element of making the most of social media monitoring. In the recent past, there have been eminent companies specializing in social media analytics tools that have introduced social intelligence applications. These applications help to track information from various social platforms to tap into the consumer behavior. The strategy followed by these companies is to concentrate on a broader market for social commerce and attain actionable, valuable user information on real-time basis with these applications.

Social media intelligence is vast, intriguing and a mind-twisting subject. It is instrumental in deriving organizational as well as consumer psychology and their disparities. Interesting as it might seem but if not attempted with adequate guidelines and methods, it can be very misleading. Therefore, to make the process easy for you, today there are renowned companies that offer innovative social intelligence application solutions that help their users by tracking information across numerous social platforms. This is churned out from the expanding social media data.

Most service providers follow the strategy of focusing on a wider marker for social commerce and getting actionable, valuable user insights on a real-time basis with the social media analytics applications.

The Electric Car Conversion

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Jun 022013

Do you find yourself watching the gas gauge in your car? Do you find yourself hoping your car will go farther and farther on a gallon of gas? As we continue to watch gas prices hover around $4.00 a gallon and take a bigger chunk out of our budgets, many of us are searching for ways to stretch our transportation dollars. Luckily, there is a solution to the outrageous price of gasoline, an electric car conversion; it is possible to convert a regular gasoline car to an electric car. By converting your car to electric, you will be changing your car into a more cost efficient vehicle, and you also be helping the environment by using a “clean” fuel. If you are thinking that it is going to be beyond your ability to manage this project,and even if you are mechanically disinclined, you will still be able to convert any car to electric with the help of a detailed plan. Detailed plans can be found at your local library, online, and in some of the better electric car conversion forums. With the wealth of bad information floating around the internet these days, if you manage to get your hands on a good manual for electric car kits, consider yourself lucky!
It is true that this project will cost you a few dollars, but you will find the investment worth it. The median cost of the electric car conversion kit is roughly the equivilent of two months worth of gasoline for a commuter.Should you decide to do the smart thing, the list of advantages is long indeed. Lower maintenance cost, huge tax incentives, reduction of your carbon footprint, and reduced CO2 Emissions.
Now if you are thinking that the price is too high, and that you will not be able to afford it, please consider all the advantages I have mentioned!

For only pennies a day, you can drive your electric car around town. That is much better than spending $4.00 a gallon to put gas in the tank. In general, an electric car can drive for one hundred miles before needing a charge. For a full charge the cost is around one dollar. Think of how much gasoline your car uses to drive a hundred miles and how much that costs. An electric car conversion can save hundreds, maybe thousands, of dollars in fuel costs in a year.

If you want to transform your car to electric, there are a few genuine good things about the project. And not just completing on the gasoline station, you possibly can power port in auto. Your carbon online canada pharmacy foot print might be eloquently reduced. Alteration is naturally simple to do some mechanical skills. Normally using this method can run fewer than $15000 which enable it to easily conserve your funds long term.

Better benefits:

-You can convert car to electric in week -You can follow specific conversion process -You can travel as much as 65 miles per hour

You are in a position to convert their cars on their own. As a result of car conversion depends on exactly how much the car weighs and ways in which many batteries make use of. Charging your vehicle isn’t that expensive. You will want to enter vehicle to charge.

The Artificial Neural Network

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May 212013

Artificial Neural Networks (ANNs) are biologically inspired. Specifically, they borrow ideas from the manner in which the human brain works. The human brain is composed of special cells called neurons. Estimates of the number of neurons in a human brain cover a wide range (up to 150 billion), and there are more than a hundred different kinds of neurons, separated into groups called networks. Each network contains several thousand neurons that are highly interconnected. Thus, the brain can be viewed as a collection of neural networks.

Today’s ANNs, whose application is referred to as neural computing, use a very limited set of concepts from biological neural systems. An artificial neural network (ANN), usually called “neural network” (NN), is a mathematical model or computational model that tries to simulate the structure and/or functional aspects of biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learningphase. Neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.

Predicting is making claims about something that will take place, often based on  information from past and from current state. Everyone solves the problem of prediction every day with various degrees of success. For example weather, harvest, energy consumption, movements of forex (foreign exchange) currency pairs or of shares of stocks, earthquakes, and a lot of other stuff needs to be predicted.

In technical analysis predictable parameters of a system can be often be expressed and evaluated using equations – prediction is then simply evaluation or solution of such equations. However, practically we face problems where such a description would be too complicated or not possible at all. In addition, the solution by this method could be very complicated computationally, and sometimes we would get the solution after the event to be predicted happened.

It is possible to use various approximations, for example degeneration of the dependency of the predicted variable on other events that is then extrapolated to the future. Finding such approximation can be also difficult. This approach generally means creating the model of the predicted event.

This Is All About Artificial Intelligence

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Apr 012013

Artificial Intelligence is a concept that concerned people from all around the world and from all times. Ancient Greeks and Egyptians represented in their myths and philosophy machines and artificial entities which have qualities resembling to those of humans, especially in what thinking, reasoning and intelligence are concerned.

Artificial intelligence is a branch of computer science concerned with the study and the design of the intelligent machines. The term of “artificial intelligence”, coined at the conference that took place at Dartmouth  in 1956 comes from John McCarthy who defined it as the science of creating intelligent machine.

Along with the development of the electronic computers, back in 1940s, this domain and concept known as artificial intelligence and concerned with the creation of intelligent machines resembling to humans, more precisely, having qualities such as those of a human being, started produce intelligent machines.

Mechanical or “formal” reasoning has been developed by philosophers and mathematicians since antiquity. The study of logic led directly to the invention of the programmable digital electronic computer, based on the work of mathematician Alan Turing and others. Turing’s theory of computation suggested that a machine, by shuffling symbols as simple as “0” and “1”, could simulate any conceivable act of mathematical deduction. This, along with recent discoveries in neurology, information theory and cybernetics, inspired a small group of researchers to begin to seriously consider the possibility of building an electronic brain.

The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956. The attendees, including John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, became the leaders of AI research for many decades. They and their students wrote programs that were, to most people, simply astonishing: computers were solving word problems in algebra, proving logical theorems and speaking English. By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense and laboratories had been established around the world. AI’s founders were profoundly optimistic about the future of the new field: Herbert Simon predicted that “machines will be capable, within twenty years, of doing any work a man can do” and Marvin Minsky agreed, writing that “within a generation … the problem of creating ‘artificial intelligence‘ will substantially be solved”.

The disciplines implied by the artificial intelligence are extremely various. Fields of knowledge such as Mathematics, Psychology, Philosophy, Logic, Engineering, Social Sciences, Cognitive Sciences and Computer Science are extremely important and closely interrelated are extremely important when it comes to artificial intelligence. All these fields and sciences contribute to the creation of intelligent machines that have resemblance to human beings.

The application areas of artificial intelligence are extremely various such as Robotics, Soft Computing, Learning Systems, Planning, Knowledge Representation and Reasoning, Logic Programming, Natural Language Processing, Image Recognition, Image Understanding, Computer Vision, Scheduling, Expert Systems and more others.

Recursion An algorithmic technique where, in order to accomplish a task, a function calls itself with some part of the task.

Symbolic computation AI programming involves (mainly) manipulating symbols and not numbers. These symbols might represent objects in the world and relationships between those objects – complex structures of symbols are needed to capture our knowledge of the world.

Term The fundamental data structure in Prolog is the term which can be a constant, a variable or a structure. Structures represent atomic propositions of predicate calculus and consist of a functor name and a parameter list.

PROGRAMMING LANGUAGES IN ARTIFICIAL INTELLIGENCE (AI) are the major tool for exploring and building computer programs that can be used to simulate intelligent processes such as learning, reasoning and understanding symbolic information in context. Although in the early days of computer language design the primarily use of computers was for performing calculations with numbers, it was also found out quite soon that strings of bits could represent not only numbers but also features of arbitrary objects. Operations on such features or symbols could be used to represent rules for creating, relating or manipulating symbols. This led to the notion of symbolic computation as an appropriate means for defining algorithms that processed information of any type, and thus could be used for simulating human intelligence. Soon it turned out that programming with symbols required a higher level of abstraction than was possible with those programming languages which were designed especially for number processing, e.g., Fortran.

The Artificial Intelligence Around Us

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Mar 052013

During the 1980’s, in America there was much interest in the field of Artificial Intelligence. The great expectations of the 1980’s were followed by the skepticism of the 1990’s, at which time the limitations of capabilities of our current computers were emphasized. The skepticism of the 1990’s has now for the most part passed, and one of the main scientific and industrial challenges of the 21st century is the development of Artificial Intelligent Systems (AIS).

 The development of AIS is aimed at the creation of new technologies that will provide solutions to problems in the areas of electronics and heavy industries, agriculture, energy and resource conservation, transportation, human health, public safety, national security, and other fields.

Speaking at a conference in Buenos Aires in 1995, Albert Arnold Gore, Jr. (Vice President of the United States from 1993-2001 under President Bill Clinton) remarked, ‘These highways, buy pills online or more accurately, networks of distributed intelligence, will allow us to share information, to connect, and to communicate as a global community.

The field was founded on the claim that a central property of humans, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine. This raises philosophical issues about the nature of the mind and limits of scientific hubris, issues which have been addressed by myth, fiction and philosophy since antiquity. Artificial intelligence has been the subject of breathtaking optimism, has suffered stunning setbacks and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.

AI research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other. Subfields have grown up around particular institutions, the work of individual researchers, the solution of specific problems, longstanding differences of opinion about how AI should be done and the application of widely differing tools. Artificial intelligence is a very broad field, and far from being isolated to computing it encompasses many other disciplines such as philosophy, neuroscience and psychology. It is important to note though, that rather than just seeking to understand intelligence, AI practitioners seek also to build or create it. The uses and applications of AI are many and varied, and although Canada pharmacy online many think of humanoid robots when we discuss AI, you may be surprised to know that we already encounter applied AI in our day-to-day lives.
AI is full of big questions – how does an entity (either biological or mechanical) think? How does it understand or solve a problem? Can a machine truly be intelligent? What is intelligence? The answer to these questions may not be easy, but there is an answer staring us in the mirror so we know the quest to find out is achievable.

Uses Of Solar Energy

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Jan 162013

Solar energy is derived from the rays of the sun, as is evident from its name itself. Solar energy has numerous benefits for users. However, until recent years, this source of energy could be used only during the day. Presently though, the current technology enables individuals to effectively store and use solar power, even during the night. We would be discussing some of the important benefits of solar energy in this article. However, there are certain challenges associated with harnessing solar power in an optimal fashion. For instance, the following three questions arise when you first think about utilizing solar power:

• Earth receives only a minuscule amount of sun’s energy. How can the heat reception and utilization at our planet be maximized?

• We receive the solar radiations only during the day. How can we make up for the energy to last through the night?

• Even after all the evolutions in technology to harness solar power, we can still only manage to use the energy falling on a given surface. How can we concentrate all this energy at one specific spot?

Researchers are still carrying-out studies and at present, the technology is improving fast. Significant alterations were made for enhancing the solar energy panels’ price and visual appearance. With invention of higher quality photovoltaic cells, solar power was greatly enhanced. The cells will be added to steel  substrate to capture sunlight and the energy could be stored in the battery. They contain many layers of cells thus expanding its efficiency. These innovative panels use the amorphous silicon thin alloy technique. Thru this technique, the cells will not be as bulky as they nce were and they are highly efficient. With more stylish models on the market the home or business office will still look pleasing with the panel on top.

The cost of photovoltaic cells is also more cost efficient. This would help to extend the usage of solar energy to areas which were using other conventional energy sources thus far, and in newer forms too. The advantages of solar energy are also becoming evident over time. Clean, toxin-free environment can be guaranteed too, since internal combustion engines would soon be replaced by solar power systems. Thus, solar energy systems are fast emerging as the most important source of mainstream power.

All About Intelligent Motion

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Dec 092012

Intelligent Motion Systems (IMS) manufactures a broad selection of step motor drives and indexers. From basic half/full step drives to high end driver indexers, there is an IMS product for your application. Servo Systems Co. offers the full IMS product line, including the IMS MDrive series and new MForce MicroDrive and MForce PowerDrice series, which feature an integrated motor and driver in a single, small and powerful package. We use indigenous technology and practice cost competence while giving appropriate
engineering solution. Diamond cutting machine market has enriched our experience in the field and has enabled us to give a complete solution including laser system. The Intelligent Motion Lab studies motion planning and control for intelligent robotic and biological systems. Our research applies computational, mathematical, and statistical techniques to address the complexity of high-dimensional motion in unstructured, dynamic, and uncertain environments, and seeks applications to domestic and industrial robotic manipulation, human-robot interaction, robot- and computer-assisted surgery, and legged locomotion. We are also interested in using computational theories to inform the study of motor cognition in humans and other cheap health coverage biological systems. The lab is directed by Prof. Kris Hauser and is part of the Indiana University School of Informatics and Computing. Semi-autonomous robots have the potential to combine the adaptability, contextual awareness, and intuition of humans with the precision, availability, and consistency of robots. We are interested in studying tightly coupled, real-time human and robot decision-making, where high performance of the overall system requires synthesizing both agents’ strengths in cognition and sensing. We are considering applications to puzzle solving, active safety systems for automobiles, interactive CAD systems, and industrial inspection and material handling.

Artificial Intelligence Software

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Nov 282012

The artificial intelligence software is used for achieving rather different set of objectives that lies beyond the capabilities of pure AI programming languages. As an example, to forecast the tropical storms, one need to use artificial intelligence software as these software contains a full system for forecasting what is going to happen next. For this, the artificial intelligence software needs to interface with a number of other AI and non-AI systems for getting information, sorting them, and calculations. When it comes to AI programming languages, they can write programming code to build parts of the artificial intelligence software, but cannot do more than that. Essentially, artificial intelligence programming languages are the building blocks of artificial intelligence software.

Potatoes, tomatoes, brown bananas, melons, grapes, squash and any non-refrigerated produce that you get from the garden or market are susceptible.

Scientists in Taiwan believe they have a way to use artificial intelligence software to throw a red flag on the field. This software is designed to forecast a future outbreak.

On the island, growers use traps that are checked every 10-day, counting the contents. Researchers at the National Taiwan University in Taipei wanted to make that process more automated.

AI research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other. Subfields have grown up around particular institutions, the work of individual researchers, the solution of specific problems, longstanding differences of opinion about how AI should be done and the application of widely differing tools. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence (or “strong AI”) is still among the field’s long term goals.

Early AI researchers developed algorithms that imitated the step-by-step reasoning that humans were often assumed to use when they solve puzzles, play board games or make logical deductions. By the late 1980s and ’90s, AI research had also developed highly successful methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.

For difficult problems, most of these algorithms can require enormous computational resources — most experience a “combinatorial explosion”: the amount of memory or computer time required becomes astronomical when the problem goes beyond a certain size. The search for more efficient problem solving algorithms is a high priority for AI research.

Human beings solve most of their problems using fast, intuitive judgments rather than the conscious, step-by-step deduction that early AI research was able to model. AI has made some progress at imitating this kind of “sub-symbolic” problem solving: embodied agent approaches emphasize the importance of sensorimotor skills to higher reasoning; neural net research attempts to simulate the structures inside human and animal brains that give rise to this skill.

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