What is Artificial Intelligence?

From cash registers at the grocery store all the way to personal computers, artificial intelligence has come a long way. So has the way that humans have applied and interacted with it. Artificial Intelligence (AI) can be seen in several aspects of human life.

How To Use Artificial Intelligence

Some familiar implementations of AI include video games, spam filtering, search engines, text analysis, and fraud detection. Artificial intelligence has become integral to the way our society functions and has great importance to many people. It has been used to protect many company’s valuable assets. Remarkably, artificial intelligence has even been used to save human lives.

1) Making Recommendations to Your Users

With the amount of data now available through the internet, being able to accurately predict what type of information your users want has become mandatory to maintaining an audience. This can be seen in sites like Amazon, Netflix, and YouTube where products, movies, and videos are recommended to visitors based on their previous actions.

Collaborative filtering is the name of a tool that allows for the combination of user input and data sources in order to predict what a user will need, like or be interested in.

Collaborative filtering is able to make automatic predictions by looking at the past interests of a large pool of users and looking for correlations. It then assumes that users who liked the same thing in the past will also like the same things in the near future. That way the system can use other users’ preferences to predict the preferences of future users.

2) Creating Killer Video Games

Video game developers have been a major driving force in artificial intelligence innovation. Video game artificial intelligence was conceived in 1951 by Christopher Strachey and Dietrich Prinz who wrote a chess and checkers game using a Ferranti Mark 1 machine at the University of Manchester.

The first video games used something called discrete logic intelligence, which provides the illusion of intelligence in the "virtual player." Video games began utilizing neural networks in 1996 with Battlecruiser 3000 AD. Video game developers can target a variety of decisions to a specific neural network. By using neural networks, the video game AI infrastructure can adapt to user behavior.

AI In Video Games

The more complex video games utilize additional technologies referred to as ‘evaluation of player actions’ and ‘emergent behavior’. Modern day video games have hundreds, perhaps thousands, of characters. New AI systems learn how the player interacts with the system and positions the characters accordingly. Artificial intelligence can utilize real-life military tactical planning and strategies, as seen in First-person shooter games such as Halo and Counterstrike.

Two new artificial intelligence technologies have emerged since 2008, including techniques dubbed "The Director" and "Milo." The Director is a strategy that provides players a different experience each time they play the game. This means that no two gaming sessions would be exactly the same. The Director also uses a procedural narrative strategy in which the AI learns how the players interact with the game and gives them increasingly difficult challenges while following a narrative.

Unveiled at E3 2009, ‘Milo’ is an up and coming technology that will allow users to interact with voice recognition AI within video games.Milo allows users to have full (and logical) conversations with the system and offers the ability to pass "items" through the TV to the AI system. Milo is currently under development by Lionhead Studios.

3) Intelligent search

Probably the most widely used form of AI is in Intelligent Search. We benefit from it when we use the mighty Google search engine, while finding Amazon products, and when we get driving directions off Mapquest. Intelligent search is everywhere. There are so many different technologies that are employed in Intelligent search it's hard to know where to begin.

Intelligent Search

One popular method uses and records the interaction of users to increasingly improve the intelligence of their search results. If a user clicks on a certain document or page it is given added weight in terms of its relevance. If web site owners link to one document or page more often than others that too is taken into account. This of course is only scratching the surface.

Another way intelligence is used in search is to find what a user is looking for as quickly as possible. The search algorithm A* (a star) is used to make sure that the amount of time, which is used for some information to travel on given paths, is decreased drastically. A function known as F(x) or the distance + cost heuristic function is utilized by the A*algorithm.

If you'd like to add intelligent search to your application we recommend you check out Lucene. Lucene is a free open source and a very innovative system which was created by Apache. It produces and optimizes intelligent searches. It's very easy to use and you don't have to make drastic changes in your system environment because it's completely file independent.

4) Document Filtering

Artificial intelligence is important and highly effective in its use of text and document classification. One common example is filtering out SPAM from your email inbox. This is accomplished by using a common classification algorithm like Naive Bayes classifier or employing an artificial neural network.

When using this kind of technology in your applications, it's not uncommon to see success rates of up to 99.5%. So how does this all work? In a nutshell, it takes sample data that you give to it and it learns from it. For example, if an email arrives in your mailbox and you mark it as SPAM, your software then parses that email to learn what elements make it SPAM. Then when the program sees other messages with similar elements it then classifies that as SPAM and deletes it.

By no means is document filtering restricted to its application in SPAM filtration. This kind of technology can be used in countless ways. If you have interest in integrating this kind of technology in your program you might want to check out the free open source library WEKA (Waikato Environment for Knowledge Analysis).

5) Using AI In Data Mining

Thanks to the large amount of information available on the internet today - the amount of which doubles every three years - data mining has become an important tool.

The object of data mining is to find and extract patterns from large data sets. Some common uses of data mining are marketing, scientific discovery, and even surveillance. A classic example is a company mining their database to find the customers that would be most likely to accept a certain offer. Companies are also using data mining extensively for getting to know more about consumer interest, spending trends, and habits. Data mining is a cost effective way for large and small companies to get the necessary data when required.

If you'd like to explore this technology further do some searching on important algorithms like, Classification, Segmentation, Association, Regression and Sequence analysis. Also, decision trees, rules, and neural networks are utilized in the modern data mining.

6) Fraud Detection

AI & Fraud Detection

Data mining, and many other forms of AI, are also extremely useful in fraud detection. This use of data mining for fraud detection takes the form of looking at any abnormalities, which might turn up in the records of the client. For example, Mr. XYZ uses his credit card to the tune of $500 per month. A large number of major and consecutive purchases against that card are going to be noted down and the information conveyed to Mr. XYZ as soon as possible.

Conclusion

The large number of AI developer systems and tools present in the market today has completely changed the manner in which Americans interact and communicate with each other. Artificial intelligence can be integrated in any sort of infrastructure, be it small or large. By now we hope it is evident that artificial intelligence is capable of increasing your business opportunities by making sure that your product reaches the target market. Artificial intelligence is a complex and detailed science so, if you want to know more about it, browse through various informational libraries online (brought to you by intelligent search!).

The full memory-prediction framework theory was first introduced by Jeff Hawkins in the book On Intelligence. The theory says that the physical arrangement of brain cortex tissue is uniform and means that there is a single principle that defines all brain and memory processing. It also notes that the brain's intelligence comes from the ability to predict future events by relying on past data.
 
Memory-prediction framework theory The memory-prediction framework gives a unified theory about complex behaviors and allows us to better understand what intelligence is.
 
This theory focuses on the cortex because we're only concerned about what makes us intelligent and not how our entire body works. After all we're trying to create intelligent software, not recreate human beings.

The Central Concept

Memory-prediction framework posits that inputs coming from the bottom of the hierarchy to the top are analyzed and ranked in a hierarchy of recognition. This then invokes a list of expectations ranked in order of potential. The framework comes into play when the brain has to compare and match up these inputs and expectations. The memory-prediction framework means that the brain does not have to consider every option at every level of the process because it uses past sequences as a guide to predict likely future sequences.

The further up the framework, the longer the past sequences are and consequently the fewer options there are to finish them so the process actually accelerates as it nears the end. For example in looking at a scene, the brain first recognizes lines, then shapes and colors and finally recognizes them as objects. At the same time in the same framework, predictions about what to expect from these objects flows down to speed up our interpretation of the scene.

The framework changes as we age because we add new memories to the system. Since we start with none, as babies we truly see things for the first time. But as we age, we collect memories and this bank of expectations about the way things are and the way things work. This helps us to understand the world around us and to process external information and stimuli more rapidly, but it helps explains how different people can interpret the same situation differently because they bring different memory structures to the experience.

The Pioneer of the Memory-prediction framework theory

Jeff Hawkins originally trained as an electrical engineer and perhaps this gives us a bit of insight on how he approached the problems of brain theory and discovered the memory prediction framework theory. His theory is one of the brain as an organ capable of future predicting and error correction. The brain predicts future events by relying on past data.

The system is a hierarchy so the steps of the analysis are performed in order and if the current even deviates from past experience at any level then a new string of events, or memory, is created. This string can then be used in the analysis of future situations. In this way, the system itself learns and evolves.

It continuously grows more complex and better at making predictions. Just as the theory describes a brain that is constantly adding details and sequences of understanding, the theory itself needs to grow and be fleshed out with details before it will be fully accepted.

The human brain. Amazing. It processes information and is felt through the senses of our touch, our smell, our taste, our vision and our hearing. It allows us to reason and lets us dream. It handles our processes of emotions, such as anger, sadness and joy. It controls our internal bodily functions, such as our heart, our breathing, and our blood pressure.
 
How the brain works The brain has several parts to it. One of the parts in our complex brain is the cerebral cortex, and when it come to artificial intelligence this is the section we really care about. Your cortex is divided into four lobes or sections.
 
These sections are the frontal lobe, the occipital lobe, the temporal lobe and the parietal lobe. This part of the brain helps us to pay attention and allows us to remember things. It is also responsible for our thoughts, our language processes, our conscious, awareness and most importantly our intelligence. It is a grey color and is made up of neurons.

One of the things that the cerebral cortex allows to function in the human brain is our senses. The cerebral cortex receives this message through our thalamus, which is then transmitted to what is known as our primary sensory area. It is the visual cortex, the auditory cortex, and the somatosensory cortex that allows us to see, hear and experience touch.

This all comes together with the use of the opposite sides of the brain. This means that when you are touched on the right side of your body, the left side of your brain is the side that is responsible for the transmission of this.

The part of your brain that controls your motor skills is located in the cerebral cortex as well. Within the cerebral cortex you have your primary motor cortex and your supplementary motor cortex. The primary motor cortex is responsible for carrying out your voluntary movements.

The supplementary motor cortex is responsible for choosing which voluntary movement that we will use. Like the portion of your cerebral cortex that controls your senses, the part that controls your motor skills is also controlled by the opposite side of your brain. If you are moving something with your left hand the right side of your brain is working to process your movements.

Another one of these components is the neurons, which are nerve cells. There are billions of these in your brain. They all work to transmit a signal to your brain, which is unique because they are electrochemical signals. The neurons are made up of a cell body, which is the main portion of the cell. If the cell body expires the neuron will not survive.

The second part of a neuron is an axon, which carries the signal to its destination. Lastly, there are the dendrites, which make it possible for the neuron to make their connections to the other cells.

The brain is present in every living creature in this world, but there is none as remarkably unique as the brain of a human.

With the huge volume of data now available, being able to accurately predict what kinds of information your users want has become mandatory if you want to keep your audience or users.
 
Collaborative Filtering How To Collaborative filtering is a tool that allows for the combination of user input and data sources to predict what your user will need, like or be interested in. It is most useful when dealing with large data sets and pools of users. So far it has been used for mineral exploration, environmental data collection, financial data, commerce and even social networking sites.
 
Collaborative filtering makes automatic predictions about what users will be interested in. It accomplishes this by looking at the past interest of a large pool of users and looking for correlations.

It then assumes that users who liked the same thing in the past will also like the same things in the future. That way the system can use other users preferences to predict the preferences of future users.

This direct connection between similar users is much more accurate and useful than the averages that other systems use. An average tells you what the most common choice is but not what any individual might want. The key to the whole system of collaborative filtering takes two steps. The first step is to find users that share ratings system patterns from past decisions. The second step is to use those shared ratings to predict future ratings for an individual user. The more accurate the results of the first step, the better the results will be from step two.

Collaborative filtering can be used with older information if the information can be sorted correctly. The use of collaborative filtering makes the same information much more valuable. The first company to make use of collaborative filtering was Xerox. They used the system to locate documents using comments from other users. The system had some problems because it only worked if the specific keywords were exact matches. As the system was perfected, it was used with greater success by MIT, Microsoft and Firefly.

A new method of collaborative filtering called active filtering has been developed in recent years and is increasing in popularity. Active filtering uses a peer to peer filtering system that makes the internet much more accurate than before. Instead of just measuring and recording user action it allows users to rate items and publish it on the web for everyone to see.

These ratings can then be used to predict the preferences of future users.It can rule out totally irrelevant search results by comparing them to the highest rated results of other users with similar interests.

There are some potential problems with collaborative filtering due to the fact that it's not a completely passive system. It needs the input of users instead of just passively harvesting information. Another potential problem arises when you're dealing with smaller sample sizes. A few very biased reviews could skew the entire rating system. The future of collaborative filtering will certainly include safeguards that will eliminate these problems because its benefits too great for companies to not want to take advantage of it.

One company that uses collaborative filtering successfully is Netflix, the online DVD movie rental company. Customers pay a monthly subscription to Netflix which allows them rent as many movies as they want each month. This means that customers are constantly visiting the site to look up new movies. Netflix has a star rating system that allows users to rate how well they liked the movies they've already seen. Netflix then uses collaborative filtering to make recommendations on other films they might like.

It does this by comparing their past preferences to that of other users and then finding others with similarly rated movies. It then looks for other highly ranked movies by those users and then recommends them to the primary user. Netflix also shows the average star rating for all users for a particular film, but also shows a predicted rating for an individual using the same collaborative filtering technique.

The collaborative filtering method works well for Netflix for several reasons. First of all, they have a captive audience who is constantly returning to the site and on whom they have collected a considerable amount of data. Second, the users have a clear benefit from participating in the collaborative filtering since it generates recommendations for them that lead to a better movie watching experience. Perhaps most importantly, the system works because Netflix makes it so easy for customers to use. One click of the mouse is all it takes to rate a film.