Patent Publication Number: US-11036825-B2

Title: Computer architecture for maintaining a distance metric across correlithm objects in a correlithm object processing system

Description:
TECHNICAL FIELD 
     The present disclosure relates generally to computer architectures for emulating a processing system, and more specifically to computer architectures for maintaining a distance metric across correlithm objects in a correlithm object processing system. 
     BACKGROUND 
     Conventional computers are highly attuned to using operations that require manipulating ordinal numbers, especially ordinal binary integers. The value of an ordinal number corresponds with its position in a set of sequentially ordered number values. These computers use ordinal binary integers to represent, manipulate, and store information. These computers rely on the numerical order of ordinal binary integers representing data to perform various operations such as counting, sorting, indexing, and mathematical calculations. Even when performing operations that involve other number systems (e.g. floating point), conventional computers still resort to using ordinal binary integers to perform any operations. 
     Ordinal based number systems only provide information about the sequence order of the numbers themselves based on their numeric values. Ordinal numbers do not provide any information about any other types of relationships for the data being represented by the numeric values such as similarity. For example, when a conventional computer uses ordinal numbers to represent data samples (e.g. images or audio signals), different data samples are represented by different numeric values. The different numeric values do not provide any information about how similar or dissimilar one data sample is from another. Unless there is an exact match in ordinal number values, conventional systems are unable to tell if a data sample matches or is similar to any other data samples. As a result, conventional computers are unable to use ordinal numbers by themselves for comparing different data samples and instead these computers rely on complex signal processing techniques. Determining whether a data sample matches or is similar to other data samples is not a trivial task and poses several technical challenges for conventional computers. These technical challenges result in complex processes that consume processing power which reduces the speed and performance of the system. The ability to compare unknown data samples to known data samples is crucial for many security applications such as face recognition, voice recognition, and fraud detection. 
     Thus, it is desirable to provide a solution that allows computing systems to efficiently determine how similar different data samples are to each other and to perform operations based on their similarity. 
     SUMMARY 
     Conventional computers are highly attuned to using operations that require manipulating ordinal numbers, especially ordinal binary integers. The value of an ordinal number corresponds with its position in a set of sequentially ordered number values. These computers use ordinal binary integers to represent, manipulate, and store information. These computers rely on the numerical order of ordinal binary integers representing data to perform various operations such as counting, sorting, indexing, and mathematical calculations. Even when performing operations that involve other number systems (e.g. floating point), conventional computers still resort to using ordinal binary integers to perform any operations. 
     Ordinal based number systems only provide information about the sequence order of the numbers themselves based on their numeric values. Ordinal numbers do not provide any information about any other types of relationships for the data being represented by the numeric values such as similarity. For example, when a conventional computer uses ordinal numbers to represent data samples (e.g. images or audio signals), different data samples are represented by different numeric values. The different numeric values do not provide any information about how similar or dissimilar one data sample is from another. Unless there is an exact match in ordinal number values, conventional systems are unable to tell if a data sample matches or is similar to any other data samples. As a result, conventional computers are unable to use ordinal numbers by themselves for comparing different data samples and instead these computers rely on complex signal processing techniques. Determining whether a data sample matches or is similar to other data samples is not a trivial task and poses several technical challenges for conventional computers. These technical challenges result in complex processes that consume processing power which reduces the speed and performance of the system. The ability to compare unknown data samples to known data samples is crucial for many applications such as security application (e.g. face recognition, voice recognition, and fraud detection). 
     The system described in the present application provides a technical solution that enables the system to efficiently determine how similar different objects are to each other and to perform operations based on their similarity. In contrast to conventional systems, the system uses an unconventional configuration to perform various operations using categorical numbers and geometric objects, also referred to as correlithm objects, instead of ordinal numbers. Using categorical numbers and correlithm objects on a conventional device involves changing the traditional operation of the computer to support representing and manipulating concepts as correlithm objects. A device or system may be configured to implement or emulate a special purpose computing device capable of performing operations using correlithm objects. Implementing or emulating a correlithm object processing system improves the operation of a device by enabling the device to perform non-binary comparisons (i.e. match or no match) between different data samples. This enables the device to quantify a degree of similarity between different data samples. This increases the flexibility of the device to work with data samples having different data types and/or formats, and also increases the speed and performance of the device when performing operations using data samples. These technical advantages and other improvements to the device are described in more detail throughout the disclosure. 
     In one embodiment, the system is configured to use binary integers as categorical numbers rather than ordinal numbers which enables the system to determine how similar a data sample is to other data samples. Categorical numbers provide information about similar or dissimilar different data samples are from each other. For example, categorical numbers can be used in facial recognition applications to represent different images of faces and/or features of the faces. The system provides a technical advantage by allowing the system to assign correlithm objects represented by categorical numbers to different data samples based on how similar they are to other data samples. As an example, the system is able to assign correlithm objects to different images of people such that the correlithm objects can be directly used to determine how similar the people in the images are to each other. In other words, the system is able to use correlithm objects in facial recognition applications to quickly determine whether a captured image of a person matches any previously stored images without relying on conventional signal processing techniques. 
     Correlithm object processing systems use new types of data structures called correlithm objects that improve the way a device operates, for example, by enabling the device to perform non-binary data set comparisons and to quantify the similarity between different data samples. Correlithm objects are data structures designed to improve the way a device stores, retrieves, and compares data samples in memory. Correlithm objects also provide a data structure that is independent of the data type and format of the data samples they represent. Correlithm objects allow data samples to be directly compared regardless of their original data type and/or format. 
     A correlithm object processing system uses a combination of a sensor table, a node table, and/or an actor table to provide a specific set of rules that improve computer-related technologies by enabling devices to compare and to determine the degree of similarity between different data samples regardless of the data type and/or format of the data sample they represent. The ability to directly compare data samples having different data types and/or formatting is a new functionality that cannot be performed using conventional computing systems and data structures. 
     In addition, correlithm object processing system uses a combination of a sensor table, a node table, and/or an actor table to provide a particular manner for transforming data samples between ordinal number representations and correlithm objects in a correlithm object domain. Transforming data samples between ordinal number representations and correlithm objects involves fundamentally changing the data type of data samples between an ordinal number system and a categorical number system to achieve the previously described benefits of the correlithm object processing system. 
     Using correlithm objects allows the system or device to compare data samples (e.g. images) even when the input data sample does not exactly match any known or previously stored input values. For example, an input data sample that is an image may have different lighting conditions than the previously stored images. The differences in lighting conditions can make images of the same person appear different from each other. The device uses an unconventional configuration that implements a correlithm object processing system that uses the distance between the data samples which are represented as correlithm objects and other known data samples to determine whether the input data sample matches or is similar to the other known data samples. Implementing a correlithm object processing system fundamentally changes the device and the traditional data processing paradigm. Implementing the correlithm object processing system improves the operation of the device by enabling the device to perform non-binary comparisons of data samples. In other words, the device is able to determine how similar the data samples are to each other even when the data samples are not exact matches. In addition, the device is able to quantify how similar data samples are to one another. The ability to determine how similar data samples are to each other is unique and distinct from conventional computers that can only perform binary comparisons to identify exact matches. 
     The problems associated with comparing data sets and identifying matches based on the comparison are problems necessarily rooted in computer technologies. As described above, conventional systems are limited to a binary comparison that can only determine whether an exact match is found. Emulating a correlithm object processing system provides a technical solution that addresses problems associated with comparing data sets and identifying matches. Using correlithm objects to represent data samples fundamentally changes the operation of a device and how the device views data samples. By implementing a correlithm object processing system, the device can determine the distance between the data samples and other known data samples to determine whether the input data sample matches or is similar to the other known data samples. In addition, the device is able to determine a degree of similarity that quantifies how similar different data samples are to one another. 
     Certain embodiments of the present disclosure may include some, all, or none of these advantages. These advantages and other features will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings and claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of this disclosure, reference is now made to the following brief description, taken in connection with the accompanying drawings and detailed description, wherein like reference numerals represent like parts. 
         FIG. 1  is a schematic view of an embodiment of a special purpose computer implementing correlithm objects in an n-dimensional space; 
         FIG. 2  is a perspective view of an embodiment of a mapping between correlithm objects in different n-dimensional spaces; 
         FIG. 3  is a schematic view of an embodiment of a correlithm object processing system; 
         FIG. 4  is a protocol diagram of an embodiment of a correlithm object process flow; 
         FIG. 5  is a schematic diagram of an embodiment a computer architecture for emulating a correlithm object processing system; 
         FIG. 6  illustrates an embodiment of a process for maintaining a distance metric across correlithm objects; 
         FIG. 7  illustrates an embodiment of a correlithm object processing system that performs error detection and correction; 
         FIG. 8  illustrates an embodiment of a correlithm object processing system that performs error correction using demultiplexers and multiplexers; 
         FIG. 9  illustrates an embodiment of a correlithm object processing system that implements transparency and traceability; 
         FIG. 10  illustrates an embodiment of a correlithm object processing system that implements coding; 
         FIGS. 11A and 11B  illustrate an embodiment of distance tables used by the correlithm object processing system of  FIG. 10 ; and 
         FIG. 12  illustrates an embodiment of a correlithm object processing system that uses mobile correlithm object devices. 
     
    
    
     DETAILED DESCRIPTION 
       FIGS. 1-5  describe various embodiments of how a correlithm object processing system may be implemented or emulated in hardware, such as a special purpose computer.  FIGS. 6-12  describe different embodiments of correlithm object processing systems and methods to achieve numerous technical advantages. 
       FIG. 1  is a schematic view of an embodiment of a user device  100  implementing correlithm objects  104  in an n-dimensional space  102 . Examples of user devices  100  include, but are not limited to, desktop computers, mobile phones, tablet computers, laptop computers, or other special purpose computer platform. The user device  100  is configured to implement or emulate a correlithm object processing system that uses categorical numbers to represent data samples as correlithm objects  104  in a high-dimensional space  102 , for example a high-dimensional binary cube. Additional information about the correlithm object processing system is described in  FIG. 3 . Additional information about configuring the user device  100  to implement or emulate a correlithm object processing system is described in  FIG. 5 . 
     Conventional computers rely on the numerical order of ordinal binary integers representing data to perform various operations such as counting, sorting, indexing, and mathematical calculations. Even when performing operations that involve other number systems (e.g. floating point), conventional computers still resort to using ordinal binary integers to perform any operations. Ordinal based number systems only provide information about the sequence order of the numbers themselves based on their numeric values. Ordinal numbers do not provide any information about any other types of relationships for the data being represented by the numeric values, such as similarity. For example, when a conventional computer uses ordinal numbers to represent data samples (e.g. images or audio signals), different data samples are represented by different numeric values. The different numeric values do not provide any information about how similar or dissimilar one data sample is from another. In other words, conventional computers are only able to make binary comparisons of data samples which only results in determining whether the data samples match or do not match. Unless there is an exact match in ordinal number values, conventional systems are unable to tell if a data sample matches or is similar to any other data samples. As a result, conventional computers are unable to use ordinal numbers by themselves for determining similarity between different data samples, and instead these computers rely on complex signal processing techniques. Determining whether a data sample matches or is similar to other data samples is not a trivial task and poses several technical challenges for conventional computers. These technical challenges result in complex processes that consume processing power which reduces the speed and performance of the system. 
     In contrast to conventional systems, the user device  100  operates as a special purpose machine for implementing or emulating a correlithm object processing system. Implementing or emulating a correlithm object processing system improves the operation of the user device  100  by enabling the user device  100  to perform non-binary comparisons (i.e. match or no match) between different data samples. This enables the user device  100  to quantify a degree of similarity between different data samples. This increases the flexibility of the user device  100  to work with data samples having different data types and/or formats, and also increases the speed and performance of the user device  100  when performing operations using data samples. These improvements and other benefits to the user device  100  are described in more detail below and throughout the disclosure. 
     For example, the user device  100  employs the correlithm object processing system to allow the user device  100  to compare data samples even when the input data sample does not exactly match any known or previously stored input values. Implementing a correlithm object processing system fundamentally changes the user device  100  and the traditional data processing paradigm. Implementing the correlithm object processing system improves the operation of the user device  100  by enabling the user device  100  to perform non-binary comparisons of data samples. In other words, the user device  100  is able to determine how similar the data samples are to each other even when the data samples are not exact matches. In addition, the user device  100  is able to quantify how similar data samples are to one another. The ability to determine how similar data samples are to each other is unique and distinct from conventional computers that can only perform binary comparisons to identify exact matches. 
     The user device&#39;s  100  ability to perform non-binary comparisons of data samples also fundamentally changes traditional data searching paradigms. For example, conventional search engines rely on finding exact matches or exact partial matches of search tokens to identify related data samples. For instance, conventional text-based search engines are limited to finding related data samples that have text that exactly matches other data samples. These search engines only provide a binary result that identifies whether or not an exact match was found based on the search token. Implementing the correlithm object processing system improves the operation of the user device  100  by enabling the user device  100  to identify related data samples based on how similar the search token is to other data sample. These improvements result in increased flexibility and faster search time when using a correlithm object processing system. The ability to identify similarities between data samples expands the capabilities of a search engine to include data samples that may not have an exact match with a search token but are still related and similar in some aspects. The user device  100  is also able to quantify how similar data samples are to each other based on characteristics besides exact matches to the search token. Implementing the correlithm object processing system involves operating the user device  100  in an unconventional manner to achieve these technological improvements as well as other benefits described below for the user device  100 . 
     Computing devices typically rely on the ability to compare data sets (e.g. data samples) to one another for processing. For example, in security or authentication applications a computing device is configured to compare an input of an unknown person to a data set of known people (or biometric information associated with these people). The problems associated with comparing data sets and identifying matches based on the comparison are problems necessarily rooted in computer technologies. As described above, conventional systems are limited to a binary comparison that can only determine whether an exact match is found. As an example, an input data sample that is an image of a person may have different lighting conditions than previously stored images. In this example, different lighting conditions can make images of the same person appear different from each other. Conventional computers are unable to distinguish between two images of the same person with different lighting conditions and two images of two different people without complicated signal processing. In both of these cases, conventional computers can only determine that the images are different. This is because conventional computers rely on manipulating ordinal numbers for processing. 
     In contrast, the user device  100  uses an unconventional configuration that uses correlithm objects to represent data samples. Using correlithm objects to represent data samples fundamentally changes the operation of the user device  100  and how the device views data samples. By implementing a correlithm object processing system, the user device  100  can determine the distance between the data samples and other known data samples to determine whether the input data sample matches or is similar to the other known data samples, as explained in detail below. Unlike the conventional computers described in the previous example, the user device  100  is able to distinguish between two images of the same person with different lighting conditions and two images of two different people by using correlithm objects  104 . Correlithm objects allow the user device  100  to determine whether there are any similarities between data samples, such as between two images that are different from each other in some respects but similar in other respects. For example, the user device  100  is able to determine that despite different lighting conditions, the same person is present in both images. 
     In addition, the user device  100  is able to determine a degree of similarity that quantifies how similar different data samples are to one another. Implementing a correlithm object processing system in the user device  100  improves the operation of the user device  100  when comparing data sets and identifying matches by allowing the user device  100  to perform non-binary comparisons between data sets and to quantify the similarity between different data samples. In addition, using a correlithm object processing system results in increased flexibility and faster search times when comparing data samples or data sets. Thus, implementing a correlithm object processing system in the user device  100  provides a technical solution to a problem necessarily rooted in computer technologies. 
     The ability to implement a correlithm object processing system provides a technical advantage by allowing the system to identify and compare data samples regardless of whether an exact match has been previous observed or stored. In other words, using the correlithm object processing system the user device  100  is able to identify similar data samples to an input data sample in the absence of an exact match. This functionality is unique and distinct from conventional computers that can only identify data samples with exact matches. 
     Examples of data samples include, but are not limited to, images, files, text, audio signals, biometric signals, electric signals, or any other suitable type of data. A correlithm object  104  is a point in the n-dimensional space  102 , sometimes called an “n-space.” The value of represents the number of dimensions of the space. For example, an n-dimensional space  102  may be a 3-dimensional space, a 50-dimensional space, a 100-dimensional space, or any other suitable dimension space. The number of dimensions depends on its ability to support certain statistical tests, such as the distances between pairs of randomly chosen points in the space approximating a normal distribution. In some embodiments, increasing the number of dimensions in the n-dimensional space  102  modifies the statistical properties of the system to provide improved results. Increasing the number of dimensions increases the probability that a correlithm object  104  is similar to other adjacent correlithm objects  104 . In other words, increasing the number of dimensions increases the correlation between how close a pair of correlithm objects  104  are to each other and how similar the correlithm objects  104  are to each other. 
     Correlithm object processing systems use new types of data structures called correlithm objects  104  that improve the way a device operates, for example, by enabling the device to perform non-binary data set comparisons and to quantify the similarity between different data samples. Correlithm objects  104  are data structures designed to improve the way a device stores, retrieves, and compares data samples in memory. Unlike conventional data structures, correlithm objects  104  are data structures where objects can be expressed in a high-dimensional space such that distance  106  between points in the space represent the similarity between different objects or data samples. In other words, the distance  106  between a pair of correlithm objects  104  in the n-dimensional space  102  indicates how similar the correlithm objects  104  are from each other and the data samples they represent. Correlithm objects  104  that are close to each other are more similar to each other than correlithm objects  104  that are further apart from each other. For example, in a facial recognition application, correlithm objects  104  used to represent images of different types of glasses may be relatively close to each other compared to correlithm objects  104  used to represent images of other features such as facial hair. An exact match between two data samples occurs when their corresponding correlithm objects  104  are the same or have no distance between them. When two data samples are not exact matches but are similar, the distance between their correlithm objects  104  can be used to indicate their similarities. In other words, the distance  106  between correlithm objects  104  can be used to identify both data samples that exactly match each other as well as data samples that do not match but are similar. This feature is unique to a correlithm processing system and is unlike conventional computers that are unable to detect when data samples are different but similar in some aspects. 
     Correlithm objects  104  also provide a data structure that is independent of the data type and format of the data samples they represent. Correlithm objects  104  allow data samples to be directly compared regardless of their original data type and/or format. In some instances, comparing data samples as correlithm objects  104  is computationally more efficient and faster than comparing data samples in their original format. For example, comparing images using conventional data structures involves significant amounts of image processing which is time consuming and consumes processing resources. Thus, using correlithm objects  104  to represent data samples provides increased flexibility and improved performance compared to using other conventional data structures. 
     In one embodiment, correlithm objects  104  may be represented using categorical binary strings. The number of bits used to represent the correlithm object  104  corresponds with the number of dimensions of the n-dimensional space  102  where the correlithm object  102  is located. For example, each correlithm object  104  may be uniquely identified using a 64-bit string in a 64-dimensional space  102 . As another example, each correlithm object  104  may be uniquely identified using a 10-bit string in a 10-dimensional space  102 . In other examples, correlithm objects  104  can be identified using any other suitable number of bits in a string that corresponds with the number of dimensions in the n-dimensional space  102 . 
     In this configuration, the distance  106  between two correlithm objects  104  can be determined based on the differences between the bits of the two correlithm objects  104 . In other words, the distance  106  between two correlithm objects can be determined based on how many individual bits differ between the correlithm objects  104 . The distance  106  between two correlithm objects  104  can be computed using Hamming distance or any other suitable technique. 
     As an example using a 10-dimensional space  102 , a first correlithm object  104  is represented by a first 10-bit string (1001011011) and a second correlithm object  104  is represented by a second 10-bit string (1000011011). The Hamming distance corresponds with the number of bits that differ between the first correlithm object  104  and the second correlithm object  104 . In other words, the Hamming distance between the first correlithm object  104  and the second correlithm object  104  can be computed as follows: 
                   1001011011           1000011011         0001000000         
In this example, the Hamming distance is equal to one because only one bit differs between the first correlithm object  104  and the second correlithm object. As another example, a third correlithm object  104  is represented by a third 10-bit string (0110100100). In this example, the Hamming distance between the first correlithm object  104  and the third correlithm object  104  can be computed as follows:
 
                   1001011011           0110100100         1111111111         
The Hamming distance is equal to ten because all of the bits are different between the first correlithm object  104  and the third correlithm object  104 . In the previous example, a Hamming distance equal to one indicates that the first correlithm object  104  and the second correlithm object  104  are close to each other in the n-dimensional space  102 , which means they are similar to each other. In the second example, a Hamming distance equal to ten indicates that the first correlithm object  104  and the third correlithm object  104  are further from each other in the n-dimensional space  102  and are less similar to each other than the first correlithm object  104  and the second correlithm object  104 . In other words, the similarity between a pair of correlithm objects can be readily determined based on the distance between the pair correlithm objects.
 
     As another example, the distance between a pair of correlithm objects  104  can be determined by performing an XOR operation between the pair of correlithm objects  104  and counting the number of logical high values in the binary string. The number of logical high values indicates the number of bits that are different between the pair of correlithm objects  104  which also corresponds with the Hamming distance between the pair of correlithm objects  104 . 
     In another embodiment, the distance  106  between two correlithm objects  104  can be determined using a Minkowski distance such as the Euclidean or “straight-line” distance between the correlithm objects  104 . For example, the distance  106  between a pair of correlithm objects  104  may be determined by calculating the square root of the sum of squares of the coordinate difference in each dimension. 
     The user device  100  is configured to implement or emulate a correlithm object processing system that comprises one or more sensors  302 , nodes  304 , and/or actors  306  in order to convert data samples between real-world values or representations and to correlithm objects  104  in a correlithm object domain. Sensors  302  are generally configured to convert real-world data samples to the correlithm object domain. Nodes  304  are generally configured to process or perform various operations on correlithm objects in the correlithm object domain. Actors  306  are generally configured to convert correlithm objects  104  into real-world values or representations. Additional information about sensors  302 , nodes  304 , and actors  306  is described in  FIG. 3 . 
     Performing operations using correlithm objects  104  in a correlithm object domain allows the user device  100  to identify relationships between data samples that cannot be identified using conventional data processing systems. For example, in the correlithm object domain, the user device  100  is able to identify not only data samples that exactly match an input data sample, but also other data samples that have similar characteristics or features as the input data samples. Conventional computers are unable to identify these types of relationships readily. Using correlithm objects  104  improves the operation of the user device  100  by enabling the user device  100  to efficiently process data samples and identify relationships between data samples without relying on signal processing techniques that require a significant amount of processing resources. These benefits allow the user device  100  to operate more efficiently than conventional computers by reducing the amount of processing power and resources that are needed to perform various operations. 
       FIG. 2  is a schematic view of an embodiment of a mapping between correlithm objects  104  in different n-dimensional spaces  102 . When implementing a correlithm object processing system, the user device  100  performs operations within the correlithm object domain using correlithm objects  104  in different n-dimensional spaces  102 . As an example, the user device  100  may convert different types of data samples having real-world values into correlithm objects  104  in different n-dimensional spaces  102 . For instance, the user device  100  may convert data samples of text into a first set of correlithm objects  104  in a first n-dimensional space  102  and data samples of audio samples as a second set of correlithm objects  104  in a second n-dimensional space  102 . Conventional systems require data samples to be of the same type and/or format in order to perform any kind of operation on the data samples. In some instances, some types of data samples cannot be compared because there is no common format available. For example, conventional computers are unable to compare data samples of images and data samples of audio samples because there is no common format. In contrast, the user device  100  implementing a correlithm object processing system is able to compare and perform operations using correlithm objects  104  in the correlithm object domain regardless of the type or format of the original data samples. 
     In  FIG. 2 , a first set of correlithm objects  104 A are defined within a first n-dimensional space  102 A and a second set of correlithm objects  104 B are defined within a second n-dimensional space  102 B. The n-dimensional spaces may have the same number of dimensions or a different number of dimensions. For example, the first n-dimensional space  102 A and the second n-dimensional space  102 B may both be three dimensional spaces. As another example, the first n-dimensional space  102 A may be a three dimensional space and the second n-dimensional space  102 B may be a nine dimensional space. Correlithm objects  104  in the first n-dimensional space  102 A and second n-dimensional space  102 B are mapped to each other. In other words, a correlithm object  104 A in the first n-dimensional space  102 A may reference or be linked with a particular correlithm object  104 B in the second n-dimensional space  102 B. The correlithm objects  104  may also be linked with and referenced with other correlithm objects  104  in other n-dimensional spaces  102 . 
     In one embodiment, a data structure such as table  200  may be used to map or link correlithm objects  104  in different n-dimensional spaces  102 . In some instances, table  200  is referred to as a node table. Table  200  is generally configured to identify a first plurality of correlithm objects  104  in a first n-dimensional space  102  and a second plurality of correlithm objects  104  in a second n-dimensional space  102 . Each correlithm object  104  in the first n-dimensional space  102  is linked with a correlithm object  104  is the second n-dimensional space  102 . For example, table  200  may be configured with a first column  202  that lists correlithm objects  104 A as source correlithm objects and a second column  204  that lists corresponding correlithm objects  104 B as target correlithm objects. In other examples, table  200  may be configured in any other suitable manner or may be implemented using any other suitable data structure. In some embodiments, one or more mapping functions may be used to convert between a correlithm object  104  in a first n-dimensional space and a correlithm object  104  is a second n-dimensional space. 
       FIG. 3  is a schematic view of an embodiment of a correlithm object processing system  300  that is implemented by a user device  100  to perform operations using correlithm objects  104 . The system  300  generally comprises a sensor  302 , a node  304 , and an actor  306 . The system  300  may be configured with any suitable number and/or configuration of sensors  302 , nodes  304 , and actors  306 . An example of the system  300  in operation is described in  FIG. 4 . In one embodiment, a sensor  302 , a node  304 , and an actor  306  may all be implemented on the same device (e.g. user device  100 ). In other embodiments, a sensor  302 , a node  304 , and an actor  306  may each be implemented on different devices in signal communication with each other for example over a network. In other embodiments, different devices may be configured to implement any combination of sensors  302 , nodes  304 , and actors  306 . 
     Sensors  302  serve as interfaces that allow a user device  100  to convert real-world data samples into correlithm objects  104  that can be used in the correlithm object domain. Sensors  302  enable the user device  100  to compare and perform operations using correlithm objects  104  regardless of the data type or format of the original data sample. Sensors  302  are configured to receive a real-world value  320  representing a data sample as an input, to determine a correlithm object  104  based on the real-world value  320 , and to output the correlithm object  104 . For example, the sensor  302  may receive an image  301  of a person and output a correlithm object  322  to the node  304  or actor  306 . In one embodiment, sensors  302  are configured to use sensor tables  308  that link a plurality of real-world values with a plurality of correlithm objects  104  in an n-dimensional space  102 . Real-world values are any type of signal, value, or representation of data samples. Examples of real-world values include, but are not limited to, images, pixel values, text, audio signals, electrical signals, and biometric signals. As an example, a sensor table  308  may be configured with a first column  312  that lists real-world value entries corresponding with different images and a second column  314  that lists corresponding correlithm objects  104  as input correlithm objects. In other examples, sensor tables  308  may be configured in any other suitable manner or may be implemented using any other suitable data structure. In some embodiments, one or more mapping functions may be used to translate between a real-world value  320  and a correlithm object  104  in an n-dimensional space. Additional information for implementing or emulating a sensor  302  in hardware is described in  FIG. 5 . 
     Nodes  304  are configured to receive a correlithm object  104  (e.g. an input correlithm object  104 ), to determine another correlithm object  104  based on the received correlithm object  104 , and to output the identified correlithm object  104  (e.g. an output correlithm object  104 ). In one embodiment, nodes  304  are configured to use node tables  200  that link a plurality of correlithm objects  104  from a first n-dimensional space  102  with a plurality of correlithm objects  104  in a second n-dimensional space  102 . A node table  200  may be configured similar to the table  200  described in  FIG. 2 . Additional information for implementing or emulating a node  304  in hardware is described in  FIG. 5 . 
     Actors  306  serve as interfaces that allow a user device  100  to convert correlithm objects  104  in the correlithm object domain back to real-world values or data samples. Actors  306  enable the user device  100  to convert from correlithm objects  104  into any suitable type of real-world value. Actors  306  are configured to receive a correlithm object  104  (e.g. an output correlithm object  104 ), to determine a real-world output value  326  based on the received correlithm object  104 , and to output the real-world output value  326 . The real-world output value  326  may be a different data type or representation of the original data sample. As an example, the real-world input value  320  may be an image  301  of a person and the resulting real-world output value  326  may be text  327  and/or an audio signal identifying the person. In one embodiment, actors  306  are configured to use actor tables  310  that link a plurality of correlithm objects  104  in an n-dimensional space  102  with a plurality of real-world values. As an example, an actor table  310  may be configured with a first column  316  that lists correlithm objects  104  as output correlithm objects and a second column  318  that lists real-world values. In other examples, actor tables  310  may be configured in any other suitable manner or may be implemented using any other suitable data structure. In some embodiments, one or more mapping functions may be employed to translate between a correlithm object  104  in an n-dimensional space and a real-world output value  326 . Additional information for implementing or emulating an actor  306  in hardware is described in  FIG. 5 . 
     A correlithm object processing system  300  uses a combination of a sensor table  308 , a node table  200 , and/or an actor table  310  to provide a specific set of rules that improve computer-related technologies by enabling devices to compare and to determine the degree of similarity between different data samples regardless of the data type and/or format of the data sample they represent. The ability to directly compare data samples having different data types and/or formatting is a new functionality that cannot be performed using conventional computing systems and data structures. Conventional systems require data samples to be of the same type and/or format in order to perform any kind of operation on the data samples. In some instances, some types of data samples are incompatible with each other and cannot be compared because there is no common format available. For example, conventional computers are unable to compare data samples of images with data samples of audio samples because there is no common format available. In contrast, a device implementing a correlithm object processing system uses a combination of a sensor table  308 , a node table  200 , and/or an actor table  310  to compare and perform operations using correlithm objects  104  in the correlithm object domain regardless of the type or format of the original data samples. The correlithm object processing system  300  uses a combination of a sensor table  308 , a node table  200 , and/or an actor table  310  as a specific set of rules that provides a particular solution to dealing with different types of data samples and allows devices to perform operations on different types of data samples using correlithm objects  104  in the correlithm object domain. In some instances, comparing data samples as correlithm objects  104  is computationally more efficient and faster than comparing data samples in their original format. Thus, using correlithm objects  104  to represent data samples provides increased flexibility and improved performance compared to using other conventional data structures. The specific set of rules used by the correlithm object processing system  300  go beyond simply using routine and conventional activities in order to achieve this new functionality and performance improvements. 
     In addition, correlithm object processing system  300  uses a combination of a sensor table  308 , a node table  200 , and/or an actor table  310  to provide a particular manner for transforming data samples between ordinal number representations and correlithm objects  104  in a correlithm object domain. For example, the correlithm object processing system  300  may be configured to transform a representation of a data sample into a correlithm object  104 , to perform various operations using the correlithm object  104  in the correlithm object domain, and to transform a resulting correlithm object  104  into another representation of a data sample. Transforming data samples between ordinal number representations and correlithm objects  104  involves fundamentally changing the data type of data samples between an ordinal number system and a categorical number system to achieve the previously described benefits of the correlithm object processing system  300 . 
       FIG. 4  is a protocol diagram of an embodiment of a correlithm object process flow  400 . A user device  100  implements process flow  400  to emulate a correlithm object processing system  300  to perform operations using correlithm object  104  such as facial recognition. The user device  100  implements process flow  400  to compare different data samples (e.g. images, voice signals, or text) to each other and to identify other objects based on the comparison. Process flow  400  provides instructions that allows user devices  100  to achieve the improved technical benefits of a correlithm object processing system  300 . 
     Conventional systems are configured to use ordinal numbers for identifying different data samples. Ordinal based number systems only provide information about the sequence order of numbers based on their numeric values, and do not provide any information about any other types of relationships for the data samples being represented by the numeric values such as similarity. In contrast, a user device  100  can implement or emulate the correlithm object processing system  300  which provides an unconventional solution that uses categorical numbers and correlithm objects  104  to represent data samples. For example, the system  300  may be configured to use binary integers as categorical numbers to generate correlithm objects  104  which enables the user device  100  to perform operations directly based on similarities between different data samples. Categorical numbers provide information about how similar different data sample are from each other. Correlithm objects  104  generated using categorical numbers can be used directly by the system  300  for determining how similar different data samples are from each other without relying on exact matches, having a common data type or format, or conventional signal processing techniques. 
     A non-limiting example is provided to illustrate how the user device  100  implements process flow  400  to emulate a correlithm object processing system  300  to perform facial recognition on an image to determine the identity of the person in the image. In other examples, the user device  100  may implement process flow  400  to emulate a correlithm object processing system  300  to perform voice recognition, text recognition, or any other operation that compares different objects. 
     At step  402 , a sensor  302  receives an input signal representing a data sample. For example, the sensor  302  receives an image of person&#39;s face as a real-world input value  320 . The input signal may be in any suitable data type or format. In one embodiment, the sensor  302  may obtain the input signal in real-time from a peripheral device (e.g. a camera). In another embodiment, the sensor  302  may obtain the input signal from a memory or database. 
     At step  404 , the sensor  302  identifies a real-world value entry in a sensor table  308  based on the input signal. In one embodiment, the system  300  identifies a real-world value entry in the sensor table  308  that matches the input signal. For example, the real-world value entries may comprise previously stored images. The sensor  302  may compare the received image to the previously stored images to identify a real-world value entry that matches the received image. In one embodiment, when the sensor  302  does not find an exact match, the sensor  302  finds a real-world value entry that closest matches the received image. 
     At step  406 , the sensor  302  identifies and fetches an input correlithm object  104  in the sensor table  308  linked with the real-world value entry. At step  408 , the sensor  302  sends the identified input correlithm object  104  to the node  304 . In one embodiment, the identified input correlithm object  104  is represented in the sensor table  308  using a categorical binary integer string. The sensor  302  sends the binary string representing to the identified input correlithm object  104  to the node  304 . 
     At step  410 , the node  304  receives the input correlithm object  104  and determines distances  106  between the input correlithm object  104  and each source correlithm object  104  in a node table  200 . In one embodiment, the distance  106  between two correlithm objects  104  can be determined based on the differences between the bits of the two correlithm objects  104 . In other words, the distance  106  between two correlithm objects can be determined based on how many individual bits differ between a pair of correlithm objects  104 . The distance  106  between two correlithm objects  104  can be computed using Hamming distance or any other suitable technique. In another embodiment, the distance  106  between two correlithm objects  104  can be determined using a Minkowski distance such as the Euclidean or “straight-line” distance between the correlithm objects  104 . For example, the distance  106  between a pair of correlithm objects  104  may be determined by calculating the square root of the sum of squares of the coordinate difference in each dimension. 
     At step  412 , the node  304  identifies a source correlithm object  104  from the node table  200  with the shortest distance  106 . A source correlithm object  104  with the shortest distance from the input correlithm object  104  is a correlithm object  104  either matches or most closely matches the received input correlithm object  104 . 
     At step  414 , the node  304  identifies and fetches a target correlithm object  104  in the node table  200  linked with the source correlithm object  104 . At step  416 , the node  304  outputs the identified target correlithm object  104  to the actor  306 . In this example, the identified target correlithm object  104  is represented in the node table  200  using a categorical binary integer string. The node  304  sends the binary string representing to the identified target correlithm object  104  to the actor  306 . 
     At step  418 , the actor  306  receives the target correlithm object  104  and determines distances between the target correlithm object  104  and each output correlithm object  104  in an actor table  310 . The actor  306  may compute the distances between the target correlithm object  104  and each output correlithm object  104  in an actor table  310  using a process similar to the process described in step  410 . 
     At step  420 , the actor  306  identifies an output correlithm object  104  from the actor table  310  with the shortest distance  106 . An output correlithm object  104  with the shortest distance from the target correlithm object  104  is a correlithm object  104  either matches or most closely matches the received target correlithm object  104 . 
     At step  422 , the actor  306  identifies and fetches a real-world output value in the actor table  310  linked with the output correlithm object  104 . The real-world output value may be any suitable type of data sample that corresponds with the original input signal. For example, the real-world output value may be text that indicates the name of the person in the image or some other identifier associated with the person in the image. As another example, the real-world output value may be an audio signal or sample of the name of the person in the image. In other examples, the real-world output value may be any other suitable real-world signal or value that corresponds with the original input signal. The real-world output value may be in any suitable data type or format. 
     At step  424 , the actor  306  outputs the identified real-world output value. In one embodiment, the actor  306  may output the real-world output value in real-time to a peripheral device (e.g. a display or a speaker). In one embodiment, the actor  306  may output the real-world output value to a memory or database. In one embodiment, the real-world output value is sent to another sensor  302 . For example, the real-world output value may be sent to another sensor  302  as an input for another process. 
       FIG. 5  is a schematic diagram of an embodiment of a computer architecture  500  for emulating a correlithm object processing system  300  in a user device  100 . The computer architecture  500  comprises a processor  502 , a memory  504 , a network interface  506 , and an input-output (I/O) interface  508 . The computer architecture  500  may be configured as shown or in any other suitable configuration. 
     The processor  502  comprises one or more processors operably coupled to the memory  504 . The processor  502  is any electronic circuitry including, but not limited to, state machines, one or more central processing unit (CPU) chips, logic units, cores (e.g. a multi-core processor), field-programmable gate array (FPGAs), application specific integrated circuits (ASICs), graphics processing units (GPUs), or digital signal processors (DSPs). The processor  502  may be a programmable logic device, a microcontroller, a microprocessor, or any suitable combination of the preceding. The processor  502  is communicatively coupled to and in signal communication with the memory  204 . The one or more processors are configured to process data and may be implemented in hardware or software. For example, the processor  502  may be 8-bit, 16-bit, 32-bit, 64-bit or of any other suitable architecture. The processor  502  may include an arithmetic logic unit (ALU) for performing arithmetic and logic operations, processor registers that supply operands to the ALU and store the results of ALU operations, and a control unit that fetches instructions from memory and executes them by directing the coordinated operations of the ALU, registers and other components. 
     The one or more processors are configured to implement various instructions. For example, the one or more processors are configured to execute instructions to implement sensor engines  510 , node engines  512 , and actor engines  514 . In an embodiment, the sensor engines  510 , the node engines  512 , and the actor engines  514  are implemented using logic units, FPGAs, ASICs, DSPs, or any other suitable hardware. The sensor engines  510 , the node engines  512 , and the actor engines  514  are each configured to implement a specific set of rules or process that provides an improved technological result. 
     In one embodiment, the sensor engine  510  is configured implement sensors  302  that receive a real-world value  320  as an input, determine a correlithm object  104  based on the real-world value  320 , and output the correlithm object  104 . An example operation of a sensor  302  implemented by a sensor engine  510  is described in  FIG. 4 . 
     In one embodiment, the node engine  512  is configured to implement nodes  304  that receive a correlithm object  104  (e.g. an input correlithm object  104 ), determine another correlithm object  104  based on the received correlithm object  104 , and output the identified correlithm object  104  (e.g. an output correlithm object  104 ). A node  304  implemented by a node engine  512  is also configured to compute distances between pairs of correlithm objects  104 . An example operation of a node  304  implemented by a node engine  512  is described in  FIG. 4 . 
     In one embodiment, the actor engine  514  is configured to implement actors  306  that receive a correlithm object  104  (e.g. an output correlithm object  104 ), determine a real-world output value  326  based on the received correlithm object  104 , and output the real-world output value  326 . An example operation of an actor  306  implemented by an actor engine  514  is described in  FIG. 4 . 
     The memory  504  comprises one or more non-transitory disks, tape drives, or solid-state drives, and may be used as an over-flow data storage device, to store programs when such programs are selected for execution, and to store instructions and data that are read during program execution. The memory  504  may be volatile or non-volatile and may comprise read-only memory (ROM), random-access memory (RAM), ternary content-addressable memory (TCAM), dynamic random-access memory (DRAM), and static random-access memory (SRAM). The memory  504  is operable to store sensor instructions  516 , node instructions  518 , actor instructions  520 , sensor tables  308 , node tables  200 , actor tables  310 , reference tables  704 , sensor output tables  910 , node output tables  920 , actor output tables  930 , distance tables  1008  and  1012 , and/or any other data or instructions. The sensor instructions  516 , the node instructions  518 , and the actor instructions  520  comprise any suitable set of instructions, logic, rules, or code operable to execute the sensor engine  510 , node engine  512 , and the actor engine  514 , respectively. 
     The sensor tables  308 , the node tables  200 , and the actor tables  310  may be configured similar to the sensor tables  308 , the node tables  200 , and the actor tables  310  described in  FIG. 3 , respectively. 
     The network interface  506  is configured to enable wired and/or wireless communications. The network interface  506  is configured to communicate data with any other device or system. For example, the network interface  506  may be configured for communication with a modem, a switch, a router, a bridge, a server, or a client. The processor  502  is configured to send and receive data using the network interface  506 . 
     The I/O interface  508  may comprise ports, transmitters, receivers, transceivers, or any other devices for transmitting and/or receiving data with peripheral devices as would be appreciated by one of ordinary skill in the art upon viewing this disclosure. For example, the I/O interface  508  may be configured to communicate data between the processor  502  and peripheral hardware such as a graphical user interface, a display, a mouse, a keyboard, a key pad, and a touch sensor (e.g. a touch screen). 
       FIG. 6  illustrates one embodiment of a correlithm object process flow  600 . A user device  100  implements process flow  600  to emulate a correlithm object processing system  300  to perform operations using correlithm objects  104 . The user device  100  implements process flow  600  to compare different data samples (e.g. images, voice signals, or text) to each other and to identify other objects based on the comparison. Process flow  600  provides instructions that allows user devices  100  to achieve the improved technical benefits of a correlithm object processing system  300 . 
     An example is provided to illustrate how the user device  100  implements process flow  600  to emulate a correlithm object processing system  300  to perform facial recognition on an image to determine the identity of the person in the image. In other examples, the user device  100  may implement process flow  600  to emulate a correlithm object processing system  300  to perform voice recognition, text recognition, or any other operation that compares different data. 
     At step  602 , the memory  504  stores a node table  200  that links source correlithm objects  104  with target correlithm objects  104 , as illustrated in  FIGS. 2 and 3 . At step  604 , node  304  receives an input correlithm object  104  and at step  606  the node  304  determines distances  106  between the input correlithm object  104  and each source correlithm object  104  in node table  200 . In one embodiment, the distance  106  between two correlithm objects  104  can be determined based on the differences between the bits of the two correlithm objects  104 . In other words, the distance  106  between two correlithm objects can be determined based on how many individual bits differ between a pair of correlithm objects  104 . The distance  106  between two correlithm objects  104  can be computed using Hamming distance, anti-Hamming distance, or any other suitable technique. In another embodiment, the distance  106  between two correlithm objects  104  can be determined using a Minkowski distance such as the Euclidean or “straight-line” distance between the correlithm objects  104 . For example, the distance  106  between a pair of correlithm objects  104  may be determined by calculating the square root of the sum of squares of the coordinate difference in each dimension. 
     At step  608 , node  304  identifies a source correlithm object  104  from node table  200  with the shortest distance  106 . A source correlithm object  104  with the shortest distance from the input correlithm object  104  is a correlithm object  104  that either matches or most closely matches the received input correlithm object  104  from among all of the source correlithm objects  104  in node table  200 . 
     At step  610 , the node  304  identifies a first target correlithm object  104  in the node table  200  linked with the source correlithm object  104  identified at step  608 . In some embodiments, it may be technically advantageous to output the first target correlithm object  104  identified at step  610  for use by the rest of the correlithm object processing system  300  despite the fact that input correlithm object  104  and the source correlithm object  104  identified at step  608  are not exact matches. For example, in a system where the input correlithm object  104  becomes altered during communication, or is modified in some other way during processing, then it may be advantageous from an error correction standpoint to find the closest match to input correlithm object  104  among source correlithm objects  104  in node table and proceed with the corresponding target correlithm object  104 . This approach is described with reference to  FIG. 4 . In other embodiments, however, it may be advantageous to propagate any alterations experienced by input correlithm object  104  or any differences between input correlithm object  104  and the source correlithm object  104  identified at step  608  to the target correlithm object  104  that is output by node  304 . This approach is described herein with respect to  FIG. 6 . 
     At step  612 , node  304  generates a second target correlithm object  104  that is offset from the first target correlithm object  104  by the distance in n-dimensional space between the input correlithm object  104  received at step  604  and the source correlithm object  104  identified at step  608 . For example, assume that input correlithm object  104 , source correlithm objects  104  and target correlithm objects  104  are each 64-bit binary strings. Further assume that the Hamming distance between input correlithm object  104  received at step  604  and source correlithm object  104  identified at step  608  was determined to be four. In this example, node  304  may generate a second target correlithm object  104  at step  612  that differs from the first target correlithm object  104  identified at step  610  by four bits. Thus, second target correlithm object  104  generated at step  612  will be the same n-dimensional distance away from first target correlithm object  104  identified at step  610  as the n-dimensional distance between the input correlithm object  104  received at step  604  and the source correlithm object  104  identified at step  608 . In this way, correlithm object processing system  300  can propagate any errors or modifications to correlithm objects  104  as they are processed while still gaining the benefits of using correlithm objects to represent data. 
     In another example, assume that input correlithm object  104  and source correlithm objects  104  are each 64-bit binary strings and target correlithm objects  104  are each 128-bit binary strings (i.e., they represent data in different n-dimensional spaces). Further assume that the Hamming distance between input correlithm object  104  received at step  604  and source correlithm object  104  identified at step  608  was determined to be four. In this example, node  304  may still generate a second target correlithm object  104  at step  612  that differs from the first target correlithm object  104  identified at step  610  by four bits. Thus, second target correlithm object  104  generated at step  612  will be the same n-dimensional distance away from first target correlithm object  104  identified at step  610  as the n-dimensional distance between the input correlithm object  104  received at step  604  and the source correlithm object  104  identified at step  608  despite residing in different n-dimensional spaces. In this way, correlithm object processing system  300  can propagate any errors or modifications to correlithm objects  104  as they are processed while still gaining the benefits of using correlithm objects to represent data in different n-dimensional spaces. 
     In still another example, assume that input correlithm object  104  and source correlithm objects  104  are each 64-bit binary strings and target correlithm objects  104  are each 128-bit binary strings (i.e., they represent data in different n-dimensional spaces). Further assume that the Hamming distance between input correlithm object  104  received at step  604  and source correlithm object  104  identified at step  608  was determined to be four. In this example, node  304  may generate a second target correlithm object  104  at step  612  that differs from the first target correlithm object  104  identified at step  610  by eight bits. Thus, second target correlithm object  104  generated at step  612  will be proportionally the same n-dimensional distance away from first target correlithm object  104  identified at step  610  (in a 128-bit space) as the n-dimensional distance between the input correlithm object  104  received at step  604  and the source correlithm object  104  identified at step  608  (in a 64-bit space) despite residing in different n-dimensional spaces. In this way, correlithm object processing system  300  can propagate any errors or modifications to correlithm objects  104  as they are processed while still gaining the benefits of using correlithm objects to represent data in different n-dimensional spaces. 
     At step  614 , node  304  outputs the second target correlithm object  104  generated at step  612 . For example, node  304  may output the second correlithm object  104  to the actor  306  illustrated in  FIG. 4 . In this way, some or all of steps  410  through  416  illustrated in  FIG. 4  may be substituted by steps  602  through  614  of  FIG. 6  in the operation of a correlithm object processing system  300 . 
       FIG. 7  is a schematic view of an embodiment of a correlithm object processing system  700  that is implemented by one or more user devices  100  to perform operations using correlithm objects  104 . In one embodiment, system  700  is a part of system  300  illustrated in  FIG. 3  and can be implemented using the computer architecture  500  illustrated in  FIG. 5 . For example, system  700  and its constituent components can be implemented by processor  502 , one or more of the engines  510 ,  512 ,  514 , and  522 , and other elements of computer architecture  500 , described above with respect to  FIG. 5 . The system  700  generally includes a first node  304   a  communicatively coupled to a second node  304   b  by a communication channel  702 . Communication channel  702  may be a wired and/or wireless medium. For example, communication channel  702  may be a satellite link between any number and combination of transmitting earth stations, satellites, and receiving earth stations. In another example, communication channel  702  may be a telecommunications link between one or more of a mobile device, transceiver, and base station in a cellular network. In still another embodiment, communication channel  702  may be a circuit trace that provides an electrical connection between components of an integrated circuit chip. These examples are not meant to be exhaustive and it should be understood that communication channel  702  can be any physical transmission medium between elements or components of a system. System  700  further includes a reference table  704  that stores a plurality of correlithm objects  104  that may be used by and communicated between nodes  304 . Memory  504  stores reference table  704  organized in any suitable data format. In one embodiment, reference table  704  is centrally stored and accessible by one or both nodes  304   a  and  304   b . In another embodiment, one or both of nodes  304   a  and  304   b  stores a copy of reference table  704  locally. In still another embodiment, one or both of nodes  304   a  and  304   b  may store reference table  704  locally and/or access it remotely. 
     In operation, second node  304   b  communicates a particular correlithm object  104   a  to first node  304   a  over communication channel  702 . Correlithm object  104   a  may be one of the plurality of correlithm objects  104  stored in reference table  704 . In this example, assume that communication channel  702  experiences noise  706  that can degrade the quality and/or accuracy of the information being communicated such as, for example, correlithm object  104   a . Sources of noise  706  on communication channel  702  may include intermodulation noise, crosstalk, interference, atmospheric noise, industrial noise, solar noise, or cosmic noise, among others. For example, although second node  304   b  may transmit correlithm object  104   a  over communication channel  702 , due to noise  706 , first node  304   a  might receive an altered version of correlithm object  104 , referred to as correlithm object  104   a ′. Depending on the magnitude and type of noise  706 , the effect on correlithm object  104  might range from small to large. In such a situation, second node  304   b  may be able to leverage the nature of correlithm objects  104  to perform error correction, as described below. 
     To do this, first node  304   a  may compare received correlithm object  104 ′ with the plurality of correlithm objects  104  stored in reference table  704  to identify the particular correlithm object  104  that was transmitted by second node  304   b . In particular, first node  304  may determine the distances in n-dimensional space between correlithm object  104   a ′ and each of the plurality of correlithm objects  104  stored in reference table  704 . In one embodiment, these distances may be determined by calculating Hamming distances between correlithm object  104   a ′ and each of the plurality of correlithm objects  104 . In another embodiment, these distances may be determined by calculating the anti-Hamming distances between correlithm object  104   a ′ and each of the plurality of correlithm objects  104 . As described above, the Hamming distance is determined based on the number of bits that differ between the binary string representing correlithm object  104   a ′ and each of the binary strings representing each of the correlithm objects  104  stored in reference table  704 . The anti-Hamming distance may be determined based on the number of bits that are the same between the binary string representing correlithm object  104   a ′ and each of the binary strings representing each of the correlithm objects  104  stored in reference table  704 . In still other embodiments, the distances in n-dimensional space between correlithm object  104   a ′ and each of the correlithm objects  104  stored in reference table  704  may be determined using a Minkowski distance or a Euclidean distance. 
     Upon calculating the distances between correlithm object  104   a ′ and each of the plurality of correlithm objects  104  stored in reference table  704  using one of the techniques described above, first node  304   a  determines which calculated distance is the shortest distance. This is because the correlithm object  104  stored in reference table  704  having the shortest distance between it and the correlithm object  104   a ′ received by first node  304   a  is likely to be the correlithm object  104   a  that was transmitted by second node  304   b . For example, if correlithm object  104   a  transmitted by second node  304   b  and the correlithm object  104   a ′ received by first node  304   a  are 64-bit binary strings and they differ by only four bits (e.g., Hamming distance is four) whereas the distance in n-dimensional space between correlithm object  104   a ′ and each of the other correlithm objects  104  stored in reference table  704  is somewhere between twenty-four and forty bits (e.g., Hamming distance of between twenty-four and forty), then the correlithm object  104  that only had a four bit difference is most likely the unaltered version of the correlithm object  104   a ′ received at first node  304   a . Accordingly, first node  304   a  may perform error correction by outputting the correlithm object  104  that was determined to have the shortest distance between it and correlithm object  104   a′.    
     In a particular embodiment, if the distance between the correlithm object  104   a ′ and each of the correlithm objects  104  stored in reference table  704  is not within a predetermined number of standard deviations in n-dimensional space, then node  304  may discard correlithm object  104   a ′ and, instead, output an alert reporting that correlithm object  104   a ′ was too corrupted to use for further processing in the system  700 . In this embodiment, second node  304   b  may be prompted to communicate correlithm object  104   a  again, or to communicate it over a different communication channel to first node  304   a . To account for the situation where communication channel  702  experiences too much noise causing correlithm object  104   a ′ to be unusable, the system  700  may be modified as illustrated in  FIG. 8 , and described further below. 
       FIG. 8  is a schematic view of an embodiment of a correlithm object processing system  800  that is implemented by one or more user devices  100  to perform operations using correlithm objects  104 . System  800  and its constituent components can be implemented by processor  502 , one or more of the engines  510 ,  512 , and  514 , and other elements of computer architecture  500 , described above with respect to  FIG. 5 . In one embodiment, system  800  is a part of system  300  illustrated in  FIG. 3 . The system  800  generally includes a first node  304   a  communicatively coupled to a second node  304   b  by a plurality of communication channels  702   a ,  702   b , and  702   c . Although  FIG. 8  illustrates three communication channels  702  between first node  304   a  and second node  304   b , it should be understood that system  800  may include any suitable number of communication channels  702  greater than one. System  700  further includes demultiplexer  802  and multiplexer  800 , which can be implemented by processor  500  of the computer architecture  500 , described above with respect to  FIG. 5 . Demultiplexer  802  is a device that takes information from a single input line and routes it to over several output lines. Conversely, multiplexer  800  is a device that combines information received over several input lines and forwards it over a single output line. Although the other elements of system  800  refer to the corresponding elements of system  700 , the operation of system  800  is slightly different from the operation of system  700  in that correlithm object  104  is split into different portions that are communicated to first node  304   a  over different communication channels  702   a ,  702   b , and  702   c  in order to minimize the risk that any given communication channel  702  is too noisy. 
     In operation, second node  304   b  communicates a particular correlithm object  104   a  to first node  304   a  via demultiplexer  802 , communication channels  702   a ,  702   b , and  702   c , and multiplexer  804 . Correlithm object  104   a  may be one of the plurality of correlithm objects  104  stored in reference table  704 . 
     Demultiplexer  802  receives correlithm object  104  from second node  304   b  and divides it into a plurality of different portions, such as first portion  104   a , second portion  104   b , and third portion  104   b . In a particular embodiment where correlithm object  104  is a 64-bit binary string, for example, demultiplexer node  802  may divide correlithm object  104  into a first portion  104   a  that includes the first sixteen bits of the binary string, second portion  104   b  that includes the second sixteen bits of the binary string, and third portion  104   c  that includes the last sixteen bits of the binary string. In other embodiments, the grouping of bits from the 64-bit binary string of correlithm object  104  may be different among first portion  104   a , second portion  104   b , and third portion  104   c . In still other embodiments, the sizes of the first portion  104   a , second portion  104   b , and third portion  104   c  are not necessarily the same as each other. For example, a larger portion of the correlithm object  104  may be sent over communication channels  702  that are reported to have a lower bit error rate which a smaller portion of the correlithm object  104  may be sent over communication channels  702  that are reported to have a higher bit error rate. Demultiplexer  802  transmits first portion  104   a  over communication channel  702   a , second portion  104   b  over communication channel  702   b , and third portion  104   c  over communication channel  702   c.    
     Multiplexer  804  is communicatively coupled to demultiplexer  802  by communication channels  702   a ,  702   b , and  702   c . Multiplexer  804  receives first portion  104   a  over communication channel  702   a , second portion  104   b  over communication channel  702   b , and third portion  104   c  over communication channel  702   c . As with communication channel  702  described in system  700 , communication channels  702   a ,  702   b , and  702   c  may experience different levels of noise  706   a ,  706   b , and  706   c , respectively, which might cause multiplexer  804  to receive altered versions of correlithm objects  104   a ,  104   b , and  104   c . Because multiplexer  804  combines first portion  104   a ′, second portion  104   b ′, and third portion  104   c ′ into correlithm object  104 ′, the noise  706   a ,  706   b , and  706   c  in communication channels  702   a ,  702   b , and  702   c , respectively, might cause first node  304   a  to receive an altered version of correlithm object  104 , referred to as correlithm object  104   a ′. Depending on the magnitude and type of noise  706   a ,  706   b , and  706   c , the effect on correlithm object  104  might range from small to large. 
     As mentioned briefly above, multiplexer  804  receives first portion  104   a ′, second portion  104   b ′, and third portion  104   c ′ and combines them to form correlithm object  104 ′. In general, multiplexer  804  combines the various bits of first portion  104   a ′, second portion  104   b ′, and third portion  104   c ′ in a manner corresponding to and consistent with the manner in which demultiplexer  802  divided correlithm object  104  into first portion  104   a , second portion  104   b , and third portion  104   c . For example, multiplexer node  804  may combine the sixteen bits of first portion  104   a ′ with the sixteen bits of second portion  104   b ′ and the sixteen bits of third portion  104   c ′ to form a 64-bit binary string that represents correlithm object  104 ′. Because the levels of noise  706   a ,  706   b , and  706   c  may be different among communication channels  702   a ,  702   b , and  702   c , the risk of a singular noisy channel that renders correlithm object  104 ′ unusable is reduced by splitting up correlithm object  104 , communicating it to first node  304   a  over multiple different communication channels  702   a ,  702   b , and  702   c , and then recombining those different portions into correlithm object  104 ′. 
     In a particular embodiment, however, it is possible that a particular communication  702  in system  800  is so noisy that it fails. For example, if communication channel  702   b  is extremely noisy such that it fails, then multiplexer node  804  may receive first portion  104   a ′ and third portion  104   c ′, but it may not receive second portion  104   b ′. In this example, multiplexer node  804  may randomly generate a suitable binary string (e.g., 16-bit binary string) that may substitute for the second portion  104   b ′ that multiplexer  804  was supposed to receive over communication channel  702   b . In particular, multiplexer node  804  may randomly select 1&#39;s and 0&#39;s to create a suitable binary string that may substitute for the second portion  104   b ′ that multiplexer  804  was supposed to receive over communication channel  702   b . Then, multiplexer  804  may combine first portion  104   a ′ with the randomly created binary string and third portion  104   c ′ to generate correlithm object  104 ′. 
     As described above with respect to system  700  and elsewhere above, first node  304   a  of system  800  compares correlithm object  104 ′ with each of the correlithm objects  104  stored in reference table  704  to determine which has the shortest distance between them in n-dimensional space. The correlithm object  104  stored in reference table  704  having the shortest distance between it and the correlithm object  104 ′ received by first node  304   a  is likely to be the correlithm object  104  that was transmitted by second node  304   b . Accordingly, first node  304   a  may perform error correction by outputting the correlithm object  104  that was determined to have the shortest distance between it and correlithm object  104 ′. 
       FIG. 9  is a schematic view of an embodiment of a correlithm object processing system  900  that is implemented by a user device  100  to perform operations using correlithm objects  104 . The system  900  is a variation of the system  300  illustrated in  FIG. 3  and can be implemented using the computer architecture  500  illustrated in  FIG. 5 . For example, system  900  and its constituent components can be implemented by processor  502 , one or more of the engines  510 ,  512 , and  514 , and other elements of computer architecture  500 , described above with respect to  FIG. 5 . As with system  300 , system  900  may be configured with any suitable number and/or configuration of sensors  302 , nodes  304 , and actors  306 . In one embodiment, a sensor  302 , a node  304 , and an actor  306  may all be implemented on the same device (e.g. user device  100 ). In other embodiments, a sensor  302 , a node  304 , and an actor  306  may each be implemented on different devices  100  in signal communication with each other, for example over a network. In other embodiments, different devices  100  may be configured to implement any combination of sensors  302 , nodes  304 , and actors  306 . 
     Sensors  302  serve as interfaces that allow a user device  100  to convert real-world data samples into correlithm objects  104  that can be used in the correlithm object domain. In general, sensors  302  are configured to receive an input signal  902  associated with a timestamp  904  and that includes a real-world value  320  representing a data sample. Sensor  302  is further configured to determine a correlithm object  104  based on the real-world value  320 , and to output the correlithm object  104 . In one embodiment, sensors  302  are configured to use sensor tables  308  that link a plurality of real-world values with a plurality of correlithm objects  104  in an n-dimensional space  102 . Real-world values are any type of signal, value, or representation of data samples. As explained with respect to  FIG. 3 , a sensor table  308  may be configured with a first column  312  that lists real-world value entries and a second column  314  that lists corresponding correlithm objects  104  as input correlithm objects. In other examples, sensor tables  308  may be configured in any other suitable manner or may be implemented using any other suitable data structure. In some embodiments, one or more mapping functions may be used to translate between a real-world value  320  and a correlithm object  104  in an n-dimensional space. 
     In operation, for example, sensor  302  may receive a first input signal  902   a  associated with a first timestamp  904   a  where the input signal  902   a  includes a first real-world value  320   a . First real-world value  320   a  may correspond to value 3 listed in column  312  of sensor table  308  and map to input correlithm object  3  listed in column  314  of sensor table  308 . Thus, sensor  302  may communicate input correlithm object  3  as an output. 
     System  900  may further include a sensor output table  910  that is configured with a first column  912  that lists real-world values  320  received in input signals  902 , second column  914  that lists input correlithm objects that are mapped to those real-world values  320  by sensor  302  using sensor table  308 , and third column  916  that lists timestamps  904  associated with those real-world values  320 . Thus, for example, because first real-world value  320   a  was received in conjunction with input signal  902   a  and corresponded to value 3 in sensor table  308 , the first entry of sensor output table  910  lists value 3 in column  912 . Furthermore, because value 3 was mapped to input correlithm object  3  in sensor table  308 , the first entry of sensor output table  910  lists input correlithm object  3  in column  914 . Finally, because input signal  902   a  that contained real-world value  320   a  was associated with timestamp  904   a , the first entry of sensor output table  910  lists timestamp  904   a , represented by t 1 , in column  916 . 
     In further operation of system  900 , sensor  302  may receive a second input signal  902   b  associated with a second timestamp  904   b  where the second input signal  902   b  includes a second real-world value  320   b . Second real-world value  320   b  may correspond to value  2  listed in column  312  of sensor table  308  and map to input correlithm object  2  listed in column  314  of sensor table  308 . Thus, sensor  302  may communicate input correlithm object  2  as an output. Because second real-world value  320   b  was received in conjunction with second input signal  902   b  and corresponded to value  2  in sensor table  308 , the second entry of sensor output table  910  lists value  2  in column  912 . Furthermore, because value  2  was mapped to input correlithm object  2  in sensor table  308 , the second entry of sensor output table  910  lists input correlithm object  2  in column  914 . Finally, because second input signal  902   b  that contained real-world value  320   b  was associated with timestamp  904   b , the second entry of sensor output table  910  lists timestamp  904   b , represented by t 2 , in column  916 . Sensor output table  910  may include any number of additional entries associated with other input signals  902  received by sensor  302 . Thus, sensor output table  910  logs the inputs, outputs, and associated timestamps of a corresponding sensor  302  to provide transparency into the operation of sensor  302  for future reference and analysis. In particular, the sensor output table  910  supports examination into what inputs and outputs are associated with sensor  302  over time. 
     Nodes  304  are configured to receive a correlithm object  104  (e.g. an input correlithm object  104 ), to determine another correlithm object  104  based on the received correlithm object  104 , and to output the identified correlithm object  104  (e.g. an output correlithm object  104 ). In one embodiment, nodes  304  are configured to use node tables  200  that link a plurality of correlithm objects  104  from a first n-dimensional space  102  with a plurality of correlithm objects  104  in a second n-dimensional space  102 . A node table  200  illustrated in  FIG. 9  may be configured similar to the table  200  described in  FIG. 2 . 
     In general, node  304  receives the input correlithm object  104  from sensor  302  and determines distances  106  between the input correlithm object  104  and each source correlithm object  104  in a node table  200 . In one embodiment, the distance  106  between two correlithm objects  104  can be determined based on the differences between the bits of the two correlithm objects  104 . In other words, the distance  106  between two correlithm objects can be determined based on how many individual bits differ between a pair of correlithm objects  104 . The distance  106  between two correlithm objects  104  can be computed using Hamming distance, anti-Hamming distance, or any other suitable technique. In another embodiment, the distance  106  between two correlithm objects  104  can be determined using a Minkowski distance such as the Euclidean or “straight-line” distance between the correlithm objects  104 . For example, the distance  106  between a pair of correlithm objects  104  may be determined by calculating the square root of the sum of squares of the coordinate difference in each dimension. 
     The node  304  identifies a source correlithm object  104  from the node table  200  with the shortest distance  106 . A source correlithm object  104  with the shortest distance from the input correlithm object  104  is a correlithm object  104  that either matches or most closely matches the received input correlithm object  104 . The node  304  identifies and fetches a target correlithm object  104  in the node table  200  linked with the source correlithm object  104 . The node  304  outputs the identified target correlithm object  104  to the actor  306 . In this example, the identified target correlithm object  104  is represented in the node table  200  using a categorical binary integer string. The node  304  sends the binary string representing the identified target correlithm object  104  to the actor  306 . 
     In an example operation, node  304  may receive a first input correlithm object  104   a  associated with a third timestamp  904   c  where the first input correlithm object  104   a  was determined by sensor  302  from first real-world value  320   a  (i.e., value 3) of first input signal  902   a . Node  200  may determine that first input correlithm object  104   a  has the shortest n-dimensional distance to source correlithm object  1  listed in column  202  of node table  200 , using any suitable technique explained above (e.g., Hamming distance of eight for 64-bit correlithm objects), and map to target correlithm object  1  listed in column  204  of node table  200 . Thus, node  304  may communicate target correlithm object  1  as an output, referenced as target correlithm object  104   c  in  FIG. 9 . 
     System  900  may further include a node output table  920  that is configured with a first column  922  that lists source correlithm objects determined by node  304  from input correlithm objects, second column  924  that lists target correlithm objects that are mapped to those source correlithm objects in node table  200 , third column  926  that lists timestamps  904  associated with those source correlithm objects, and fourth column  928  that lists the n-dimensional distance calculation made by node  304  to determine a source correlithm object from the input correlithm object (e.g., Hamming distance). Thus, for example, because first input correlithm object  104   a  was received by node  304  from sensor  302  and corresponded to source correlithm object  1  in node table  200 , the first entry of node output table  920  lists source correlithm object  1  in column  922 . Furthermore, because source correlithm object  1  was mapped to target correlithm object  1  in node table  200 , the first entry of node output table  920  lists target correlithm object  1  in column  924 . Additionally, because input correlithm object  104   a  that corresponded to source correlithm object  1  was associated with timestamp  904   c , the first entry of node output table  920  lists timestamp  904 , represented by t 3 , in column  926 . Finally, consider that node  304  determined a Hamming distance of eight for the n-dimensional distance between input correlithm object  3  received from sensor  302  and source correlithm object  1  in node table  200  (e.g., assuming 64-bit correlithm objects). Accordingly, first entry of node output table  920  lists a Hamming distance of eight in column  928 . 
     In further operation of system  900 , node  304  may receive a second input correlithm object  104   b  associated with a fourth timestamp  904   d  where the second input correlithm object  104   b  was determined by sensor  302  from second real-world value  320   b  (i.e., value  2 ) of second input signal  902   b . Node  200  may determine that second input correlithm object  104   b  has the shortest n-dimensional distance to source correlithm object  3  listed in column  202  of node table  200 , using any suitable technique explained above (e.g., Hamming distance of ten for 64-bit correlithm objects), and map to target correlithm object  3  listed in column  204  of node table  200 . Thus, node  304  may communicate target correlithm object  3  as an output, referenced as target correlithm object  104   d  in  FIG. 9 . 
     Because second input correlithm object  104   b  was received by node  304  from sensor  302  and corresponded to source correlithm object  3  in node table  200 , the second entry of node output table  920  lists source correlithm object  3  in column  922 . Furthermore, because source correlithm object  3  was mapped to target correlithm object  3  in node table  200 , the second entry of node output table  920  lists target correlithm object  3  in column  924 . Additionally, because input correlithm object  104   b  that corresponded to source correlithm object  3  was associated with timestamp  904   d , the second entry of node output table  920  lists timestamp  904   d , represented by t 4 , in column  926 . Finally, consider that node  304  determined a Hamming distance of ten for the n-dimensional distance between input correlithm object  1  received from sensor  302  and source correlithm object  3  in node table  200  (e.g., assuming 64-bit correlithm objects). Accordingly, second entry of node output table  920  lists a Hamming distance of ten in column  928 . Node output table  920  may include any number of additional entries associated with other input correlithm objects  104  received by node  304 . 
     Thus, node output table  920  logs the inputs, outputs, and associated timestamps of a corresponding node  304  to provide transparency into the operation of node  304  for future reference and analysis. In particular, the node output table  920  supports examination into what inputs and outputs are associated with node  304  over time. In addition, node output table  920  also logs the n-dimensional distance calculations (e.g., Hamming distances) determined by node  304  to provide traceability into the operation of node  304  for future reference and analysis. In particular, the node output table  920  supports examination into why certain inputs and outputs were selected from node table  200  and used during the operation of the node  304  over time. 
     Actors  306  serve as interfaces that allow a user device  100  to convert correlithm objects  104  in the correlithm object domain back to real-world values or data samples. Actors  306  enable the user device  100  to convert from correlithm objects  104  into any suitable type of real-world value. Actors  306  are configured to receive a correlithm object  104  (e.g. an output correlithm object  104 ), to determine a real-world output value  326  based on the received correlithm object  104 , and to output the real-world output value  326 . The real-world output value  326  may be a different data type or representation of the original data sample. In one embodiment, actors  306  are configured to use actor tables  310  that link a plurality of correlithm objects  104  in an n-dimensional space  102  with a plurality of real-world values. As described above with respect to  FIG. 3 , an actor table  310  may be configured with a first column  316  that lists correlithm objects  104  as output correlithm objects and a second column  318  that lists real-world values. In other examples, actor tables  310  may be configured in any other suitable manner or may be implemented using any other suitable data structure. In some embodiments, one or more mapping functions may be employed to translate between a correlithm object  104  in an n-dimensional space and a real-world output value  326 . 
     In general, actor  306  receives the target correlithm object  104  and determines distances between the target correlithm object  104  and each output correlithm object  104  in an actor table  310 . The actor  306  may compute the distances between the target correlithm object  104  and each output correlithm object  104  in an actor table  310  using one of the techniques described above. Actor  306  identifies an output correlithm object  104  from the actor table  310  with the shortest distance  106 . An output correlithm object  104  with the shortest distance from the target correlithm object  104  is a correlithm object  104  that either matches or most closely matches the received target correlithm object  104 . 
     Actor  306  identifies and fetches a real-world output value in the actor table  310  linked with the identified output correlithm object  104 . The real-world output value may be any suitable type and format of data sample. Actor  306  outputs the identified real-world output value. In one embodiment, the actor  306  may output the real-world output value to a peripheral device. In another embodiment, the actor  306  may output the real-world output value to a memory or database. In still another embodiment, the real-world output value is sent to another sensor  302 . For example, the real-world output value may be sent to another sensor  302  as an input for another process. 
     In operation, for example, actor  306  may receive a first target correlithm object  104   c  associated with a fifth timestamp  904   e  where the first target correlithm object  104   c  was determined by node  304  from first source correlithm object (i.e., source correlithm object  1 ) and first input correlithm object  104   a . Actor  306  may determine that first target correlithm object  104   c  has the shortest n-dimensional distance to output correlithm object  2  listed in column  316  of actor table  310 , using any suitable technique explained above (e.g., Hamming distance of three for 64-bit correlithm objects), and map to real-world value  2  listed in column  318  of actor table  310 . Thus, actor  306  may communicate real-world value  2  as an output, referenced as real-world value  326   a  in  FIG. 9 . 
     System  900  may further include actor output table  930  that is configured with a first column  932  that lists output correlithm objects determined by node  306  from target correlithm objects, second column  934  that lists real-world values that are mapped to those output correlithm objects in actor table  310 , third column  936  that lists timestamps  904  associated with those output correlithm objects, and fourth column  938  that lists the n-dimensional distance calculation made by actor  306  to determine a output correlithm object from the target correlithm object (e.g., Hamming distance). Thus, for example, because first target correlithm object  104   c  was received by actor  306  from node  304  and corresponded to output correlithm object  2  in actor table  310 , the first entry of actor output table  930  lists output correlithm object  2  in column  932 . Furthermore, because output correlithm object  2  was mapped to real-world value  2  in actor table  310 , the first entry of actor output table  930  lists real-world value  2  in column  934 . Additionally, because target correlithm object  104   c  that corresponded to output correlithm object  2  was associated with timestamp  904   e , the first entry of actor output table  930  lists timestamp  904   e , represented by t 5 , in column  936 . Finally, consider that actor  306  determined a Hamming distance of three for the n-dimensional distance between target correlithm object  1  received from node  304  and output correlithm object  2  in actor table  310  (e.g., assuming 64-bit correlithm objects). Accordingly, first entry of actor output table  930  lists a Hamming distance of three in column  938 . 
     In further operation of system  900 , actor  306  may receive a second target correlithm object  104   d  associated with a sixth timestamp  904   f  where the second target correlithm object  104   d  was determined by node  304  from second source correlithm object (i.e., source correlithm object  3 ) and second input correlithm object  104   b . Actor  306  may determine that second target correlithm object  104   d  has the shortest n-dimensional distance to output correlithm object  1  listed in column  316  of actor table  310 , using any suitable technique explained above (e.g., Hamming distance of seven for 64-bit correlithm objects), and map to real-world value  1  listed in column  318  of actor table  310 . Thus, actor  306  may communicate real-world value  1  as an output, referenced as real-world value  326   b  in  FIG. 9 . 
     Because second target correlithm object  104   d  was received by actor  306  from node  304  and corresponded to output correlithm object  1  in actor table  310 , the second entry of actor output table  930  lists output correlithm object  1  in column  932 . Furthermore, because output correlithm object  1  was mapped to real-world value  1  in actor table  310 , the second entry of actor output table  930  lists real-world value  1  in column  934 . Additionally, because target correlithm object  104   d  that corresponded to output correlithm object  1  was associated with timestamp  904   f , the second entry of actor output table  930  lists timestamp  904   f , represented by t 6 , in column  936 . Finally, consider that actor  306  determined a Hamming distance of seven for the n-dimensional distance between target correlithm object  3  received from node  304  and output correlithm object  1  in actor table  310  (e.g., assuming 64-bit correlithm objects). Accordingly, second entry of actor output table  930  lists a Hamming distance of seven in column  938 . 
     Thus, actor output table  930  logs the inputs, outputs, and associated timestamps of a corresponding actor  306  to provide transparency into the operation of actor  306  for future reference and analysis. In particular, the actor output table  930  supports examination into what inputs and outputs are associated with actor  306  over time. In addition, actor output table  930  also logs the n-dimensional distance calculations (e.g., Hamming distances) determined by actor  306  to provide traceability into the operation of actor  306  for future reference and analysis. In particular, the actor output table  930  supports examination into why certain inputs and outputs were selected from actor table  310  and used during the operation of the actor  306  over time. 
     The sensor output table  910  captures a historical record of the real-world values  320  received by sensor  302 , the input correlithm object output by the sensor  302 , and the timestamps associated with the mapping of real-world values  320  to input correlithm objects by sensor  302 . The node output table  920  captures a historical record of the source correlithm objects determined by node  304 , the target correlithm objects output by the node  304 , the timestamps associated with the mapping of source correlithm objects with target correlithm objects by node  304 , and the n-dimensional distance calculations (e.g., Hamming distances) associated with the operation of nodes  304 . The actor output table  930  captures a historical record of the output correlithm objects determined by actor  306 , the real-world values  326  output by actor  306 , the timestamps associated with the mapping of output correlithm objects to real-world values  326  by actor  306 , and the n-dimensional distance calculations (e.g., Hamming distances) associated with the operation of actors  306 . These historical records can be individually or collectively retrieved and communicated in response to one or more requests in order to perform an audit or other function. For example, where system  900  is used in an artificial intelligence engine of an autonomous vehicle that gets into an accident, the data from one or more of sensor output table  910 , node output table  920 , and actor output table  930  may be used to examine the decision-making that was made by the autonomous vehicle leading to the accident. The contents of tables  910 ,  920 , and  930  may be periodically stored in remote memory devices for long-term storage and retrieval. 
       FIG. 10  is a schematic view of an embodiment of a correlithm object processing system  1000  that is implemented by a user device  100  to perform operations using correlithm objects  104 . System  1000  and its constituent components can be implemented by processor  502 , one or more of the engines  510 ,  512 , and  514 , and other elements of computer architecture  500 , described above with respect to  FIG. 5 . System  1000  includes sensors  302   a ,  302   b , and  302   c  communicatively coupled to node  304 . System  1000  may be configured with any suitable number and/or configuration of sensors  302  and nodes  304  to achieve an appropriate scale for the operation to be performed. In one embodiment, sensors  302   a ,  302   b ,  302   c , and node  304  may all be implemented on the same device (e.g. user device  100 ). In other embodiments, sensors  302   a ,  302   b ,  302   c , and node  304  may each be implemented on different devices in signal communication with each other, for example over a network. In other embodiments, different devices may be configured to implement any combination of sensors  302   a ,  302   b ,  302   c , and node  304 . 
     In general, sensors  302   a - c  serve as interfaces that allow a user device  100  to convert real-world data samples into correlithm objects  104  that can be used in the correlithm object domain. Sensors  302   a - b  are configured to receive sample text strings as real-world values and to determine one or more correlithm objects  104  based on these values. Sensor  302   c  is configured to receive a test text string as real-world values and to determine one or more correlithm objects  104  based on these values. Node  304  is configured to receive the correlithm objects  104  output by sensors  302   a - c , determine which sample text string is the closest match, in n-dimensional space, to the test text string, and output a correlithm object representing the determined sample text string. 
     In operation, sensor  302   a  receives a first sample text string  1002  including a plurality of characters. In the example illustrated in  FIG. 10 , the first sample text string  1002  is “DOG DAYS”. Sensor  302   a  assigns correlithm objects  104  to subsets of the characters of the first sample text string  1002 . For example, sensor  302   a  assigns a first correlithm object C 11  to the first two characters of the string  1002 : “DO”; a second correlithm object C 12  to the second and third characters of the string  1002 : “OG”; a third correlithm object C 13  to the third and fourth characters of the string  1002 : “G_”; a fourth correlithm object C 14  to the fourth and fifth characters of the string  1002 : “_D”; a fifth correlithm object C 15  to the fifth and sixth characters of the string  1002 : “DA”; a sixth correlithm object C 16  to the sixth and seventh characters of the string  1002 : “AY”; and a seventh correlithm object C 17  to the seventh and eighth characters of the string  1002 : “YS”. The entirety of the string  1002  may be represented by an eighth correlithm object C 1 . In this way, sensor  302   a  represents the real-world value of “DOG DAYS” according to pairwise combinations of characters progressing and overlapping from the beginning of the string to the end of the string  1002 . As will be described below, this allows for a more granular and accurate comparison with the test text string  1006 . Although sensor  302   a  is described above with respect to generating correlithm objects C 11 -C 17  according to a successive and overlapping pairwise subset of characters from string  1002 , the correlithm objects  104  generated by sensor  302   a  from first sample text string  1002  may be any number and combination of characters from the string  1002  to suit particular needs. 
     Sensor  302   b  receives a second sample text string  1004  including a plurality of characters. In the example illustrated in  FIG. 10 , the second sample text string  1004  is “DOWNTOWN”. Sensor  302   b  assigns correlithm objects  104  to subsets of the characters of the second sample text string  1004 . For example, sensor  302   b  assigns a first correlithm object C 21  to the first two characters of the string  1004 : “DO”; a second correlithm object C 22  to the second and third characters of the string  1004 : “OW”; a third correlithm object C 23  to the third and fourth characters of the string  1004 : “WN”; a fourth correlithm object C 24  to the fourth and fifth characters of the string  1004 : “NT”; a fifth correlithm object C 25  to the fifth and sixth characters of the string  1004 : “TO”; a sixth correlithm object C 26  to the sixth and seventh characters of the string  1004 : “OW”; and a seventh correlithm object C 27  to the seventh and eighth characters of the string  1004 : “WN”. The entirety of the string  1004  may be represented by an eighth correlithm object C 2 . In this way, sensor  302   b  represents the real-world value of “DOWNTOWN” according to pairwise combinations of characters progressing and overlapping from the beginning of the string to the end of the string  1004 . As will be described below, this allows for a more granular and accurate comparison with the test text string  1006 . Although sensor  302   b  is described above with respect to generating correlithm objects C 21 -C 27  according to a successive and overlapping pairwise subset of characters from string  1004 , the correlithm objects  104  generated by sensor  302   b  from second sample text string  1004  may be any number and combination of characters from the string  1004  to suit particular needs. 
     Sensor  302   c  receives a test text string  1006  including a plurality of characters. As will be described in greater detail below, node  304  will determine which of the first sample text string  1002  and second sample text string  1004  are the closest match to the test text string  1006  in n-dimensional space using correlithm objects  104 . In the example illustrated in  FIG. 10 , the test text string  1006  is “BULLDOG.” Sensor  302   c  assigns correlithm objects  104  to subsets of the characters of the test text string  1006 . For example, sensor  302   c  assigns a first correlithm object T 11  to the first two characters of the string  1006 : “BU”; a second correlithm object T 12  to the second and third characters of the string  1006 : “UL”; a third correlithm object T 13  to the third and fourth characters of the string  1006 : “LL”; a fourth correlithm object T 14  to the fourth and fifth characters of the string  1006 : “LD”; a fifth correlithm object T 15  to the fifth and sixth characters of the string  1006 : “DO”; and a sixth correlithm object T 16  to the sixth and seventh characters of the string  1006 : “OG”. The entirety of the string  1006  may be represented by a seventh correlithm object T 1 . In this way, sensor  302   c  represents the real-world value of “BULLDOG” according to pairwise combinations of characters progressing and overlapping from the beginning of the string to the end of the string  1006 . As will be described below, this allows for a more granular and accurate comparison between test text string  1006  and first and second sample text strings  1002  and  1004 . Although sensor  302   c  is described above with respect to generating correlithm objects T 11 -T 16  according to a successive and overlapping pairwise subset of characters from string  1006 , the correlithm objects  104  generated by sensor  302   c  from test text string  1006  may be any number and combination of characters from the string  1006  to suit particular needs. 
     Node  304  receives correlithm objects C 1 , C 2 , and T 1  from sensors  302   a ,  302   b , and  302   c , respectively. In general, node  304  determines the n-dimensional distances between each of the correlithm objects T 11 -T 16  of test text string  1006  against each of the correlithm objects C 11 -C 17  associated with first sample text string  1002  and stores it in distance table  1008 , an example of which is illustrated in more detail in  FIG. 11A . Node  304  determines a first composite value  1010  of the distances calculated and stored in distance table  1008 . Node  304  determines the n-dimensional distances between each of the correlithm objects T 11 -T 16  of test text string  1006  against each of the correlithm objects C 21 -C 27  associated with second sample text string  1004  and stores it in distance table  1012 , an example of which is illustrated in more detail in  FIG. 11B . Node  304  determines a second composite value  1014  of the distances calculated and stored in distance table  1012 . Node  304  determines which of first sample text string  1002  and second sample text string  1004  is the closest match to test text string  1006  based on a comparison of composite values  1010  and  1014 , as described in greater detail below. 
       FIG. 11A  illustrates one embodiment of a distance table  1008  stored in memory  504  that is used by node  304  to compare the correlithm objects C 11 -C 17  of first sample text string  1002  with the correlithm objects T 11 -T 16  of test text string  1006  in n-dimensional space  102 . Such a comparison can be used to determine how closely any portion of the first sample text string  1002  matches the test text string  1006 . In operation, node  304  compares each correlithm object T 11 -T 16  pairwise against each correlithm object C 11 -C 17 , and determines Hamming distances (or anti-Hamming distances in an alternative embodiment) based on this pairwise comparison. As described above with regard to  FIG. 1 , the Hamming distance (or anti-Hamming distance) calculation can be used to determine the similarity between correlithm objects T 11 -T 16  and correlithm objects C 11 -C 17 . The average number of bits between a random correlithm object and a particular correlithm object is equal to n/2 (also referred to as standard distance), where ‘n’ is the number of dimensions in the n-dimensional space  102 . The standard deviation is equal to √{square root over (n/4)}, where ‘n’ is the number of dimensional in the n-dimensional space  102 . Thus, if a correlithm object C 11 -C 17  is statistically dissimilar to a corresponding correlithm object T 11 -T 16 , then the Hamming distance is expected to be roughly equal to the standard distance. Therefore, if the n-dimensional space  102  is 64-bits, then the anti-Hamming distance between two dissimilar correlithm objects is expected to be roughly 32 bits. If a correlithm object C 11 -C 17  is statistically similar to a corresponding correlithm object T 11 -T 16 , then the Hamming distance is expected to be roughly equal to six standard deviations less than the standard distance. Therefore, if the n-dimensional space  102  is 64-bits, then the Hamming distance between similar correlithm objects is expected to be eight or less (i.e., 32 (standard distance)−24 (six standard deviations)=8). In other embodiments, if the Hamming distance is equal to four or five standard deviations less than the standard distance, then the correlithm objects are determined to be statistically similar. The use of six standard deviations away from the standard distance to determine statistical similarity is also appropriate in a larger n-dimensional space  102 , such as an n-space of 256-bits. 
     Node  304  determines the Hamming distances between correlithm object T 11  and each of correlithm objects C 11 -C 17  and stores those values in a column of distance table  1008  labeled “T 11 ”. Node  304  determines the Hamming distances between correlithm object T 12  and each of correlithm objects C 11 -C 17  and stores those values in a column of distance table  1008  labeled “T 12 ”. Node  304  repeats this pairwise determination of Hamming distances between correlithm objects T 13 -T 16  and each of correlithm objects C 11 -C 17 , and stores those values in columns of table  1008  labeled “T 13 ”, “T 14 ”, “T 15 ”, and “T 16 ”, respectively. The Hamming distance values represented in the cells of table  1008  indicate which correlithm objects T 11 -T 16  of test text string  1006  are statistically similar to which correlithm objects C 11 -C 17  of first sample text string  1002 . Cells of table  1008  having a Hamming distance value of zero in them, for example, indicate a similarity. Cells of table  1008  with an “SD” in them (for standard distance), for example, indicate a dissimilarity. In table  1008 , for example, the cell indicating the Hamming distance value of zero between correlithm object T 15  (representing the characters “DO”) and correlithm object C 11  (representing characters “DO”) indicates that there is a statistical similarity (“DO” is a match with “DO”). Similarly, the cell indicating the Hamming distance value of zero between correlithm object T 16  (representing the characters “OG”) and correlithm object C 12  (representing characters “OG”) indicates that there is a statistical similarity (“OG” is a match with “OG”). The combination of these comparisons between successive correlithm objects (T 15 -T 16  and C 11 -C 12 ) also indicates that the combination of the characters represented by those correlithm objects are statistically similar (“DOG” is a match with “DOG”). The Hamming distances represented by the cells of table  1008  can be added together to form an aggregate Hamming distance  1010  (e.g., aggregate Hamming distance calculation of  1280  for table  1008 ). The smaller the aggregate Hamming distance calculation, the closer the statistical similarity between correlithm objects T 11 - 16  and correlithm objects C 11 -C 17 . By representing subsets of characters in text strings in n-dimensional space using correlithm objects and then comparing those correlithm to each other as described above with respect to table  1008 , system  1000  can find statistical similarities among text strings. 
       FIG. 11B  illustrates one embodiment of a distance table  1012  stored in memory  504  that is used by node  304  to compare the correlithm objects C 21 -C 27  of second sample text string  1004  with the correlithm objects T 11 -T 16  of test text string  1006  in n-dimensional space  102 . Such a comparison can be used to determine how closely any portion of the second sample text string  1004  matches the test text string  1006 . In operation, node  304  compares each correlithm object T 11 -T 16  pairwise against each correlithm object C 21 -C 27 , and determines Hamming distances (or anti-Hamming distances in an alternative embodiment) based on this pairwise comparison. As described above, if a correlithm object C 21 -C 27  is statistically dissimilar to a corresponding correlithm object T 11 -T 16 , then the Hamming distance is expected to be roughly equal to the standard distance. If a correlithm object C 21 -C 27  is statistically similar to a corresponding correlithm object T 11 -T 16 , then the Hamming distance is expected to be roughly equal to the six standard deviations less than the standard distance (e.g., Hamming distance of eight for 64-bit n-dimensional space  102 ). 
     Node  304  determines the Hamming distances between correlithm object T 11  and each of correlithm objects C 21 -C 27  and stores those values in a column of distance table  1012  labeled “T 11 ”. Node  304  determines the Hamming distances between correlithm object T 12  and each of correlithm objects C 21 -C 27  and stores those values in a column of distance table  1012  labeled “T 12 ”. Node  304  repeats this pairwise determination of Hamming distances between correlithm objects T 13 -T 16  and each of correlithm objects C 21 -C 27 , and stores those values in columns of table  1008  labeled “T 13 ”, “T 14 ”, “T 15 ”, and “T 16 ”, respectively. The Hamming distance values represented in the cells of table  1008  indicate which correlithm objects T 11 -T 16  of test text string  1006  are statistically similar to which correlithm objects C 21 -C 27  of second sample text string  1004 . Cells of table  1012  having a Hamming distance value of zero in them, for example, indicate a similarity. Cells of table  1012  with an “SD” in them (for standard distance), for example, indicate a dissimilarity. In table  1012 , for example, the cell indicating the Hamming distance value of zero between correlithm object T 15  (representing the characters “DO”) and correlithm object C 21  (representing characters “DO”) indicates that there is a statistical similarity (“DO” is a match with “DO”). Unlike with correlithm objects C 11 -C 17  representing first sample text string  1002 , none of the other correlithm objects T 11 -T 16  are statistically similar to any other correlithm objects C 21 -C 27 . The Hamming distances represented by the cells of table  1012  can be added together to form an aggregate Hamming distance  1014  (e.g., aggregate Hamming distance calculation of  1312  for table  1012 ). The smaller the aggregate Hamming distance calculation, the closer the statistical similarity between correlithm objects T 11 - 16  and correlithm objects C 21 -C 27 . By representing subsets of characters in text strings in n-dimensional space using correlithm objects and then comparing those correlithm to each other as described above with respect to table  1012 , system  1000  can find statistical similarities among text strings. 
     Node  304  compares the aggregate Hamming distance  1010  of table  1008  (e.g.,  1280 ) with the aggregate Hamming distance  1014  of table  1012  (e.g.,  1312 ) to determine which is smaller to indicate which of the first sample text string  1002  or the second sample text string  1004  is more statistically similar to the test text string  1006 . In this example, because the aggregate Hamming distance  1010  associated with first sample text string  1002  is smaller than the aggregate Hamming distance  1014  associated with the second sample text string  1004 , node  304  determines that first sample text string  1002  is a closer match to test text string  1006  than second sample text string  1004 . This result is supported by the fact that first sample text string  1002  and test text string  1006  have common characters that spell “DOG” whereas the second sample text string  1004  and test text string  1006  only have common characters that spell “DO”. Accordingly, node  304  outputs a correlithm object C 1  representing the first sample text string  1002 . 
       FIG. 12  is a schematic view of an embodiment of a correlithm object processing system  1200  that is implemented by a user device  100  to perform operations using correlithm objects  104 . The system  1200  is a variation of the system  300  illustrated in  FIG. 3  and can be implemented by processor  502 , one or more engines  510 ,  512 , and  514 , and other elements of computer architecture  500 , described above with respect to  FIG. 5 . As with system  300 , system  1200  may be configured with any suitable number and/or configuration of sensors  302 , nodes  304 , and actors  306 . As illustrated in  FIG. 12 , a collection of sensors  302  may be communicatively coupled to a collection of nodes  304 , which may be further communicatively coupled to a collection of actors  306 . The collection of sensors  302  have access to one or more sensor tables  308  to perform various functions associated with mapping real-world input values  320  to input correlithm objects  104 , among others, as described above with respect to  FIGS. 1-11 . The collection of nodes  304  have access to one or more node tables  200  to perform various functions associated with mapping input correlithm objects  104  to output correlithm objects  104 , among others, as described above with respect to  FIGS. 1-11 . The collection of actors  306  have access to one or more actor tables  310  to perform various functions associated with mapping output correlithm objects  104  to real-world output values  326 , among others, as described above with respect to  FIGS. 1-11 . 
     System  1200  illustrated in  FIG. 12  adds mobility and collective processing to the sensors  302 , nodes  304 , and actors  306 , as described in greater detail below. In particular, system  1200  includes any suitable number and combination of mobile correlithm object devices  1210  (e.g., mobile correlithm object devices  1210   a - f  described below) that each comprise correlithm object “parts” such as sensors  302 , nodes  304 , and/or actors  306 , or higher-level aggregates of such parts, where such aggregates fulfill higher-level correlithm object functions (e.g., such as language or facial recognition or robotic control, among others). A mobile correlithm object device  1210  can be implemented by processor  502 , one or more engines  510 ,  512 , and  514 , and other elements of computer architecture  500 , described above with respect to  FIG. 5 . 
     Each mobile correlithm object device  1210  functions as a single organism while maintaining continuous or periodic communication with the other elements of system  1200  as a whole. In various embodiments, the mobile correlithm object devices  1210  may communicate with each other, using light signals, audio signals, radio frequency signals, all or a portion of a telecommunications network (e.g., Internet), or using any other suitable communications technique applicable to computer or networking systems. System  1200  further includes a central command module  1220  that communicates with and functions as an executive director for an affiliated group of mobile correlithm object devices  1210 . Processor  502  of computer architecture  500  is configured to implement central command module  1220 . 
     A mobile correlithm object device  1210  has the ability to communicate with other mobile correlithm object devices  1210 . In one embodiment, a group of mobile correlithm object devices  1210  are affiliated with one another by being assigned common tasks to perform. In another embodiment, a group of mobile correlithm object devices  1210  are unaffiliated with one another by being assigned different tasks to perform. A mobile correlithm object device  1210  has the ability to communicate with other mobile correlithm object devices  1210  whether they are affiliated or unaffiliated. Each mobile correlithm object device  1210  has sufficient intelligence and functionality to perform individual tasks as a part of a collective function performed by its affiliated group of mobile correlithm object devices  1210 . For example, a collection of mobile correlithm object devices  1210  can collectively implement some or all portions of a computer program in a computer system. The collective intelligence and functionality of the mobile correlithm object devices  1210  and their associated central command module  1220  is distributed across the entire aggregate system  1200  and all or most of its components. One common term for this kind of organization is “swarm intelligence” which is a collective behavior of decentralized, self-organized components. 
       FIG. 12  illustrates example mobile correlithm object devices  1210   a - f  which are now described herein. A mobile correlithm object device  1210   a  may embody a sensor  302  (indicated by “S”) from the collection of sensors  302  illustrated in  FIG. 12 . This means that device  1210   a  can perform at least the functionalities of a sensor  302 , as described above. Device  1210   a  includes an address  1212   a  and a destination table  1214   a . The address  1212   a  is any suitable logical or physical identifier for device  1210   a , such as a network address, a MAC address, an IP address, a computer address, or any other suitable communications address. Destination table  1214   a  is a physical or logical data structure that identifies other mobile correlithm objects  1210  with which to connect and communicate information, and their respective addresses  1214 . Although only a single mobile correlithm object device  1210   a  is prominently illustrated and described in  FIG. 12 , system  1200  can include and deploy any suitable number and combination of mobile correlithm object devices  1210   a.    
     In another example, mobile correlithm object device  1210   b  may embody a node  304  (indicated by “N”) from the collection of nodes  304  illustrated in  FIG. 12 . This means that device  1210   b  can perform at least the functionalities of a node  304 , as described above. Device  1210   b  includes an address  1212   b  and a destination table  1214   b  which are similar to address  1212   a  and destination table  1212   a  but are specific to mobile correlithm object device  1210   b . Although only a single mobile correlithm object device  1210   b  is prominently illustrated and described in  FIG. 12 , system  1200  can include and deploy any suitable number and combination of mobile correlithm object devices  1210   b.    
     In still another example, mobile correlithm object device  1210   c  may embody an actor  306  (indicated by “A”) from the collection of actors  306  illustrated in  FIG. 12 . This means that device  1210   c  can perform at least the functionalities of an actor  306 , as described above. Device  1210   c  includes an address  1212   c  and a destination table  1214   c  which are similar to address  1212   a  and destination table  1212   a  but are specific to mobile correlithm object device  1210   c . Although only a single mobile correlithm object device  1210   c  is prominently illustrated and described in  FIG. 12 , system  1200  can include and deploy any suitable number and combination of mobile correlithm object devices  1210   c.    
     Particular mobile correlithm objects  1210  may embody a combination of sensors  302 , nodes  304 , and actors  306 . For example, mobile correlithm object device  1210   d  may embody each of a sensor  302  and node  304  (indicated by “SN”) from the collection of sensors  302  and nodes  304  illustrated in  FIG. 12 . This means that device  1210   d  can perform the functionalities of at least both sensors  302  and nodes  304 , as described above. Device  1210   d  includes an address  1212   d  and a destination table  1214   d  which are similar to address  1212   a  and destination table  1212   a  but are specific to mobile correlithm object device  1210   d . Although only a single mobile correlithm object device  1210   d  is prominently illustrated and described in  FIG. 12 , system  1200  can include and deploy any suitable number and combination of mobile correlithm object devices  1210   d.    
     In another example, mobile correlithm object device  1210   e  may embody each of a sensor  302 , node  304 , and an actor  306  (indicated by “SNA”) from the collection of sensors  302 , nodes  304 , and actors  306  illustrated in  FIG. 12 . This means that device  1210   e  can perform the functionalities of at least each of sensors  302 , nodes  304 , and actors  306 , as described above. Device  1210   e  includes an address  1212   e  and a destination table  1214   e  which are similar to address  1212   a  and destination table  1212   a  but are specific to mobile correlithm object device  1210   e . Although only a single mobile correlithm object device  1210   e  is prominently illustrated and described in  FIG. 12 , system  1200  can include and deploy any suitable number and combination of mobile correlithm object devices  1210   e.    
     In still another example, mobile correlithm object device  1210   f  may embody each of a node  304  and an actor  306  (indicated by “NA”) from the collection of nodes  304  and actors  306  illustrated in  FIG. 12 . This means that device  1210   f  can perform the functionalities of at least both of nodes  304  and actors  306 , as described above. Device  1210   f  includes an address  1212   f  and a destination table  1214   f  which are similar to address  1212   a  and destination table  1212   a  but are specific to mobile correlithm object device  1210   f . Although only a single mobile correlithm object device  1210   f  is prominently illustrated and described in  FIG. 12 , system  1200  can include and deploy any suitable number and combination of mobile correlithm object devices  1210   f.    
     In some embodiments, one or more of devices  1210  are mobile in some capacity such as, for example, physically, logically, or otherwise. In this context, mobility may be reflected as physical separation and deployment away from the other elements of system  1200 , including central command module  1220 . Therefore, in a collective system  1200 , the mobile correlithm object devices  1210  can be deployed across a logical or physical space instead of or in addition to being located as a single, local computational module. One beneficial environment for mobile correlithm object devices  1210  to be deployed is the Internet. For example, correlithm object based mobile devices embodying sensors, nodes, and/or actors could be distributed across the Internet, with components lying in different machines, servers, physical or logical robots, or other suitable constructs. Another beneficial environment for mobile correlithm object devices  1210  to be deployed is within different elements of a computer system. Such deployment of mobile correlithm object devices  1210  may provide technical advantages in the form of significant and robust resistance to physical damage to any host system because the elements of system  1200 , in this case, would not necessarily reside in only one specific locality. 
     Mobility may also be reflected as logical separation. The mobile correlithm object devices  1210  could be dispersed across a network, so that its functionalities are ubiquitous. Such mobile correlithm object devices  1210  could be shifted from logical host system  1200  to another logical host system  1200  piecemeal, with each mobile correlithm object device  1210  itself remaining functionally intact and able to communicate effectively with other mobile correlithm object devices  1210 , as appropriate. 
     The design and implementation of individual mobile correlithm object devices  1210  could range across a spectrum from individual components to complex subsystems akin to “lobes,” which are higher-level aggregate functionalities and functional units of correlithm object systems. This approach could accomplish several technical advantages. For example, a physical machine might have a correlithm object-based intelligence that is in fact a subordinate component of the collective functionality, with enough logically local functionality to deal with specific local situations. Such an architecture would minimize response and reflex time. The actions of an individual machine would be communicated to a general central correlithm object system, such as central command module  1220 , for further evaluation and direction. Each mobile correlithm object device  1210  could be designed to meet a specific purpose. These purposes span many dimensions, such as, for example, mobility choices (e.g., where and how to move next), action choices (e.g., “fight or flee”), coordination choices (e.g., if, and how best to cooperate and support nearby groupings of mobile correlithm object devices  1210 ), communications choices (e.g., how much and what to pass on to a central correlithm object system or other mobile correlithm object devices  1210 ), and others. 
     One embodiment of these mobile correlithm object devices  1210  is “containerized” mobile correlithm object devices  1210 . In this embodiment, each mobile correlithm object device  1210 , or perhaps a collection of them, and other components or even the entire central correlithm object system  1200  (as is appropriate for the task at hand) are logically placed into a “container.” In this context, a container is an operating system feature in which the kernel allows the existence of multiple isolated user-space instances. A containerized environment, such as this, is good for the distribution of the mobile correlithm object devices  1210  across a network in any suitable manner for the task at hand. In this way, mobile correlithm object devices  1210  that embody one or more of sensors  302 , nodes  304 , and actors  306  can be deployed across a network. 
     System  1200  that deploys mobile correlithm object devices  1210  as described above add redundancy and security against the damage or loss of any given mobile device  1210 . In some embodiments, a subset of mobile correlithm objects  1210  are deployed temporarily, akin to sending out a scouting party of mobile devices  1210  instead of individual mobile devices  1210 . Furthermore, system  1200  facilitates reproduction of the entire system  1200  or particular parts of it (e.g., particular mobile devices  1210 ) to add to the robustness of the system  1200 . System  1200  similarly provides techniques for growth of the correlithm object system or its component parts at any level from simple individual mobile correlithm object devices  1210 , to more complex mobile correlithm object devices  1210 , to a central correlithm object system itself, by adding parts to the system according to several strategies (cloning, adding virgin (untrained) parts, generating new virgin parts, etc.). 
     In operation, one or more mobile correlithm object devices  1210 , such as any number and combination of mobile correlithm object devices  1210   a - f , are deployed to different parts of a network or system to perform particular assigned tasks. Each of the deployed mobile correlithm object devices  1210  perform one or more of the functionalities of a sensor  302 , node  304 , and/or actor  306 , as described above with respect to  FIGS. 1-11 . In one embodiment, one or more of the deployed mobile correlithm object devices  1210  communicate with one another using, for example, their assigned addresses  1212  and destination tables  1214 . In another embodiment, one or more of the deployed mobile correlithm object devices  1210  communicate with other nearby deployed mobile correlithm object devices  1210  regardless of their destination tables  1214 . Mobile correlithm object devices  1210  periodically report back to and take instruction from central command module  1220  to coordinate their functionality, deployment, and other activities. 
     While several embodiments have been provided in the present disclosure, it should be understood that the disclosed systems and methods might be embodied in many other specific forms without departing from the spirit or scope of the present disclosure. The present examples are to be considered as illustrative and not restrictive, and the intention is not to be limited to the details given herein. For example, the various elements or components may be combined or integrated in another system or certain features may be omitted, or not implemented. 
     In addition, techniques, systems, subsystems, and methods described and illustrated in the various embodiments as discrete or separate may be combined or integrated with other systems, modules, techniques, or methods without departing from the scope of the present disclosure. Other items shown or discussed as coupled or directly coupled or communicating with each other may be indirectly coupled or communicating through some interface, device, or intermediate component whether electrically, mechanically, or otherwise. Other examples of changes, substitutions, and alterations are ascertainable by one skilled in the art and could be made without departing from the spirit and scope disclosed herein. 
     To aid the Patent Office, and any readers of any patent issued on this application in interpreting the claims appended hereto, applicants note that they do not intend any of the appended claims to invoke 35 U.S.C. § 112(f) as it exists on the date of filing hereof unless the words “means for” or “step for” are explicitly used in the particular claim.