Introduction
Machines were invented to ease the work of human beings. The complexities required to achieve certain objectives proved to be complex or overwhelming for the human mind. Apart from it, tasks that were achievable by the human mind, when done over tie, became weighing for the human capabilities. Machines were then invented to cater for the occurrence of such events. To achieve it, machines were programmed in a way to make them understand what was required and how to achieve the main objective of the task (Christian 2011).
Programming of machines took place in specific languages that were oriented towards the machines’ capabilities; these languages are also known as machine languages. Machines could not be programmed in a language commonly used by human beings for communication, i.e. natural languages. If it was done, the efficiency of the machine would be lowered, as the machines’ resources would be spent in interpreting the language to its form. Over the years, a common ground was achieved between the languages by utilizing a machine language that adapts to commands that are comprehensible to natural languages.
The programming involved a declaration of the inputs utilized by the machine and the protocol involved, which is the set of instructions used to process the input to a meaningful output. Machines of this nature were used to perform complex algorithmic operations; later on, it has advanced to the conceptualization of logical operations. The machine could thus compute values using a stipulated formula or make an informed decision by comparing variables associated with options. All these activities depended on the programmed instructions to the device; thus, the machine depended on how the human operator deemed it to perform associated errors would mostly be on the programmer’s side. These errors were based on the machine not being able to perform certain operations due to limitations of the code provided or an incorrect set of instructions provided to the machine. A need was created for the machine to adapt to such changes and advance in its functionality.
The Need for Machine Intelligence
As technology advanced over the years, more machines were required to perform various tasks and calculations. It created the need for developing more machines or having a single machine that could perform autonomous functions. Based on the functionality of arithmetic and logical functions, a single machine could perform this task, provided the input was according to machines demand and an output of the same nature such as a display screen. The machine was programmed according to the current objectives at hand. However, during operation, new processing needs in the environment emerged over the period; at other times, errors that were not previously envisioned began to come about. To cater for these new developments the machine needed to be programmed continuously to meet these new demands.
Programming the machine continuously becomes complex and tiresome, as it is the characteristic of any repetitive process. Machines thus needed to be advanced so as to adapt to these new changes; it involved a process of enabling the machine to learn by itself and change its instructions to cater to arising needs or developments in its environment. It became the inception of machine learning by itself a consequent branch of machine intelligence. It is because it enhances the ability of the machine to be smart (Christian 2011).
Achieving this goal reduced errors that were inevitable overtime and increased the ability of the machine to cater to new functions previously not addressed by the programmer. It involved no new lines of the program; however, the program on the device equips the machine with the necessary ability to adapt to new changes through learning. It is adapted to several types of ways the first is supervised learning, whereby the machine is given examples of inputs and their desired outputs. This software acts as a teacher, as it helps the machine learn different inputs and their outputs; through this step, it is able to develop a rule on the relationship between inputs and outputs. Thereby, when the machine comes across a new input with no defined output, utilizing the learned matching mechanism, it is able to map it out to the required output (Christian 2011). The other version of ML was known as unsupervised learning, whereby the machine had to learn on its own without being given an example of inputs and their desired output. It later advanced into reinforced learning, where the machine was trained through a mechanism that gave it a form of reward when it operated correctly and a punishment when it operated in the wrong way (Christian 2011).
Interaction with Human Beings
Advancement in technology led to the incorporation of machines in more complex day-to-day operation done by human beings. Current systems were mechanical in that they simplified work for human beings in the aspect of strength required to perform the task, consequently, it reduced the operation time. A need was developed to equip these machines with electrical and electronic components that could simplify the work further. Soon enough, electrical components were integrated, while for other machines, a completely new advancement was developed. It led to the inception of automation, whereby devices could operate on their own with minimal supervision or none at all.
Automation led to a new phase of machine intelligence; as machines had to take human aspects of activities, they had to sense outputs of the environment in operation and develop outputs that could be used in another process. However, it was for low-level production. As developments took course, the automation of machines was also notable in complex environments, thereby eliminating human experts. Despite this aspect, the machines had to learn the expert’s mechanism through other subsequent branches of machine intelligence that involved analyzing big data. This data was a characterization of the operation of the machines under the influence of the expert, artificial intelligence was used to simulate this nature of events for the machine, and slowly, the process took the course of the machine revealing end results that were close to or above that of the human expert.
The development of robots led to a lot of negative effects on unskilled human labor, as most of them were left without jobs. In consideration of these groups, simple tasks were left for human labor, as automating them was also too expensive compared to the cost of running the production division utilizing human labor. Interaction of human beings and machines became a necessity at the time; thus, the machine had to be programmed in such a way as to work in the same environment as human beings. In other areas, where machines eliminated human labor, the machine still had to learn how to integrate with human beings especially in service industries, whereby the user has to communicate with the machine (Lemley et al. 2017, p.48). In such scenarios, human beings communicate using natural and not machine language. The machine thus had to learn how to understand natural human languages.
Natural Human Languages
Natural human languages are utilized in day-to-day communication with human beings. The aspect of communication using natural languages is ambiguous, as it evolves beyond the type of words used to aspects of context. Context reveals the environment in which the words are communicated; it includes the use of words in specific disciplines. In one environment, an aspect of speech is inclined towards a specific meaning, while in others, it means the opposite thing. The context incorporates the use of punctuation marks, tonal variations, gestures, or facial expressions (Rouse 2017).
These aspects of speech completely change the meaning of words used for communication. Computers begun incorporating the use of speech through machine learning; it bridged the gap of understanding various words and what they meant as per the definition or meaning of the word when used in a specific sentence. The machine intelligence utilized singled out the new words of the speech through categorically searching for adjectives and nouns in the speech it was used to give meaning to the words.
It gave birth to Natural Language Processing; this aspect of machine intelligence gave meaning to words and enabled analysis to be done on natural words captured. The analysis could then be performed on words utilized in a specific setting for advanced analytical tasks that can also be incorporated in aspects of machine intelligence and learning. Currently, Natural Language Processing has advanced to utilize a subsequent branch of machine intelligence called deep learning that is able to bring out the concept of context. Communication using natural language for machines takes place in two ways – recognizing speech and responding back in natural language.
Human communication utilizes several sensory elements such as tonal variation and visual elements such as gestures. All these aspects enable human-to-human communication to be efficient in that communication involves a lot of extra information that is quite not useful in the current setting. Human beings are therefore capable of utilizing these additional elements to make meaning out of the communication. A machine is not able to achieve this form of communication. Deep learning is an advanced complex division of artificial intelligence that sets out to bridge the gap between biological aspects not relatable to a digital environment close to machine intelligence (Han et al. 2014, p.225). It is achieved through advanced analytical capabilities on data from the environment. The machine is thus able to collect data from different forms of speech in different settings and characterize them in accordance with context and meaning (Stubbs and Pustejovsky 2011, p.256). Once it is achieved, the machine learns more about the complex nature of human communication. This machine is now able to interpret different forms of speech based in different contexts and respond to the appropriate level of speech. This use of analytics is also helpful when the data in speech is required to be compared alongside other sets of information. It is achievable, as the speech data is now in a form understandable in machine language or characteristics.
Machines utilizing human speech were also found to be of annoyance to human beings, as the communication was not flat lined nor did it not take any consideration of the context or emotions present. Thus, since humans are emotional beings, they felt the machine did not address their current concerns; also, when someone is in a rush, the machine should be able to communicate the important aspects that are needed (Rouse 2017). It is recognizable in human-to-human communication, but for machine-to-human communication, one had to seek out where there was meaning in the speech.
Deep learning through analytics is the future of understanding different contexts of human speech through an advanced platform that bridges the gap of human characteristics deviant form machine aspects of in natural language. Previous versions implemented did not incorporate the aspect of context in speech, as they utilized an algorithm that sets out to search for specific words in speech and give a defined response if those words are noticed. This advancement through the use of deep learning incorporates context in speech and helps the machine to know the intent of the person communicating apart from the words he or she used.
Nuanced Distinctions
Machine intelligence incorporates the use of systems that resemble the architecture of human reasoning. It is because machine intelligence was oriented to make machines as smart as human beings are or even more. Through this step, a lot of technicalities involved utilizing the form of human reasoning. It led to the development of a branch of artificial neural networks. This neural network exemplifies the structure of the human brain adapts to the connecting structure utilized to connect different parts of the brain responsible for different functions (Lemley et al. 2017, p.49).
The different parts of the brain are used to perform various functions that collectively achieve the main objective at hand. For example, for vision, a section of the brain captures the image from the data relayed from the eyes; another section of the brain makes meaning out of the image, and other sections respond from the meaning relayed. This response may be emotionally oriented or require physical motion of body parts. It is dependent on other parts of the brain for actualization.
Neural networks adapt to the same system outlined above, the network is then adapted to perform various functions. However to make meaning out of the different inputs provided it has to learn, it is enabled through the deep learning (Lemley et al. 2017, p.51). The machine learns through a combination of the neural networks and deep learning. For image recognition, different images are provided for the machine, and different objects in the image are classified; the machine then learns to recognize the different sets of objects available in an image. The neural network develops a mechanism on how to recognize these kinds of images.
Deep learning enables the machine to utilize analytics for comparison of recognized objects to the objects identified in its database based on relational elements of the objects. However, the machine does not apply the complexities of human reasoning to base its decision but the structure of the human brain. Thus, the machine has to utilize machine-oriented nature of characterization which is based on mathematical models that provide a relational basis to commonly observed features of an image that make them the same. It achieves a method of detection through shape, structure, orientation, and other similar characteristics (Lemley et al. 2017, p.53).
Utilizing such a system brings about specific discrepancies that are similar to different objects in this case a gun and an ice cream cone pointed to the device. In both cases, the shape and orientation of both objects are similar; therefore, the machine will classify the devices to be the same in accordance with the object stored in the database. As for the human mind, it will adapt to utilize additional aspects of the image such as pattern, color, or texture that might not be quite achievable for the current machine
Nevertheless, it does not mean it is impossible to clarify the image correctly. However, it concludes that additional information is required to classify the image – in this case, the aspect of texture pattern and previous knowledge from such an item. The machine thus has to incorporate these additional data elements recognizable from a picture. It requires more comparisons that are complex, which is achievable through the incorporation of big data with deep learning (Lemley et al. 2017, p.54). Big data is a collection of various data models that can be distributed in accordance with the different sets of data to be recognized form the image. Then once each data item is correctly matched, using the neural network a correct relational model can be created to assign the object type to the relationship obtained from the various sets of data. Big data and deep learning are, therefore, the keys to enabling precise vision recognition.