Meaning of Artificial Intelligence; Types, Pros/Cons Explained
To understand the concept of Artificial Intelligence, the following questions may be asked;
What is Artificial Intelligence? And in what ways is it relevant or applicable in the present and future?
This article will discuss the following issues within the context of the topic;
-Meaning of Artificial Intelligence
-Types of Intelligence
-Developments in Artificial Intelligence
-Types/Categories of Artificial Intelligence
-Phases/Stages of Artificial Intelligence
-Advantages and Disadvantages of Artificial Intelligence
-Opportunities in Artificial Intelligence
-Present and Future Prospects of Artificial Intelligence
* Meaning of Artificial Intelligence
Basically, artificial intelligence (AI), refers to the capability of a computer system; which could be either a computer-driven robot or a simple digital computer, to handle responsibilities and tasks that have been more often handled by intelligent beings like humans.
In order to easily understand it, artificial intelligence must be looked at in terms of its component words. It basically represents a form of intelligence exhibited or simulated by artificial media, a typical example of which is machines.
This is in opposition to the intelligence naturally exhibited and utilized by rational and cognitive beings like humans
Artificial intelligence comprises of different forms and functionalities, including automation and robotics, big data analytics, internet of things, machine learning, quantum computing and augmented processes. The roles of these various functionalities and their relevance in today’s society, cut across a relatively wide scope of application.
In essence, artificial intelligence appeals to the ideology that computers could be capable of performing human tasks. Frequently, the term (which may also be represented by A.I for short) is applied to the computerization or digitalization of scenarios and processes that are typical of humans. These scenarios and processes include intellectual reasoning, interpretation, generalization, and experiential learning.
Following the advent of digital computers which saw its full bloom in the 1940s, further developments and modifications have proved that digital computers are capable of handling highly complicated tasks through programming.
These tasks can be exemplified simply using the analysis and validation of mathematical codes and theoretical models. However, the full actualization of ‘Artificial Intelligence’ as the definition of the tern implies, is yet to be accomplished, as there are no computers which can completely match the intellectual flexibility and cognition of intelligent (human) beings.
Computers are also generally yet to meet the diversified and vast domains of knowledge and learning which humans are capable.
This is in spite of numerous and consistent modifications and advancements in computer technology on a continuous basis. The results of these efforts have been mainly the development of digital computer programs that can handles select professional tasks with a high degree of expertise and professionalism.
However, most of these programs are not cognitive, intuitive or entirely autonomous, therefore require manning of some form or the other, to perform. We may observe the achievements in the line of artificial intelligence efforts, by evaluating some modern computer functionalities like search engines, medical diagnosis units, and recognition-based authentication systems.
* Definitions of Intelligence and Learning within the Context of Artificial Intelligence
What is intelligence?
In order to fully appreciate the concept of artificial intelligence, it is crucial to understand in clear terms the meaning of Intelligence itself.
While this term has been portrayed in a similar guise to other terms like intuition, learning and cognition, artificial intelligence is not an instinctual or habitual concept, as is intuition, and is neither linked intensively to learning, as is cognition.
Rather, intelligence is a broader concept that encompasses learning and cognition, adaptation, automation, sustainability and implementation.
It therefore includes not only the ability to perform tasks through training or command; but the ability to adapt to changing scenarios independently, and the capacity to learn and improve from experience in a sustainable and consistent manner.
* Misconceptions about Artificial Intelligence
What are the limits of the concept and definition of Artificial Intelligence?
The well-known and generally perceived nature of artificial intelligence may be illustrated using the image below;
This is however a very stereotypical conception and barely portrays the full meaning of artificial intelligence. In order to properly envisage what artificial intelligence signifies, it may be necessary to understand the different types of intelligence.
What are these types of Intelligence?
Intelligence, as exhibited by humans; includes various traits and capabilities, which are both individualistic and interdependent in nature. What this must imply, is that human intelligence creates capacity in several dimensions. These ‘dimensions’ are what make humans capable of critical reasoning, problem solving, intuition, learning, self-improvement and teamwork, among others.
On the other hand, Intelligence, as may be exhibited by machines and all non-humans, is usually unidimensional. This means that the ‘intelligence’, so to speak, is not diversify-able or multifaceted as in humans. The result of this condition, is the ability of such entities to function properly or expertly in one of few tasks, while being unable to perform several others. It also results in a lack of the capacity to self-develop, or practice experiential learning.
These observations are in line with the identification of two main types of intelligence. These include;
1). Natural Intelligence
2). Artificial Intelligence
Essentially, the ‘natural’ intelligence refers to that which is exhibited by humans, including versatility of knowledge, intuition, cognition, flexibility of application, independence or autonomy, and learning. What these capabilities do is to imbue humans with intelligence which is adaptable and innately diverse. Therefore, we are able to perform more diverse functions and adapt to changing needs and scenarios.
On the contrary, however, artificial intelligence embodies the efforts to make machines powerful enough to supplement, or even totally match and exceed, human capability. The current limitations encountered in this attempt include streamlined functionalities like detection. authentication, language utilization and problem solving.
What is the major way in which Natural and Artificial Intelligence differ?
Mainly, natural and artificial intelligence differ with regards to the ability to learn.
In general, artificial intelligence may be capable of exhibiting some attributes of learning, such as the repetition of an approach used to solve a complex problem or handle a task.
This is however not equivalent to actual, flexible and cognitive learning. It may be more conveniently termed ‘rote learning’, which is more similar to memorization of applied methods and approaches based on repetition. It is less diverse and instrumental than cognitive learning, by not being adaptable, but rather routine in nature.
Natural intelligence is capable of generalizing, which is the act of applying the knowledge of past experience, in new, diverse scenarios. This is not yet achievable by artificial intelligence, which is more inclined to handle responsibilities and solve problems within its scope of programming, repetition and memorization.
How does Artificial Intelligence work? How do we evaluate Artificial Intelligence?
Alan Mathison Turing (23 June 1912 – 7 June 1954), the renowned English computer scientist, who is also considered by many to be the father of artificial intelligence, gives perhaps the most insightful way to evaluate artificial intelligence. In 1950, he introduced the Imitation Game, also famously known as the Turing Test.
This test basically functions to test the capability of artificial intelligence, by evaluating how well it is able to mimic natural intelligence. In other words, the Turing Test is geared toward determining the capacity of the computer to think.
What the Turing Test is about
The Turing Test simply involves interrogating a human and a computer, and determining the degree of difficulty involved in identifying the computer among the two of them. According to the approach used in this test, a computer system or program may be said to have passed if it is mistaken for a human, for up to 30% or more of the time during a five-minute (keyboard) interrogation session.
Outcomes of the Turing Test so far. Has any Computer passed the Turing Test?
While there have been numerous significant advances in artificial intelligence and several evaluations of computer programs over the years, it is fairly evident that artificial intelligence is not yet fully capable of exhibiting the thinking, learning, diversification and generalization attributes of natural intelligence.
There have however been a few outstanding performances, such as the chatbot Eugene Goostman, which is a simulation of a 13-year-old boy from Odessa, Ukraine, who has a gynecologist father and a pet guinea pig.
On the 7th of July, 2014, Eugene Goostman was able to convince 33% of moderators at the 60th anniversary of Alan Turing’s death, that it was human.
While this may be technically interpreted as a ‘pass’ of the Turing Test, sever flaws have been noted to this assumption, by some critics. One of these is the fact that the chatbot did not display natural intelligence of any form, and another is that it neither exhibited the capacity to think.
The means by which Eugene Goostman convinced the moderators was rather by evading and misdirecting questions without actually addressing them. This is in essence, no different from the capability of any computer program or system, since it involves coding the program to respond in certain predetermined and repetitive ways. It therefore presents no actual generalization, adaptation, cognition, or learning capabilities, and cannot solve any problem, as a human would, using natural intelligence. In essence, therefore, there is still yet to be a provable pass of the Turing Test of artificial intelligence.
How can Artificial Intelligence be improved to match Natural Intelligence? Can Intelligence be Artificial indeed?
Improving artificial intelligence is no doubt a huge necessity and responsibility. However, it is highly questionable, as to whether actual Intelligence (that is; including thinking, learning and generalization capacity) can ever be fully achieved artificially, by computers. If this is ever achieved, it will involve extremely complex developments that may be completely unsustainable. The more likely improvements and achievements in artificial intelligence border around developing very smart (but not extremely intelligent) computers. Smartness here, will be simply an enhancement of currently achievable computer attributes, such as rite learning, computation, analysis, detection and authentication.
* Artificial Intelligence Types/Categories
What are the types of Artificial Intelligence?
Generally, there are a number of types or categories into which artificial intelligence can be grouped. These are as follows;
1). Visual Artificial Intelligence
As the term implies, this type of A.I is inclined toward the use of visual material. It is used in geospatial applications; monitoring; image classification, recognition, sorting and conversion; computer vision and augmented reality. A common example of its application can be cited in face-recognition technologies.
2). Limited Memory Artificial Intelligence
This type of A.I is observational, repetitive, memorizing and predictive in nature. It best exemplifies the use of rote learning, which is the artificial simulation of natural learning and adaptation. However, as earlier pointed; it is not adaptive.
Limited Memory A.I works by accumulating knowledge based on observations, and forming functional patterns which will be used on a repetitive (rather than adaptive) basis. It uses data which has been stored in its (limited) memory to form a history of trends and patterns which it applies in future scenarios.
This would be similar to experiential learning, which is typical of natural intelligence, except for the fact that it is not applied on a generalized basis. It is rather only repetitive, as stated earlier.
A good example of the use of limited memory A.I is in the development and utilization of autonomous vehicles. This application is basically hinged on the ability of the computer system or program to observe and record environmental factors that are crucial to the navigation of the vehicle, such as the speed and direction of other vehicles within its vicinity.
By recording these observations, it simply forms an observational history or reference list of data to guide its own operations; thereby preventing it from colliding with other vehicles or exiting its lane, among others.
The ‘limited’ nature of the A.I memory, within this context, enables the system to function based on short-term observations. Again this is much different from experiential learning within the context of natural intelligence, which makes use of long and short-term observations to direct its functions. In any case, limited A.I represents one of the most prominent types of artificial intelligence in the world today.
3). Theory of Mind Artificial Intelligence
The basic ideology behind the theory of mind A.I can be analyzed within the context of the Turing Test. While the requirements of the test may very well never be accomplished- at least not entirely -the theory of mind A.I scheme is designed to achieve as much imitation of human cognizance as possible, using computer systems.
A more convenient way of conceptualizing the theory of mind A.I is to put to consideration, the manner in which highly advanced robotic machines have been portrayed in Hollywood sci-fi movies, in the last few decades. Exemplification can be found in popular plots like Terminator, Iron man, Avengers and the Transformers.
These screenplays portray advanced Artificial Intelligence as being highly emotionally intelligent, and therefore capable of making decisions and performing critical and rational thinking. To make the concept more vivid, such A.I systems are often embodies by humanoid outer-frame designs.
The lofty endeavor of theory of mind A.I has seen some progress in the real world, as well. While there are yet many obstacles facing its actualization, some examples exist which suggest that efforts so far have not been too far from successful.
One of these is the media-renowned Robot Sophia; a humanoid computer system developed by Hong Kong-based company Hanson Robotics, and activated on February 14, 2016.
While it is fairly detectable that the robot is designed to operate based on rote learning, as most other A.I systems, it exhibits a few attributes which may be associated with natural intelligence, such as recognition of human emotional expressions, replication of such expressions, and an ability to provide meaningful (although, of course; repetitive) responses to questions and other stimuli.
4). Analytic Artificial Intelligence
Analytic A.I is very unique, in the sense that it most vividly highlights and exploits the link between artificial intelligence and machine learning, both of which constitute prominent topics in today’s computer-driven society.
Artificial Intelligence and Machine Learning
What is Machine Learning?
In its simplest terms, machine learning refers to a branch of computer science and artificial intelligence, which bases on enabling computer systems and programs to handle tasks which they have not been exclusively programed to handle.
A variant of this definition states that machine learning is the utilization of algorithms to simulate human learning patterns, through observation and implementation. Machine learning is no doubt an effort to achieve experiential learning capabilities for computer systems.
The link between machine learning and A.I is mainly contextual, because machine learning exists within the context of the broader A.I concept.
However, there is also a serious, codependent relationship between the two.
The future and advancement of artificial intelligence, are both highly predicated on how successful computer scientists around the world, are, in enhancing machine learning in years to come.
How is Machine Learning important to A.I?
Returning to the original concept being discussed, analytical A.I is the type of artificial intelligence which basically scans large volumes of data to find recurrent trends, patterns and interdependencies that can be used to develop a framework for evaluation, attribution; recommendation, and decision-making. It is used in forecasting trends and optimizing inventories, among other applications.
The utilization of analytical A.I is driven by rote learning and mind-intelligence models. These models are in turn built using machine learning platforms, tools, functions and processes.
5). Functional Artificial Intelligence
As the term implies, functional A.I is specialized and streamlined to perform a series of preselected functions.
While this type of artificial intelligence is highly similar to the analytic A.I model, it is more inclined to perform commands and take action, than to provide recommendations. However, both of them function by scanning and analyzing data to identify significant and usable trends or patterns.
6). Interactive or Reactive Artificial Intelligence
Interactive A.I can be viewed within a similar context as theory of mind A.I. This is simply because both types of A.I are geared primarily toward designing computer programs and systems that are similar to humans in their interactive and cognitive capabilities.
Classic examples of the implementation of reactive A.I can be cited in the form of chatbots. These programs, which may serve as smart personal assistants in many scenarios, are diverse in their capabilities, that include handling conversational tasks through the use of pre-assigned contextual responses. These programs have found a vast range of application, for all forms of commercial and developmental purposes.
7). Text Artificial Intelligence
The use of text A.I varies across a range of very important functions. These include machine translation, speech-to-text conversion, content generation and text recognition.
Text A.I is multidimensional in its applicability, having analytical and interactive attributes. Therefore, it may be used in natural language processes, database analysis and semantic search, among other applications.
8). Self-aware Artificial Intelligence
Self-aware A.I is highly relevant as an example of efforts to imbibe some crucial aspects of natural intelligence, into the general scheme of A.I.
It literarily involves the development of computer programs which are introspective, and can attribute outcomes to their various causes. Such programs are usually diagnostic in their function and can be used in problem-solving processes.
* Stages/Phases of Artificial Intelligence (may also be called Types of Artificial Intelligence in some cases)
What are the stages of Artificial Intelligence (A.I)?
Basically, the stages of artificial intelligence refer to a depiction (or rather; an attempt ro depict) the major milestones covered in the process of development of A.I. They also present A.I as a conglomerate of developmental phases.
The stages are discussed shortly as follows;
1). Artificial Narrow Intelligence
Artificial narrow intelligence (ANI) is simply the phase or stage of A.I which is signified by the capability of the computer program/system to efficiently and professionally handle a single kind of responsibility or task.
ANI is believed to have been accomplished in the form of analytic and interactive systems since 2016.
2). Artificial General Intelligence
Artificial general intelligence (AGI) refers to the stage of A.I that is immediately subsequent to the narrow A.I phase.
It is typically characterized by a slight enhancement in A.N.I characteristics, so that the computer becomes capable of handling more complex responsibilities.
Generally, Artificial General Intelligence is seen as having already been accomplished, with the notable rise in advanced A.I applications since the year 2020.
3). Artificial Super Intelligence
Artificial super intelligence (ASI) refers to AI which is highly efficient and smart, capable of performing better in intellectual and cognitive features, than humans.
This form of A.I is hypothetical and projected to be achievable by the year 2050, with the accomplishment of natural intelligence functionalities in A.I systems.
* Advantages and Disadvantages of Artificial Intelligence
This section is geared toward answering the question;
What are the advantages/merits, and disadvantages (demerits) of A.I?
Below are a number of points that may satisfactorily address this question.
* Advantages of Artificial Intelligence
1). Elimination of Risks
A.I systems and programs have been very helpful in reducing risks especially in the workplace. This is simply because robotic machines- powered by A.I -have been increasingly assigned to handle many potentially hazardous tasks in place of humans.
Such functions and tasks cut across carious sectors of commerce and industry, such as in bomb detonation, nuclear testing missions, rocket launch expeditions and unmanned space exploration projects, manufacturing controls, deep sea exploration, offshore rig decommissioning, among others.
In addition to reducing risks for humans in these scenarios, A.I-enabled robots are usually capable of making performance more efficient in general.
2). Reduction in the Rate of Human Error
Another significant advantage presented by artificial intelligence, is the capacity to reduce the rate of occurrence, and the general prospects, of human error.
This is possible, because analytics and decision-making by A.I systems, are usually both guided by detailed and predefined algorithms, as well as assembled and evaluated datasets that portray the full conditions and potentials of the scenario at hand. This helps make accurate considerations, decisions and calculations, for most of the time.
3). Consistency and Availability
Numerous studies have been used to show that humans are highly limited in their capacity to work productively for prolonged periods of time. The result of this is a severe level of inconsistency in work, where extended periods and complex tasks are directly proportion to a sharp decline in efficiency, effectiveness and productivity.
These challenges are generally tackled by A.I, which is capable of working consistently for much longer periods of time than humans.
A.I systems can handle tasks with the same level of efficiency without any significant breaks; provided the systems are well configured and managed. They are also able to perform faster calculations and executions than humans and may take on tedious, monotonous and repetitive roles with ease. This is a huge advantage in today’s tedious work environment.
The advantage of unbridled consistency as presented by A.I may be appraised by considering internet and mobile banking applications such as USSD transactions, electronic operations and ATM utilization. These functionalities are all A.I-enabled, and have been immensely helpful to bank users and operators in recent decades.
4). Innovation
Given the fast-rising trend of innovation and technological advancement in our modern society, artificial intelligence has played a highly significant role so far. Research and development (R & D) efforts have grown sophisticated and rigorous, requiring the help of robotic systems and smart analytical platforms to handles various types of responsibilities.
Additionally, artificial intelligence may itself be viewed as a major trend in innovation. While proving that technology can be used to solve increasingly difficult problems in our society, A.I has laid the foundation for numerous scientific and technological discoveries in various fields that include aeronautics, petroleum engineering, computer science, biotechnology, medicine, pharmacy, geology and civil construction, among others.
5). Unbiased Processes of Decision-Making
This function can also be explained from the human-error point of view. Decision-making processes, when solely handled by humans, are highly susceptible to various forms of alteration. This is simply as a result of the emotional tendency of all humans.
The probability of bias in human-controlled decision-making grossly affects the reliability and validity of decisions and developments produced from such circumstances. However, using theory of mind and analytic A.I systems to support or fully control the process of decision-making, can totally eliminate bias that is usually influenced by human emotionality. The accuracy in usage of data also optimizes such decision-making processes.
6). Smart Assistance and Digital Applications
The utilization of smart assistants in recent times is a widespread practice, which is prominent across the commercial, corporate and bureaucratic sectors of the society. In the commercial sector, smart assistants can be observed in the form of intelligent, interactive programs that aid in the process of initiating and executing transactions.
* Disadvantages of Artificial Intelligence
1). High Cost of Development and Implementation
The utilization of A.I programs and systems is usually associated with very high costs. The reason for this is simply because of the high demands in terms of material and intellectual resources required to create, manage and implement A.I functionalities in general.
Because these systems are highly demanding and require continuous modification to be aligned with latest methods and requirements; they may often be too cost-intensive to be considered sustainable.
2). Unemployment Rate Increase
While it has been fully acknowledged that A.I has a vast number of merits and potential advantages, it is important to consider the huge threat of unemployment posed by this form of technology.
Given the fact that robotic machines (often driven solely by A.I) are progressively replacing human resources in different tasks and roles in the workplace across various sectors of commerce and industry, several potential job opportunities have been eliminated and are unavailable to the human populace. It has been predicted that this trend will increase, with A.I taking up a vast percentage of human roles in the next decade to come.
3). Lack of Creativity
The lack of creativity of A.I is simply its inability to think or execute beyond its immediate programming. While several A.I systems have been enabled to function through rote learning, this is basically a functionality that is based on already existent data that is fed into the A.I system.
Artificial intelligence is simply not capable of exhibiting critical reasoning or creativity in its approach to solving problems. This is a serious limitation, since the increasing complexity of industrial processes implies that creativity is needed to provide meaningful resolutions to encountered problems.
4). Effect on Human Work Ethic
The capacity of A.I to replace human effort in several aspects of work has had the negative effect of reducing the working capability of many professionals today. In other words, it may be said that the support provided by A.I in the work environment has made several workers, and a majority of the human populace, lazy.
Asides the reduced capacity to handle physical responsibilities, A.I is believed to have reduced the tendency of humans to perform much intellectual work. This is because most A.I systems come along with computation and analytical capabilities. The reduction of work efficiency and enthusiasm within the human is potentially detrimental to the development of the society in the near future.
5). Lack of Empathy
A.I systems lack a very vital capability which is embedded in natural intelligence. This is empathy.
The importance of empathy can be observed in its ability to foster teamwork and cooperation among human workers. Teamwork is very vital to provide an avenue for collective decision-making and critical reasoning.
There are also concerns regarding the future potentials of the relationship between humans and artificial intelligence. The lack of empathy in such systems implies that they may be capable of driving mass destruction and devastation, thereby causing harm to the human populace, in the near future. This is a likelihood especially considering the rate of growth and enhancement of A.I at the present, and its implied future prospects.
* Branches or Aspects of Artificial Intelligence
Machine Learning
This is the branch of A.I that works toward developing intelligent systems that are able to perform tasks that are beyond the immediate scope of their programming.
The concept of machine learning seeks to utilize algorithms and stored data, to autonomously simulate responses and functionalities.
Robotics
This branch of A.I is predicated upon the design, development and implementation of machines which may carry our human functions.
Basically, robotics involves the design of a physical (usually humanoid) embodiment of the otherwise disembodied A.I programs. Robotics may, however, deviate slightly from humanoid designs and applications. There are about five main fields of robotics technology.
What are the Five main Fields of Robotics?
The five main fields of robotics include Mobility Design, Human-Robot Interface, Sensors, Programming, and Manipulation.
Computer Vision
Computer vision is simply a branch of artificial intelligence which is geared toward developing understanding and functionality in computer systems through the use of images and videos.
Essentially, the concept which drives computer vision is the effort to imitate the visual component and system of the human brain, which is an attribute of natural intelligence. Provided computer systems can be made to learn, and extract meaningful information/interpretation, from visualization, it will represent a huge leap in the advancement of A.I.
Computer Vision poses prospects of increase in computer efficiency, through advanced analytical and interpretive capabilities. The ability to extract meaningful information from images and video will imply that computer interaction will be more advanced and accurate, and less time-consuming. Its application can be seen in spatial analysis and text extraction functionalities, among others.
Natural Language Processing
The intention which drives natural language processing (or NLP) is in relation to the goal of making computer systems to be more interactive, such that they may efficiently analyze and communicate using human languages.
NLP is therefore a branch of artificial intelligence that seeks to enable smart systems to read, analyze, and interpret, as well as communicate effectively, in human languages. It has found application in computer-computer and human-computer communication.
Internet of Things
Internet of Things (IoT) is a branch of artificial intelligence which revolves around internet-driven smart systems that are comprised of interconnected, physical components. The physical components usually vary across a range of devices, that include sensors, data collectors and processors, among others.
The ideology behind internet of things, is to develop an integrative system that is capable of handling data (through collection, processing, analysis, and interpretation) efficiently.
Internet of things, if properly developed and harnessed, can help speed up the accomplishment of the global goal for digitalization.
The importance of this branch of A.I can be seen in applications such as smart building technology and smart city development, integrated systems, smart wearables, industrial collaboration, traffic monitoring and automated vehicular transport. These are prominent uses of IoT, among others.
Recommender Systems
Also known as Recommendation systems, these A.I programs are generally driven by machine learning. Their key function to make recommendations based on collected and analyzed historical data.
Recommender systems find much application in the field of commerce, and can be encountered on e-commerce web platforms like Amazon and eBay. On such platforms, the systems use search history and other forms of stored data to suggest product and service options to potential customers and clients.
Deep Learning
What is Deep Learning?
Deep learning simply refers to a branch of artificial intelligence, which specializes in developing and implementing exponential and multi-linear learning pathways for computer systems.
In essence, deep learning can be considered to be a subset of machine learning. This implies, that while machine learning would encompass deep learning, there are some differences which exist between the two concepts/practices
What is the difference between machine learning and deep learning?
Deep Learning Vs Machine Learning
| Deep Learning | Machine Learning |
| This is a subset of Machine Learning | This is a core aspect of A.I which encompasses Deep Learning |
| Was developed subsequent to, and on the basis of the concept of machine learning | Was developed as a sub-discipline of A.I and therefore prior to Deep Learning |
| Involves the use of multi-linear and sequential, exponential algorithm and dataset patterns | Makes use of solely linear algorithm and dataset patterns |
| The ideology behind deep learning is to imitate and achieve the exponential, progressive capacity of the human brain (natural intelligence) | The aim is to enable computer programs and systems to perform tasks without being exclusively programmed to handle them, through a data analytical mechanism |
| Seeks to improve on rote learning tendencies of conventional machine learning | Is underlain by a basic, practical foundation of rote learning (historical data analysis) patterns |
| Utilizes a hierarchical, exponential and progressive approach to artificial learning | Utilizes an essentially linear approach to artificial learning |
What are Uses of Deep Learning?
Some important uses of deep learning today include text generation, and autonomous vehicle development. The future would likely see more applications however.
Augmented Processes
There are various forms in which we may encounter augmented processes, and they may go by a variety of names. These include Cyber-Augmented Operations (CAO); Robotic Process Automation (RPA); and Augmented Reality (AR).
As the term implies, Augmented Processes are all inclined toward establishing supplementary functionalities through A.I. In essence, such processes support the integration of natural and artificial intelligence, in the form of human-computer interactions.
Augmented processes are based on a model of human-centered A.I collaboration, whereby functionalities of A.I are managed, controlled and supported by humans. These systems therefore, augment the efforts of individuals, rather than entirely replace them.
The applications of augmented processes can be seen in various sectors of commerce and industry today. These include the manufacturing sector, where augmented processes are implemented in the form of expert data management support; predictive maintenance, efficient product design, streamlined logistics, and optimized assembly schemes.
Quantum Computing
What is Quantum Computing and how does it work?
This is a branch of A.I that is based on the concept of the quantum theory and quantum mechanics. Quantum computing systems, therefore, are designed to execute computations and analyses using the quantum theory.
These systems are able to handle high-volume, complex data by assessing these data in bits, where a bit represents the probability of the potency of the state of a variable; and is represented as either 0 or 1. These values may otherwise be referred to as quantum states.
Quantum Computing Vs Classical Computing: What are the major differences?
However, quantum computing differs from classical computing in that these quantum states do not always have to exist individualistically. This means, the computation of a variable can be in terms of 0, 1, or a combination of both 0 and 1. As a result, while 0 and 1 are referred to as Bits in classical or conventional computer systems, these variables are rather called Quantum Bits or Qubits in quantum computing (owing to their combinatory and orbital tendencies).
Relevance/Importance/Uses of Quantum Computing
How does Quantum Computing help us?
The main intended use of quantum computing is to handle and solve problems that are otherwise too complex to be handled by conventional or classical computer programs. Quantum computing therefore, finds its relevance and helpfulness in addressing analytical issues in today’s big data-dominated technological sector. Its high-performance characteristics make it an ideal approach for managing large databases.
Artificial Intelligence Opportunities
The field of A.I poses huge potentials to the modern society. A.I is gradually becoming a very large and elaborate sector, on a consistent and progressive basis.
One of the predictable implications of this is an increase in employment opportunities in A.I and general computer technology. As a matter of fact, artificial intelligence applications have permeated basically every sector of the society, ranging from its use in education, to manufacturing, construction, finance management, agriculture, health, and engineering.
Various opportunities exist, and more are being created, in the A.I sector. This is in line with the potentially significant role which it will play in the future of humanity and the entire universe.
In education, there has been an increase in the creation and participation of A.I-related courses. All of these fall under the broader concept of computer science, and include the different aspects of artificial intelligence learning like robotics, computer vision, etc. The near future will see more available roles for computer programmers, robotics engineers, and data scientists among others, in order to drive the required growth and advancement of artificial intelligence.
Some notable companies which specialize in artificial intelligence-related projects and developments have also sprung up in recent time, and continue to spring up across the world. They include; Docusign, IBM, Nvidia, Mobidev, Toptal and Unucsoft, among several others.
Conclusion
- Relevance of Artificial Intelligence at the Present
As this article has rightly shown, A.I presently has a notable degree of relevance in the modern society. This relevance is envisaged in the utilization of A.I in various industries, where it is being used to enhance the efficiency and consistency of various forms of work-related responsibilities.
- Relevance of Artificial Intelligence in the Future
While it has been highlighted that the future holds prospects of detriments due to artificial intelligence, the threats associated with A.I do not generally outweigh the positive prospects.
A.I is undoubtedly a major factor in the future of science and technology. In the near future it will be developed further and optimized to improve industrial and professional functions and processes. It will also be supportive toward the achievement of sustainable development in our global society, through functions in carbon emissions reduction, environmental remediation, waste management, lean manufacturing and space exploration.
