Artificial Intelligence

Humans beings are endowed with extraordinary intellectual capabilities which are incomparable to any other beings in the known universe. These abilities are highlighted by conscious mental activities that are characterized by the ability and desire to learn both simple and complex phenomena, reason and act logically, information retention, communication with others and problem-solving among other notable faculties. The computer technology has witnessed tremendous growth over the ages leading to unprecedented changes in the way humans live. Continuous innovation in this regard has and will continually keep changing the landscape of what is natural and what is not.

Among the future technologies that are mindboggling, and which will test the boundary of the natural world and the artificial world and be the emblem of scientific discovery is Artificial Intelligence (AI). Also christened as Machine Intelligence, Artificial Intelligence is defined as the intellectual prowess displayed by machines which reflect human intelligence. The machines imitate human cognitive functions such as logical reasoning, decision making, and learning. The advances in Artificial Intelligence are made possible through successful attempts in modeling the human brain and how human understand the reality around them. Therefore, if the progress made in making this technology operational is anything to go by, humans may have competitors or rather partners in cognitive reasoning.

Robotics

Artificial intelligence no doubt is a product of robotics and its advances. Robotics essentially deals with the design, construction, and maintenance of robots and relates to AI in the way these robots are supposed to imitate human behavior. robotics encompasses components of design such as the mechanics, electronics and computer programmes. Application of robots has been witnessed in diverse sectors such as medicine, agriculture, manufacturing, military and households among others.

Key Disciplines

Since its inception in the 20th century, the field of Artificial Intelligence has grown in leaps and bounds helped in principal by research in the field labeled as AI research. AI researched is built on other subjects of study key of which are psychology, information engineering, computer science, mathematics, and psychology.

Psychology

Understanding the human conscious and unconscious mind requires intensive study achieved through the science of psychology. Psychology researches the common and specific principles of the human brain by interrogating the biological and physical processes underscoring cognitive functions. Understanding human cognitive processes make it possible for them to be modeled and imitate proving the field’s importance to Artificial Intelligence.

Information Engineering

Information engineering plays a key role in the automation of processes useful in Artificial Intelligence. It is defined as the amalgamation of automation techniques for the design of data and process models that create and maintain an organization’s knowledge base leading to automated systems. Through information engineering, information engineers can create a base of knowledge about human cognitive functions for Artificial Intelligence systems. With Information Engineering, Artificial Intelligence system components created separately can be conjoined to work as a unit. These information systems can generate computer code for the Artificial Intelligence with little or no human input.

Computer Science

Computer science is a field of study that focuses on the design and use of computers and computer systems through theory and practical processes. Some aspects, if not all, of computer science, directly influence the future of Artificial Intelligence. The integral ones are data structures and computational methods and algorithms and their analysis to achieve computational efficiency. Programming languages are fundamental to Artificial Intelligence. Other areas of Computer Science particular to Artificial Intelligence are computer and computer systems architecture, computer networks and computer databases. Experts combine all these areas through software development to achieve computational understanding.

Mathematics

Artificial Intelligence is built on key mathematics principles which play a complementary role. Principally, the mathematics principles are calculus, linear algebra, probability, and optimization. A solid background in mathematics is essential for professionals working in the field of Artificial Intelligence. The representation of ideas in Artificial Intelligence is built upon their mathematical representation through properties, categories, objects, and relations among other techniques. Artificial Intelligence problems of Search and Representation reflect those in mathematics.

Targets of AI Research

The growth of Artificial Intelligence through AI research is focused in certain areas of interest which reflect on human intelligence. some of these are explored below.  

Problem-solving

Faced by puzzles or problems, humans employ a step-wise procedure to come up with a solution. Fairly efficient Artificial Intelligence systems also employ a similar technique when presented with an uncertain or unpredictable situation. However, highly efficient systems must incorporate algorithms that mirror humans’ quick and intuitive problem-solving. This can be achieved by drawing upon principles in statistics and economics.

Planning

To be able to stay relevant Artificial Intelligence systems should be able to mimic human tendencies of planning ahead. This also guarantees maximum efficiency of the system and narrows the problem of uncertainty. While it is relatively easier to plan for one factor, planning for multiple factors present enormous challenges. Overcoming these challenges will present enormous opportunities for the advancement of Artificial Intelligence.

Knowledge Representation

Humans possess extensive knowledge about the world in terms of objects, their properties, concepts, events and time among other things. This is represented as a knowledge base paramount for an Artificial Intelligence system to mimic human behavior. the problem of knowledge representation comes in terms of what a machine’s default reasoning will be, its extent of knowledge and its subconscious mind.

Learning

Artificial Intelligence systems are expected to be fully independent with zero input from humans. This requires machines to find patterns alone and be self-teaching which is made possible through a research in Artificial Intelligence christened as Machine Learning. Machine learning enables Artificial Intelligence systems to continually improve themselves through supervised and unsupervised learning.

Natural Language Processing

To achieve human-machine interactions, machines must be able to read and understand human language. This is made possible through Natural Language Processing (NLP). Present progress in addressing this problem has been done through systems which read directly from text written by human sources. When the challenges about natural language processing are overcome, machines will be able to fully read and understand human language, therefore, depicting common sense.

Perception

Perception requires machines to receive and process information from their sensors. These sensors include those that read speech, facial, motion detection and object recognition. Overcoming this problem requires modeling of all objects and their attributes, therefore, a database from which an Artificial Intelligence system will refer.

Motion

Healthy humans possess the capability to move from one place to another. Machines must, therefore, be able to move in whole or part to be considered as intelligent. As in robotics, efficient motion is essential to Artificial Intelligence systems. Future machines are expected to scan and map their immediate environment. while static environments are easy to scan and map, dynamic environments present a monumental challenge for Artificial Intelligence research. The use of proximity sensors in the case of driverless cars presents a monumental step in having motion intelligent machines.

Social Intelligence

Social skills are an emblem of humanity as they enable interactions with each other underscored by the ability to understand and be relatable. Artificial Intelligence systems must be able to imitate human emotions and understand their emotions and motives. Achievement of these will lead to them being labeled as socially intelligent beings. This will make human-computer and computer-computer interactions possible. Progressive research in this regard is being done under the umbrella of artificial emotional intelligence which employs parameters and techniques such as speech descriptors, facial expression detection systems, and physiological monitors.

General Intelligence

The steps and progress made in Artificial Intelligence are projected to peak with general intelligence of a machine mirroring the brain and function of a human being. Attempts at these have been done with no success with focus constrained to the application in a specific domain. The future intelligent machines are expected to overcome this limitation and be cross-domain encompassing all the individual application domains. Some forecasters predict that with general intelligence some Artificial Intelligence systems may surpass human intelligence. An application of this can be witnessed in machine translation, transfer learning and reading the unstructured web. This can be achieved through a hypothetical master algorithm.

AI Research Techniques

Through AI research, steps are being made in the desire to achieve a fully functional AI system. The techniques employed are elaborated below.

Cybernetics

Cybernetics integrates various disciplines in coming up with control systems for machines using technology. The system works on the causal effect analogy where action by one component generates a reaction by the other components.  Through cybernetics, researchers can explore and solve AI system concepts such as communication and learning.

Symbolic and Sub-Symbolic Intelligence

These research techniques were the pioneers of research in the field where they focused on representing problems in human-readable forms. Through concepts such as expert systems, symbols manipulation is used to represent AI concepts by relying on simple rules. The application of these techniques is however only useful where inputs are certain. 

Embodied Intelligence

Proposed by opposers to the symbolic research technique, embodied intelligence research technique dictates that higher intelligence of machines can be achieved by achieving basic cognitive and motor functions.

Computing Intelligence

This approach to AI enables machines to learn from data and apply the knowledge acquired to solve complex real-world phenomena which are beyond the scope of conventional modeling. These problems are those which cannot be represented as binary for the machine. This technique is necessary for concepts such as Natural Language Learning and reasoning.

Statistical Learning

Statistical learning technique uses mathematical and statistical concepts such as the Bayesian decision theory and hidden Markov models. The success made using these concepts to perform data mining paves way for their application in AI.

Logic-based and Knowledge-based Research

The Logic-based technique tries to map abstract reasoning to solve learning and planning problems while the application of knowledge-based techniques in expert systems enables the creation of a knowledge base for AI.

AI Research Tools

The following Artificial Intelligence research tools are currently being used in technology to overcome some of the problems explored above. These are:

Search Algorithm

Like the mind of a human being when presented with a challenge requiring a solution, Artificial Intelligence systems can use a search algorithm to look for the best solution in a myriad of possible solutions. Search algorithms can be used to overcome all the Artificial Intelligence problems. For example, through searches performed in machine configurations, robots can be able to move any part of its body as is desired.

Probabilistic Methods

Artificial Intelligence systems are more likely than not to face uncertain situations. Overcoming these problems warrants the operationalization of certain probabilistic methods such as the Bayesian networks. Variants of Bayesian networks are used to solve individual Artificial Intelligence problems. For example, Bayesian decision networks can be used to solve the planning problem. Decision networks are used as a tool to model how Artificial Intelligence systems can make decisions when faced with multiple options.

Classifiers

Mathematical classifiers are used in grouping objects, concept or any other phenomena. For Artificial Intelligence systems, classifiers use pattern matching to perform their job. This helps search algorithms to use less time and resources to sort through a large knowledge base.  Classifiers are widely used for machine learning operations. Some of the classifiers widely used for Artificial Intelligence systems are decision tree and the naïve Bayes classifier.

Artificial Neural Networks

Artificial neural networks imitate the design and operation of human brain neurons where the function of a neuron is influenced by other neurons. Learning and motion `require the successful application of artificial neural networks. Learning of a complex and chained event such as speech recognition and natural language processing is achieved through deep learning.

Logic Programming

Logic programming helps solve problems such as knowledge representation. Logic programmes use such basic truth functions as ‘and’, ‘or’ and ‘and-or’ to help Artificial Intelligence systems perform simple reasoning. Other techniques employ fuzzy sets which use a linear scale and fuzzy logic for adding control to systems. Default logic is used successfully to give Artificial Intelligence systems default reasoning.

AI: The Applications

AI is projected to impact peoples’ lives by having it used in virtually every sector of the economy. the application in some sectors is explored below.

In the automotive industry, the development of efficient autonomous vehicles heavily relies on AI. These vehicles will incorporate things such as proximity sensors, mapping technologies, and navigation systems which will combine into an AI system. With such a system, an autonomous vehicle can map their surroundings and be able to respond accordingly to their environment by for instance braking or accelerating.

AI can be effectively applied in the health industry to boost the health of patients. This will be done by the AI system complementing the doctors’ efforts by helping them find the right medication and quantities for different ailments. AI systems in the industry will rely on a massive base of knowledge of diseases and their medication. Using search and optimization algorithms the AI system will select the right combination of medicines. Diagnosis of patients will also be another important application of AI.

The finance industry is characterized by data analysis and strategic decision making. Various elements of AI such as big data mining, neural networks, and machine learning can perform these tasks efficiently. Some of the techniques of AI applicable in the finance industry are the Monte Carlo simulations and sentiment analysis. Further AI developments will make it applicable to solving the current uncertain, nonlinear and highly evolving financial situations. Audit of large datasets will be made efficient and reliable.

AI is expected to impact national security and the military in many ways. For instance, through machine learning, AI systems will be able to analyze complex satellite imagery and produce reliable output such as the safest path. Cyber defense is another probable application of AI. Further advances will prove important to countries in terms of military prowess with investments in robotic weaponry.

AI in Education (AIED) is a phrase coined in reference to the application of AI in helping students learn and teachers teach. Future AI systems will implement the use of robots which use sensors to monitor their environment. With large classrooms, human teachers may be unable to effectively serve each student. This caveat is overcome by robotic aides who can help teachers focus specific content to students. Future AI systems will be able to have interactive sessions with students making classrooms smart.

Intelligent AI systems may in the future be used to better explore the outer space expanding the sphere of space exploration and determine the possibility of life beyond earth.

Through AI the sectors of sales and marketing will be revolutionized by having the AI systems sift through large volumes of data to correctly learn customer preferences. With this companies can target their customers based on their preferences reducing the cost of advertising. AI algorithms will also be able to monitor and advise sales representatives on ways of improving sales and optimal pricing.

The application of AI in dangerous or toxic environments is a no-brainer as a substitute to humans. These environments can range from manufacturing processes of toxic products to bomb deactivation, from deep sea exploration to space exploration and mine exploration.

Potential Issues in AI

Implementation of fully operation AI systems is expected to have some detrimental impact on society. The issues will touch on the economy, social ethics and security. For example, several fictional movies have made to project that AI systems may take over the universe wiping out humanity. This may be if the machines override all human control such as refusing to power off in case of emergency and as such overpowering humanity. Weaponized AI systems, artificial soldiers and drones may also fall under the control of criminals who will use them for illegal activities. General intelligent AI systems will eventually devalue humanity as the super intelligent beings will be a competition to the singularly intelligent humans. AI systems may take over their control to continually improve themselves beyond the limit envisioned by the programmer.

While AI creates jobs in terms of developers and researchers, AI systems will eventually replace human labor leading to joblessness. Some of the jobs in the line of fire relate to those involving repetitive and therefore predictable. These jobs are extremely suited for AI systems. Since AI systems are projected to be efficient in terms of time and resources, profit-seeking companies will rather employ AI than a multitude of workers.

The issue of what the rights of AI systems will be has attracted a lot of interest. Proponents of machine rights argue that since intelligent machines will mimic human emotions and cognitive functions, they should hence have rights like human beings.

Conclusion

Advances in various research fields such as robotics, computing, technology, psychology, mathematics, and information engineering in the recent past have made AI a real possibility. While notable innovations have been made in the field, researchers are still holding out and working towards General Intelligence AI at par or even surpassing humans. The Internet of Things technology will complement rather than substitute AI. At its peak, no other technology will compete with or replace AI. AI will impact work and lives both positively and negatively. It is upon Governments to shape AI policies in such a way that the negatives are deemed.