What is Artificial Intelligence?

What is Artificial Intelligence?

Several scholars have described Artificial Intelligence (AI) to mean different things based on their terms of preference. The earliest definitions of AI focused on systems' ability to act like humans, thus birthing the descriptions of John McCarthy in 1956, who coined the term "Artificial Intelligence." McCarthy submitted that AI is “the science and engineering of making intelligent machines.”

In addition, AI has been described as the intelligence demonstrated by machines. Contrary to what is obtainable in natural intelligence possessed by animals and humans, AI is mostly displayed by programmed machines who learn to interact with their environment by studying human behaviors and how they act in different situations. The term is also used to describe machines that mimic cognitive behaviors associated with the human mind and intellectual capacity to learn and solve problems. The other names for AI include; computational intelligence, computational rationality, or synthetic intelligence.

What, then, is the most acceptable definition of AI?

Experts are still working to find a solution to this one. One of the more palatable definitions of AI is "intelligent machines." According to some contemporary definitions, AI is the study and creation of intelligent agents, with an intelligent agent being a system that can recognise its surroundings and take actions to improve its chances of success. For the sake of this essay, we will use Chuck Williams' terminology from 1983, who called artificial intelligence (AI) "a multidisciplinary field whose goal is to automate activities that currently require human intelligence."

Although artificial intelligence (AI) was first developed as a field of study in 1955, it has since evolved into a new wave of technology that will shape our world in the near future. AI has significantly improved our world because to the novel ideas it has brought about as well as its many related sub-fields, including robotics, machine learning, deep learning, computer vision, artificial neural networks, and more. Although AI is still in its infancy, we have already witnessed good effects on our transportation system, medical diagnosis, research, and therapy, to name just a few. AI-based solutions have the advantage of adapting to various work situations without being specifically tailored to a user's preferences.

How does AI work?

While creating robots or systems that can use human intellect to solve issues is the primary objective of AI, depending on the field of AI, they often do this in a variety of ways. At the time this article was being written, machine learning, deep learning, neural networks, cognitive computing, natural language processing, and computer vision were the most often encountered disciplines of artificial intelligence. The last section of this paper will describe how they function.

In order for computers and/or software to automatically learn from patterns repeating characteristics in such data, massive volumes of data must be combined with quick, repetitive processing and clever algorithms. This is true regardless of the branch and goal of constructing the AI model. The following sub-fields of AI contain a wide range of theories, technologies, and methodologies.

• Machines that learn from experience (Machine Learning)

  Machine learning (ML) is a subfield of AI that deals with computers that pick up knowledge via interacting with people. Even without being trained, these models may constantly do a task and automatically learn to improve. The majority of AI models in this area are able to easily and reliably analyse data and anticipate events. Businesses like Netflix, Amazon, and Facebook frequently use them to make recommendations for films, items, and friends based on user interests. They have also been used to speed up the discovery of new drugs and detect disorders in the pharmaceutical, healthcare, and life science sectors.

  Machine Learning is further divided into two broad categories; Supervised, Unsupervised, and Reinforcement learning.

  • Supervised Learning:

    Here, an algorithm learns to associate a set of labelled input data with a certain output. These are used to forecast unknown data that will be discovered in the future. Most of the time, the machine or algorithm under the supervision of the algorithm makes the appropriate choices, which are rewarded. It resembles the interaction between a teacher and pupil. This is used in voice recognition models, bioinformatics, and spam detection.

  • Unsupervised Learning:

    In this case, the data is not labeled, and the outputs are quite unsure. The algorithms are labeled to understand patterns from the data and provide the required answer. This is also a form of self-learning where the algorithm can find unknown patterns in the dataset that do not have any labels. It comes in handy in finding anomalies in data, predicting products on Amazon, and credit card fraud detection.

  • Reinforcement Learning:

    The algorithm gains experience interacting with its surroundings and is rewarded for its efforts. There is no need for input data for this kind of learning. The machine develops better application skills as it learns to take the rewarding path. Machine learning is a technique that is used to diagnose illnesses and prescribe treatments in self-driving cars and in healthcare.

• The self-educating Deep Learning models

  Deep Learning refers to AI models that use Artificial Neural Networks (ANN) to process data. These neural networks can mimic the activity of the human brain (neurons) to solve problems. In this model, several layers of ANN work to determine a single output from many forms of input. They are designed to exhibit how humans think and act when solving problems. It comes in handy in identifying a face from a mosaic of tiles. Deep Learning machines work by learning through positive and negative reinforcement of the tasks they carry out often. The commonly utilized forms of Deep Learning models in AI include but are not limited to autoencoders, Deep Belief Net, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Reinforcement learning to neural networks. Please note that Deep Learning is often identified as a sub-field of Machine Learning.

  Deep learning's self-educating nature has found use in visual recognition, fraud detection, self-driving cars, Natural Language Processing, news aggregation, and fake news detection. It is the technology behind virtual assistants and several healthcare AI-based models. Deep Learning is the technology behind speech recognition used by virtual assistants (VA) like Siri. It allows the VA to understand simple commands such as "Hey Siri, what's the weather report today?"

• Association based Neural Networks

  The core of deep learning models are neural networks. They were developed to replicate how the human brain works. These models' algorithms are capable of making judgements like people do without any outside intervention.

  Processing training samples and connecting them to specific occurrences is how neural networks operate. In order to identify relationships and provide sense to the previously undefined data, neural networks often analyse data numerous times.

  There are several different types of neural networks, including feed-forward neural networks, radial basis function neural networks, Kohonen self-organizing neural networks, recurrent neural networks, convolutional neural networks, and modular neural networks. These neural networks are used for speech recognition, fraud detection, fraud diagnosis, machine diagnosis, credit rating, and medical diagnosis.

• Cognitive Computing that makes inferences from context

  These AI models aim to imitate and improve the interaction between machines and their operators (humans). It seeks to replicate the human thought process in computer models and, by so doing, understand human languages and image processing capability. Just like Neural Networks, cognitive learning strives to create machines with human-like attributes and processing abilities. Cognitive Computing models can possess human-like attributes via implicit, explicit, associative, cooperative, discovery, observation, imitation, and collaborative learning. This technology has been utilized to create chatbots, face detection, risk assessment, and fraud detection models.

• Image Processing Computer Vision

  With the use of Deep Learning and pattern recognition, this AI technology can recognise and decipher the contents of images, such as photos, tables, and graphs from documents and movies. It includes the capacity of computer systems to decipher visual information. Health care, security, scientific research, and development all employ this technology. Both medical diagnostics and criminal detection have made extensive use of it.

• Natural Language Processing

  This is the branch of AI focused on interpreting spoken data. It comes with algorithms that allow computers to interpret, recognize, and produce human language and speech. These models teach machines to learn our natural languages and produce logical responses. This model has been widely utilized in Skype Translator to translate several languages in real-time and facilitate communication.

Source: Bitsenze

Types of AI

• Narrow or Weak AI:

  This describes the application of AI to certain, limited activities, such as data processing. In this situation, human involvement is necessary since the AI models are not sophisticated enough to do their task. These artificial intelligence (AI) systems have been demonstrated to surpass human expert players in games like chess and poker. General AI: In gene

• General AI:

  In general AI, machines can perform any intellectual task with efficiency like humans. These machines are capable of cognitive thinking without any human input. Many AI-based models used today fall under this category.

• Strong AI:

  In this instance, the AI models are able to reason for themselves and complete difficult tasks on their own. These devices/technologies, which have the ability to power autonomous cars and ships, are frequently seen as AI's future.

  Other classifications may describe reactive machines, limited memory, the theory of the mind, and self-aware. While reactive machines are the most primitive forms of AI, the self-aware machines are regarded as the future of AI where machines will be capable of cognitive activities without any external interference. These machines may even outperform their human counterparts with ease.

The future of AI

We have witnessed AI evolve from science fiction novels to a reality we can engage with in less than a century. With increasing adoption and application, artificial intelligence will have a bright future with a number of smart robots that are capable of doing jobs better than humans. As of right now, AI can reliably identify criminals from camera video, accurately anticipate stock market movement, and much more. It has surpassed a panel of physicians in identifying cancer. More autonomous ships and smart cities with minimal human oversight are anticipated to be powered by AI by the year 2050.

The unemployment rate may rise as more businesses use AI-based technologies to address their problems, opening up additional opportunities for AI-based job possibilities. We have witnessed AI evolve from science fiction novels to a reality we can engage with in less than a century. With increasing adoption and application, artificial intelligence will have a bright future with a number of smart robots that are capable of doing jobs better than humans. As of right now, AI can reliably identify criminals from camera video, accurately anticipate stock market movement, and much more. It has surpassed a panel of physicians in identifying cancer. More autonomous ships and smart cities with minimal human oversight are anticipated to be powered by AI by the year 2050.

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