Table of Contents

What is Artificial Intelligence Technology?

We describe Artificial Intelligence Technology as a category in the software development program where the computer robot can perform a human task. To what extent artificial intelligence will kill human labour cannot be foretold, but the factors behind it are known.

Three main concepts drive the AI tech ability that digital computers utilize for implementation. These are machine learning, deep learning, problem-solving, reasoning, and neural networks.

History

Artificial Intelligence Technology is a term representing the self-thinking of computers and machines. Ancient computers in the first half of the 20th Century could not attain self-thinking ability. Computers could only execute commands but could not store them for reference. A simple program demonstration at the University of Manchester in 1951, called the draughts program that could play a complete game of checkers at a reasonable speed, ignited the Artificial Intelligence idea, enabling computers to store commands. John McCarthy coined the term, “artificial intelligence” and had the first AI tech conference in 1956. He brought together specialists from different fields for open discussions on AI tech.

In 1958, McCarthy built the first general-purpose mobile robot, to name it Shaky, with a list of instructions still in use today. Arthur Samuel created an algorithm, thought to play chess games better than humans. He termed it as machine learning since it could let machines understand the chess game. In 1965, Edward Feigenbaum and Joshua Lederberg designed an expert system that could replicate human thinking and decision-making abilities.

Later, in the year 1966, Joseph Weizenbaum created the first chatterbot through natural language processing. In 1979, a group of American Association of Artificial Intelligence Technology came up, laying the foundation of advanced research. The deep blue supercomputer came along in 1997 when it defeated the world chess champion in a chess competition. In 2002, developers built the first commercially successful robotic vacuum cleaner.

Intelligence

A focusable behaviour applied by the human brain towards fulfilling a function, is described as intelligence. Intelligence behaviour can be learnt naturally from body response or environmental factors over some time. This function can apply to humans and animals in life adaptation and environmental situations. Below are intelligence manners that apply to Artificial intelligence, and they are

Reaction- through sensor components by a computer, data used to develop machine learning tools and augment generates in response to a motive at hand.
Limited memory- these are supervised Artificial Intelligence technology systems that generate methods from experiments or real-life experiences. These methods can store previous data and use the data to make better predictions in future.
Theory of mind- For a long time, Artificial Intelligence machines have proven to outsmart humans at analytical tasks but are less capable of skills like intuition and inference.
Self-awareness- In humans, self-awareness comes through mindfulness, self-compassion, reflection, and feedback. Artificial Intelligence machines, through a collaborative cloud environment, can enable predictive awareness. For instance, in the health sector, self-aware machines provide more accurate, faster data for human diagnosis.

Related Content: What is Grok AI?

What are the components of AI?

machine learning

Machine learning is an AI tech component that is applied when machines or computer systems acquire and store specific new information. The first method used is trial and error, where developers expect the machine to remember specific tasks after successfully achieving the most accurate process.

The machine implements the same ideology in future for a similar task the machine comes across. The machine learning method can be able to find connections that affect the implementation of the task to be able to predict any failure.

The main challenge witnessed by this method is called generalization. This method involves the usage of a past method in achieving a task to a similar new one. If it cannot be applied to achieve the task accurately, you command the machine with a rule to implement the new task.

deep learning

Deep learning can acknowledge complex patterns from different sources like pictures, videos, texts, and other information. This Artificial Intelligence Technology component enables computers to process data similar to a human brain. Deep learning technology is applied in our daily lives products. These applications include:

Automatic facial recognition
Fraud detection
Voice-activated television remotes
Digital assistants

Its component is designed and modified like the human brain. Its algorithms are neural networks that can function by creating multiple levels of abstraction to represent the data. The Artificial neurons also known as nodes are software models which use mathematical calculations to operate.

reasoning

When applying reasoning, the machine uses inferences to make conclusions depending on the information at hand. Since this ability is limited to the human mind, AI tech mainly relies on programs that can draw conclusions and inferences from specific tasks without human inclusion.
The reasoning attribute is for developers to create software that achieves consistent results from a particular issue. There has been a great milestone in machines concluding inferences, but reasoning requires more than just reference but an accurate solution for a specific problem at hand. To this day, it is the most complex implementation by the AI tech.

problem solving

Problem-solving algorithm components can either be a familiar problem or an uninformed one. The action that triggers the component is a systematic search through a checkup of possible solutions. On the other hand, an uninformed problem can contain a wide range of issues that include a series of specific problems unaware by AI machines.

Artificial Intelligence program selects various specific software solutions that exist in a systematic manner aiming to solve a problem. From this, we can see that without the specific problem-solving solutions it is aware of, the Artificial Intelligence program cannot fully solve an uninformed problem it has never encountered.

natural language processing

Natural language processing is a category in artificial intelligence that anchors on giving computer machines, the ability to understand text and spoken words. Similarly, human beings can analyse, understand, and generate human language in an understood manner. The AI component is essential in various applications, such as:

Chatbots and virtual assistants: Developers design chatbots with user interfaces that enable chart-like communications with users. On the other hand, virtual assistants can have a chat-based interface operated by voice commands. Agents such as Amazon’s Alexa use speech recognition to understand patterns in voice commands.
Machine translation: This category involves more than replacing words in one language with words of another. This factor is vital in enabling communication between people who speak different languages.
Effective translation has to accurately express the meaning of the input language and translate it to text. For accuracy, machine translating tools play a key role in translating text into one language and back to the original.
Summarization of Text: The natural processing language retrieves information from text data, such as search engine results and question-answering systems. Its techniques apply in processing large quantities of information to create summaries and synopses for indexes and research databases.
Spam detection: Natural processing language’s capabilities scan emails for language that often indicates spam or phishing. These indicators identify the overuse of financial processes, threatening language, misspelt characters, and other factors.
Computer vision: Computer vision is another vital element in the Artificial Intelligence field, enabling machines to interpret and understand visual information.
Developers train a computer robot to capture and interpret information from image and video data by applying machine learning to the provided data. The process can be through image classification, object detection, image segmentation and object tracking.

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Daily life uses of AI

The tech industry, the driving factor behind Artificial Intelligence, has invented abilities to enable machines to improve the human brain’s capabilities. From our daily activities, Artificial Intelligence usage can be categorised by:

Software platform– One common application in life includes voice assistants and image recognition for face unlock in mobile phones.
Embodied- This category involves the utilization of drones, self-driven vehicles, assembly-line robots, and the Internet of Things.

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Summary

The use of applications in machines to perform tasks that resemble human brain functions, is referred to as Artificial Intelligence. Well, what the purpose of Artificial Intelligence Technology?
The end goal is to implement daily human tasks by machines, but till today, no machine has been able to match the human intelligence capacity. The closest performance to achieve this is only through applications such as health diagnosis, voice recognition and computer search engines.

AI and human Intelligence

As outlined earlier, the components of artificial intelligence are a human character reflection that generates decision-making. From this point of view, we can find similarities and differences between an Artificial Intelligence machine and the human mind.

Almost all tasks that Artificial Intelligence technology can perform are improvements of existing solutions. The main differences are that humans implement them less accurately and slower. The real work is behind the human mind that identifies these problems and comes up with solutions.

The Artificial Intelligence machines are fed with data to speed up the solution process in a more outlined manner. For instance, the Artificial Intelligence computer cannot identify a new problem as a real-life problem unless through the machine learning process. For example, humans identify real-life problems from past life experiences that machines lack.

AI tech machines exist to automate wasteful operations in human activities. Most of the human tasks taken over by Artificial Intelligence machines are either complex for the human mind’s accuracy or require a lot of human resources. Industries have not eliminated human labour in such fields but are reducing numbers.

As it has displaced human labour, AI has also generated new jobs. It has created new jobs that never used to exist before like, AI start-ups and consulting, AI policy and ethnic development, data annotation and labelling, etc.

Related: Artificial Intelligence revising jobs, how to save yours.

Comparison on performances

For a human brain, the information does not necessarily stick permanently at the instance for future reference. For that to happen, it should be unique or repetitive at times. The human brain stores information hinted from behaviour or external sources with limited access. If the information is lost, humans refer to somewhere or get reminded. For Artificial Intelligence, once the data allocation on the memory is successful, the information is easily traced unless deleted. On the other hand, AI machines can connect to external sources, giving them an upper hand in reference.
How Artificial Intelligence machines perform tasks has no output determinants. It is because there are little or no factors affecting the output manner once the required resources are in place. The human mind relies on different factors to perform a task. The physical mind state is the most common factor, and multiple determinants like mood, priority, etc.
Emotions, feelings and beliefs are some of the common determinants. These determinants come hand in hand to stimulate human reasoning to a certain extent. AI in analytic applications can apply the knowledge by simulating human reasoning to make predictions, recommendations or decisions but from a constant state set by models.

Conclusion

Human intelligence mind state varies and changes with natural causes, but with AI technology, the intelligence state can only change when manually triggered through machine learning or deep learning.

Due to self-awareness and integrity, human intelligence is self-driving depending on the physical state, unlike AI tech, which is apathetic, lacking integrity, and influenced by human actions at a particular state. Therefore, any effect of Artificial Intelligence technology on humans and the environment, is only generated by humans. In most AI applications in labour output, some bodies and stakeholders monitor its performance in different industries. It is to verify that as much as AI plays a big role in modern technology, it heavily depends on human intelligence.