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Artificial Intelligence

What is Artificial Intelligence?

Artificial intelligence (AI) is a branch of computer science that makes it possible for machines to mimic human intelligence and perform human-like tasks by learning from experience and adjusting to new inputs.

Computers can be trained in artificial intelligence to perform specific tasks by processing large datasets and recognising patterns in those datasets.

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Start Your Business in 30 Days Even If You Don’t Have An Idea

This means that AI may take your job; you either evolve or die.

History of Artificial Intelligence (AI)

John McCarthy coined the term Artificial Intelligence during the Dartmouth Summer Research Project on Artificial Intelligence in 1956.

In 1950, Alan Turing explored the mathematical possibility of machines making intelligent decisions and solving human-like problems in his academic paper titled Computing Machinery and Intelligence. He posed the question – Can machines think?

He discussed how to build intelligent machines and test their intelligence, and proposed the Turing Test (named after him) – a method for determining if a machine is intelligent.

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Types of Artificial Intelligence (AI)

Artificial Intelligence (AI) classification can be based on capabilities and functionalities.

Artificial Intelligence Based on Capabilities

1. Narrow AI

Narrow artificial intelligence, also known as Weak AI, can repeatedly perform a dedicated intelligent task. However, this type of artificial intelligence cannot perform beyond its field or limits as it targets a single subset of cognitive functions and advances its performance.

Examples of Narrow AI
  • Virtual assistants: Apple Siri, Amazon Alexa, Microsoft Cortana, etc.
  • Recommendation engines
  • Search engines: Google, Bing, Yahoo, etc.
  • Chatbots

2. Artificial General Intelligence (AGI)

The objective of AGI also known as strong AI is to apply knowledge and skills in different contexts with machines or applications that can reason and think like a human. The development of this type of artificial intelligence is still in its infant stage.

3. Super AI

Super AI is also known as artificial super-intelligence. It is a form of AI that in theory is capable of surpassing human intelligence through the manifestation of advanced cognitive skills.

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Go from idea to starting your business in 30 days

Machines with these capabilities can be self-aware and think in complex abstractions and interpretations that humans cannot do.

See Also: Cryptocurrency- Everything you need to know about cryptocurrency.

Artificial Intelligence Based on Functionality

 1. Reactive Machines

A reactive machine is the most basic form of artificial intelligence. It does not store memories or use past experiences to aid future actions.

The machine focuses only on current scenarios to determine its course of action.

Examples of Reactive Machines
  • Email spam filters
  • Chess-playing supercomputer

2. Limited Memory

Limited Memory AI systems have short-lived memories. These systems train from past data to make present decisions, and their memory retention capacity is short-lived. They can use past data for a limited period, but they cannot add it to their library of user experience.

Example of Limited Memory AI
  • Self-driving cars (Autonomous vehicles).

3. Theory of Mind

Theory of Mind AI is an advanced class of technology that can understand human emotions, alter feelings and behaviours, and can be able to interact socially like humans. However, this level of artificial intelligence technology exists only as a concept as there are many knowledge improvements to be done before it becomes a reality.

4. Self-awareness

This is the future of Artificial Intelligence, and these machines will be super intelligent. Moreover, these machines will be conscious, self-aware, and exhibit personal sentiments.

Self-awareness machines will be smarter than humans. However, at present, Self-awareness AI only exists hypothetically.

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Sub-fields of Artificial Intelligence (AI)

Machine Learning                                                

Arthur Samuel, the man who coined the term Machine Learning in 1959, defines it as a “Field of study that gives computers the capability to learn without being explicitly programmed”.

This learning process is automated and improved based on the availability of good quality data to these machines. Different machine learning algorithms are used to build models to train machines on various computational activities.

See Also: Blockchain technology and how it works.

Types of Machine Learning Algorithms

Supervised Learning Algorithm

Supervised learning uses labelled datasets to train models to identify, classify, or predict data outcomes accurately.

In supervised learning, models are trained using a well-labelled dataset. These models learn about each type of dataset, and once the training process is completed, the models are tested based on test data (a subset of the training set), and then they predict the output.

Unsupervised Learning Algorithm

Unsupervised learning models the exact opposite of supervised learning models. Here, models are exposed to unlabeled data and are left to discover the hidden patterns and insights from the given data without any supervision.

Reinforcement Learning Algorithm

Reinforcement learning is a type of machine learning method where an intelligent agent or model interacts with its environment and, based on that, finds the best way to solve a problem. The model learns from its mistakes and gets rewards when delivering accurate results.

Reinforcement learning is based on the hit and trial process because the model takes the next action and changes its state according to the feedback of its previous action.

See Also: How to develop a business model for your business.

Deep Learning

As a subset of machine learning, deep learning is implemented through neural networks architecture inspired by the structure and function of the human brain.

Deep learning algorithms work with large amounts of both structured and unstructured data and require lots of data together with significant computational power.

Examples of Deep Learning Application

  • News aggregation and fake news detection systems
  • Fraud Detection systems
  • Customer relationship management (CRM) systems

Natural Language Processing

 Natural Language Processing (NLP) algorithms make it possible for computers to understand texts and spoken words just like human beings.

NLP makes human language intelligible to machines by combining the duo of linguistics and computer science to study the rules and structure of language. It is capable of understanding, analysing, and extracting meaning from text and speech.

Neural Networks

Artificial neural networks are designed to mimic the neural pathways in the human brain. They are comprised of node layers, containing both an input layer and output layer in addition to other hidden layers.

Neural networks learn to improve their accuracy over time with constant training data. As a result, and having been fine-tuned for accuracy, these learning algorithms are powerful tools used in various computational tasks in real-world applications.

One of the most well-known neural networks is Google’s search engine algorithm.

Computer Vision

Visual information is an integral part of artificial intelligence. For example, computer vision is the subset of artificial intelligence responsible for reading and analysing videos, images, and other visual information in the same way humans do.

Computer vision involves teaching computers how to process an image at a pixel level and understand it at the technical level. This visual information can then be retrieved, worked on, and interpreted through special software algorithms.


Robots are machines that are capable of sensing, interpreting, and interacting with their environment. They are becoming much smarter and more intuitive due to the incorporation of artificial intelligence in their design and operation.

As an interdisciplinary field of science and engineering, robotics is powered through the convergence of computer science, mechanical engineering, electrical engineering, and artificial intelligence.

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Applications of Artificial Intelligence (AI)

Over the last ten years, research and development in artificial intelligence have taken a giant leap of progress. Today, AI applications are pervasive across multiple sectors, and efforts are always in top gear to conquer new technological grounds.

Below are some of the real-world applications of artificial intelligence.

1. Healthcare Delivery

Expert systems applications designed to diagnose and predict diseases accurately, wearable devices that monitor body functions, surgical robotic arms… These are some of the real-life examples of how AI is improving healthcare delivery and saving millions of lives.

2. Financial Services

Chatbots: Banks are deploying chatbots to offer real-time service engagement to their clients. These conversational AI applications can even provide rudimentary services like new account opening, account balance inquiry, etc to their customers.

Examples of these chatbots include U.B.A’s Leo chatbot, Zenith bank’s Ziva chatbot, etc.

Robo-traders: These are financial markets trading bots powered by complex algorithms trained in reading market trends and making the best financial decisions for traders.

Fraud dictation: Financial institutions can use AI algorithms to understand a customer’s online spending habits. These real-time insights can then be used to detect any anomaly within the system and determine if a transaction is genuine or not. In addition, suspicious transactions can be flagged for further investigation and verification by the customer.

3. Security and Surveillance

AI-based surveillance systems use face recognition tools to monitor millions of CCTV cameras worldwide in real-time. The automation of these systems brings on board an enhanced public safety while eliminating error and fatigue-prone manual security monitoring.

4. E-commerce

The e-commerce sector is currently being disrupted by artificial intelligence technology in the following ways:

  • Consumers’ spending patterns are monitored and analysed to predict future behaviour with AI applications on complex algorithms.
  • Recommendation engines are deployed to projects enticing new offers to consumers as they shop online.
  • GPS and laser technology tools are used to track goods inventory on/off the store shelves, track supply shipment, and delivery to customers.

5. Automobile

Self-driving cars are here courtesy of artificial intelligence. Limited memory machine learning algorithms are the systems behind the development and deployment of autonomous vehicles.

Tesla Inc., an American automotive company, is leading the pack in developing and deploying autopilot capability across its vehicle models.

Benefits of Artificial Intelligence (AI)

1. Process Automation

Process automation has increased productivity and efficiency across various industries – communications, transportation, consumer products, and service industries. This is a core advantage of Artificial intelligence systems.

For instance, AI systems’ autonomous nature means that they are never prone to fatigue or wear and tear; hence they are highly efficient and reliable.

2. Customer Experience Delivery

Businesses can now respond to customer queries and grievances promptly and address situations efficiently with AI-powered solutions like chatbots.

The deployment of Natural Language Processing technology chatbots provides opportunities for businesses to connect with customers at a more personalised level.

3. Repetitive Tasks Management

Recurring business tasks can lead to a productivity slump over time among employees as these tasks are not just time-consuming but also monotonous.

AI-powered automation tools are never prone to fatigue; their deployment in the execution of these tasks provides the opportunity for businesses to maximise their productive activities.

4. Smart Decision Making

Given the enormous amount of data available to AI systems, they ultimately make smarter decisions than humans.

Their sheer ability to ingest and analyse massive datasets, identify and analyse trends, develop data consistency, and provide forecasts put them in a better position to help businesses make the right decision to support business efficiency.

5. Data Analysis

In today’s modern business environment, data is key to organisations’ productivity and profitability. As a result, business organisations deploy deep learning algorithms for analysis and pattern identification to make sense of these massive datasets.

AI systems can process and analyse massive amounts of data at a remarkable speed faster than humans. In addition, they can identify trends, make decisions, and offer recommendations based on historical data.

Risks of Artificial Intelligence (AI)

1. Job Loses

Artificial intelligence-powered automation systems are a serious threat to workers whose jobs involve predictable and repetitive tasks. Millions of workers in this category are at risk of losing their jobs as the deployment of machine automation continues in various sectors of the economy.

2. The Danger of Blackbox Algorithm

There is a possible risk that some AI algorithms can become so complex (Blackbox) that even the programmers cannot exactly explain and arrive at the variables initially used in creating these algorithms.

Blackbox Algorithms can pose a significant danger to society at large if used to power autonomous weapon systems due to a lack of transparency and accountability for instance.

3. Bias in Decision Making

Artificial Intelligence systems rely on sufficient dataset training for performance. The likelihood for the dataset to reflect the assumptions and bias of the compilers exist, and these biases could influence the decision-making process of these AI systems.

Frequently Asked Questions on Artificial Intelligence

1. How Dangerous is Artificial Intelligence (AI)?

Artificial intelligence (AI) is simply a tool to drive positive change and improve productivity and quality of life. But, like every other tool, its positive potential or abuse lies in our hands and the way we use it.

AI has tremendous potential for the general good, let’s join hands and put it to great use.

2. How Can I Take Advantage of Artificial Intelligence (AI)?

As stated earlier, AI technology is with us everywhere we go – on our mobile phones, virtual assistants, Google search engine when we type a query, etc. As a result, the key to fully utilising the potential of artificial intelligence is first to be intentional about its usage.

There are millions of AI applications on the internet on a wide range of subjects, and you can begin your exciting journey by checking out Google AI tools here.

3. I Already Have a Job; How Useful is AI to Me?

Artificial intelligence is a significant technological disruptive force that has come to stay with far-reaching positive consequences for organisations and individuals.

Being AI aware and, to some extent, compliant is gradually becoming the new productivity benchmark for the modern-day worker. This is so because of the massive deployment of AI applications by organisations across multiple sectors of the economy.

It is important to remember that an AI-compliant worker remains a relevant asset in today’s workforce. Therefore, go ahead and stay at the top of your game. Cheers!

4. What is a Chatbot?

A chatbot is an application that simulates and processes written or spoken human conversation between users and digital devices.

A chatbot uses conversational artificial intelligence (AI) technology to simulate real-time chat interactions with users in such a way as if they were communicating with a real person.

In conclusion, the artificial intelligence (AI) technology revolution holds massive potential for organisations and individuals due to the productive efficiency that process automation offers at both corporate and individual levels.

Now is the right time to tap into this new world of possibilities; now is the time to skill up and build up.

Do you have any questions or concerns? We are willing and ready to walk with you on this “exciting possibility boulevard”.

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