If you’re looking for a clear definition of what is artificial intelligence, you’re in luck!
I’m super stoked to deliver this definitive guide of 6000+ words about what is artificial intelligence.
It will definitely help you understand how AI is going to change our lives, what it means for business, and how new technologies are intertwining into an ever-evolving force that’s quickly taking over our lives.
AI and Machine Learning are now converging on our world at a rapid pace.
The new future is one where AI is embedded into appliances, cars, gadgets, and all kinds of products.
We’re at the dawn of a new era, which is frightening for some and exciting for others.
One thing we cannot deny is that AI and its army of technologies are making a great deal of noise more than ever.
All of the major players (Microsoft, Google, IBM, Apple, and Meta) are building or acquiring AI-powered apps that aim to change our lives.
But there are a few thought-provoking questions that pop into society’s mind when it comes to AI:
Let’s dive right into the subject.
What is Artificial Intelligence?
Artificial intelligence is a term used to describe the ability of machines to perform tasks that usually require human intelligence, like reasoning and understanding language.
Artificial Intelligence can be programmed into devices and they’re capable of learning over time.
Artificial intelligence also refers to the concept of creating systems that have cognitive capabilities comparable or superior to the human brain.
Some people believe artificial intelligence will create a better world by freeing people from tedious work, however, others worry about the increased reliance on technology, which leaves us vulnerable to malicious hacks or glitches.
Ahead of the 21st century and due to the rapid growth in data generation and processing requirements, humans are becoming conscious information processors that require more intelligent software.
The fear of these technologies is that robots may one day take over, enslaving or harming humans.
The myth of the “Terminator” robot causing a global apocalypse has been around for decades and there are many other similar movies that have created this fear (and rightly so).
When was Artificial Intelligence Created?
Let’s take a trip down memory lane to 1956 when the term Artificial Intelligence first came into existence.
That’s when John McCarthy, a young American mathematician and computer scientist, coined the term and outlined its basic concepts at a conference of scientists at Dartmouth.
The Dartmouth Summer Research Project Conference
Artificial Intelligence was birthed during the Dartmouth Summer Research Project on Artificial Intelligence, an 8-week conference, at Dartmouth College in Hanover, New Hampshire.
During this time, the term was used to refer to the science of making machines do what the human mind can do.
The project was based on McCarthy’s definition, which states that human intelligence is a body of knowledge about information, its origin, existence, and uses.”
It’s interesting to note that one of the known references to artificial intelligence is a book written by some ancient philosophers who were trying to predict if there would be real people born posthumously through Artificial Intelligence.
So how did the term artificial intelligence come to be?
Back in the 1950s, there were a few different names that would describe this world of thinking machines such as cybernetics, automata theory, and complex information processing.
However, it wasn’t until 1956 that he coined the term artificial intelligence and introduced his definition.
John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon are considered the founding fathers of AI.
When was The First AI Program Introduced?
The first AI program was introduced in 1959 by Arthur Samuel, who familiarized the computer science field with the word Machine Learning.
One of the very first ai projects was his game of checkers where he had the computer learn and adjusted its own tactics so that he would be able to play against a human.
Arthur worked on refining his checkers game until 1970 when the program he developed was skilled enough to compete against a reputable player in the game.
Artificial Intelligence and the Turing Test
Alan Turing was a British mathematician and logician who is considered to be the father of computer science and also one of the very first ai researchers.
In 1947, Alan predicted that computers would one day be able to play chess just like a human. And a few decades after that, we saw the materialization of IBM’s chess computer, Deep Blue, which beat the world champion, Garry Kasparov.
The Turing Test was created by Alan Turing primarily as a tool for measuring progress toward artificial general intelligence (AGI).
The Turing Test is basically a conversation between a human and a programmed computer and there was an evaluator that would judge if the conversation would sound natural.
Therefore the test would determine if the computer was able to respond like a human in communication with a real person, then we have achieved artificial intelligence.
Turing proposed that an effective way to test for artificial intelligence was to hold a conversation with the machine, which would demonstrate that the machine possessed thinking skills (like humans).
Although it has been criticized as being too narrow compared to the scope of other early AI research, the goal of passing the test is still held in high regard.
Interesting AI Facts & Stats for 2022 & Beyond
General Artificial Intelligence Statistics
Top 10 Countries That Are Using AI
Top Ten Factors Driving AI Adoption
Top 10 User Groups Using AI in 2022
7 Types of Artificial Intelligence
To better understand the concept behind AI development, we need to know what are these things that are powering AI itself.
Artificial Intelligence can be broken into two main umbrellas which gather all the AI technologies:
1. Narrow AI
Narrow AI is a subset of AI that is designed to train a learning algorithm to perform a single task.
Another definition for Narrow AI would be an ai system that has human-like capabilities but can only be applied in specific areas and can’t be transferred to perform other tasks.
The concept of narrow AI is closely related to another term: weak AI.
In order to better understand this concept, let’s break down a bit further the above paragraph.
2. General AI
Also known as artificial general intelligence (AGI), General AI is concerned with creating artificial intelligence agents which possess general intelligence as well as the flexibility to deal with any kind of situation.
This means that it should be able to engage in conversations, interact with humans and make complex decisions.
In a nutshell, artificial general intelligence is able to do tasks that are normally done by people.
General AI is considered to be the most advanced form of AI which is still far from being totally exploited.
3. ASI (Artificial Super Intelligence)
The term Artificial Super Intelligence (or super-human intelligence) is not yet used but is a proposed type of AI that would possess superhuman abilities such as human-level intelligence or even more.
A super-intelligent computer (or AI system) would be able a true problem-solving machine, able to carry on sophisticated conversations, solve problems that are too complex for people to solve, and possess other human-like qualities.
This would leave no doubt in one’s mind as to whether they were actually talking to a person or a machine.
A super-intelligent AI would be able to supply answers that are not possible for even the most educated people to know, or produce results that have no possible explanation.
It could even do things beyond the capability of computers today.
Let’s now take a look at the other type of AI.
4. Reactive Machines
Reactive machines are computer systems that act as a response to an external stimulus.
This means that the behavior of reactive machines is limited only to what has been programmed into them and they will not be able to think independently or come up with new ideas.
An example of Reactive AI is Deep Blue, the IBM Computer we discussed earlier.
Reactive machines can be further broken down into something like this:
AI agents are systems that can make decisions and function independently from their human creators.
These agents have been largely developed over the last 20 years and have characteristics that define them as artificial intelligence.
What makes them artificial intelligence is the fact that they are able to solve complex problems and adapt to new situations.
The level of independence from the source of programming also defines whether AI is considered intelligent or not.
5. Limited Memory
Limited Memory is a type of AI that consists of machines that have the power to store data from past experiences but temporarily.
It operates by observing past actions, patterns, and variables and then uses this to make decisions and solve problems.
Limited memory AI is not capable of thinking independently and beyond since its data is transient, therefore will only perform actions based on the knowledge that it has been fed.
For instance, Long Short-Term Memory (LSTM) is a model of Machine Learning that is powered to process a succession of data (feedback connections), which helps it learn long-term dependencies.
6. Theory of Mind
Theory of Mind, another field of study within AI, is concerned with the understanding of how both humans and artificial intelligent systems attribute mental states to others.
Examples of mental states include:
Theory of mind is therefore a part of the interaction between an agent and its environment, and it can be used to give computers (or virtual agents) a better understanding of the world around them by discerning certain types of emotional states.
Theory of mind is considered to be one of the most challenging tasks in AI since the machines will have to be trained to understand that they are dealing with emotions, consciousness, and other forms of emotional conditions.
No wonder why it is still under Research and Development.
In the world of Artificial Intelligence, the concept of self-awareness will definitely be the final frontier.
It is a concept that states that machines can understand their own existence, meaning they can comprehend and relate to themselves.
And two words to describe this are “conscious machines”.
Not only will they be aware of human emotional states but of theirs as well.
Self-awareness is a hard one to achieve though and many experts are skeptical about it.
It is believed that if an AI can understand itself and its existence, then it will be able to learn new things and would be able to develop its own personality traits.
However, the concept of self-awareness in computer programs is even harder to approach than Theory of Mind.
The difficulties lie in making computer programs that can learn about themselves, and realize their own emotional states.
A computer that is able to do this would not only be able to be aware of its own existence, but it will also be able to understand the world around it in a way that goes far beyond what we know today.
What is the Purpose of Artificial Intelligence?
It is believed that when the term artificial intelligence first came into existence back in the early 1950s, the general purpose was to augment human intelligence.
There have been significant improvements and discoveries in artificial intelligence research and development over the past decades and we are testifying ot its ability to help humans in many areas of life and business.
The advancements in AI technology have led to self-learning systems that can learn from past actions and adapt accordingly for better performance.
They do this through the use of data and AI algorithms, which are used to simulate a real-world scenario.
Based on the accomplishments of AI in today’s day and age, we can say that the purpose of AI is to:
Pros & Cons of Artificial Intelligence
As with any form of technology, artificial intelligence has its benefits and drawbacks.
Some of the popular current uses of AI include self-driving cars, medical care assistance, aviation, financial modeling, and eCommerce
However, some of these applications also come with risks as well as ethical issues regarding privacy and security.
There are many potential pros to artificial intelligence such as:
Additionally, AI systems can be programmed to detect fraud and financial corruption in the market that could allegedly have devastating effects on the economy and cause inflation.
Unfortunately, there are many pitfalls and risks of AI technology that will definitely impact the lives of many. Some of these include:
11 Most Common AI Technologies
If you’re new to the AI world, all this may seem so futuristic but the AI technologies we’re about to dissect are realities in various areas of life.
They have been around for years, some of them dating back to the 1950s, but they have come a long way since then.
Let’s dive in.
1. Machine Learning
The first one on our list of AI technologies being used today is Machine Learning. So, what is Machine Learning?
Machine Learning is a branch of AI technology that teaches computers how to learn.
It helps them derive meaning from the data and identify patterns that are otherwise impossible for a human mind to detect.
It gives the computer the capability to make decisions that are in accordance with previous experiences and knowledge.
It is a type of AI technology that is used by businesses to give computers the ability to learn from their actions and experience, much like a human would.
It allows for the computer or device to operate in a more autonomous fashion.
Machine Learning can be used in various applications including web services, online shopping, image recognition, and natural language processing among many others.
It has been used by companies, for years, like Netflix, Google, and Pinterest among many others. This is one of the most commonly used AI technologies today.
2. Natural Language Processing
Natural Language Processing or NLP is a type of AI technology that enables machines to understand language the way humans do.
It can interpret and understand human language, as well as produce speech.
NLP utilizes machine learning models to analyze the language data and returns statistical summaries of the text, which provide a holistic analysis of the speech that it is analyzing.
One particular example of an NLP implementation is Siri, which allows you to access different functions on your phone through spoken commands.
In some sense, NLP gives the machine a way to interpret human speech in the same way we would and in some cases, even better.
Some common applications of Natural Language Processing include:
So, what does this mean for the average user? Don’t worry, nothing too scary.
We have seen it in movies and we’ve even seen robots that can converse with people and understand them, but they aren’t going to be replacing humans anytime soon.
Your phone should not plan your wedding or dance with you to “Fame.”
At this point, most NLP applications are mainly used to help create improve efficiency, learning curves, content creation, enhance search engines and provide more personalized results.
3. Deep Learning
Deep Learning is another branch of AI that has been around for a while.
Deep learning is a statistical learning process that involves several layers of artificial neural networks.
This type of AI technology is designed to sift through data and figure out patterns and relationships, just like our brains do.
In fact, deep learning works by discovering complex patterns in data that are not easily discernable.
This type of AI technology has had a lot of success in image recognition, natural language processing, and voice recognition among others.
It’s been used by companies such as Google and Facebook to help analyze all types of data, and they have used it to teach machines how to identify images, recognize voices or understand human language.
The role of Deep Learning and statistical learning in AI is going to be the most important role to watch as the technology continues to go mainstream.
4. Cognitive Computing
Cognitive computing is a particular type of AI that uses a combination of machine learning and neuroscience.
It aims to replicate the way human brains work, i.e, the human thought process.
The machine learning part of cognitive computing uses algorithms to learn patterns and relationships in data, much like deep learning but for more complex reasoning.
The neuroscience part is what sets cognitive computing apart.
The systems of cognitive computing rely on what we call “neural networks.”
This type of network uses an artificial neural network that consists of nodes and links, much like the human mind.
The nodes represent neurons and the links represent the synapses used by them to communicate with each other.
In this case, these are represented by weights between the nodes and have a data input with a set of features and outputs a set of predictions.
Additionally, cognitive computing systems process large amounts of data because they make decisions based on correlation and not pre-determined rules as traditional AI would.
This is what makes them so different from computer programs and traditional AI technologies.
The lines are blurring, slowly but surely, between human intelligence and machine intelligence.
If you look at the way people learn, it parallels the way these systems learn.
For example, a human child goes through many stages of learning including sensations, actions, abstraction, and finally thought.
In cognitive computing systems, expert can observe similar processes to occur between artificial neural networks.
5. Neural Network
Another subset of AI is Neural Network, which is also referred to as an artificial neural network.
It is basically an unsupervised learning model, designed to mimic human intelligence, by replicating the neurons and their connections.
The neural network does require training data, in order to learn a particular task, just like the way a human brain does.
It can be trained with supervised learning or unsupervised learning.
With supervised learning, the network is trained using an algorithm and given test data, either by labeling and then comparing it to the original data or by labeling a small part of the data and letting it learn from the rest.
Unsupervised neural networks are trained based on unlabeled input data and there is no comparison made between the expected output and actual output.
The neural network is made up of many layers of interconnected nodes. These nodes are also called artificial neurons.
They are organized in a way to emulate the human brain, with inputs and outputs between each node.
Neural networks are mathematical models that accurately simulate the relationship between stimuli and a set of responses – given their values.
They allow machines to recognize patterns in data and make predictions based on these patterns.
An important part of neural networks is their ability to learn and store memories.
By training neural networks with new data, you can use them to analyze large amounts of information and create models for predicting different outcomes based on variables such as size, color, proximity, or feeling certain emotions.
Various types of the neural network include:
However, these are just a few of the many different types of artificial neural networks. One more advanced type is Deep Neural Network (DNN).
This is a more complex and advanced machine learning model that contains multiple layers of nodes that extracts powerful information and converts it into another innovative module.
This type of network also has a sophisticated computer architecture that allows it to learn and produce multiple solutions for one problem as opposed to traditional AI, which only produces one solution for each problem.
6. Computer Vision
Computer vision manages to interpret images, video, and data with a high degree of accuracy.
This subset of AI uses machine learning to build statistical models to generate AI algorithms that can inspect, analyze, extract information and interpret visual data.
The AI uses various visual features such as color, structure, lighting, and texture to classify objects effectively in different cases.
Computer vision can be used by organizations to inspect products that are being produced by robots or machines and ensure they meet an expected quality standard.
It can also be used by organizations that process images to detect anomalies or any flaws in the data at a faster rate through algorithms working at the edge of the network.
A few examples of CV in action are:
7. Augmented Reality & Virtual Reality
Augmented Reality (AR) & Virtual reality (VR) are two types of computer applications that use a technology called computer vision.
Augmented reality is another subset of AI that enhances the real world with computer-generated information and graphics, whereas virtual reality completely replaces the real world with simulated objects and environments.
This technology can be used by organizations and companies in many ways such as:
8. Robotic Process Automation
Robotic Process Automation (RPA) is an AI that mimics the way humans process business processes.
RPA is used to automate manual processes and make them work like a human would do.
The major benefit of using RPA is that it takes away a lot of the labor-intensive work associated with manual processes and that it can be done in a fraction of a second meaning automation can produce quicker results than human employees which reduces error-prone human errors.
Nowadays, robots are being used both in homes and businesses.
Robots are quite simply parts of AI systems that are being used to automate processes.
This means that robots are used in businesses to take care of simple and mundane tasks that don’t need a lot of human interaction.
That doesn’t mean that robots cannot perform high-level duties.
Robots can’t replace humans altogether but they do help humans work more productively and efficiently, therefore, boosting productivity.
9. Speech Recognition
The first time I learned about speech recognition, I immediately thought about Siri.
Speech recognition allows you to use your voice to communicate with computers and other devices.
It is a very well-known technology that’s used in a variety of products and services including Apple’s Siri, Google Now, Amazon Alexa, and Microsoft’s Cortana.
It is also used to add voice control options to mobile apps. In fact, in the next generation of SDK for mobile apps, developers will have access to more neural network-based speech recognition services.
Spoken language is used by a computer to correctly identify sentences and their meaning such as asking a question that a computer can understand, using commands to operate your devices, booking tickets, and even arranging an appointment.
In fact, we are seeing a rise in the use of voice-based AI assistants and an improvement in speech recognition technology.
According to a study by PWC, three out of four consumers use their mobile voice assistants at home.
10. AI Chatbots
AI Chatbots have taken the world by storm and are expected to have a huge impact on multiple industries such as healthcare, automotive, and insurance.
Chatbots are AI-powered applications that use natural language processing (NLP) to understand what a person wants and then provide it.
In recent years, there’s been an incredible upsurge in the usage of chatbots by businesses because:
The way AI chatbots work is by asking a series of questions and then applying machine learning algorithms to the user’s responses.
This means that the more a user interacts with an AI chatbot, the smarter it gets.
Each time a user interacts with a chatbot, it is learning from their responses, the information they provide, and what they actually search for in terms of products and services.
As this happens, the user’s experience improves.
The many advantages of chatbots allow them to be used by businesses in a number of ways such as:
11. Self-Driving Cars
Artificial intelligence has been playing a big role in the development of self-driving cars because computer vision combined with machine learning enables autonomous vehicles to drive without human intervention.
Computer vision can detect objects, obstacles, and processes happening in traffic.
According to TechCrunch, there are over 1400 self-driving cars on the US roads and another study reveals that the German car brand, Audi will be spending $16 billion on self-driving cars by 2023.
Despite the fact that a few hundred car crashes have been reported, we are not seeing a decline in the investment for the production of driverless cars.
We have yet to see the improvement of autonomous vehicles but we can already see how computers are able to do a human driver’s job such as:
There are obviously many ways that AI is poised to make a huge impact on our lives.
And, with almost every device and app being able to communicate with each other, it’s possible to imagine the real potential of AI and robots.
AI Examples in Various Industries
Artificial Intelligence is getting more accessible, more accurate, and more powerful every day.
That’s why many businesses across various industries have already embraced it and are using it to transform their businesses.
Below is a list of popular industries that use AI technologies and the top companies in each field.
Artificial Intelligence in Healthcare
The healthcare industry is booming and so is the use of AI. Artificial intelligence has made a lot of positive impacts in healthcare because it helps doctors make critical decisions by providing them with information and tools that can help improve the lives of patients.
Market in 2020
Expected Reach by 2023
Top AI Healthcare Companies
Artificial Intelligence in Finance
Many financial institutions like banks and fintech companies are already using AI to improve their risk management systems to curb financial crimes and detect fraud in a more efficient manner.
Market in 2020
Expected Reach by 2026
Top AI Finance Companies
Artificial Intelligence in Retail
The retail industry is one of the big players in AI, using robotics and predictive analytics to improve customer experience. With AI, retailers can respond to customer needs and demands in real-time, creating a faster and more efficient experience for the customers.
Market in 2020
Expected Reach by 2028
Top AI Finance Companies
Artificial Intelligence in Digital Marketing
Digital marketing is getting more competitive by the day and Artificial Intelligence is now being used as a way to help businesses stand out from the crowd. The main components of AI being used in digital marketing include machine learning, NLP, image recognition, and semantic search.
Market in 2020
Expected Reach by 2028
Top AI Digital Marketing Companies
Artificial Intelligence in Cybersecurity
Cybersecurity is an area that is experiencing exponential growth alongside all the digital advancements we are making in the global economy. The cybersecurity industry is a highly specialized field that requires expertise in many technical and scientific fields.
Market in 2019
Expected Reach by 2026
Top AI Cybersecurity Companies
Artificial Intelligence in Education
The education field is one of the industries that has already embraced AI and its technologies. It’s not just the use of AI in education that is changing the way we learn but in projects and innovation. They’re being tested and implemented to better help us understand how to learn.
Market in 2021
Expected Reach by 2030
Top AI Education Companies
I hope you found this guide to AI interesting, and that you’ve also learned a few things along the way.
So, what is artificial intelligence really bringing to our modern society? Well, as we saw earlier, AI has its benefits and its disadvantages.
But for businesses, artificial intelligence is developing at a rapid pace and could eventually render many human jobs obsolete.
In light of this concept, it is necessary to understand whether there are ways we can keep our businesses competitive by incorporating the benefits of AI into them.
Seeing how AI is making an impact on various industries, it won’t be long before it evolves beyond our imagination!
While you’re at it, don’t forget to share this definitive guide with others!
1. AI vs Machine Learning
The terms AI and Machine Learning are often used interchangeably, but there are subtle differences between AI and ML. AI is a broad term that encompasses all modern technologies and innovations in the advancement of computing, while Machine Learning is one of the many subcategories of Artificial Intelligence.
Machine Learning can also be defined as the “set of techniques that enable computers to learn from data without being explicitly programmed by human intervention.
It allows computer programs to improve their performance based on the outcomes observed from previous behavior patterns and interactions with other systems.”
2. Is Machine Learning Artificial Intelligence?
Machine Learning is a subset of Artificial Intelligence that relates to the ability of a computer to ‘learn’ to perform a task, by analyzing large amounts of data and applying them to learn new skills. Machine Learning is made possible through the use of powerful automated algorithms that are trained on a set pattern or set of rules.
3. How Does Deep Learning Work?
Deep Learning is a statistical learning technique that relies on an architecture of neural networks to simulate the human brain and its ability to learn. Deep Learning is based on a hierarchical decomposition of tasks into layers.
Also, Deep Learning typically consists of a large number of modules, each serving a specific role. Finally, Deep Learning uses structured and unstructured data to train the system.
4. What is an Example of Deep Learning?
One popular example of deep learning is the use of chatbots. Chatbots are computer programs that are capable of carrying out a conversation with a human user and interacting with them in a way that appears to be natural.
Ai-powered chatbots utilize deep learning techniques to build a deep understanding of the context of conversations, to be able to carry out more complex tasks. The more data the chatbots receive, the more accurate they become.
5. What is Machine Learning With Neural Networks?
Machine learning neural networks is a subset of artificial intelligence that was introduced by the “Neural Network” computer program, which belongs to the subcategory of machine learning or deep learning.
6. Is AI Possible Without Big Data?
AI & Big Data are interdependent on each other. Big data refers to the large amount of data that computer systems collect, process, and analyze and AI generates insights from Big Data to make decisions. So, you see, AI needs Big Data as much as Big Data needs AI!