What Is Artificial Intelligence?

It’s a term that has been used for decades and is often used to describe the antagonist of science fiction films and novels. We have all heard the term artificial intelligence, and now it seems that it’s finally becoming more than a fantasy. So What is artificial intelligence?

Artificial Intelligence, or AI for short, is a field of computer science that focuses on creating machines or computer programs that can perform tasks that typically require human intelligence. It involves developing algorithms and models that enable devices to learn from experience, recognize patterns, make decisions, and perform tasks with a level of autonomy.

At its core, AI is about creating intelligent agents, which are systems that can perceive their environment, reason about it, and take action to achieve specific goals. These agents can be designed to perform a wide range of tasks, from simple ones like recognizing objects in images or translating languages, to more complex ones like playing games, driving cars, or even assisting doctors in diagnosing diseases.

The Different Forms Of Creating Artificial Intelligence

The term Ai has become relatively abstract in the modern age is used to generally define a program or system that can learn to improve its capabilities for a specific task. An example of this in the real real is midjourney which uses a database of images and artworks to be able to form any image based on a simple command prompt.

Exactly how these ai systems are created and how they learn changes based on the purpose of the tool. so let’s take a look at a few examples of artificial intelligence systems.

Rule Based Systems

Rule-based systems are a type of artificial intelligence (AI) that rely on a set of predefined rules and logic to make decisions or solve problems. In a rule-based system, the rules are typically represented in the form of “if-then” statements, where the “if” part specifies a condition, and the “then” part specifies an action to be taken if the condition is true.

For example, let’s say we wanted to create a rule-based system that could help diagnose medical conditions based on a patient’s symptoms. We might start by defining a set of rules like:

  • If the patient has a fever and a cough, they might have a respiratory infection.
  • If the patient has a headache and light sensitivity, they might have a migraine.
  • If the patient has a rash and swollen lymph nodes, then they might have an allergic reaction.

These rules could be represented in a knowledge base, which the rule-based system could use to reason about the patient’s symptoms and make a diagnosis.

One of the advantages of rule-based systems is that they are easy to understand and modify, since the rules are explicit and can be easily changed or updated as needed. However, rule-based systems can be limited by the fact that they rely on predefined rules, and may not be able to handle complex or unpredictable situations that fall outside of the rules.

As such some would argue that this is not true Ai as the restrictions are more notable than in other forms of artificial intelligence.

In contrast, machine learning-based approaches like deep learning can learn from data and adapt to new situations, making them more flexible and powerful in some cases. However, they may also be more complex and difficult to understand and may require large amounts of training data to perform well.

Machine Learning

Machine learning is a subfield of artificial intelligence that focuses on creating algorithms to learn from data and make predictions or decisions based on that learning.

The primary goal of machine learning is to enable software and programs to automatically improve their performance on a specific task over time, without being explicitly programmed to do so.

The category of machine learning can again be divided up into three smaller sub categories. For example, one subcategory is Supervised Learning. In supervised learning, the machine is trained on labeled data, where the correct answers are known in advance. The machine uses this labeled data to learn how to make predictions or classifications on new, unseen data.

Another subcategory is Unsupervised Learning. With this form of learning, the machine is trained on unlabeled data, where the correct answers are not known in advance. The application uses this data to find patterns and structure within the data, without specific guidance on what to look for.

Then we have Reinforcement Learning. In this subcategory, the applications learn by interacting with an environment and receiving feedback in the form of rewards or punishments. The goal is to learn how to take actions that maximize the total reward over time.

There are advantages to using this form of artificial intelligence, depending on the way the application is programmed to learn.

For example, machine learning algorithms can process vast amounts of data and extract patterns that might be difficult for humans to detect.

The algorithms can learn from new data and adjust their behavior accordingly, making them useful in situations where the problem is complex or constantly changing. This is an example of adaptability.

And it can automate repetitive tasks, freeing up humans to focus on more complex or creative work.

Again, machine learning is not perfect and there are many limitations, one such limitation is that machine learning algorithms typically require large amounts of labeled data to learn effectively, which can be costly and time-consuming to obtain.

Another key weakness of Machine Learning can be the appearance of biased data. These algorithms can sometimes perpetuate biases in the data they are trained on, leading to unfair or discriminatory outcomes.

Deep Learning

Deep learning is a subfield of machine learning that uses neural networks to learn representations of data. Neural networks are modeled after the structure of the human brain and consist of layers of interconnected nodes, or neurons, that can learn to recognize patterns and relationships in data.

In deep learning, neural networks are organized into multiple layers, with each layer learning increasingly complex representations of the input data.

The input data is fed into the first layer, and each subsequent layer processes the output of the previous layer, gradually transforming the input data into a form that is useful for the desired task.

The final layer produces the output of the network, which could be a prediction, classification, or some other form of analysis.

Deep learning algorithms can learn to recognize patterns in data without being explicitly programmed to do so. Instead, they learn by adjusting the weights of the connections between neurons in the network, based on feedback from the training data.

During training, the network is presented with labeled examples of the input data and the desired output, and the weights are adjusted to minimize the difference between the predicted output and the actual output.

The strength of deep learning is that it can learn to recognize patterns in complex and high-dimensional data, such as images, speech, and text. Some typical applications of deep learning include image recognition, speech recognition, natural language processing, and robotics.

You will find many forms of deep learning ai models on the internet now, the most well known at this point being Chat GPT.

However, deep learning also has some limitations. One of the main challenges is that deep neural networks can be computationally expensive to train, especially for large datasets.

Additionally, the black-box nature of deep learning models can make them difficult to interpret, which can be a concern in applications where transparency is important.

In What Industries Can Ai Be Used In?

Artificial intelligence has the potential to transform many industries by providing new ways to analyze data, automate processes, and optimize systems.

AI can be used in healthcare to improve patient care, diagnosis, and drug discovery. In Finance for example, AI can help detect fraud, manage risks, and analyze investment opportunities.

Retail can benefit from artificial intelligence through personalized advertising, inventory management, and supply chain optimization.

Transportation can leverage AI for autonomous driving, route optimization, and predictive maintenance.

Manufacturing uses AI for quality control, predictive maintenance, and process optimization.

Energy and utilities can use AI for demand forecasting, grid optimization, and predictive maintenance.

Media and entertainment use AI for content recommendation, image and speech recognition, and natural language processing.

Last but not final, Education could potentially use AI for personalized learning, student assessment, and intelligent tutoring systems.

Artificial intelligence has the potential to revolutionize a wide range of industries, and its impact is likely to continue growing as technology advances.

Will Artificial Intelligence Continue To Grow?

There is absolutely no doubt that over the coming months and years, artificial intelligence will play a key role in all of the various industries and fields that exist in the modern world.

Towards the end of 2022, we started to see applications like ChatGPT really take off in the public eye and in the early months of 2023 dozens more AI-based applications have surfaced.

Even though the large majority of these applications are still in their infancy, with many improvements still to be made, the potential is clear to see. Midjourney version 5 is an example of a text-to-image application that is often used within Discord.

In the past six months alone, the results from my journey have improved dramatically and now you can use the AI tool to generate almost any image that you want to a high standard. Such an application can be invaluable in any field where images are used.

For example, we can use AI based images on our sales pages when trying to promote a specific course or digital products.

You could also use these applications to create things like YouTube thumbnails for your latest videos. Canvas is another example of an application that has AI-based tools that allow you to generate the results you want far more quickly.

And yet, text to image is just one example of artificial intelligence in the modern age. There are many other ways in which we can use AI to improve the various work flows related to our fields. AI will continue to grow, and the most alarming aspect of this is that it’s going to continue to grow at an incredible rate in the coming months, let alone the next few years.

Artificial intelligence, in many ways, is as big a discovery for the modern world as the first computers were many years ago. It’s a development on par with the introduction of the Internet, made accessible in your own home. AI is going to have a strong impact on jobs and businesses around the world.

Should I Learn More About Artificial Intelligence

AI is going to become such a key tool in the future, and even in the present, that you should absolutely learn as much about artificial intelligence as possible, especially the forms of AI that will relate to your field, industry, and job role. If you are an artist, then you’re going to need to learn as much as you can about text to image software.

If you are in the e-mail marketing business, then you want to use language models like chat, GTFO in order to help generate better emails and sales copy for your business.

If you are in the business of video production, then there are other applications such as NVIDIA Broadcast that can be used to improve the audio quality of your videos.

Take a few minutes to consider all of the things that you do in your daily job role. What aspects of your role could potentially be automated? It’s possible that an application already exists to at least attempt the automation of that specific task. If you want to remain employable in the future, then artificial intelligence is not something that we can ignore. It’s something that we need to learn. Because it’s going to change the way we do things forever.


My name is Joe and I am the lead writer and content creator for this site. Since it first starting to make waves in 2022 I have believed that a lot of what we will be able to do in the future will be assisted by artificial intelligence, and that the more you know about how to best use ai, the better a position you will be in regardless of your field and industry.

Recent Posts