Artificial Intelligence. Machine Learning. Data Science. More words you don't know the meaning.

If you're a curious cat like me who wants to know everything, you'd have probably searched the web to know what this “Artificial Intelligence" is all about?

Is it some kind of fancy robot? Is it a part of Data Science? Is it the same thing as Machine Learning? What is even Machine Learning? - crash! Beeping noises.

Sadly - or not - Artificial Intelligence is not some sort of fancy robot that will overpower humans and take over the world, soon.

What Is Artificial Intelligence?

Artificial Intelligence is a computer-controlled robot or software that thinks intelligently like the human mind.

Hold up, so why are Data Science and Machine learning always included in the Artificial Intelligence discussion?

The key thing to note is, Data science is a broad discipline that includes Machine Learning and the study of AI.

Data Science is the process of discovering useful insights from data through the use of scientific processes and methods, steps and procedures, like; extraction, manipulation, visualization, and maintenance of data.

Machine Learning involves the use of mathematical models to make a computer learn by giving it instructions directly.

Artificial Intelligence has to do with the use of a predictive model to predict events. Machine learning is a subset of AI.

The introduction of AI technology was a result of studying the patterns of the human brain and how it thinks and works.

Applications of AI include natural language processing, speech recognition, machine vision, and object detection.

Categories of Artificial Intelligence:

Artificial intelligence generally falls under two broad categories:

Narrow AI - Narrow AI is also known as Weak AI. It is an Artificial Intelligence system that is created and trained to carry out a single task extremely well.

It mimics human intelligence and only functions under specific circumstances. They usually operate under a lot of restrictions and limitations than even the most basic human intelligence, so they're not so intelligent - lol.

Narrow AI is used by Digital Assistants like Apple's Siri, Industrial Robots, Google Search, Image recognition software, and self-driving cars.

Artificial General Intelligence (AGI) : You already guessed it, this one can also be referred to as Strong AI.

AGI is a machine that possesses general intelligence and, like a human, can use such intelligence to address any issue. It is a computer program that can mimic the cognitive functions of the human brain.

Types of Artificial Intelligence:

Artificial Intelligence can be divided into four types:

1. Purely Reactive Artificial Intelligence.

2. Limited Memory Artificial Intelligence.

3. Theory of Mind Artificial Intelligence.

4. Self-aware Artificial Intelligence.

Purely Reactive : Purely Reactive AIs usually do not have any data to work with because they can't store any form of memory, they usually specialize in one type of work.

It is only capable of only using its intelligence to perceive the world in front of it, it follows the basic principles of AI.

Expert systems, natural language processing, speech recognition, and machine vision are some examples of specific AI applications.

They're designed to complete only a certain number of specific duties, they're more trustworthy and reliable as they will react to the same stimuli the same way every time.

Limited Memory : This is a more complex AI than the Purely Reactive AI, it can collect past data, store it, and continuously add data to its memory - remember we said the Purely Reactive AI doesn't store data, so they work with past data.

Limited Memory AIs come to life when a team trains a model continuously on how to analyze new data when it's given.

There are usually six steps that must be followed when creating a Limited Memory AI:

• The training data must be created/available.
• The Machine Learning model must also be created/available- I'm sure it's becoming clear how Machine Learning and Artificial Intelligence are usually mixed up together.
• The Model should've been tested and be sure that it's able to make predictions.
• The Model must be able to receive feedback, either human or environmental feedback.
• The feedback gotten must be stored as data.
• Finally, the steps should be repeated as a cycle.

Theory of Mind : AIs that belong to this group can interact socially, and understand emotions and thoughts. Bad news - or not? - this type of AIs hasn't been built yet.

Theory of Mind is purely academic. We still lack the technological and scientific advancements required to develop artificial intelligence to this level.

The idea behind the Theory of Mind AI is founded on the psychological knowledge that one's behaviour is influenced by the thoughts and feelings of other living creatures.

This would imply that these types of AIs might understand how people, animals, and other machines feel and make decisions through self-reflection and determination and would use that knowledge to make their own decisions.

To establish two-way communication between humans and artificial intelligence, computers would essentially need to be able to understand and interpret the idea of "mind," the variations of emotions in decision-making, and a litany of other psychological concepts in real-time.

Self Aware : Just like the Theory of Mind AIs, these ones aren't yet in existence. They're to come after the Theory of Mind.

Once Theory of Mind have become established, the next thing will be for the AIs to become self-aware.

This kind of AI will have a certain level of consciousness that's equivalent to that of humans.

It would be able to read body language and understand what others may need not just on what they communicate, but how they communicate.

Self-awareness in artificial intelligence depends on human researchers being able to recreate consciousness so that it may be incorporated into computers.

How Does Artificial Intelligence Work?

They work by ingesting a vast volume of labelled training data and then, examining the data for correlations and patterns, before using the patterns discovered to predict future states.

For example, by studying millions of instances, an image recognition tool can learn to recognize and describe objects in photographs, just as a chatbot is given examples of text chats so it can learn to make similar exchanges with people.

Applications of AI:

Some of the most common commercial business uses of AI are:

1. Banking Fraud Detection - The AI learns to determine if a new transaction is fraudulent or not fraudulent through an analysis of enormous data of past fraudulent and non-fraudulent transactions.

2. Online Customer Support - Online customer support and voice messaging systems are being made possible through the use of AI.

3. Cyber Security - AI is used to detect abnormalities, adapt, and respond to threats using machine learning algorithms and lots of sample data.

4. Virtual Assistants - Voice recognition is now used by Alexa, Google, Cortana, and Siri to carry out user orders.

They gather data, decipher the question, and then provide the solution using data that has been fetched.

Pros and cons of AI:

Pros:

• It never sleeps, it's active 24/7.
• There's a decrease in human error.
• It can easily handle repetitive tasks.
• It's fast.
• It's very good at detail-oriented tasks.

Cons:

• It cost money to implement.
• It doesn't have human creativity.
• It can lead to unemployment, as it can replace some jobs.
• It requires “deep” technical knowledge.
• It can specialize as it only knows what it has been shown.

There you have it, the basics of Artificial Intelligence, now you can take part in the next conversation and tell them everything you know. Even better, you could just share this article. 🚀

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