Worth over $136 billion today, the artificial intelligence (AI) industry is quickly growing into a giant, leaving measly technology that we once awed at in its shadow.
It is moving so fast that some of the biggest tech leaders are requesting that it be postponed for 6 months. But is AI really growing that fast? What can it do, and where will it go next?
To start chipping away at the tip of this artificial iceberg, we have put together this guide on the 6 major branches of artificial intelligence so that you can be prepared for the next AI evolution.
1. Machine learning
Let’s get stuck in and start with machine learning, which can predict patterns, human behavior and learn speech recognition as found in Siri and Alexa.
This branch of AI was created all the way back in the 1950s by IBM employee Arthur Samuel. He programmed a computer to play checkers and continue to get better the more it played – this is the first instance of machine learning.
Now it has many more talents than just checkers; it can learn almost anything if it has enough data to analyze and spot patterns or trends. The more data it has, the more accurate it will be.
Nasa is a big fan of this particular branch of AI and uses it to drive rover, find other planets, and take them to the moon.
In simple terms, machine learning uses a model consisting of algorithms and data. When new data is put in, the model’s knowledge expands, and can begin learning from experience and make predictions.
The way it is programmed or ‘trained’ as they like to refer to it in the AI community, is categorized into 4 different methods:
Supervised machine learning
Unsupervised machine learning
Semi-supervised machine learning
Supervised machine learning
Supervising does not equate to someone babysitting the AI to make sure they don’t hurt themselves – instead, the supervisor is the data programmed into them.
With supervised learning, the data input is labeled extensively, making it easier for the AI to predict the output as some are already labeled.
The process is pretty straightforward. To begin with, the AI models are trained with a clearly labeled dataset. Then, it is tested with data it has never seen before but has the resources in its existing knowledge bank to predict the output.
Regression is one of the most common supervised models, where the AI’s algorithm will look at the variables within the data and estimate the relationships.
As you can imagine, this type of machine learning takes more time to program and test, and due to it needing a bit of hand-holding, it is inefficient at complex tasks requiring more problem-solving.
Unsupervised machine learning
Unsupervised learning can find patterns and predict outputs without the need for a training dataset. It uses these patterns in data, such as weight, color, decimal place, etc., to put it into clusters of similar or dissimilar values.
Plagiarism checks, data organization, and fraud detections are areas where unsupervised machine learning works best because the user doesn’t necessarily know what data it is looking for, but the AI does.
Semi-supervised machine learning
Now that you know what supervised and unsupervised machine learning is, it’s pretty self-explanatory as to what semi-supervised means.
It is the perfect solution for when there is not enough labeled data to input into the system to create accurate output. But, when you add some unlabeled data, the machine learning model can connect the dots and paint a full picture.
By only having to label a fraction of the data, you can save lots of time without sacrificing the accuracy of the output.
Finally, we have reinforcement learning, a machine learning method quite different from the previous 3 we have covered.
Essentially, the AI system has the freedom to trial and error when exploring a new environment, e.g., a platform in which it interacts. Successful outputs are rewarded, and unsuccessful ones are punished. Therefore reinforcing the desired behavior as seen in robotics and self-driving cars.
This type of machine learning can assess the main goal and the best way to achieve it and even understands that small rewards are not as valuable as long-term benefits.
Machine learning examples
Machine learning algorithms can come in many forms, but what exactly does that look like when applied to real-world problems? Here are some examples you may be familiar with:
Media recommendations. You know when you’re scrolling through Netflix looking for something to watch? The movies and TV shows that pop in your recommendations are put there using machine learning.
Machine learning algorithms can use data analysis to predict what you will want to watch next based on your previous viewing behavior, such as genres or actors you watch most. So the more Netflix you watch, the more personalized it will be!
Image recognition. By analyzing the numeric property in each pixel, machine learning uses computer vision to put them together like a jigsaw and label the image for what it is, whether a house or a park bench.
his doesn’t come naturally to computers, so it demands a lot more processing power. It requires them to categorize an image, environmental context, object detection, and pick up on every other object in the image.
The result of what image it recognizes is based on a database program, usually with the programming language python, by clever humans so that they have a base knowledge of images and what they should look like.
Healthcare. The healthcare industry has adopted machine learning capabilities to help predict diseases using pattern and trend recognition and how to diagnose them. Machine learning can also use its image recognition ability to analyze medical scans such as X-rays and MRIs.
2. Natural language processing
Natural Language Processing (NLP) is the AI branch that focuses on understanding and replicating human language. According to Forbes, we have struck the golden age of NLP, and after learning more about it, you will understand why.
NLP is an expert on the rules of human language, such as grammar, context, and spelling, and can combine this with deep learning models to fully comprehend and predict text and speech.
When we read something, we understand it immediately, but it’s much more complicated for AI systems to do the same. It has to analyze the context, structure, semantics, and syntactic properties of the text before it can comprehend the meaning and tone.
Natural language processing is also capable of speech recognition. This is commonly seen when you get a message while driving that you can’t respond to. Instead, you just speak to the smartphone, and it sends it as a text. All without having to take your eyes off the road.
And that’s not the end of it; it utilizes algorithms and deep learning to use this understanding of the text to paraphrase, summarize, and apply learning concepts.
It doesn’t matter what format the text comes in, whether it be social media comments, articles, or academic essays; NLP gives AI the ability to analyze thousands and thousands of words almost instantly and do with it what you please.
But how does it work?
It uses a tool called text vectorization which must be done in order for the computer system to understand it. It then dips into its training data to find correlating patterns between them to produce accurate output.
Let’s take a look at an example. A student has written a history essay but wants to have it spellchecked before it is submitted. The student turns to an AI using NLP to scan through the text and flag spelling mistakes.
The essay will be inputted using text vectorization and then compared to the training data knowledge bank of language rules. If there are discrepancies, then they will be shown in the output.
It’s as easy as that!
This is just one of the many examples of where you will see natural language processing in day-to-day life. Here are some more:
Chatbots. Almost every website has a chatbot pop-up at the bottom of the screen asking if you need help. This is NLP in action and can answer your questions, promote products to you, and more.
Chatbots are not limited to customer service; they can also be a virtual assistant capable of mimicking human intelligence and conversation. Like most AI, the more you talk to it, the more it will learn.
Search results. In order for a search engine to display relevant results, it needs to understand what you type into the search bar. You will have noticed that it can even predict what you are going to say before you finish typing based on similar searches from other users. You only need to give it a few words, and the NLP will start working its magic and produce search results by filling in the gaps.
Translation. NLP can accurately translate text and speech much faster than a bilingual human can. Combining faster computer processing power, machine translation (MT), and NLP, we can translate whole books from one language to the next in a matter of minutes.
3. Neural networks
This is where AI gets a little bit freaky as it uses similar networks to a human brain.
They may not be able to feel human emotion, but they can use a set of algorithms full of mathematical equations that cluster information in the same way that we do to recognize patterns in the forms of numbers, vectors, and audio.
Artificial neural networks (ANN) date back to the 1940s, beginning as a simple algorithm created by Walter Pitts and Warren McCulloch, mathematicians who set out to emulate human intelligence.
In neural networks, there are usually 3 layers; input, hidden, and output layer. If there are more, it is classified as deep learning.
The input layer is where data in the form of neurons are received and fed through to the hidden layer, where the magic computation happens. The neurons in the hidden layer are able to transform the input data and send the results to the output layer.
In the human brain, we have cells that connect to one another through electrical signals that carry information. It creates a sort of highway, and the more connection we make, the more efficient we become with processing information and learning new things.
In contrast, artificial neural networks are made of wires and silicon but have the same ability to evolve as they continue to learn. And, like humans, ANN doesn’t enter this world knowing everything.
They are trained with lots of data with expectations of how they are to process the input data to achieve the desired output. By giving direction, the AI can use decision-making ability to reach its goal in a better way, increasing efficiency and accuracy.
Neural network examples
If you can’t quite wrap your head around it, here are some examples of what this branch of AI is capable of:
Facial recognition. A subset of neural networks called convolutional neural networks (CNN) can scan our faces numerous times a day. From unlocking our smartphones to making payments, our faces are the only keys we need.
Aerospace engineering. Neural network AI has also been adopted into the aerospace industry, focusing on the visual inspection process to maintain aircraft. It can scan areas and flag any faults that the engineers should know about, such as paint scratches, dents, and cracks.
Weather forecasting. ANN can accurately predict the weather days in advance by using a combination of neural networks, deep learning, machine learning, and pattern recognition. They are given large data sets of previous events, which they use to predict weather and climate patterns.
4. Expert systems
As the name suggests, these types of AI have expertise in a particular field and are a reliable, high-performing practical application of expert knowledge. How it got to be so smart in the first place comes down to the information already being inputted by one or more human experts.
The knowledge is stored inside the expert systems and acts as a domain for those who are not experienced in the particular subject to access and learn from.
With a huge amount of expert knowledge stored in the same place, this AI branch is incredibly valuable as a source for others in an easy-to-understand presentation.
It works as the middleman between the user and the information they are wanting to access. The main function is to comprehend what is asked of them, e.g., what the person wants to know about a particular subject so that it can dive into its knowledge base and extract relevant data using reasoning skills.
Expert systems are comprised of 5 components that all work in tandem to create a seamless stream of knowledge; user interface, inference engine, knowledge base, knowledge acquisition module, and explanation module. They all work together to understand what is asked of the model and how it can use the data it has to best meet the demand.
An early example of this AI branch helping people access information is the early backward chaining expert system MYCIN, an artificial intelligence program from the 1970s that used data science and expert systems to store extensive knowledge on bacterial infections, antibiotics, and diagnosis.
AI robots are not just those creepy humanoid ones like Sophia who can recognize human faces and emotions. They come in all different shapes and sizes and are found in factories, hospitals, restaurants, and on mars.
Computer science, data science, and engineering are combined to develop robots that can carry out tasks without intervention. They can learn, solve complex problems, and make decisions based on their environment and previous trial and error.
Expert systems and neural networks are also integrated into the robotics branch of AI, enabling them to learn like humans and apply the level of knowledge needed to carry out their tasks without requiring help.
Not all robots are programmed with AI; some are merely engineered to drill a hole, and that’s it. Many boring, repetitive tasks can be released from humans due to robotic automation, as seen mostly in factories.
Artificial intelligence systems in robotics can be categorized into 4 types:
Theory of mind
Robotics with reactive AI are the most basic and do not learn from past experiences, living in the moment instead. Hence the name; it reacts to what is happening in its immediate environment and produces the same output every time.
Unlike reactive machines, robots with limited memory can refer to the past and make more accurate predictions with time. All models with machine learning need limited memory in order to function properly.
Theory of mind
Robots with theory of mind AI are able to function as a human theoretically would. This means they better understand emotions, thought processes, beliefs, and needs, which they can then apply to their decision-making.
Finally, we have self-awareness which sounds quite existential and has not become a reality as of yet. We first have to fully understand the human consciousness in order even to begin teaching AI how to replicate it.
6. Fuzzy Logic
AI is essentially a computer system that works with yes/no, black/white values known as boolean. Fuzzy logic gives AI the freedom to explore beyond this point and opens the door to human reasoning, such as maybe, definitely, and don’t know.
It may sound a bit useless,like a magic 8 ball, but fuzzy logic opens up the possibility for many more solutions to problems, realistic to how humans operate in the rollercoaster of life.
Fuzzy logic is valuable for finding solutions to complex, vague data that other machine learning techniques would struggle with as there is no yes/no output.
In fact, even though fuzzy logic is often categorized with machine learning, it is a different system altogether. Remember, machine learning is modeled after human brain function, whereas fuzzy logic is a large data set from which it functions and understands rules.
These two systems share the fact that humans program their algorithms to give them artificial intelligence to carry out intricate problem-solving.
There are 4 components to fuzzy logic:
This component stores the conditions for decision-making and problem-solving. Fuzzy logic doesn’t need many rules as it can understand a concept with only a small rule base. Other AI systems need a lot of data to grasp a concept in order to be as accurate.
The name doesn’t sound very technical, but a lot of clever stuff goes on in the fuzzifier. This is where the data input is transformed into fuzzy sets defined by blurred boundaries. The fuzzy sets are sent off to be processed further.
This part of fuzzy logic tells the system what outputs it should ideally produce and then applies it to the data.
Finally, the fuzzy sets are transformed once again into detailed output data.
Fuzzy logic examples
To further drive home how useful fuzzy logic can be, here are some use cases:
Data mining. This is where vast amounts of information are searched to find meaningful trends and patterns. Businesses usually use data mining to group their customers together based on age, location, etc. It can be used for any instance where lots of data need to be simplified.
Washers and Dryers. Fuzzy logic can also be found running your home appliances, such as the washer and dryer, by controlling temperature, water input, velocity, and wash time. Sensors use fuzzy logic to monitor cycles that are stored in the dataset and used to improve future performances.
To wrap up
The 6 branches of artificial intelligence have evolved beside us for nearly 100 years, and seeing them take such big strides due to the intervention of the greatest programmers is inspiring.
From saving human lives to saving us from getting caught in the rain, there is an AI branch for whatever aspect of life you need help with.
By learning of all the ways in which AI can take the form, you may be feeling a bit on edge. t’s not just a gimmick robot you see at a tesla convention; it is all around you and has been for years.
Artificial intelligence is like a giant looming tree, with multiple branches continuing to grow and wrap themselves around us, but how tight these branches get is yet to be seen.
Were you aware that artificial intelligence had 6 branches? If you want to learn more about AI and its capabilities, visit Top Apps.