Machine Learning

The six main subsets of AI: (Machine learning, NLP, and more)

by
Ekin Keserer
,
February 3, 2022

Once seen as mere hype, artificial intelligence is now widely accepted as a transformative technology. Its ability to enable machines to learn and work on their own is opening up new possibilities in business, and 95.8% of organizations have AI initiatives underway, at least in pilot stages.

But despite this broad consensus, there is still a lot of confusion about what AI is and how to use it. Businesses need a solid understanding of the six main subsets of AI in order to make the most of this transformative technology.

To help executives get up to speed, we’ve identified the six main subsets of AI as machine learning, deep learning, robotics, neural networks, natural language processing, and genetic algorithms. We'll also explore how to effortlessly deploy AI in your business with our no-code action plan.

What is AI Vs. Machine Learning?

Often used interchangeably, AI and machine learning (ML) are actually quite different. AI is the umbrella term that refers to anything that allows a machine to do something that ordinarily requires human intelligence, such as recognizing objects or sounds, understanding natural language, or solving complex probabilistic problems. 

Machine learning is a type of AI that enables a machine to learn on its own by analyzing training data, so that it can improve its performance over time.

The vast majority of advancements in AI today are due to machine learning models. In fact, many of the applications billions of people use every day, such as Google Search, YouTube, Amazon, and Netflix, are powered by machine learning.

Google Search was once based solely on rules written by engineers, which limited the number of queries it could handle. Today, Google Search is powered by machine learning, which allows it to handle billions of queries per day and get smarter over time. Further, YouTube uses machine learning to recommend videos to users, Amazon uses it to personalize product recommendations, and Netflix uses it to provide customized recommendations for TV shows and movies.

The Top 6 Subsets of AI

As artificial intelligence (AI) evolves and becomes more sophisticated, its various applications and use cases are becoming more apparent. There are six distinct subsets of AI that are worth keeping an eye on:

  • Machine learning
  • Deep learning
  • Robotics
  • Neural networks
  • NLP

These aren't mutually exclusive categories, and AI technologies are often used in combination. But they provide a useful framework for understanding the current state of AI and where it's headed.

The graphic below illustrates how AI is the broadest category, encompassing specific subsets like machine learning, which itself has more specific subfields like deep learning.

 A graphic highlighting a couple major subsets of AI: Machine learning and deep learning. Deep learning is itself one of the subsets of machine learning.

Machine learning

Machine learning is a broad subset of artificial intelligence that enables computers to learn from data and experience without being explicitly programmed. In recent years, machine learning has helped to solve complex problems in areas such as finance, healthcare, manufacturing, and logistics.

There are different types of machine learning algorithms, but the most common are regression and classification algorithms. Regression algorithms are used to predict outcomes, while classification algorithms are used to identify patterns and group data.

Machine learning algorithms can be further divided into two categories: supervised and unsupervised. Supervised algorithms require a training dataset that includes both the input data and the desired output. Unsupervised algorithms do not require a training dataset, and instead rely on data to "learn" on its own.

Machine learning itself has several subsets of AI within it, including neural networks, deep learning, and reinforcement learning. 

A machine learning example: Housing price prediction

Let's look at the example of housing price prediction. In this example, a supervised machine learning algorithm called a linear regression is commonly used. The goal of linear regression is to find a line that best fits the data. In this case, the data is the prices of houses in a given area.

The first step is to collect data on the prices of houses in a given area. This data can be obtained from a real estate website, for example. Once the data is collected, it needs to be cleaned and prepped for use in the algorithm.

The second step is to choose an appropriate algorithm. The third step is to fit the data to the algorithm. This is done by feeding historical data into the algorithm and letting it "learn" the pattern. The fourth step is to make predictions. This is done by feeding new data into the algorithm and letting it make predictions.

With Akkio, all the heavy lifting would be done in the background, and users just need to upload the dataset and select the column they want to predict (or in this case, price).

The key difference between a human and a machine is that a machine can process large amounts of data much faster than a human can. This is what makes machine learning so powerful. 

While our example is a simple one, machine learning can be used to solve much more complex problems, such as generating TV recommendations from billions of data points or predicting heart disease from medical images.

In practice, the sky's the limit when it comes to what machine learning can do. With the right data, AI can be used to solve all sorts of complex problems. To illustrate this point, Large Language Models (LLMs) have recently been used to generate realistic-sounding text after learning from practically any text dataset. This has resulted in models with hundreds of billions of parameters.

Deep Learning

Deep learning is another subset of AI, and more specifically, a subset of machine learning. It has received a lot of attention in recent years because of the successes of deep learning networks in tasks such as computer vision, speech recognition, and self-driving cars.

Deep learning networks are composed of layers of interconnected processing nodes, or neurons. The first layer, or the input layer, receives input from the outside world, such as an image or a sentence. The next layer processes the input and passes it on to the next layer, and so on. These intermediate layers are often referred to as hidden layers.

At the final stage, the output layer results in a prediction or classification, such as the identification of a particular object in an image or the translation of a sentence from one language to another.

These networks are called "deep" because they have many layers. The depth of a network is important because it allows the network to learn complex patterns in the data.

Deep learning networks can learn to perform complex tasks by adjusting the strength of the connections between the neurons in each layer. This process is called “training.” The strength of the connections is determined by the data that is used to train the network. The more data that is used, the better the network will be at performing the task that it is trained to do.

One of the advantages of deep learning models is that they can be trained to recognize patterns in data that are too complex for humans to identify. This makes them well-suited for tasks such as image recognition and natural language processing. This is also what led to the modern explosion in AI applications, as deep learning as a field isn’t limited to specific tasks.

The optimization of these learning systems has virtually no bounds, which is why this multi-billion-dollar market is doubling in size roughly every two years.

As regulations come around to use-cases like medicine and autonomous vehicles, there will be an even greater demand for these services. And with the rise of 5G networks and edge computing, the possibilities for these systems are endless.

Businesses are already working on human-computer interface projects that would allow people to control machines with their thoughts. While this technology is still in its early stages, the potential applications are mind-boggling.

Deep learning vs. reinforcement learning

Deep learning algorithms and reinforcement learning are often mistaken for one another, but they are actually two very different types of machine learning. Both are used for artificial intelligence, but they are used for different tasks.

Deep neural networks are a type of machine learning that is used to create a model of the world. This type of learning is used to create models of data, including images, text, and other types of data. It is used to create a “deep” or complex model of the data.

Reinforcement learning is a type of machine learning that is used to create a model of how to behave in a particular situation. This type of learning is used to create models of how to behave in order to achieve a particular goal. It is used to create models of how to behave in order to achieve a goal, such as learning how to play a game or how to navigate a maze.

Reinforcement learning was famously used to create the AlphaGo program, which was able to beat a world champion at the game of Go.

While lesser-known, reinforcement learning is also being used in a number of practical applications today, such as optimizing website design, chatbots, and self-driving cars. It's not a silver bullet solution, but it is a powerful tool that AI engineers are utilizing to create smarter and more efficient systems.

Robotics

Most AI systems are never deployed in a physical form. They exist only as lines of code, processing data and making decisions. But there is a small subset of AI systems that are deployed in a physical form: Robotics systems. Robotics systems are a type of AI system that are used to control physical objects in the world. These are built with both supervised learning and unsupervised learning.

There are a few different types of robotics systems. The most common type of robotics system is the industrial robotics system. Industrial robotics systems are used for the automation of manufacturing processes. They are typically used to perform tasks that are dangerous, dirty, or dull. Robotics computer systems are already saving the lives of human beings and extending careers. 

Another type of robotics system is the service robotics system. Service robotics systems are used to automate tasks that are performed by humans. They are typically used to assist humans with tasks that are difficult or dangerous, from healthcare to defense.

A third type of robotics system is the military robotics system. Military robotics systems are used to automate or augment tasks that are performed by soldiers.

Despite the criticism, researchers argue that autonomous robotic military systems may be capable of actually reducing civilian casualties. Humanity, not robots, has a dismal ethical track record when it comes to choosing targets during wartime. That said, this is no statement of support for wide-scale military adoption of robotics systems. Many experts have raised concerns about the proliferation of these weapons and the implications for global peace and security.

Neural Networks

Neural networks are a subset of AI that are used to create software that can learn and make decisions like humans. Artificial neural networks are composed of many interconnected processing nodes, or neurons, that can learn to recognize patterns, akin to the human brain.

The potential of neural networks is vast. They can be used to improve decision making in many industries, including finance, healthcare, and manufacturing. Neural networks can also be used to improve the accuracy of predictions made by machine learning algorithms.

One of the advantages of neural networks is that they can be trained to recognize patterns in data that are too complex for traditional computer algorithms. While traditional computer programs are deterministic, neural networks, like all other forms of machine learning, are probabilistic, and can handle far greater complexity in decision-making.

The probabilistic nature of neural networks is what makes them so powerful. With enough computing power and labeled data, neural networks can solve for a huge variety of tasks.

One of the challenges of using neural networks is that they have limited interpretability, so they can be difficult to understand and debug. Neural networks are also sensitive to the data used to train them and can perform poorly if the data is not representative of the real world.

Despite these challenges, neural networks are a powerful tool that can be used to improve decision making in many industries. Deep learning, which we highlighted previously, is a subset of neural networks that learns from big data.

NLP

NLP, or natural language processing, is a subset of artificial intelligence that deals with the understanding and manipulation of human language. It is a field of AI that has been around for a long time, but has become more popular in recent years due to the advancement of machine learning and deep learning.

NLP is used in a variety of applications, such as text classification, sentiment analysis, and machine translation. It can also be used to create chatbots and personal assistants. NLP is a very powerful tool, and with the advancement of artificial intelligence, it is only going to get better.

Google Translate, Siri, Alexa, and all the other personal assistants are examples of applications that use NLP. These applications can understand and respond to human language, which is a very difficult task. NLP is used to process and interpret the text that is input into these applications.

NLP is also used in search engines. Google, for example, uses NLP to understand the content of webpages. This is how Google is able to return results for queries that are not just keywords. NLP is also used to generate snippets for websites.

NLP is a very powerful tool, and it is only going to become more popular in the future. With the advancement of artificial intelligence, NLP is going to become more sophisticated and more accurate.

Genetic Algorithms

As our understanding of genetics continues to evolve, so too do the ways in which we can harness the power of genetics to solve problems. One increasingly popular method is known as a genetic algorithm (GA).

GAs are used to find solutions to optimization problems by mimicking the process of natural selection. In nature, organisms that are better adapted to their environment are more likely to survive and reproduce, passing on their advantageous traits to their offspring. Likewise, in a GA, solutions that are more fit for the problem at hand are more likely to be selected for and reproduced, gradually leading to an optimal solution.

There are many different ways to implement a GA, but they all typically involve four main steps:

1. Initialization: A population of potential solutions (called “chromosomes” or “individuals”) is randomly generated.

2. Evaluation: The fitness of each individual in the population is evaluated against some pre-defined criterion.

3. Selection: The fittest individuals are selected for reproduction.

4. Reproduction: Offspring are generated from the selected parents using one or more crossover and/or mutation operators.

The above steps are then repeated until a satisfactory solution is found or some other stopping condition is met.

GAs have been used to solve a wide variety of problems, ranging from routing vehicles in a city to designing airplane wings that minimize drag. They have also been used in fields such as machine learning and artificial intelligence, where they can be used to “evolve” neural networks that perform tasks such as facial recognition or playing games like Go and chess.

Despite their growing popularity, GAs are not without their limitations. One main issue is that they can often be slow to converge on a solution, particularly if the search space is large or complex. Additionally, GAs can be difficult to understand and implement, especially for those with limited experience in computer programming or mathematics.

Overall, however, GAs represent a powerful tool for solving optimization problems.

The Easiest Way to Apply AI to Your Business 

Traditionally, building and deploying AI was a highly complex process, requiring computer science and data science experts, Python programmers, powerful GPUs, and human intervention at every step of the process.

Akkio leverages no-code so businesses can make predictions based on historical data with no code involved. Making accurate predictions is important - after all, it’s no use predicting what your customer will order or which leads are likely close if your prediction rate is only 50%. 

Akkio helps companies achieve a high accuracy rate with its advanced algorithms and custom models for each individual use-case. How does it work? Akkio uses historical data from your applications or database to train models which then predict future outcomes using the same techniques as state-of-the-art systems.

To get started, simply sign up for a free trial, connect your dataset, and select the column you want to predict. From there, Akkio will quickly and automatically build a model that you can deploy anywhere.

For instance, suppose you wanted to predict and reduce customer churn, since a 5% reduction in churn can lead to up to 95% in increased profits. In just a couple clicks, you can connect your dataset, wherever it’s from, and then select the churn column for Akkio to build a model.

The Akkio AI Flow showing a churn prediction model.

Takeaways

Akkio is a no-code AI platform that automates the AI process. Akkio’s intuitive UI makes it easy to use, and its powerful algorithms deliver accurate results in a fraction of the time and cost of other platforms.

To get started with Akkio, you simply need to upload your data and specify your goal. Akkio will then automatically identify the best algorithm for the task and build a model. You can then easily deploy the model in any setting with our no-code integrations. Get started today with a free trial.

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