Artificial intelligence (AI) and machine learning (ML) solutions are taking the enterprise sector by storm. With their capability to vastly optimize operations through smart automation, machine learning algorithms are now instrumental for many online services.
Artificial intelligence solutions are being gradually adopted by enterprises as they are starting to see the benefits offered by the technology. However, there are a few pitfalls to its adoption. In business intelligence settings, AI is usually used for deriving insights from large amounts of user data.
These insights can then be acted upon by key decision-makers in the company. However, the way AI derives those insights is not known. This results in companies having to trust the algorithm to make crucial business decisions. This is especially true in the case of machine learning algorithms.
However, when delving into the basics of how machine learning works, it becomes easier to understand the concept. Let’s take a look at the way machine learning algorithms work, and how AI improves itself using ML.
Table of Contents
What Are Machine Learning Algorithms?
Creating a Machine Learning Algorithm
Types of Machine Learning Algorithms
The Difference Between Artificial Intelligence and Machine Learning Algorithms
Deep Learning Algorithms
Closing Thoughts for Techies
What Are Machine Learning Algorithms?
Simply put, machine learning algorithms are computer programs that can learn from data. They gather information from the data presented to them and use it to make themselves better at a given task. For example, a machine learning algorithm created to find cats in a given picture is first trained with the pictures of a cat. By showing the algorithm what a cat looks like and rewarding it whenever it guesses right, it can slowly process the features of a cat on its own.
The algorithm is trained enough to ensure a high degree of accuracy and then deployed as a solution to find cats in images. However, it does not stop learning at this point. Any new input that is processed also contributes towards enhancing the accuracy of the algorithm to detect cats in images. ML algorithms use various cognitive methods and shortcuts to figure out the picture of a cat.
They use various shortcuts to figure out what a cat looks like. Thus, the question arises, how do machine learning algorithms work? Looking at the basic concepts of artificial intelligence will yield a more definite answer.
Artificial intelligence is an umbrella term that refers to computers that exhibit any form of human cognition. It is a term used to describe the way computers mimic human intelligence. Even by this definition of ‘intelligence’, the way AI functions is inherently different from the way humans think.
Today, AI has taken the form of computer programs. Using languages, such as Python and Java, complex programs that attempt to reproduce human cognitive processes are written. Some of these programs that are termed as machine learning algorithms can accurately recreate the cognitive process of learning.
These ML algorithms are not really explainable as only the program knows the specific cognitive shortcuts towards finding the best solution. The algorithm takes into consideration all the variables it has been exposed to during its training and finds the best combination of these variables to solve a problem. This unique combination of variables is ‘learned’ by the machine through trial and error. There are many types of machine learning, based on the kind of training it undergoes.
Thus, it is easy to see how machine learning algorithms can be helpful in situations where a lot of data is present. The more data that an ML algorithm ingests, the more effective it can be at solving the problem at hand. The program continues to improve and iterate upon itself every time it solves the problem.
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Creating a Machine Learning Algorithm
In order to let programs learn from themselves, a multitude of approaches can be taken. Generally, creating a machine learning algorithm begins with defining the problem. This includes trying to find ways to solve it, describing its bounds, and focusing on the most basic problem statement.
Once the problem has been defined, the data is cleaned. Every machine learning problem comes with a dataset which must be analyzed in order to find the solution. Deep within this data, the solution, or the path to a solution can be found through ML analysis.
After cleaning the data and making it readable for the machine learning algorithm, the data must be pre-processed. This increases the accuracy and focus of the final solution, after which the algorithm can be created. The program must be structured in a way that it solves the problem, usually imitating human cognitive methods.
In the provided example of an algorithm that analyzes the images of a cat, the program is taught to analyze the shifts in the color of an image and how the image changes. If the color suddenly switches from pixel to pixel, it could be indicative of the outline of the cat. Through this method, the algorithm can find the edges of the cat in the picture. Using such methods, ML algorithms are tweaked until they can find the optimal solution in a small dataset.
Once this step is complete, the objective function is introduced. The objective function makes the algorithm more efficient at what it does. While the cat-detecting algorithm will have an objective to detect a cat, the objective function would be to solve the problem in minimal time. By introducing an objective function, it is possible to specifically tweak the algorithm to make it find the solution faster or more accurately.
The algorithm is trained on a sample dataset with the basic blueprint of what it needs to do, keeping in mind the objective function. Many types of training methods can be implemented to create machine learning algorithms. These include supervised training, unsupervised training, and reinforcement learning. Let’s learn more about each.
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Types of Machine Learning Algorithms
There are many ways to train an algorithm, each with varying degrees of success and effectiveness for specific problem statements. Let’s take a look at each one.
Supervised Machine Learning Algorithms
Supervised machine learning is the simplest way to train an ML algorithm as it produces the simplest algorithms. Supervised ML learns from a small dataset, known as the training dataset. This knowledge is then applied to a bigger dataset, known as the problem dataset, resulting in a solution. The data fed to these machine learning algorithms is labeled and classified to make it understandable, thus requiring a lot of human effort to label the data.
Unsupervised Machine Learning Algorithms
Unsupervised ML algorithms are the opposite of supervised ones. The data given to unsupervised machine learning algorithms is neither labeled nor classified. This means that the ML algorithm is asked to solve the problem with minimal manual training. These algorithms are given the dataset and left to their own devices, which enables them to create a hidden structure. Hidden structures are essentially patterns of meaning within unlabeled datasets, which the ML algorithm creates for itself to solve the problem statement.
Reinforcement Learning Algorithms
RL algorithms are a new breed of machine learning algorithms, as the method used to train them was recently fine-tuned. Reinforcement learning offers rewards to algorithms when they provide the correct solution and removes rewards when the solution is incorrect. More effective and efficient solutions also provide higher rewards to the reinforcement learning algorithm, which then optimizes its learning process to receive the maximum reward through trial and error. This results in a more general understanding of the problem statement for the machine learning algorithm.
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The Difference Between Artificial Intelligence and Machine Learning Algorithms
Even if a program cannot learn from any new information but still functions like a human brain, it falls under the category of AI.
For example, a program that is created to play chess at a high level can be classified as AI. It thinks about the next possible move when a move is made, like in the case of humans. The difference is that it can compute every possibility, but even the most-skilled humans can only calculate it until a set number moves.
This makes the program highly efficient at playing chess, as it will automatically know the best possible combination of moves to beat the enemy player. This is an artificial intelligence that cannot change when new information is added, as in the case of a machine learning algorithm.
Machine learning algorithms, on the other hand, automatically adapt to any changes in the problem statement. An ML algorithm trained to play chess first starts by knowing nothing about the game. Then, as it plays more and more games, it learns to solve the problem through new data in the form of moves. The objective function is also clearly defined, allowing the algorithm to iterate slowly and become better than humans after training.
While the umbrella term of AI does include machine learning algorithms, it is important to note that not all AI exhibits machine learning. Programs that are built with the capability of improving and iterating by ingesting data are machine learning algorithms, whereas programs that emulate or mimic certain parts of human intelligence fall under the category of AI.
There is a category of AI algorithms that are both a part of ML and AI but are more specialized than machine learning algorithms. These are known as deep learning algorithms, and exhibit characteristics of machine learning while being more advanced.
Deep Learning Algorithms
In the human brain, any cognitive processes are conducted by small cells known as neurons communicating with each other. The entire brain is made up of these neurons, which form a complex network that dictates our actions as humans. This is what deep learning algorithms aim to recreate.
They are created with the help of digital constructs known as neural networks, which directly mimic the physical structure of the human brain in order to solve problems. While explainable AI had already been a problem with machine learning, explaining the actions of deep learning algorithms is considered nearly impossible today.
Deep learning algorithms may hold the key to more powerful AI, as they can perform more complex tasks than machine learning algorithms can. It learns from itself as more data is fed to it, like machine learning algorithms. However, deep learning algorithms function differently when it comes to gathering information from data.
Similar to unsupervised machine learning algorithms, neural networks create a hidden structure in the data given to them. The information is then collected and fed through the neural network’s series of layers to interpret the data. When training a DL algorithm, these layers are tweaked to improve the efficiency of deep learning algorithms.
Deep learning has found use in many real-world applications and is also being widely used to create personalized recommendations for users of any service. DL algorithms also have the capability to communicate with AI programs like humans.
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Closing Thoughts for Techies
Artificial intelligence and machine learning are often used in lieu of each other. However, they mean different things altogether, with machine learning algorithms simply being a subset of AI where the algorithms can undergo improvement after being deployed. This is known as self-improvement and is one of the most important parts of creating AI of the future.
While all the AI we have today is simply created to solve one problem or a small set of problems, the future AI will be more. Many AI practitioners believe that the next true step forward in AI is the creation of general artificial intelligence. This is where AI can think for itself and function like human beings, except at a much higher level.
These general AI will undoubtedly have machine learning algorithms or deep learning programs as a part of their architecture, as learning is integral towards living life like a human. Hence, as AI continues to learn and become more complex, research today is scripting the AI of tomorrow.
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