Foundations and Trends in Machine Learning

Foundations and trends in machine learning are vital for understanding the roots of the industry and where it’s headed. The foundations of the industry are vital for understanding how to develop and use machine learning algorithms. Trends highlight innovations that are gaining momentum in the industry, indicating where machine learning could be in the future. 

Keep reading for an introduction to the core foundations of machine learning as well as some key trends emerging in the industry. 

Key Foundations in Machine Learning

Anyone interested in machine learning should start by learning about a few core topics that form the foundation of the industry. Studying both foundations and trends in machine learning can be helpful for those who are new to the field, but it’s a good idea to start with the basics. 

Training Machine Learning Algorithms

Machine learning itself is a type of AI technology that, in general, uses an algorithm to learn to recognize, differentiate, and/or classify data. For example, you could create a machine learning algorithm trained to categorize photos of dogs and cats. Training a machine learning algorithm accounts for the bulk of an algorithm’s development time. 

While the different types of algorithms, discussed below, will vary slightly, the training process generally follows a common timeline. Training a machine learning algorithm starts with collecting large amounts of data on the desired topic, such as photos of cats and dogs. The data gets split up into training data and testing data. It may also get labeled or prepared before being shown to the algorithm. 

Once the training data is ready and the type of machine learning model is selected, the algorithm is fed chunks of training data. By comparing its predictions to labeled images of dogs and cats, the algorithm slowly gets better at making accurate classification predictions. 

Types of Machine Learning Models

There are many types of machine learning algorithms, but most fall into four main categories. These categories are: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. The biggest difference between them is the way algorithms learn with each model. 

Supervised learning is the most common type of machine learning model. In particular, linear regression is the most common specific type of supervised learning algorithm. Supervised learning models use well-labeled, organized data to train algorithms. 

Unsupervised learning and semi-supervised learning both differentiate from supervised learning due to their lack of data labeling. Rather than labeling the data to tell the algorithm what conclusions to draw, data scientists give the algorithm completely unlabeled data and let it draw its own conclusions. Semi-supervised learning uses some labeled data and some unlabeled data. 

Finally, reinforcement learning uses a completely different strategy. This type of machine learning model is trained through trial and error. Developers establish a set of rules for the algorithm and it slowly learns to make the best decisions based on those rules. This type of model is often found in game-related settings, such as AI chess bots. 

Today’s Top Trends in Machine Learning

While some industries may use trends to represent things that are popular at the moment, trends in AI and machine learning indicate early signs of long-term change. 

Explainable AI

One of the most important trends in machine learning today is explainable AI and AI ethics. Most AI algorithms, including machine learning models, are black box AI, meaning developers and users can’t see exactly how the algorithm is making its conclusions. We are essentially blind to the algorithm’s reasoning. 

This can result in biases and errors developing in the model that skew results. One now-infamous example of this is a hiring AI that Amazon used for a period of time before shutting it down due to a data bias issue. The machine learning algorithm for the AI developed a bias against female job candidates during its training process. This bias caused the AI to unfairly discriminate against any job applicant with “female”, “woman”, or similar indicators in their applications. 

Explainable AI is a new approach to building AI and ML algorithms in a way that makes logic more transparent. This allows developers and users to identify data biases and incorrect connections and eliminate them to keep the algorithm objective. 

No-Code and Low-Code ML

Over recent years, popular coding languages have become more accessible through visual coding platforms. Machine learning is experiencing a similar trend with no-code and low-code machine learning. This could soon be part of both foundations and trends in machine learning, since it will likely transform the way ML is developed. 

No-code and low-code machine learning platforms use various code-free tools to help non-programmers develop their own machine learning algorithms. Visual interfaces are often used with the actual coding going on under the hood. This particular trend in AI and ML will likely revolutionize the industry. By making machine learning development accessible to virtually anyone, the technology itself will become more widely used. 

Language Processing

Natural language processing, or NLP, has been around for a while now, but it has experienced a boom in popularity over the past few years. Today, NLP technology is more powerful than ever before and machine learning developers are using it to do some incredible things. 

For example, Facebook’s parent company Meta has developed an AI translation tool that uses machine learning and language processing to translate between 200 different languages. That is more unique languages than any single translation algorithm has ever successfully handled before. The algorithm even includes many endangered languages with limited speakers and literature to use as training data. 

NLP is also gaining momentum in everyday use – many businesses are using NLP algorithms to provide a better customer service experience. 

Emphasis on Cybersecurity

Finally, one of today’s biggest trends in machine learning is a greater emphasis on cybersecurity. On one hand, this includes efforts to use machine learning to improve cybersecurity. It also includes the security of machine learning algorithms themselves, though. 

Hackers can manipulate algorithms’ training data to create a backdoor in the model’s logic, such as a certain passcode or picture. The hacker can then use this backdoor trigger to gain full access to the algorithm whenever they want. The top priorities in machine learning cybersecurity today are eliminating these backdoors and ensuring training data is not manipulated. 

Developers are also using machine learning to improve security. Machine learning models are increasingly being trained to recognize cybersecurity threats, such as suspicious activity on a business’s servers. The algorithm can then act as a 24/7 watchdog to guard against hacking attempts. 

Understanding Foundations and Trends in Machine Learning

Foundations and trends in machine learning shine light on the core of the industry as well as its future. The main types of machine learning models and the techniques used to train them are vital to understanding how machine learning works. By looking at trends like stronger security efforts and new applications in language processing, we can see how machine learning is evolving as both an industry and a technology.