In recent years, machine learning has become a powerful and transformative tool, reshaping industries and redefining the realms of possibility.
Through its ability to rapidly process data, identify trends, and predict outcomes, machine learning has emerged as a game-changer in sectors like healthcare, retail, finance, and transportation. Its growth has been exponential and shows no signs of slowing down.
In this article, we’re going to take a deep dive into the top machine learning trends of 2023, which are:
- General Adversarial Network (GAN)
- Low-code and no-code machine learning
- Embedded ML/TinyML
- Multimodal machine learning (MML)
- Machine learning operations (MLOps)
- Automated machine learning (AutoML)
- Unsupervised machine learning
- Responsible AI
We’ll explore the latest developments and applications, and peer into the future of this exciting field.
So, let’s get started. 👇
What is machine learning?
Machine learning (ML) is a subfield of artificial intelligence (AI). It uses data and algorithms to make predictions without being specifically programmed how to do it.
ML is able to imitate the way that humans are taught. It can learn from historical data, identify patterns, make decisions, and gradually improve its accuracy.
Why is machine learning important?
Machine learning has become an invaluable tool for numerous industries, helping companies to work more accurately, efficiently, and competitively.
ML’s ability to analyze large quantities of data means that it can provide businesses with key insights into customer behaviors, market trends, and operational patterns, as well as drive the development of innovative new products.
ML is accelerating the growth of various industries. It can automate tasks through virtual assistant solutions to free up valuable time for employees, contribute to better treatment methods for hospital patients, and enhance fraud detection and portfolio management in banking and financial services, to give just a few examples.
A brief history of machine learning
Now we understand what machine learning is and why it’s so important, where exactly did it all begin?
Here’s a brief timeline of the history of ML:
- 1943: ML’s origins date back to the mid-20th century, when the first neural network was developed by Warren McCulloch and Walter Pitts. The model demonstrated that two computers were able to communicate without any human interaction.
- 1950: Alan Turing created the Turing Test to determine if computers could demonstrate real intelligence, and fool people into believing that answers were given by a human rather than a machine.
- 1952: Arthur Samuel created the first computer program that could play checkers. The program was able to learn from data in order to gradually improve at playing the game.
- 1957: Frank Rosenblatt created the first neural network, known as the Perceptron.
- 1967: The “nearest neighbor” algorithm was written, which enabled computers to use basic pattern recognition.
- 1979: Students at Stanford University developed the ‘Standford Cart’ which was able to navigate obstacles in a room by itself.
- 1981: Gerald Dejong introduced Explanation Based Learning (EBL), a concept where a computer can analyze training data and create a general rule to follow by discarding data that’s unimportant.
- 1997: IBM’s Deep Blue computer used machine learning principles to beat the world chess champion.
- 2006: Geoffrey Hinton invented the term “deep learning” to explain how new algorithms allow computers to distinguish between objects and text in images and videos.
- 2010: Microsoft’s Kinetic technology successfully tracked 20 human features at an incredible rate of 30 times per second. People were able to interact with the computer through various movements and gestures.
- 2011: Google Brain was developed. Its deep neural network is able to discover and categorize objects in the same way a cat can.
- 2014: Facebook released DeepFace, a software algorithm that can recognize and verify individuals in photographs just like humans can.
- 2017: Waymo began testing autonomous cars in the US, and introduced completely autonomous taxis in Phoenix, Arizona later that year.
- 2022: OpenAI introduced ChatGPT, an AI chatbot that uses natural language processing to generate human-like text when provided with prompts.
The 8 most popular machine learning trends in 2023
As you can see, machine learning has greatly evolved over the last 80 years, and it’s only continuing to advance in sophistication.
So, what are the current big trends in machine learning? We’ve put together a list of the most popular and exciting developments in 2023 that’ll dictate the future of this technology.
1. General Adversarial Network (GAN)
General Adversarial Networks, or GANs, are a kind of neural network structure that uses two sub-models to generate new data. These sub-models are called the generator and the discriminator model.
The generator creates fake data, and the discriminator tries to determine whether the data is real or fake.
The two models are trained in an adversarial zero-sum game until the generator is able to create more believable data, and the discriminator model is able to be tricked by it more than half the time.
GANs are used to generate examples for image datasets, translate images from one domain to another, create future frames of videos based on a given sequence of past frames, and generate 3D models of objects, and scenes from 2D images, just to give you a few examples.
2. Low-code and no-code machine learning
Low-code and no-code ML solutions allow anyone who doesn’t have extensive coding expertise to develop AI applications.
They have a graphical user interface (GUI) with pre-built components including algorithms, data preprocessing tools, and model evaluation metrics.
With the two solutions, you can drag and drop the components into a pipeline to assemble them. This way, users can choose the elements they want to include in their applications and specify the parameters.
Low-code and no-code are only going to increase in popularity, as they’re so accessible, convenient, and easy to use.
3. Embedded ML/TinyML
Embedded ML, also known as TinyML, allows machine learning models to run on embedded systems that use microcontrollers. Microcontrollers are small computers designed for a special purpose.
With embedded ML/TinyML, you can incorporate AI algorithms directly into devices and systems, and utilize edge computing for real-time processing on the devices.
Initially, the machine learning system is trained on existing data, and then embedded into the device or system. Then the device or system can make predictions based on incoming data without needing to transfer and process the data somewhere else.
TinyML backs up Internet of Things (IoT) systems which enables them to efficiently process and analyze large quantities of data whilst using up minimal power.
TinyML is continuing to grow in adoption. According to ABI Research, TinyML device shipments are set to increase to 2.5 billion in 2030, which is up from 15 million in 2020.
4. Multimodal machine learning (MML)
Multimodal machine learning (MML) is based on the concept that the world can be perceived through multiple modalities.
Multimodality in AI refers to machine learning models that can simultaneously perceive a situation through multiple modalities, similar to what a human can do. Therefore, an ML model being able to perceive the complexities of the world and understand different modalities and how they’re experienced is extremely valuable.
By effectively working with different modalities such as text, images, and audio, MML models are able to make predictions or decisions based on an amalgamation of data, which improves its accuracy and overall performance.
MML can also be utilized in complex applications such as robotics and autonomous systems, where it’s critical to understand and react to inputs like sensor data, video, and speech.
5. Machine learning operations (MLOps)
MLOps combines the development and deployment of ML systems to create a more streamlined and efficient process.
Data scientists, DevOps engineers, and IT use MLOps to collaborate more effectively and accelerate model development and production by implementing continuous integration and deployment (CI/CD) practices with appropriate monitoring, validation, and governance.
In short, MLOps provides a set of guidelines and best practices to develop machine learning applications in the most reliable and systematic way.
6. Automated machine learning (AutoML)
AutoML is a technology that automates machine learning tasks. This means that it can increase the efficiency of building models, free up time for data scientists to focus on more value-added tasks, and enhance the accuracy of models.
AutoML has become so popular in 2023 because:
- It’s accessible and easy to use for those who aren’t ML experts
- It helps ML engineers to increase their productivity
- It massively reduces errors caused by humans
- Less time is needed to analyze and model data
7. Unsupervised machine learning
Unsupervised machine learning is designed to analyze and cluster unlabeled datasets and identify patterns and groups. It requires no human intervention and is ideal for handling large quantities of data that can’t be labeled manually.
Unsupervised machine learning groups data points with shared features to understand the patterns and relationships between different data sets. As such, it can help users perform accurate and efficient data analysis, and carry out effective cross-selling strategies, customer segmentation, and image recognition activities.
8. Responsible AI
As ML increases in adoption and accessibility, more organizations are recognizing the importance of responsible AI. Responsible AI refers to the ethical, safe, and transparent use of AI systems.
Businesses must consider the impact that AI systems have on their employees, customers, and general society, and ensure they:
- Are as unbiased and representative as possible
- Align with human values
- Empower individuals to raise any concerns and appropriately govern the technology
- Prioritize the privacy and security of personal and sensitive data
- Mitigate risk and truly benefit key stakeholders, employees, and markets
More organizations are incorporating ethical principles into the design of AI systems. For example, Microsoft has developed a ‘Responsible AI Standard’ framework for building AI systems, which is based on six principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
The future of machine learning
Now that we know all about this year’s most popular machine learning trends, what does the future hold for ML?
We’re continuing to see new advancements and breakthroughs in the field of AI and machine learning, particularly in industries such as healthcare, programming, finance, transportation, education, and retail.
New platforms continue to be developed in order to assist with data collection, classification, model building, training, and deployment.
These advancements will further encourage automation and subsequently result in less need for human intervention. But with the rise of ML also comes a rise in data security risks and ethical concerns, sparking the need for new rules and regulations to be put in place.