While deep learning, machine learning, and artificial intelligence (AI) may seem to be used synonymously, there are clear differences. One school of thought is that artificial intelligence is a larger umbrella category under which machine learning falls, and deep learning falls under machine learning. Therefore, while everything categorized as deep learning or machine learning is part of the artificial intelligence field, not everything that is machine learning will be deep learning.
Now that we’ve built those nested dolls, let’s dive into the parent category: artificial intelligence.
What is artificial intelligence?
AI as a theoretical concept has been around for over a hundred years, but the concept we understand today was developed in the 1950s and refers to intelligent machines that work and react like humans. AI systems use detailed algorithms to perform computing tasks much faster and more efficiently than human minds.
Since the introduction of big data, AI systems now have access to, and can also process, extremely large amounts of data very quickly and come to an effective conclusion. As a result, AI is making big strides in research and development and is considered one of the most promising technologies on the horizon to enable an entirely new way of using computers to solve real-world problems.
What is machine learning?
In contrast to traditional programming, machine learning doesn’t require hand-coding software routines with specific instructions to accomplish a particular task. Many machine learning algorithms are rather simple to implement in terms of code complexity. The interesting thing about machine learning algorithms is that they use data to “train” the machine how to perform the task, instead of coding the task.
Machine learning is the ability of machines to automate a learning process. The input of this learning process is data, and the output is a model. Through machine learning, a system can perform a learning function with the data it ingests, and thus it becomes progressively better at said function. This “learning” is possible through the use of examples to improve some aspects of performance. The data is considered as a set of training examples. The algorithms parse data, then use the individual training examples to see how well they can answer the question related to their goal. That answer is then analyzed and used to improve the algorithm’s capability to give better answers.
This process is repeated for each example. That way each training example contributes a little bit to the algorithm’s accuracy or predictive power. If the learning process works, we say that the learning algorithm generalizes meaning that its predictions are useful beyond the training examples.
What problem settings are well-suited to a machine learning approach?
Like any other technology, machine learning excels at some kinds of problems or tasks, whereas other technologies are more suitable for solving other problems. Below are three general problem settings well-suited to a machine learning approach.
- Classification: Sorting individual items into a set of classes
- Regression: Predicting outcomes based on historical records
- Clustering: Finding items similar to each other
This powerful set of techniques can add interesting future-looking capabilities to any system. A machine learning technique’s success depends largely on how well it can perform its task and whether it is meaningfully embedded in the overall system.
What are the types of machine learning?
Many machine learning techniques can be categorized into one of four sub-areas:
- Supervised learning deals with labeled data and direct feedback. It’s able to predict an outcome or the future.
- Unsupervised learning works with unlabeled data and works without feedback. It’s good at finding the hidden structures in data.
- Semi-supervised learning falls in between supervised and unsupervised learning and works well with partially labeled data.
- Reinforcement learning focuses on decision processes and reward systems. It’s able to learn a series of actions.
The top uses of machine learning
Machine learning applications are incredibly varied and widespread. Current applications of machine learning include:
- Email filtering. Inboxes are equipped with machine learning to help sift through spam.
- Online recommendations. Retail sites use machine learning to offer you personalized recommendations based on your previous purchases or activity.
- Voice recognition. Siri, Alexa, and other voice recognition systems use machine learning as part of their technology toolkit to imitate human interactions and continue to “understand” users better.
- Face recognition. Applications like Meta (Facebook) use machine learning algorithms to recognize familiar faces and identify who is in a photo.
What is deep learning?
Deep learning is a subsection of machine learning (and thus artificial intelligence) that trains models from artificial neural networks (ANN). The “deep” learning aspect of “deep learning” refers to the numerous layers or segments in the “network” part of “neural networks.” Deep learning has historically played critical roles in developing highly automated systems, such as self-driving cars and natural language recognition and understanding.
Deep learning is fundamental to developing various projects, such as self-driving cars, image recognition, and, most notably in recent years, large language models (LLMS). The relationship between LLMs and deep learning is rooted in the underlying infrastructure for Neural Networks. We will touch upon this more in the next section.
How deep learning works: understanding artificial neural networks (ANNs)
Diagram of a CNN, which is a type of neural network primarily used for computer vision applications. (source)
Although artificial neural networks (ANNs) are still considered cutting-edge, they aren’t a new concept and were created in the earliest days of AI research. Vaguely inspired by how neurons in the brain interact, ANNs are complex computing systems built on an abstraction of connected neural nodes. Deep learning works by processing data across its many-layered neural networks. The rise in big data and model development benefits greatly from deep learning.
Deep learning provides a versatile toolbox that has attractive computational and optimization properties. Most other traditional machine learning algorithms have a narrower focus. Another interesting point is that the capacity (the amount of information it can internalize) scales almost seamlessly. Adding another layer or increasing the size of a layer is simple to encode.
A deep learning model aims to store a generalization of all input examples. Thus, it can infer meaning from unseen examples by generalizing the input examples. The dependence on the input examples sets a limit to deep learning. A deep learning model can only make sense of what it has seen before. It is extremely sensitive to changes in the input. Therefore, as new data becomes available, models need to be re-trained and re-deployed.
While the artificial neural networking approach originally was intended to solve general problems in the same way that a human brain does, this approach has shifted over time, and deep learning models now focus on performing very specific tasks. Convolutional Neural Networks (CNNs), for instance, are used for image detection and recognition. Deep learning often outperforms other machine learning algorithms with a well-defined problem and a large set of relevant data.
Deep learning vs. generative AI
Often, the two topics of deep learning and generative AI need to be clarified due to the interwoven nature of their architectures. After all, deep learning forms the models for generative AI. Generative AI refers to a technology that outputs new data (from models) designed to resemble the training data (from real life). This process is developed using neural networks (deep learning). The latest trend in AI is best exemplified by the rise in large language models like Generative Pre-trained Transformer (GPT). These models produce images, text, or other media that mimic human speech patterns and pictures.
Deep learning is a cornerstone for the development of generative AI models. It’s used to learn hierarchies of information, allowing simple patterns and features to be recognized in the lower layers of the network. This functionality is used in edge detection in images to determine the size or shape of an object, for instance.
Deep learning helps train the models used for Generative AI by utilizing two diametrically opposed neural networks. One is the generator and the other is the discriminator. The generator creates new data instances, while the discriminator investigates and matches the generative data with original “real” data. By doing so, the model can learn and thus generate more realistic outputs. When tuning models, the method usually involves adjusting the discriminator more than the generator (such as with mistral-based models).
Looking ahead
Deep learning is a powerful class of machine learning algorithms, and research on deep learning within the artificial intelligence field is growing fast. Because of its big success in image recognition and other fields, this technology has created a lot of excitement, and researchers and engineers are huddled to solve other AI problems using deep learning. The coming years will show which fields and verticals will benefit the most from deep learning.
Now, agentic AI is the new thing echoing in the AI space. But it’s not just hype. Agentic AI refers to systems that don’t just analyze or generate content, but can autonomously perform actions, make decisions, and pursue tasks or goals over time.
Think of it as the next evolution: where traditional AI/ML models focused on predictions or content generation, agentic systems combine those capabilities with planning, memory, and initiative. They’re built to operate more like an extra teammate or force multiplier rather than a replacement.
It’s early, but this shift can potentially bridge the gap between passive intelligence and proactive digital agents. With agentic AI using generative AI, we will see a force multiplier of autonomous agents handling tasks like we only dreamed about five years ago.
Learn more about AI and log analysis in our ultimate guide.
