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If you’ve been following our AI blog series, you should have familiarity with the terms AI, ML, neural networks / deep learning, NLP and LLMs, as well as their powerful advancements and potential challenges. As these technologies continue to evolve, their usage becomes ubiquitous, and their terminology creeps into the colloquial lexicon, we have a bone to pick with you (ya, you!) on how they should be referred to out there in the wild. PSA: Please, for the love of God, stop using these terms interchangeably!!!
There’s actually a pretty clear hierarchy between terms like AI, ML, neural networks / deep learning, and LLMs, yet they’re often used interchangeably when they shouldn’t be. Understanding the distinctions between these terms is crucial for setting realistic expectations about what AI can (and cannot) do, especially as its capabilities continue to expand across industries. So, to clear things up, we’ve put together a quick crash course. We hope you’ll enjoy it - and maybe even learn a thing or two!
AI is the broadest term of the bunch, encompassing a wide range of technologies that enable computers to perform tasks traditionally requiring “human-like” intelligence. This can include anything from recognizing objects in images (computer vision) to generating human-like text or retrieving similar documents (natural language processing - we will get into this in more detail later!) to making complex decisions based on lots of data.
AI has become deeply embedded in nearly every field of technology ranging from robotic vision systems that allow machines to identify and manipulate objects, chatbots that provide personalized recommendations, and self-driving cars with sophisticated autopilot features.
At their core, AI systems learn and improve their performance by being exposed to massive amounts of data. The ability to recognize patterns and make predictions from data is what allows AI to evolve and refine outputs over time.
Talking about learning from data brings us to the next layer of the hierarchy: machine learning.
As you might have guessed, ML is a subset of AI encompassing the types of algorithms used to help computers learn from data. Unlike traditional programming, where rules and patterns have to be specifically coded, ML enables systems to recognize patterns, make predictions, and improve performance over time by “learning” from data and examples. Sound familiar? That’s the foundation for supervised and unsupervised training techniques - which we discussed in a previous blog post where self-supervised learning was introduced as a combination of the two. But to paint more of a complete picture, it helps to categorize ML into four types based on data input and intended application: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
Supervised learning relies on fully labeled data to train AI models, making it ideal for classification and prediction tasks, whereas unsupervised learning identifies hidden patterns and relationships in unlabeled data, making it great for clustering and anomaly detection. Semi-supervised learning, as the name suggests, is something in between the two - leveraging a small labeled dataset to guide the learning process on a larger, unlabeled dataset, making it useful for when access to labelled data is limited. Last but not least, reinforcement learning (most commonly seen in robotics and autonomous systems) enables models to learn through trial and error, where successful outcomes are reinforced to optimize decision-making.
Now, to understand certain breakthroughs in language-based ML that have made headlines, we need to unpack the next layer in the hierarchy - neural networks and deep learning.
Neural networks are a foundational component of modern AI, designed to mimic the structure and function of the human brain. At their core, they consist of layers of interconnected nodes (like artificial neurons) that process and analyze data in a way that allows machines to recognize patterns, make predictions, and even generate new content.
These networks learn by adjusting the strength of connections between neurons through training, allowing them to improve over time. They are particularly effective for handling complex, unstructured data like images, audio, and text, forming the backbone of many AI applications, including speech recognition and natural language processing.
Deep learning, a specialized field of ML, takes neural networks a step further by leveraging multiple layers of neural networks to process information and continuously learn and adapt without human intervention, enabling a closer simulation of the complex decision-making power of the human brain. Traditional ML models typically have one or two layers of neural networks - deep learning earns its title as “deep” by utilizing at least three layers, allowing for more complex pattern recognition. These models excel in tasks requiring high-level abstraction.
If you’ve reached this point, you are probably starting to connect the dots (or “nodes” - sorry, I couldn’t help myself!), and have realized that deep learning is incredibly useful for identifying complex patterns and relationships in massive datasets, which is why they are used in natural language processing and are integral to our work at NLPatent! At last, we have made it to the last piece of the puzzle, which is near and dear to our hearts: natural language processing!
Remember how we mentioned the diverse array of industries transformed by AI? Well linguistics is no exception. NLP is a branch of AI that enables computers to understand, interpret, and generate human language. But NLP is unique! Unlike the other forms of AI discussed above, it doesn’t fit nicely into the hierarchy. Instead, it overlaps with, but isn’t wholly encompassed by AI, ML, and deep learning. In fact, not all NLP is AI-driven at all! Some of the earliest forms of NLP were rule-based, predicting linguistic rules rather than statistical learning or relying on neural networks. As I write this blog post I am reminded of a great example - spell check (!), which relies on dictionary definitions without a real understanding of language.
That said, the “sweet spot” where all the magic happens for NLPatent and modern language-based AI, is at the convergence of NLP and deep learning, giving rise to the star of the show - the real MVP - Large Language Models (LLMs).
LLMs is where all this jargon comes together - sitting prominently on the “Rodeo Drive” of the AI market map, at the perfect intersection of deep learning and natural language processing. By leveraging vast amounts of text datasets and neural networks to perform incredibly sophisticated linguistic tasks, LLMs have revolutionized AI-driven language understanding and generation. To help bring this all into perspective here is a venn diagram of all the described terms:
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Understanding the hierarchy of AI, ML, neural networks / deep learning, NLP, and LLMs isn’t just about impressing your friends at cocktail parties (although trust me, it will!), it’s seeing how these layers build on one another to create the plethora of sophisticated AI tools we use today. While AI is often used as a catch-all term, the reality is that ML provides the foundation for much of today’s AI advancements, deep learning supercharges ML’s capabilities, and LLMs represent the latest breakthrough in language-based AI.
So, the next time you hear someone throw around AI jargon incorrectly, it will not only make you cringe (!) but you’ll probably have a bone to pick with them too. 😉
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