You must have read these two terms — NLP and machine learning — when reading about AI technology. Both of these are two related but different fields within the broader spectrum of AI tech, differing in terms of their purpose and applications.
In this article, we will take a look at the differences between NLP and machine learning, including ten key differences. Let’s start from a brief definition of AI.
What is AI?
AI means artificial intelligence. It refers to “machines” that can simulate human “intelligence.” Hence, the name “artificial intelligence.”
AI’s intellectual capabilities include reasoning and problem solving, as well as “perception” and understanding of languages.
What is NLP?
NLP stands for “Natural Language Processing.” It is a branch of AI that focuses on teaching and training machines to interpret and generate human language. Examples of solutions/tools developed using NLP include generative tools like ChatGPT and humanizing tools like HumanizeAI.net.
However, tools like ChatGPT and HumanizeAI.net also rely on machine learning in addition to NLP, because the former powers the implementation of the latter — learning of language. Although it is also possible to develop purely NLP-based chatbots without machine learning (like ELIZA, developed from 1964 to 1967), such a solution would be strictly rule-based and manually scripted to respond to queries based on pattern or word recognition, leading to limited and stiff responses. It is because there is no actual “learning” of language.
What is Machine Learning?
Machine learning (abbreviated as “ML”) is a branch of AI that includes training AI tools based on data, specifically without an explicit prompt. Using ML, AI models discover and learn patterns from large amounts of existing or current data fed to them.
An example of solutions that only rely on machine learning includes a spam filter for emails. It works by learning patterns common in emails marked as spam (such as excessive use of certain emojis or the use of the term “free”), then recognizing these patterns in incoming emails and marking them as potentially spam.
10 Key Differences Between NLP and ML
1. Scope
Machine learning is a broader field compared to NLP. It covers tasks that require prediction and automation, like predicting sales or inventory trends of a store. These predictions and pattern finding are valuable for making informed decisions.
NLP’s scope differs in that it is a specialized area within AI, which specifically deals with language. NLP focuses on teaching machines to understand language, enabling them to generate it and converse with humans like a human.
2. Data Types
Machine Learning can use and process all types of data, including figures and text, audio, images, videos, etc, — both structured and unstructured.
On the other hand, NLP mainly works with text and speech data, since its focus on languages. It processes words, phrases, sentences, grammar, semantics, etc.
3. Techniques
Both ML and NLP use some techniques to be implemented.
ML relies on a range of different algorithms, like regression, clustering, decision trees, and random forests.
NLP operates on the same ML algorithms, but in a combination with its own techniques, like part-of-speech tagging and named entity recognition.
4. End Goals
The end goal of leveraging Machine learning is to create AI models for predictions and classifications that are accurate, like recognizing cats from dogs in images. This is true for all data types.
But the end goal of NLP is to create machines or enable them to be able to interact with humans.
5. Use of Context by Models
Unlike NLP, ML doesn’t usually involve contextual understanding, but only patterns. For example, a model trained to identify spam emails only recognizes patterns like phrases that sound too good to be true (like “win $1000 lottery”), but not the meaning behind it.
However, NLP relies critically on context, because languages are complex since individual words or phrases can have different meanings (like the word “record,” which carries different meanings in “for the record” vs “record him performing”).
6. Interaction With Humans
A major difference between ML and NLP is their interaction with humans.
ML models don’t interact with humans; they’re not required to do so, usually. These tools work in the background, like an email spam filter.
But NLP models are usually made to interact with humans, with chatbots like ChatGPT being the prime example.
7. Rule-Based vs. Data-Driven Reliance
ML’s strength lies in data. It is heavily data-driven, since it relies on learning patterns using data. The more quality data it is fed, the better an ML model is likely to be. This also means that ML models learn the rules rather than being coded.
NLP also heavily relies on data, however, it also uses rule-based systems, so it combines both methods. This means that NLP models learn from patterns as well as are coded to respond the right way.
8. Evaluation Metrics
ML models are simpler when it comes to their evaluation metrics. These use simpler metrics like accuracy and precision, which are well-defined.
NLP models have to deal with a lot of ambiguity, which is why they’re more complex and use NLP-specialized metrics like perplexity and BLEU, for language modeling and translation, respectively.
9. Scalability and Complexity
ML models are easier and less costly to scale compared to NLP.
NLP models are usually more complex and resource-consuming to scale. Processing vast amounts of unstructured text requires heavy computational resources, advanced architectures.
10. Applications
Machine learning has applications where prediction and repetitive automation is required, like in detecting fraud in banks and identifying spam emails. However, ML has a broader scope in this area compared to NLP.
NLP has applications in repetitive automation but also more complex tasks, like chatting with humans. It is used where machines need to interpret and generate human language. Examples include grammar checkers, paraphrasing tools, and Google Assistant.
Here’s a concise table of the comparisons:
Conclusion
NLP means Natural Language Processing, which is a branch of artificial intelligence that deals with training machines to be able to interpret and generate human language. Examples of NLP solutions include ChatGPT and Google’s Gemini. NLP models rely on special techniques as well as the ones used in machine learning. It can be more complex in various ways, including evaluation metrics and scaling, because of language complexities. These advancements also play a big role in smart writing, where AI tools help plan, draft, and polish text more effectively.
In comparison, machine learning (ML) is a broader concept, but also a branch of artificial intelligence. ML is used to create machines or tools that can predict outcomes or recognize patterns based on historical and present data. ML is also the engine that powers the implementation of natural language processing. It is relatively easier in various aspects like evaluation metrics and scaling, due to simpler algorithms.
