Differences Between NLP and Machine Learning
Artificial Intelligence (AI) has seen a lot of advancements over the last three decades, especially in machine learning (ML) and natural language processing (NLP). They are both highly useful technologies with a vast amount of applications, however, there are many differences between the two and how they function.
For consumers, the difference might be negligible, but for developers and businesses, there are huge amounts of variation between the two ML and NLP. Knowing this difference can have a profound impact on the AI products they develop and deploy, leading to more beneficial programs for consumers.
What is Machine Learning?
Machine learning is a form of AI that builds systems that can learn from data. They are trained to spot patterns and analyze them to make predictions and other results. There are three main types of ML, including supervised/unsupervised learning which relates to the amount of human oversight involved, and reinforcement learning which places an artificial agent inside a digital environment.
Machine learning has many applications that are beneficial to creating modern programs for the digital age. Image recognition is becoming more common in cameras and social media, helping photographers tag the people and items in their images. Predictive analytics is another powerful feature that is used extensively in business forecasting and meteorology because it can identify patterns for accurate predictions.
What is Natural Language Processing?
Natural language processing is a form of AI that intersects computer science and linguistics to create algorithms that can mimic human language and dialogue. It works by breaking down written or spoken language into its simplest forms and tokenizing each word. These tokens are then adjusted by weights on syntax, semantics, and pragmatics to output coherent responses and comprehension.
NLP can be used in a variety of ways, especially with the rise of smartphones and internet technology which has been a catalyst for digital communications. Virtual chatbots are becoming more common across the internet, helping businesses give customers more direct access to customer service lines. More advanced bots like ChatGPT are driving these innovations even further with the ability to produce programming languages too.
Key Differences between NLP and ML
While machine learning and natural language processing are crucial fields within AI, there are many key differences. They include:
Purpose & Applications:
ML: Algorithms are made to analyze patterns and learn from them, improving their output results. These programs are largely general-purpose and used for a variety of applications that require pattern identification.
NLP: This form of AI is dedicated to comprehending human language and creating responses that are easy for humans to interpret and understand. Advanced applications have the ability to hold entire conversations.
Techniques Used:
ML: Statistical models are implemented to process data through the algorithms layers using neural networks. These can host a wide range of applications, but convolution is specifically beneficial for image and video editing.
NLP: Language models combine statistics with linguistics to apply mathematical frameworks onto language inputs. This allows models to take a statistical approach to how language is conceived, suiting its computational strengths.
Data Types:
ML: ML algorithms can process a large variety of different data types including images, numbers, and sounds. This creates numerous applications for different businesses in finance and healthcare.
NLP: NLP models require language inputs to work properly. These inputs are usually in the form of text and verbal ques, but information can also be extracted from images of text and are great for superposing language translations.
Complexity & Challenges:
ML: Overfitting, when a model excels on training data more than input data, can cause algorithms to process information poorly, causing less accurate outputs. These can be limited with gates that prevent placing too much emphasis on trining data.
NLP: Intricacies in human language like slang or regional dialects can cause NLP models to get confused, causing low quality outputs. Likewise, learning multiple languages can also cause problems, but this can be overcome slowly with a wider variety of training data.
Interrelation Between NLP and ML
Despite their key differences, there are still many similarities between NLP and machine learning, including the way they operate. Neural networks are often incorporated into both models because they can enhance processing abilities. Moreover, deep learning, a subset of machine learning, is incredibly useful in NLP models.
Without machine learning, NLP would not exist. ML models are necessary for hosting complex transformers and architecture that NLP uses to produce results. These transformers create “attention” which weighs the significance of certain words, highlighting their contextual importance. Some popular transforms include GPT and BERT.
Future Prospects
Human language is a unique tool that we’ve created and also undergoes vast amounts of evolution over time from the ancient roots in the European language created by Latin to the revolutionary development of Korean Hangul, our ways of communicating are far from stagnant and AI’s potential to grow and adapt is breaking new technological ground.
The relationship between machine learning and natural language processing is symbiotic, helping each other evolve over time to become more efficient machines. As machine learning improves, so does NLP, leading to more advanced language outputs that will eventually understand dynamic changes in language.
