The AI industry has ushered in a number of terms that are often used interchangeably or whose differences are unclear to many end-users. Natural Language Processing (NLP), Machine Learning (ML), and Neural Networks are three terms that are often used within the context of AI tools and applications. In this article, we will clarify the terminology and explain the differences between NLP, ML, and Neural Networks.
What is NLP?
NLP is a field of AI that focuses on the interaction between computers and human language. NLP algorithms analyze and interpret human language and text, allowing machines to understand and respond to it. NLP is used in a variety of applications, including virtual assistants, language translation, and sentiment analysis.
The popularity of the NLP concept has soared with the recent introduction of Chat-GPT, one of the most powerful NLP models. Chat-GPT currently enjoys the status of the most popular generative NLP model. Generative models are adept at learning from a large amount of text data and then generating new text based on prompts and directions.
Another popular type of NLP model is classificatory. These are trained to classify text into various categories, e.g., positive, negative, and neutral. The leading model in this niche is RoBERTa, a powerful NLP algorithm used for a variety of critical processes within our social analytics platform, PUMP. For instance, one of the key tasks of RoBERTa is to classify social media signals for stocks, cryptocurrencies, and commodities into bearish, bullish, and neutral.
What is Machine Learning?
Machine Learning is a branch of AI that involves training algorithms to learn from data. ML algorithms analyse data, learn patterns, and make predictions based on those patterns. ML is used in a variety of sophisticated applications, such as image recognition and fraud detection. Machine Leaning is the broadest of the three concepts we are looking at in this article.
What are Neural Networks?
Neural Networks are a type of ML algorithm that is modeled after the structure and function of the human brain. Neural Networks are made up of layers of interconnected nodes, or neurons, that process information and make predictions. Neural Network systems are particularly adept at learning from their experience as they operate.
Differences between NLP, Machine Learning, and Neural Networks
While NLP, Machine Learning, and Neural Networks are all related to the field of AI, there are some major differences between them.
NLP vs Machine Learning
NLP and Machine Learning are related concepts, though they are not synonymous. NLP algorithms could be considered a specialised subset of ML.
The main difference between NLP and the more generalised ML is the type of data being analysed. NLP algorithms analyze, process, and interpret text-based data, while generalized ML algorithms focus more on other types of data, such as numeric data or image data.
While NLP models are a text-oriented subset of ML, and technically could be called ML models, the NLP niche has grown so popular on its own that few people normally refer to these language algorithms as ML. At least after the Chat-GPT revolution, these algorithms have earned the right to be called by their specific name – NLP models.
NLP vs Neural Networks
Neural Network are extensively used within many NLP models. NLP algorithms can use Neural Networks as a way to analyze and interpret language data, but not all NLP algorithms make use of Neural Networks.
Neural Networks are essentially self-learning systems whose operational structure is modeled after the human brain. NLP systems can use Neural Networks to leverage their self-learning capabilities and enhance the language-based tasks at the heart of NLP.
In conclusion, NLP is a text- and language-focused subset of Machine Learning, while Machine Learning is a general-purpose AI niche for processing data of all kinds, shapes, and forms. In turn, Neural Networks are a specific type of Machine Learning algorithm whose operational model is structured similarly to the way the human brain operates. Neural Networks are particularly good at self-learning, and some NLP models might use them within their general framework.
What is NLP?
NLP is a field of AI that focuses on the interaction between computers and human language. NLP algorithms analyze and interpret human language and text, allowing machines to understand and respond to it. NLP is used in a variety of applications, including virtual assistants, language translation, and sentiment analysis.
The popularity of the NLP concept has soared with the recent introduction of Chat-GPT, one of the most powerful NLP models. Chat-GPT currently enjoys the status of the most popular generative NLP model. Generative models are adept at learning from a large amount of text data and then generating new text based on prompts and directions.
Another popular type of NLP model is classificatory. These are trained to classify text into various categories, e.g., positive, negative, and neutral. The leading model in this niche is RoBERTa, a powerful NLP algorithm used for a variety of critical processes within our social analytics platform, PUMP. For instance, one of the key tasks of RoBERTa is to classify social media signals for stocks, cryptocurrencies, and commodities into bearish, bullish, and neutral.
What is Machine Learning?
Machine Learning is a branch of AI that involves training algorithms to learn from data. ML algorithms analyse data, learn patterns, and make predictions based on those patterns. ML is used in a variety of sophisticated applications, such as image recognition and fraud detection. Machine Leaning is the broadest of the three concepts we are looking at in this article.
What are Neural Networks?
Neural Networks are a type of ML algorithm that is modeled after the structure and function of the human brain. Neural Networks are made up of layers of interconnected nodes, or neurons, that process information and make predictions. Neural Network systems are particularly adept at learning from their experience as they operate.
Differences between NLP, Machine Learning, and Neural Networks
While NLP, Machine Learning, and Neural Networks are all related to the field of AI, there are some major differences between them.
NLP vs Machine Learning
NLP and Machine Learning are related concepts, though they are not synonymous. NLP algorithms could be considered a specialised subset of ML.
The main difference between NLP and the more generalised ML is the type of data being analysed. NLP algorithms analyze, process, and interpret text-based data, while generalized ML algorithms focus more on other types of data, such as numeric data or image data.
While NLP models are a text-oriented subset of ML, and technically could be called ML models, the NLP niche has grown so popular on its own that few people normally refer to these language algorithms as ML. At least after the Chat-GPT revolution, these algorithms have earned the right to be called by their specific name – NLP models.
NLP vs Neural Networks
Neural Network are extensively used within many NLP models. NLP algorithms can use Neural Networks as a way to analyze and interpret language data, but not all NLP algorithms make use of Neural Networks.
Neural Networks are essentially self-learning systems whose operational structure is modeled after the human brain. NLP systems can use Neural Networks to leverage their self-learning capabilities and enhance the language-based tasks at the heart of NLP.
In conclusion, NLP is a text- and language-focused subset of Machine Learning, while Machine Learning is a general-purpose AI niche for processing data of all kinds, shapes, and forms. In turn, Neural Networks are a specific type of Machine Learning algorithm whose operational model is structured similarly to the way the human brain operates. Neural Networks are particularly good at self-learning, and some NLP models might use them within their general framework.