Artificial intelligence, machine learning and natural language processing
There is a a lot of confusion and hype on this topic, so I thought it best to clarify this point. You probably use artificial intelligence and these related subsets every time you use a computer not it’s not as scary as you might think. Indeed, Artificial Intelligence (AI), Machine Learning (ML), and Large Language Models (LLMs) are related concepts in the field of technology, but they have distinct differences:
- Artificial Intelligence (AI):
- Definition: AI refers to the broader field of creating machines or systems that can perform tasks that typically require human intelligence. It aims to replicate human-like thinking, reasoning, problem-solving, and decision-making.
- Scope: AI encompasses a wide range of techniques and applications, including natural language processing, computer vision, robotics, and more.
- Examples: Virtual assistants like Siri and Alexa, autonomous cars, and AI-powered recommendation systems.
- Machine Learning (ML):
- Definition: ML is a subset of AI that focuses on developing algorithms and models that allow machines to learn from data and make predictions or decisions without explicit programming.
- Approach: ML algorithms use patterns and statistical analysis to improve their performance over time as they are exposed to more data.
- Examples: Spam email filters, image recognition software, and predictive text suggestions on smartphones.
- Large Language Models (LLMs):
- Definition: LLMs are a specific type of ML model designed for natural language understanding and generation tasks. They are massive neural networks trained on vast amounts of text data.
- Functionality: LLMs excel at tasks like text generation, translation, summarization, and question-answering. They can understand and generate human-like text.
- Examples: GPT-3, GPT-4, and BERT are examples of LLMs that have gained prominence for their text-based capabilities.
In summary, AI is the overarching concept that aims to create intelligent machines, while ML is a subset of AI that focuses on learning from data. LLMs, on the other hand, are specific ML models designed for natural language processing tasks. They are powerful tools within the field of AI and ML, capable of understanding and generating human language at scale.