Artificial Intelligence (AI) refers to computer systems capable of performing tasks that historically required human intelligence. These tasks include recognizing speech, making decisions, and identifying patterns. AI is an umbrella term that encompasses various technologies, including machine learning, deep learning, generative, and natural language processing (NLP).

First, machine learning involves creating models that learn from data, allowing them to make predictions or categorize information. Unlike traditional programming, where rules are explicitly defined, machine learning models adapt and improve over time based on the data they encounter. Think of programs such as Amazon or Netflix where recommendations are based on your preferences. While machine learning and AI are often used interchangeably, they have meaningful differences in that machine learning does not mimic the learning process of the human brain and relies solely on data and programming.

Deep learning is a machine learning technique that layers algorithms and computing units – or neurons – into artificial neural networks that mimic the human brain. The term “deep” refers to the use of multiple layers in the network.  Deep learning is intended to mimic the complex decision-making processes of the human brain, making it a tool for solving diverse problems.  

Generative AI refers to a type of artificial intelligence capable of creating new content, such as text, images, code, or other data, based on patterns it has learned from existing examples. When given a prompt, generative AI models generate responses that mimic human-like output2. Generative AI employs deep learning to analyze large datasets to recognize patterns. Neural networks, inspired by the human brain, play a crucial role. For instance, if you feed a generative AI model lots of fiction writing, it can eventually craft stories or elements based on the literature it has absorbed. These models become more sophisticated over time as they learn from the data they process.

Finally, natural language processing (NLP) is an interdisciplinary field that combines computer science and linguistics. Its primary goal is to equip computers with the ability to understand and manipulate human language. NLP focuses on making human communication—both speech and text—comprehensible to computers. It enables machines to process language, understand its meaning, and even infer intent and sentiment. NLP combines techniques from computational linguistics, statistical modeling, machine learning, and deep learning. Examples include voice assistants, chatbots, and translation services.

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Resources used:

What Is Machine Learning? Definition, Types, and Examples | Coursera

Deep Learning vs. Machine Learning: A Beginner’s Guide | Coursera

Generative artificial intelligence - Wikipedia

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Key Terms: AI, Artificial Intelligence, Deep Learning, Generative AI, NLP, Machine learning, Natural language processing

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