Difference Between AI, ML, DL, NN and How It Relates to ChatGPT?
Nikita Borisenko
CEO of Loqos AI
October 10, 2023
The terminology surrounding AI can be confusing. There are various buzzwords like neural networks, AI, machine learning, deep learning, language models, ChatGPT, and OpenAI that are often used, but it can be difficult to understand how they all relate to one another. To help you make sense of it all, I have organized this information for you.
Understanding the Differences: Artificial Intelligence, Machine Learning, Deep Learning, and Neural Networks
Understanding the Differences: Artificial Intelligence, Machine Learning, Deep Learning, and Neural Networks
Difference between Artificial Intelligence, Machine Learning, Deep Learning, and Neural Networks
Briefly:
• AI (Artificial Intelligence) is the broader concept of creating intelligent machines.
• ML (Machine Learning) is a subset of AI focused on training machines to improve tasks through data.
• DL (Deep Learning) is a subset of ML that leverages neural networks to learn complex patterns autonomously.
• NN (Neural Networks) are the fundamental components of DL, serving as the intricate architecture that allows deep learning models to excel in various tasks.
In more detail:
1. Artificial Intelligence (AI) refers to computer systems that can perform tasks that typically require human intelligence, such as problem-solving, speech recognition, decision-making, or language understanding. AI aims to mimic human cognitive functions, and it's the overarching concept.
2. Machine Learning (ML) is a subset of AI. ML enables machines to learn from data and improve their performance on specific tasks over time. In addition, it requires humans to explicitly define the relevant characteristics or attributes within the data that the model should pay attention to. Imagine it as someone carefully selecting and providing the machine with specific hints to perform a task better. For instance, in email spam detection, a machine learning model might require explicit rules about what characteristics make an email spammy.
3. Deep Learning (DL) goes even deeper. It's a subset of ML that's inspired by the structure of our brain, consisting of artificial neural networks. The "deep" in deep learning signifies the numerous hidden layers within these neural networks. Unlike traditional networks, which might have just 2-3 hidden layers, deep neural networks can extend to over 150 layers. This depth enables them to automatically learn intricate patterns and features from extensive datasets, particularly excelling in tasks like image and speech recognition. For example, in image recognition, deep learning can recognise complex features like cat whiskers or dog tails by itself.
4. Neural Networks (NN) are the building blocks of deep learning. They are a set of algorithms that aim to recognise underlying relationships in data through a process that mimics the human brain. Each neuron, or node, in a neural network is connected to the others, and these connections enable the network to learn and make sense of data. For instance, in your favourite social media app, NNs help recognize faces when tagging friends in photos or curate your news feed based on your interests. They're also at play in recommendation systems like Netflix, suggesting movies tailored to your preferences.
• AI (Artificial Intelligence) is the broader concept of creating intelligent machines.
• ML (Machine Learning) is a subset of AI focused on training machines to improve tasks through data.
• DL (Deep Learning) is a subset of ML that leverages neural networks to learn complex patterns autonomously.
• NN (Neural Networks) are the fundamental components of DL, serving as the intricate architecture that allows deep learning models to excel in various tasks.
In more detail:
1. Artificial Intelligence (AI) refers to computer systems that can perform tasks that typically require human intelligence, such as problem-solving, speech recognition, decision-making, or language understanding. AI aims to mimic human cognitive functions, and it's the overarching concept.
2. Machine Learning (ML) is a subset of AI. ML enables machines to learn from data and improve their performance on specific tasks over time. In addition, it requires humans to explicitly define the relevant characteristics or attributes within the data that the model should pay attention to. Imagine it as someone carefully selecting and providing the machine with specific hints to perform a task better. For instance, in email spam detection, a machine learning model might require explicit rules about what characteristics make an email spammy.
3. Deep Learning (DL) goes even deeper. It's a subset of ML that's inspired by the structure of our brain, consisting of artificial neural networks. The "deep" in deep learning signifies the numerous hidden layers within these neural networks. Unlike traditional networks, which might have just 2-3 hidden layers, deep neural networks can extend to over 150 layers. This depth enables them to automatically learn intricate patterns and features from extensive datasets, particularly excelling in tasks like image and speech recognition. For example, in image recognition, deep learning can recognise complex features like cat whiskers or dog tails by itself.
4. Neural Networks (NN) are the building blocks of deep learning. They are a set of algorithms that aim to recognise underlying relationships in data through a process that mimics the human brain. Each neuron, or node, in a neural network is connected to the others, and these connections enable the network to learn and make sense of data. For instance, in your favourite social media app, NNs help recognize faces when tagging friends in photos or curate your news feed based on your interests. They're also at play in recommendation systems like Netflix, suggesting movies tailored to your preferences.
Difference between Machine Learning and Deep Learning
In addition, language models such as BERT (by Google) and ChatGPT (by OpenAI) play pivotal roles in AI. They employ machine learning, learn from data, and often leverage deep learning techniques, like neural networks, to grasp and generate human language. In simpler terms, they represent AI systems designed to understand and produce human language.
How ChatGPT is connected with Machine Learning, Deep Learning, and Neural Networks
Take ChatGPT, for example. Neural networks form its foundation, functioning as interconnected artificial neurons similar to the human brain. They process and transmit information, enabling ChatGPT to understand and generate text by recognising patterns. Deep learning involves stacking neural network layers to extract specific data features, enhancing ChatGPT's ability to comprehend language context and generate coherent text. Machine learning, a broader field encompassing deep learning, allows ChatGPT to continuously improve its language generation skills by learning from extensive text data, making it increasingly proficient with each update.
In summary, these terms—AI, ML, DL, NNs, and language models—form a complex ecosystem where each element builds upon the others to enhance technology's capabilities.
In summary, these terms—AI, ML, DL, NNs, and language models—form a complex ecosystem where each element builds upon the others to enhance technology's capabilities.