Introduction
Artificial intelligence, machine learning, deep learning. The three popular buzzwords in this new age of technology. Three of these convey the same purpose of building intelligent systems using sophisticated algorithms and techniques, but how do they differ from each other, and how they are similar?
In this article, we will explore more about artificial intelligence, machine learning, and deep learning, and examine how they are related and how they differ from one another. We will also delve into the various ways in which these technologies are being used in various industries and the potential implications of their use in the future.
Artificial Intelligence
Artificial Intelligence! One of the most interesting technologies of bringing intelligence to a machine using methods of advanced science and engineering. That is the basic definition of AI. But what is the true purpose of AI when we are trying to dig into it even more?
Let's see an example most of us are familiar with. You may often be frustrated not winning a chess game against a computer opponent someday in your life. Have you ever wondered how a computer opponent inside a game can play as intelligent as humans or more than us? Those opponents in the game can take quick decisions and can understand how to make the next move. One idea to make these computer opponents is by hard-coding programs that do everything as the instruction specified. But it cannot beat the powerful decision-making capabilities of the human brain. Here comes another method. Why can't make a computer intelligent as humans? This is where Artificial Intelligence comes into the scene.
Artificial Intelligence is a branch of computer science that deals with the simulation of human intelligence in machines that are programmed to think and act like humans. These intelligent machines can be trained to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
So with our example of the computer chess game, we can say that the computer opponent has the intelligence to play the game against us. But is the computer opponent really intelligent? Can it play any other game apart from chess? No, it can't play any other games as it is programmed to be intelligent in playing only the chess game. This is where the different type of Artificial Intelligence comes in to picture.
There are different types of Artificial Intelligence,
- Narrow or weak AI: This type of AI is designed to perform a specific task or set of tasks, such as playing chess or recognizing speech. It is not designed to be able to perform a wide range of tasks and is not generally considered to be "intelligent" in the same way that humans are.
- General or strong AI: This type of AI is designed to be able to perform any intellectual task that a human being can. It is considered to be truly "intelligent" and is still the subject of much research and development.
- Superintelligent AI: This type of AI refers to the hypothetical future development of AI that would surpass human intelligence in a wide range of tasks. It is the subject of much speculation and debate among experts, as it is not yet clear what the implications of such a development would be.
This is just one example of Artificial Intelligence in action. Examples like self-driving cars, recommendation systems, chatbots, etc are using Artificial Intelligence more often.
Artificial Intelligence vs Machine Learning
Alright, we have discussed Artificial Intelligence, but what is the purpose of Machine Learning, and how it is related to Artificial Intelligence? Think about the same game of chess. Imagine if the computer opponent inside the game can improve in each game playing with it. The computer program can learn something new in each game and can have a new understanding of playing chess more effectively like we do. This is what Machine Learning is all about.
Machine learning is a subfield of artificial intelligence that involves the use of algorithms to enable a computer or machine to learn from data without being explicitly programmed. It involves the development of algorithms and models that can analyze and make decisions based on data, and improve their performance over time as they are exposed to more data.
The computer opponent with machine learning capabilities can learn from the data provided when playing the game, this way it can improve its capabilities over time without coding it separately.
There are different types of Machine Learning,
- Supervised Learning: This involves training a model on a labeled dataset, where the correct output is provided for each example in the training set. The model is then used to make predictions on new, unseen examples. Like teaching a machine to play chess by providing labeled data of different moves and rules in the chess game.
- Unsupervised Learning: This involves training a model on an unlabeled dataset, and allowing the model to discover patterns and relationships in the data on its own. In the context of the chess game, unsupervised learning could be used to analyze a large dataset of past chess games, without being provided with any information about the moves that were made or the outcomes of the games.
- Semi-supervised Learning: It falls under supervised and unsupervised learning techniques that use both labeled and unlabeled data to train a model. It is particularly useful when it is expensive or time-consuming to label a large dataset, as it allows the model to learn from both the labeled and unlabeled data.
- Reinforcement Learning: Reinforcement learning is a way for a computer to learn how to do a task by trying different things and getting rewards when it does well. It involves training an agent to make decisions in an environment in order to maximize a reward. The agent learns through trial and error, receiving positive or negative rewards based on its actions. At each time step, the agent observes the current state of the environment and selects an action to take. The action leads to a new state and a corresponding reward, and the process repeats. In the case of the chess game, Reinforcement learning is used to train the machine each time by providing a reward for a good move and penalizing for a bad move.
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| AI and ML Hierarchy |
Machine Learning is often used literally everywhere, like self-driving cars which learn to drive more effectively over time, Recommendation systems which recommend videos or songs as per our interest by learning about our interest over time, chatbots, personal assistants(Google Assistant, Siri, Alexa, ChatGPT), and many many more.
In contrast, the difference between AI and ML is that AI is a broad category of making computers as intelligent as humans while machine learning is a field of AI which makes machines learn from data.
Artificial Intelligence vs Machine Learning vs Deep Learning
Until now, we have discussed what is AI and ML and their relation and difference. Another important field to understand is Deep Learning. Back to the example of the chess game, imagine if a machine can learn how to make effective moves by using an Artificial Neural Network(ANN) which is inspired by the human brain. We know that all our abilities to understand, think, and make decisions are because of neurons communicating with each other inside our brains. Our brain has billions of neurons connected to each other sending signals to make things possible for us including intelligence.
Deep learning is a type of machine learning that involves training artificial neural networks on a large dataset. Neural networks are composed of layers of interconnected nodes, and they are able to learn and make intelligent decisions by analyzing and processing large amounts of data.
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| Artificial Neural Network(ANN) |
It is no wonder that Deep Learning and Neural Networks are here for many years than some of the primitive machine learning algorithms like SVM(Support Vector Machine). But recently Deep Learning got more attention because of the increase in computational capabilities. Training of Deep Learning algorithms needs a massive amount of computational power and storage. Luckily, right now we have lots of powerful CPUs, GPUs, and RAM that can process a large amount of data making Deep Learning literally possible.
Deep Learning is a very powerful technology that has a wide variety of applications including, weather forecasting, stock price prediction, chatbots, self-driving cars, Computer Vision, Natural Language Processing, and more
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| AI, ML, DL hierarchy |
Conclusion
In conclusion, we can say that Deep Learning is a subfield of Machine Learning and Machine Learning is the subfield of Artificial Intelligence. All these three technologies are related but distinct fields of study that are playing a significant role in the development of new technologies and applications. Artificial intelligence involves the use of computers to perform tasks that would normally require human intelligence, such as decision-making and problem-solving. Machine learning is a subfield of AI that involves training a model to make decisions and predictions based on data. Deep learning is a type of machine learning that involves training neural networks on large datasets, allowing them to learn and understand complex patterns and relationships in the data. Understanding the differences and connections between these fields is important in order to fully appreciate the capabilities and limitations of current and future technologies.
