The difference between Artificial Intelligence and Machine Learning
For non-tech people, the differences between Artificial intelligence and Machine learning may not be recognizable, as both of these words appear to describe the same thing, just with some technical.
However, even though they are closely related, they are not the same thing, not at all. There are still some crucial moments to understand. Especially, for people who want to implement these technologies in their businesses.
Today, we're going to cover the main differences between AI and ML as understandably as possible.
Definition and difference:
Artificial Intelligence (AI) is a broad field that refers to the simulation of human intelligence in machines programmed to think and learn like humans. Machine learning (ML) is a subset of AI that refers to the use of algorithms and statistical models that allow machines to improve their performance with experience. For a better comparison, look at the image below.
In other words, AI is the overall concept of machines being able to perform tasks that would normally require human intelligence, while machine learning is a specific way of achieving AI.
The importance of data or knowledge
Classic AI systems, like rule-based systems, don't need a lot of data to be created because they rely on predefined rules to perform tasks. These rules are typically created by experts in the field who have a deep understanding of the problem domain and can explicitly define the conditions and actions needed to perform a task. For example, a rule-based system for recognizing objects in an image might be programmed to recognize that an object is a car if it has four wheels and a certain shape or you take a temperature sensor that sends an alarm to the system if it reaches a certain treshold. These rules can be defined by an expert on knowledge or formulas and doesn't require a lot of data to be created.
On the other hand, ML systems need a lot of data to be created because they learn from data to improve their performance to find the best solution. These systems use algorithms that can analyze data and find patterns that can be used to make predictions or decisions. For example, a ML system for recognizing objects in an image might be trained on a large dataset of images and learn to recognize objects based on their so called features such as color, shape, and texture. This system needs a lot of data to be able to learn from different examples and generalize to new situations. This means that ML systems can find solutions in complex problem space where humans struggle to even understand it.
The more data the ML system has, the more accurate it can be. With enough data, the system can learn to recognize the nuances of the task and can make predictions with high accuracy. Even in uncertain situations. With not enough data, the system might not be able to learn or generalize well, leading to poor performance.
To sum up, Classic AI systems do not need a lot of data to be created because they rely on predefined rules, while ML systems need a lot of data to be created as they learn from data to improve their performance. ML system are great to use in areas e.g. where the rules are hard to define, complex or you have a lot of different features.
Abstract representation of a decision tree
Expert systems: Expert systems are rule-based AI systems that use a knowledge base to make decisions and perform tasks. The knowledge base is created by experts in the field and contains a set of if-then rules that define the conditions and actions needed to perform a task. Expert systems can be used in a wide range of applications, such as medical diagnosis, financial forecasting, and scientific research.
Decision trees: Decision trees are a type of rule-based system that uses a tree-like structure to represent a set of decisions. Each node in the tree represents a decision, and the branches represent the possible outcomes. Decision trees can be used for tasks such as classification and prediction.
These are some common techniques in rule-based AI systems, all of them use predefined rules to make decisions and perform tasks, and they can be used in a wide range of applications depending on the field they are applied.
Machine Learning Methods:
Artificial neural network
There are several common techniques used in machine learning (ML) systems:
Supervised learning: Supervised learning is a type of ML where the system is trained on a labeled dataset, where the correct output for each input is already known. The system then uses this training data to make predictions on new, unseen data. Common algorithms used in supervised learning include linear regression, logistic regression, and support vector machines (SVMs).
Unsupervised learning: Unsupervised learning is a type of ML where the system is not given labeled data. The system must find patterns and relationships in the input data on its own. Common algorithms used in unsupervised learning include k-means clustering and principal component analysis (PCA).
Semi-supervised learning: Semi-supervised learning is a type of ML where the system is given a small amount of labeled data and a large amount of unlabeled data. The system must use the labeled data to find patterns in the unlabeled data. Common algorithms used in semi-supervised learning include self-training and co-training.
Reinforcement learning: Reinforcement learning is a type of ML where the system learns by interacting with an environment and receiving feedback in the form of rewards or penalties. Common algorithms used in reinforcement learning include Q-learning and SARSA.
Deep learning: Deep learning is a type of ML that is based on neural networks with multiple layers. These networks are able to learn complex patterns in data and have been used to achieve state-of-the-art results in tasks such as image recognition and natural language processing (NLP). Common algorithms used in deep learning include convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
These are some common techniques in ML systems, depending on the problem.
There are several reasons why there have been huge advancements in machine learning (ML) systems:
1. Data availability: With the explosion of digital data, there is now a vast amount of data available for training ML models. This data can be used to train models that can learn to perform a wide range of tasks with high accuracy. In contrast, rule-based systems rely on predefined rules that are typically created by experts, which is a time-consuming and labor-intensive process.
2. Scalability: ML systems can be easily scaled to handle large amounts of data and perform complex tasks. They can be trained on large datasets and then deployed to perform tasks on new data. These systems are built to apply knowledge from one domain to another similar domain with small retraining on smaller datasets. In contrast, rule-based systems can be limited by the number of rules that can be defined and the complexity of the tasks they can perform.
3. Adaptability: ML systems can adapt to new situations and perform a wide range of tasks. They can learn from data and improve their performance over time. In contrast, rule-based systems are typically designed to perform specific tasks and can't adapt to new situations. Or they can be retrained on new data without changing the model. Rule-based systems need to be defined again from the start.
We hope that this article has answered a few basic questions about artificial intelligence and machine learning.
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