Ten common algorithms that must be understood for AI learning. How many do you know?

First of all, people need to understand what is an AI algorithm? Simply put, AI algorithms are mathematical models that enable machines to learn from data. They come in different forms, including supervised learning, unsupervised learning, and reinforcement learning.

Examples of labeled data are used to train supervised learning algorithms, while unlabeled data are used to train unsupervised learning algorithms. Tagged data is annotated with a predefined target value, while unmarked data is not assigned any target value. Trial and error is the learning method of reinforcement learning algorithms and is widely used in games such as chess and Go, as well as in the robotics industry.

Ten commonly used AI algorithms:

(1) Artificial Neural Network (ANN)

The inspiration for artificial Neural networks comes from the human brain and they are utilized in image and speech recognition as well as natural language processing.. The input data is transferred to a layer of artificial neurons, which is the core idea behind artificial neural networks. Each neuron takes information from the previous layer and computes an output, which is then passed to the next layer. Currently, almost all artificial intelligence applications use deep learning as the preferred architecture, which employs multi-layered artificial neural networks. Artificial neural networks were first used in the 1950s.

(2) Support Vector Machine (SVM)

Support vector machine (SVM) is used for classification and regression problems. Its working principle is to find and separate groups of different data points. The best straight line or curve (called a "super platform"). This super platform can then be used to predict which group a new data point belongs to. Support vector machines (SVMs) can tell people which emails are spam and are widely used in fields such as bioinformatics, finance and computer vision.

(3) Decision tree

The decision tree is a supervised learning algorithm used for prediction. It works by recursively dividing data into subsets based on the values ​​of selected features.

(4) Random Forest

Random forest is an extension of decision tree. They improve the accuracy of predictions by combining the results of multiple decision trees.

(5) K-means clustering

K-means clustering is an unsupervised machine learning algorithm that measures the similarity between data points. They are assigned to K different cluster subsets. Users can predefine or use algorithms to determine the value of K. K plays an important role in fields such as image segmentation and document clustering.

(6) Gradient boosting

The prediction model can be implemented by combining the results of many weak models to perform gradient boosting, a machine learning technique. It is used in web search rankings and online advertising.

(7) Convolutional Neural Network (CNN)

The convolutional neural network in artificial neural networks is designed inspired by the visual cortex of the human brain. It can automatically learn features such as edges and corners in images. Convolutional neural networks are specialized networks for processing grid data (such as pixels), while artificial neural networks are general-purpose, so convolutional neural networks are suitable for image and video processing.

(8) Long short-term memory network (LSTM)

The long short-term memory network is a neural network used to process sequential data such as speech and text, so it is suitable for Speech recognition, machine translation and handwriting recognition are very useful.

(9) Principal Component Analysis (PCA)

PCA is a technique that reduces the dimensionality of data by projecting the data into a low-dimensional space. It is used for face recognition and image compression.

(10) Apriori algorithm

Apriori is an association rule learning algorithm, which is a method that identifies frequent patterns, associations, or correlations between variables. Techniques for discovering relationships between variables in large data sets. In market shopping analysis, it is popular to identify items that are frequently purchased together.

When people interact with AI, they are interacting with these algorithms. People tend to anthropomorphize AI systems, but this is not necessary to understand AI. Because this is just a mathematical problem, it has limitations, one of which is its reliance on data. AI algorithms require a large amount of high-quality data to be effectively trained. In AI, more and better data are needed for training. In contrast, people are more capable of drawing inferences from one example, so they can learn more from just one example.

The widespread application of AI systems needs to meet one or more of the following conditions:

(1) The extended hypothesis is correct (simply add more data and computing will produce artificial general intelligence (AGI)).

(2) Compared with biological paths (such as airplanes that achieve flight but are not designed like birds), large language models (LLM) represent a feasible alternative path to general intelligence.

(3) New or innovative algorithms and architectures are needed to enable AI systems to learn any knowledge from one or a few examples (such systems may require a cohesive world model and virtual/physical embodiment ).

What have we learned from AI?

While artificial intelligence is powerful and awe-inspiring, it is simply a method based on accepted mathematical principles, probability and statistics. Tools for learning optimization algorithms. It is unclear at what point an information-processing system incorporating AI will become a fully realized, conscious digital being with capabilities beyond those of the human mind. What is clear is that we are entering a new era, and as data and computing resources continue to grow, the world will be changed.

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