Understanding AI, Machine Learning, and Deep Learning Concepts

Understanding AI, Machine Learning, and Deep Learning Concepts

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Introduction:In today’s information age, the concepts of artificial intelligence, machine learning, and deep learning are no longer as lofty as they were over a decade ago. They have gradually permeated various aspects of our lives, and we understand and use them. For example, the “Siri” voice assistant on iOS devices is a typical representation of artificial intelligence applied in daily life. So what truths lie behind these high-frequency terms? Let DataHunter explore this for everyone.
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Development History

First, we should clarify that artificial intelligence, machine learning, and deep learning are not independent concepts but rather a nested relationship from broad to narrow, simply put, a “concentric circle”.
Understanding AI, Machine Learning, and Deep Learning Concepts
Among them, the concept of artificial intelligence appeared first, and as the name suggests, it is to endow machines with human wisdom.At the same time, it is also the broadest concept of the three. In the summer of 1956, a group of visionary young scientists, including McCarthy and Shannon, gathered at the Dartmouth Conference to discuss a series of issues regarding simulating intelligence with machines, and the concept of “artificial intelligence” was proposed for the first time. Its long-term goal is to ultimately enable machines to achieve human-like intelligence, but we have not yet reached that point. Current research mainly focuses on the field of weak artificial intelligence, such as facial recognition and text review. Weak artificial intelligence can only focus on a specific area, showing capabilities similar to or even exceeding those of humans in certain aspects, but still falls short overall. Achieving strong artificial intelligence, where it can think critically and emotionally like humans, still requires some time.
Understanding AI, Machine Learning, and Deep Learning Concepts
Figure: Branches of Artificial Intelligence Research
Machine learning is a method to achieve artificial intelligence.This concept originated from IBM engineer Arthur Samuel in 1952, who proposed the term “Machine Learning” at the Dartmouth Conference in 1956. Machine learning is about allowing machines to learn patterns from historical data and then applying those patterns to the future. In fact, human behavior is also derived from learning and imitation, so we hope that computers can learn and imitate like humans from historical data and behaviors, thus achieving artificial intelligence.
Deep learning is a further advancement based on machine learning; it is a technology to achieve machine learning.Deep refers to the number of layers; we typically refer to neural network models with more than 8 layers as deep learning. In 2006, Geoffrey Hinton, a professor at the University of Toronto, optimized traditional neural network algorithms and proposed the concept of Deep Neural Network, which can be roughly understood as a neural network structure containing multiple hidden layers, thereby promoting the development of deep learning. Deep learning has solved many “intelligent” problems that traditional machine learning algorithms could not address, such as in image recognition, semantic understanding, and speech recognition.
Understanding AI, Machine Learning, and Deep Learning Concepts
Figure: Professor Geoffrey Hinton’s Explanation of Deep Neural Networks
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Applications in the Industry

After introducing the differences and connections among the three, let’s talk about their applications in the industry. The following content mainly describes how machine learning and deep learning realize artificial intelligence in practice.
The mainstream algorithms of machine learning include the following:Decision Trees, Random Forests, Logistic Regression,SVM, Naive Bayes, K-Nearest Neighbors, K-Means, Adaboost, Neural Networks, Markov.
Understanding AI, Machine Learning, and Deep Learning Concepts
First, in the media sector.The most well-known is the recommendation mechanisms of various Internet companies’ apps, where machine learning algorithms are used for recommending articles and videos. You may often find that when you focus on a certain type of content for a long time, more and more related content gathers in your information stream interface. This is mainly based on item-based recommendations, knowledge graph-based recommendations, and collaborative filtering algorithm recommendations. The recommendation of information materials involves algorithms like Doc2Vec and LSI, as it involves some understanding of the semantics of the materials. Of course, today, recommendation mechanisms are no longer a unique technology; social media apps like Hupu and Weibo have already deployed them.
DataHunter also recognized the importance of applying artificial intelligence in the current media convergence environment during the construction of the “Central Kitchen” of the People’s Daily. By connecting to Tencent’s interface to display hot news, integrating a CMS content editing and publishing system to track manuscript flow and approval, and integrating a big data platform to conduct key behavior data statistics for WeChat and Weibo, they timely adjust the direction based on the manuscript tracking statistics.
Understanding AI, Machine Learning, and Deep Learning Concepts
Secondly, in the retail sector.Nowadays, the digital transformation of enterprises has become a necessary condition for survival and development in the information age. DataHunter, as a professional provider of data software products and services, has developed an agile data middle platform, Data Formula, which supports complex algorithm processing capabilities, can label data, or perform complex data processing and business processing through AI machine learning algorithms.
For example,the retail product recommendation algorithm aims to provide users with products they may be more interested in, thereby increasing total sales. Currently, there are two mainstream methods in the industry: user-based collaborative filtering and item-based collaborative filtering. The former compares the characteristics of purchased products by similar users and recommends other items bought by reference users to a specific user; the latter analyzes the characteristics of products purchased by users to determine the types of products the user likes and recommends related products.
Data Formula uses labeling algorithms for data processing, creating business data labels. By associating the product label dataset generated by the labeling algorithm with the product dataset, a new retail model based on labels can be formed, thus providing corresponding label data support for the retail system. This allows enterprises to adapt to various complex businesses locally, enabling data to create value and making business decisions more intelligent.
The retail industry also utilizessmart replenishment algorithms, as the supply of goods faces increasingly complex situations in today’s information age. For enterprises undergoing digital transformation, smart replenishment has become an urgent technical need. Digital and intelligent replenishment is about how to utilize artificial intelligence technology to help convenience stores formulate replenishment strategies and improve overall store operational efficiency. Demand forecasting, a typical regression problem, uses artificial intelligence algorithms to continuously adjust model parameters based on historical data, using actual sales as data samples, to automatically learn the optimal solution for predictions, ultimately achieving scientific demand forecasting and intelligent replenishment.
Additionally, artificial intelligence is also applied in other areas of the retail industry, including2D/3D visual recognition technology, natural language processing technology, robot technology, AR/VR augmented reality technology, and sensor technology, which have greatly changed traditional sales models for merchants and brands, forming a direct link between consumers and manufacturers. The maturity of technologies like visual recognition and sensors has enabled the concept of robotic and unmanned operations to be realized, significantly reducing operational costs for producers and enhancing the shopping experience for consumers.
Understanding AI, Machine Learning, and Deep Learning Concepts
Moreover, in the medical field, artificial intelligence is currently mainly applied in medical imaging-assisted diagnosis and various intelligent detection devices. In the era of traditional artificial imaging diagnosis, doctors relied on their eyes to identify pathological features in medical images as the basis for diagnosis. However, in the AI era, by using machine learning, medical images that yield conclusions can be used as training sets, allowing machines to iteratively learn and ultimately achieve diagnostic accuracy and identification capabilities comparable to experts.
Understanding AI, Machine Learning, and Deep Learning Concepts
Finally, at the government level, artificial intelligence has made facial recognition technology possible, bringing many conveniences for the public in handling various services and travel. At the same time, AI has also made achievements in combating crime and maintaining social order. By combining facial recognition technology with electronic eyes, machines can identify and compare suspects in real-time from surveillance footage, effectively aiding public security systems in improving case-solving efficiency and timely apprehending criminals.
In addition, during the handling of police incidents, user profiling analysis for suspects is equally crucial. User profiling represents a model obtained by mining as much data information about users as possible. DataHunter closely collaborates with the Ministry of Public Security to build a smart policing system. They conduct data modeling, establish suspect data models, and collect formatted user profile data. By combining machine learning algorithms, they achieve user profiling analysis and relationship graph analysis.
End:In summary, the era of artificial intelligence has arrived. Mastering relevant conceptual knowledge can enable governments, enterprises, and even individuals to enjoy its benefits. Of course, there are no limits to technological advancement, and personal progress also knows no bounds. We hope this article is helpful to you.
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About Us
DataHunter is a well-known enterprise in the field of data governance and visual analysis, owning intelligent data analysis products – Data Analytics, large screen visualization products – Data MAX, and agile data middle platform – Data Formula. It has provided data visualization analysis services for domestic and foreign Fortune 500 companies such as Xiaomi, Sany Heavy Industry, State Grid, Nestle, Unilever, and CITIC Group.
Understanding AI, Machine Learning, and Deep Learning Concepts
Data Analytics– A business-driven BI product that provides a complete solution integrating data collection, processing, analysis, and visualization, helping clients achieve data-driven decision-making and improve business. Data Analytics is flexible in deployment, easy to operate, and powerful, widely used in scenarios such as leadership dashboards, user analysis, sales management, financial analysis, and human resource management.
Data MAX– A cool large-screen visualization product that helps enterprises quickly visualize business data on large screens, PCs, and mobile devices. Data MAX features a rich component library, theme styles, and industry templates, with simple drag-and-drop layout capabilities. Users can freely configure or customize it, widely used in exhibition reports, command centers, business dashboards, smart factories, smart transportation, and media monitoring scenarios.
Data Formula– An agile data middle platform that addresses issues such as data silos, data governance, and data assets for enterprises, helping them truly become data-driven. Data Formula can build a unified, labeled, API-based, and continuously updated data asset management platform based on the unique business architecture of enterprises, providing rapid response to decision-making, refined operations, and application support for front office departments, enabling enterprises to continuously align their capabilities with user needs.
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