The wave of artificial intelligence is sweeping the world, and many words linger in our ears all the time: artificial intelligence(AI), machine learning, and deep learning. Many people always seem to understand the meaning of these high-frequency words and the relationship behind them.
In order to better understand artificial intelligence(AI), this article explains the meaning of these words in the simplest language and clarifies the relationship between them, hoping to be helpful to people who are just getting started.
In 1956, several computer scientists put forward the concept of "artificial intelligence", dreaming of using computers to construct complex machines with the same essential characteristics as human intelligence, that had just appeared at that time. Since then, artificial intelligence(AI) has been lingering in people's minds and slowly incubated in scientific research laboratories. In the decades that followed, artificial intelligence(AI) was either called the prediction of the dazzling future of human civilization, or was thrown into the trash as the fantasies of technological madmen. Until 2012, these two voices still existed at the same time.
After 2012, artificial intelligence began to explode, thanks to the increase in data volume, the improvement of computing power and the emergence of new machine learning algorithms (deep learning). The research fields of artificial intelligence are also expanding, including expert systems, machine learning, evolutionary computing, fuzzy logic, computer vision, natural language processing, recommendation systems, etc.
However, current research work is focused on weak artificial intelligence, and it is very hopeful that major breakthroughs will be made in the near future. Most of the artificial intelligence in the movie is depicting strong artificial intelligence, and this part is difficult to truly realize in the current real world.
Artificial intelligence is usually divided into weak artificial intelligence and strong artificial intelligence. The former allows machines to have the ability to observe and perceive, and to achieve a certain degree of understanding and reasoning. And strong artificial intelligence allows the machine to acquire adaptive capabilities and solve some problems that have not been encountered before.
Weak artificial intelligence has hopes of making breakthroughs, how did it achieve it, and where does "intelligence" come from? This is mainly due to a method of achieving artificial intelligence-machine learning.
The basic method of machine learning is to use algorithms to parse data, learn from it, and then make decisions and predictions about events in the real world. Unlike traditional software programs that are hard-coded to solve specific tasks, machine learning uses large amounts of data to "train" and learn how to complete tasks from the data through various algorithms.
For example, when we browse online, product recommendations often appear. This is based on your past shopping records and a long list of favorites to identify which of these are the products you are really interested in and are willing to buy. Such a decision-making model can help the online shops provide customers with suggestions and encourage product consumption.
The application of traditional machine learning algorithms in areas such as fingerprint recognition, Haar-based face detection, and HoG feature-based object detection has basically reached the requirements for commercialization. But every step forward is extremely difficult, until the emergence of deep learning algorithms.
Deep learning is not originally an independent learning method. It also uses supervised and unsupervised learning methods to train deep neural networks. However, due to the rapid development of this field in recent years, some unique learning methods have been proposed one after another, so more and more people regard it as a learning method alone.
The initial deep learning is a learning process that uses deep neural networks to solve feature expression. Deep neural network itself is not a new concept, but can be roughly understood as a neural network structure containing multiple hidden layers. In order to improve the training effect of deep neural networks, people make corresponding adjustments to the connection method and activation function of neurons. In fact, there were many ideas in the early years, but due to the insufficient amount of training data and backward calculation ability, the final effect was not satisfactory.
Machine learning is a method to realize artificial intelligence(AI), and deep learning is a technology to realize machine learning.
At present, the industry has a common sense of error that "deep learning may eventually eliminate all other machine learning algorithms." This awareness is mainly due to the fact that the application of deep learning in the fields of computer vision and natural language processing far exceeds traditional machine learning methods, and the media has exaggerated reports on deep learning.
Deep learning, as the hottest machine learning method at present, does not mean that it is the end of machine learning. At least the following problems of deep learningcurrently exist:
The views expressed herein are for reference only and don't necessarily represent the official views of Alibaba Cloud.
Machine Learning (ML) in simple terms can be defined as the science of getting computers to act and learn without explicit programming to perform those actions. It has become quite popular in recent years, however, the term itself was coined in 1959 by Arthur Samuel who defined Machine Learning as ‘the field of study that gives computers the ability to learn without being explicitly taught’.
A more recent and formal definition of Machine Learning was created by Tom Mitchell and describes it as a well-defined learning problem – ‘A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.’
Machine Learning usage has become so common that you probably use it many times a day without even knowing it. For example, when you search on Google or Bing or any other search engine, in the background there is a learning algorithm that has learned how to rank pages based on user queries. Similarly, when you see the photodetection feature on different social media applications or see spam filter filtering out bogus/unwanted emails in your mailbox, behind the scene is a Machine learning algorithm that learns and detect faces or spams emails respectively. A more recent technology use case is the advent of self-driving cars.
In the architecture of Machine Learning Platform for AI (PAI), the underlying layer is the computing framework and data resources of PAI. It supports multiple data resources such as MaxCompute, Object Storage Service (OSS), Hadoop Distributed File System (HDFS), and NAS. The initial prototype of PAI was PAI-Studio, a visual modeling experiment platform built around data resources and a variety of computing frameworks. Studio includes more than 200 algorithms that cover the entire environment process, including data preprocessing, feature engineering, model training, and evaluation and inference. Users may easily set up experiments by dragging and dropping components in PAI-Studio. In addition, PAI has a built-in Kunpeng computing framework that supports ultra-large matrix training with tens of billions of features and samples. Initially, PAI-Studio was designed as an algorithm platform for intermediate algorithm engineers. It is easy to use and has a low technological threshold. With the visual modeling capability of Studio, PAI supports businesses with capabilities such as recommendation systems, financial risk control, disease prediction, and news classification.
With the emergence of data computing and machine intelligence algorithms in recent years, applications based on big data and AI algorithms have become increasingly popular, and big data applications have also emerged in various industries. Testing technologies, as a part of engineering technologies, are also evolving with the times. In the Data Technology (DT) era, how to test and guarantee the quality of a big data application has become a puzzle for the testing field.
Machine Learning Platform for AI provides end-to-end machine learning services, including data processing, feature engineering, model training, model prediction, and model evaluation. Machine Learning Platform for AI combines all of these services to make AI more accessible than ever.
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