AI artificial intelligence-definition, technical principles, trends, and application areas

AI artificial intelligence
AI artificial intelligence

The technology, resources, and infrastructure of artificial intelligence have reached maturity. Regardless of the size of the organization, as long as the strategic investment and development direction are planned, artificial intelligence

can bring huge commercial value to the organization. MGI even pointed out in a 2018 study that if 70% of organizations adopt part of AI technology and 50% of large organizations fully adopt AI technology, by 2030,

artificial intelligence (including machine learning) will be able to contribute to global GDP. Here comes the potential of another 13 trillion dollars.

However, even if artificial intelligence carries such a huge commercial potential, most organizations still cannot fully realize the potential of AI. The reasons are nothing more than four:

  • Lack of effective data infrastructure to obtain clean, sufficient, and commercially valuable data
  • The shortage of data scientists, AI engineers, and related data technology talents in the labor market
  • Organizations and team members do not understand AI, which leads to resistance to AI introduction
  • The lack of strategic deployment and change management in the project development process has caused AI-introduced projects to lose momentum.

To truly understand how to fully apply AI, we can start with definitions, trends, and applications, and gradually explore the introduction strategy to understand how companies deploy, develop, and organize AI applications on a large scale. .

What is artificial intelligence? ​

Many fields have different definitions and opinions on artificial intelligence, but the definition at the core level is the same. AI means to use programs to achieve things that humans need to use wisdom to accomplish.

Traditional programs use a series of instructions and specifications to make the entire program work. For example, a computer whose Inputs such as numbers and operators such as addition, subtraction, multiplication, and division

are all standardized, and this makes the program unable to handle it. The new variable being defined. So if we want this program to handle other problems, engineers must update the command to handle the new variables.

However, it is impossible for engineers and computers to put the variables of the entire world into one program. This is why new methods must be developed when solving real-world problems. Among all AI technologies,

machine learning is the most widely used, not only because of its lower technology and data costs, but also because the value of machine learning deployment is faster, which makes organizations more inclined to develop this

technology. In a cooperation with Taiwanese food industry, we at OOSGA started three machine learning programs in a short 24-week cycle, which included full-cycle forecasting and planning, ROI evaluation, and pricing and

Strategies such as special prices. At the same time, in this whole process, we continue to increase talent plans through the value generated by the first three Programs, and scale other levels of development and applications, so

that our customers’ companies can build the momentum for sustainable development of AI and become An AI-enabled company.

Similarly, deep learning, reinforcement learning and other technologies also have huge potential. A study even pointed out that more advanced AI technology has the potential to produce 3.5 to 5.8 trillion per year. The current

technological breakthroughs in object detection, natural language processing, and image recognition are heavily dependent on the development of deep learning and other more advanced AI models.

Machine Learning​

Machine learning, an artificial intelligence technology, is different from traditional programs. Machine learning uses inductive reasoning to solve problems by processing and learning huge data . Therefore, when new data appears, the machine learning model can update itself The understanding of this world and changing his perception of the original problem.

To put it simply, suppose there is a person who has no concept of beauty and ugliness, then you take him to a group of people, and point to one of them and say that it is beautiful, one is ugly, the other is beautiful, etc… When the

viewer sees more information, he will also start to have certain ideas about the concept of aesthetics. The key is that the amount of data must be large enough and the quality of the data must be good, so that the machine learning model can better judge the answer to the question.

In the following insights, we discuss the architecture of different algorithms in machine learning, unsupervised learning, supervised learning, and semi-supervised learning, as well as the practical applications of these algorithms in the industry. Click to read more.

Machine Learning-Algorithms & Commercial Applications

Deep Learning​

This branch of machine learning uses multi-level artificial neural networks to learn from data. The two most important categories are Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).

CNN is more suitable for spatial data types such as pictures and videos. It recognizes images through the characteristics of different classes, such as the characteristics of a nose, the characteristics of the eyes, the

characteristics of the mouth, the relationship between the three, and finally become one. Face. The development of CNN is essential for autonomous driving that needs to quickly recognize the surrounding environment. At the same

time, the technology of image recognition is also one of the core technologies of Industry 4.0. RNN is more suitable for sequential data such as speech and text. Unlike other neural networks, for RNN, all inputs are connected, and all processed information will be remembered during the training process. This feature makes it very suitable for processing natural language.

Although neural network technology was developed decades ago, the environment at that time was not only lack of data, but also the speed and cost of computing made deep learning unable to be successfully introduced into the

business environment. However, with the substantial increase in computing speed, the substantial reduction in computing costs, and the more mature algorithms, technologies such as deep learning have also begun to be frequently applied in business environments. 

Reinforcement Learning

Reinforcement learning is also one of the branches of machine learning, which is a method of training algorithm models through reward mechanisms and punishment mechanisms. In other words, when the algorithm does the

behavior we expect, we will express the algorithm to do more of this behavior through rewards, and vice versa. And the way we evaluate the effectiveness of the algorithm in performing each task is to measure its score (how much it is rewarded).

Reinforcement learning has a wide range of applications, from autopilot trajectory optimization, path planning, motion planning, or news recommendation models established through user behavior feedback, all the way to marketing and sales, and NLP. 

Ensemble Learning​

In order to reduce model bias, variables, and improve accuracy, ensemble learning applies different machine learning algorithms at various stages to train the model’s algorithm according to different types of data. Integrated learning is very useful when the data is very complex or there are multiple potential hypotheses, because it can build models based on different hypotheses to define a clearer direction.

Six major trends driving the future development of AI

From a technical point of view, artificial intelligence has ushered in an explosive growth in the past five years, not only because of the huge capital investment of leading technology companies, but also because of the open source

code and other community effects. Enjoy the joint research and development of experts from all over the world. From the perspective of the adoption rate and technology penetration rate of various industries, AI

technology has also successfully brought great influence in many industries such as telecommunications, finance, software platforms, and manufacturing. Deloitte research also predicts that AI will be in 2025. By 2010, the global market grew to 6.4 trillion U.S. dollars, nearly three times that of 2020. 

It is an inevitable fact that artificial intelligence is sweeping all industries around the world and changing our lives. However, under the umbrella of AI, there are six main trends that have been most prominent in recent

years. Among them, there are many technological breakthroughs in artificial intelligence itself. At the same time, it has begun to accelerate its development and its own adoption rate has increased; at the same time, because of

technological breakthroughs in other industries, a certain trend technology has begun to increase its momentum; finally, There are also trends that have been affected by the needs of consumers and users for certain developments, or changes in preferences.

1. The rapid growth of reinforcement learning

Since AlphaGo developed by DeepMind defeated South Korean chess player Lee Sedol in 2015, the proportion of reinforcement learning mentioned in artificial intelligence related research papers has grown from 4.7% at the time

to 20% after 2020. Now, reinforcement learning is gradually creating huge value in various industries. Google’s data center has reduced energy consumption by more than 50% through this technology.

2. AI-driven business decisions

Although the wisdom of AI is based on data, the so-called AI-driven and data-driven are actually very different. The former focuses on data, while the latter is the ability to process data. In 2020, artificial intelligence has participated in

more business decisions that were originally tasks for decision makers, including operations, marketing and sales , and even design. Artificial intelligence will gradually become the only link between data and business decisions.

3. Increased RPA penetration

Process automation, also known as RPA (Robotic Process Automation), is currently the most frequent application of artificial intelligence. In a study of 152 AI use cases (Use Cases), it was found that nearly half of the industry cases

are based on RPA . In recent years, due to the gradual maturity of technology, the penetration of RPA will be greatly improved in most industries, and many of our existing tasks will be completed at a near-zero error and high-efficiency rate.

4. AI will no longer rely on big data so much

In the past, training a deep learning model based on neural networks often required a very large amount of data. However, these data are not so easy to obtain in many fields such as medical care. This is why researchers often use

certain data enhancement techniques, such as flipping the same photo, to increase the amount of existing data. However, as the maturity of GAN technology gradually increases, research in many fields can directly simulate new data. , So that the environment with only a small amount of data can build many meaningful models.

5. Ethical AI and AI credibility

Based on our current many controversial developments in AI, such as simulating other people’s voices and videos, or AI-driven surveillance systems, etc., as well as our fear of the potential of AI, how humane development of

artificial intelligence technology is gradually Gain momentum in academic research. Among them, developments such as explainable artificial intelligence and transparent AI decision-making are enhancing the credibility of users and consumers in AI. At the same time, many policies and industrial regulations are gradually responding to this trend.

6. More relevant interactive modes

Cognitive Engagement, an interactive mode driven by AI, is often translated as cognitive input. It is driven by breakthroughs in NLP research and the maturity of neural networks, and now has very complete applications in

various fields. For example, a 24-hour customer service chatbot, a product and service recommendation system that provides a personalized experience through communication, or an intelligent assistant that combines expert systems and professionals to work together. AI will be used in many fields in the future. Interact with users.

How will artificial intelligence create value? (Application areas of AI)

These artificial intelligence technologies, whether it is machine learning, deep learning, integrated learning, or reinforcement learning, have huge potential in various industries, and as the urgency of transformation increases,

we also see that artificial intelligence is more extensive Is applied to various fields, such as Industry 4.0, smart city, new retail, and smart home, etc. One of the most important technologies behind these applications is artificial intelligence.

However, regardless of the application in the industry, we can divide AI into five major value levels, namely time series, image processing, audio processing, NLP, and image processing. AI applications in the industry are based on

the progress of these fields. . For example, the Google Assistant we often hear, which is also an AI-driven intelligent assistant, is an extremely mature application in audio processing and natural language processing.

Time series and forecast analysis

Time series data is a data pattern that marks values ​​according to the time before and after. Such as the historical sales data of the sales department, the temperature of each day, or the occupancy rate of a hotel per night, etc. The so-

called predictive analysis is the use of data mining and statistical models to analyze the aforementioned time series data (historical data) and extract certain specific patterns to predict the future.

There are several levels of value that AI can provide in this field. First, when the amount of data is very large, it is not only more economical to use AI to do predictive analysis, but even in most cases, the accuracy rate is also

Compared with the statistical model, it is higher. For example, the journey analysis of the webpage, suppose that the industry now wants to build a recommendation system based on the user’s journey data, and recommend other

products that the consumer may also want to purchase when the consumer is about to check out. In this case, it is impractical to build a statistical model through samples. Amazon Data Science also used machine learning to develop

a recommendation engine on the platform at a very early stage, and integrated it into every stage of the customer journey, and achieved a 29% revenue growth within one year after the introduction .

The second field suitable for time series is when the data is very complex, that is, when the independent variables of the data are very large, then the use of AI will be more able to solve the problem than the traditional statistical

model, such as many different types IoT data integration, ERP supply chain data, etc. The reason why this type of work is more suitable for AI is that the industry can build models and test faster, while traditional statistical models take longer to build insights.

Image Processing

Image processing is divided into two levels, one is image recognition, and the other is image generation. Since we have not yet seen a clear commercial application level in the field of image generation, we will focus on image

recognition to mention the application of AI. Image recognition was almost an impractical task in the past ten years ago when AI gradually matured. Imagine that an engineer inputs the logic of the world’s images into the process. It is almost impossible. Even identifying the ten numbers 0-9 was an engineering problem in the past. 

However, AI is different from logical operations. AI treats the entire picture as a binary matrix, which is the so-called unstructured data, and uses algorithms to process huge data (that is, pictures) to train the model. Now, with the maturity of AI technology, the growth of data (ImageNet library led by Professor Li Feifei), and further breakthroughs in computing power and computing methods, image recognition technology is everywhere in our lives.

Complete analysis of image recognition

Audio processing

Audio AI, another fast-developing artificial intelligence frontier technology, although the overall market size was only over 60 million U.S. dollars in 2018, its market will maintain a CAGR of 75.8% until 2025. Industries such as smart homes, security and supervision, and manufacturing create huge value.

In fact, this is not difficult to understand. The range that we can hear is often farther than the range that we can see. However, the processing of hearing by human beings is far from our visual maturity. There are many interesting AI

projects in audio processing on Kaggle, such as judging the type by the sound of birds, judging whether it is about to malfunction by the sound of machines, judging emotions by the voice of customers, and so on. In the industry example, Audio AI is also blooming in various fields, such as through

As more and more devices will be given voice recognition capabilities (it is expected to reach 600 million units in 2023), the development of audio AI in various industries uses the sounds of human organs to recognize physical conditions and machine equipment predictions. Sexual repair, abnormal detection of smart home devices, and even software that can produce real sound, etc.

Natural language processing

Natural Language Processing (NLP), abbreviated as NLP, is one of the most mainstream applications of artificial intelligence, whose purpose is to allow computers to understand and even generate the language used by humans.

In the early days, the execution method of NLP was through engineers programming a bunch of rules into natural language processing programs, such as using grammar, part of speech, and word types to build an entire decision

tree, that is, a series of “if… Then…” to help the program at the time understand the language. With the increase in computing speed and the advancement of machine learning algorithms, decades ago, we also began to bring many AI technologies into natural language processing.

The biggest recent breakthrough is the BERT model, which is further driven by the advancement of deep learning, which is the “two-way encoding representation of the translator”. BERT is different from the previous one-way

method, that is, from left to right or right to left to process words. On the contrary, BERT uses two-way word processing and training models to better understand the context of the text. Its score not only reached 86.7% in the accuracy of MultiNLI evaluation, but also reached 93.2 in SQuAD v1.1 essay question F1, which is a substantial increase compared to the previous generation of models.

Now that NLP has been adopted on a large scale in various fields, insurers can use the NLP model to automate the underwriting process, law firms can use NLP to process huge data that need to be reviewed, and consumer goods

companies can use NLP-driven communities Monitoring is used to better grasp consumer feedback, and Google Duo uses NLP to generate real voices to assist users in ordering restaurants. The effective application of natural language processing technology allows the industry not only to use it to automate many business processes and enrich the consumer experience, but also to think about how to use the essence of NLP technology to develop many innovative commercial applications.

Complete analysis of natural language processing (NLP)

Dynamic image processing

There are approximately 770 million monitors in every corner of the world monitoring the actions of citizens, and this number will grow to one billion in 2021. This means that huge amounts of moving image data are being

produced every second, and at the same time, movies are gradually replacing text and pictures, becoming our main medium for absorbing information. Finally, and more importantly, the so-called film is actually the closest of all data types to how humans perceive the world.

However, AI is at a relatively early stage in the field of image processing compared to images and text. This is mainly because the amount of data in the image is too large and there are too many variables, which makes it difficult for

the algorithm to train the model. At the same time, dynamic image processing has many branches, such as visual odometry, object detection, video tracking, and damage detection (to determine whether the video has been

modified) and so on. And since dynamic image processing is built on these technologies, growth in these fields is necessary for a good and comprehensive grasp of dynamic image processing technologies.

Although the dynamic image processing technology is relatively in its infancy, we can still see many successful applications. For example, in sports analysis, many companies have effectively used film analysis to predict the

results of the game, measure the value of team members, and optimize athletes. Evaluate. In CCTV, local governments use video analysis to better understand citizens’ every move and provide them with the most

immediate assistance, such as real-time crime mapping and real-time demand detection. Finally, in the retail industry, there are businesses who analyze videos to understand how to better optimize the display of goods on the shelves.