Category: AI

Artificial Intelligence  has increased the chances of hearing it throughout our lives. I understand it somehow, but I think there are many people who are vague and difficult to explain the concept. In the coming era, a basic background in AI will be indispensable. Therefore, in this article, we will explain the outline and history of AI from the basics. If you want to know about AI, please refer to it.

Basic knowledge of AI

First, let’s take a look at the definition of AI and its types.

What is AI?

AI is an abbreviation for “Artificial Intelligence”, which is an artificial reproduction of a part of human intelligence using software. However, the definition of AI has not been clearly defined at this time, and it is widely understood by academic experts. In any case, it’s a good idea to remember that it’s a computer system that mimics human intelligence.

What is machine learning?

Machine learning is one of the methods of analyzing data, and it is a technique in which a computer automatically learns from the data to find out the rules and patterns behind the data. Algorithms implemented by manual programming can be automatically constructed from a large amount of data, so they are applied in various fields.

What is deep learning?

Deep learning is one of the machine learning methods that utilize neural networks. A neural network is a mathematical model based on the neural circuits (neurons) of the human brain, and is characterized by its ability to perform more complex calculations and learning.

What is big data?

Big data means a huge group of data that is difficult to record, store, and analyze with conventional database management systems. With the widespread use of big data, it has become possible to handle data that could not be collected in the past.

What is a quantum computer?

A quantum computer is a computer that can decipher complicated calculations that cannot be solved by conventional computers by applying the phenomenon of quantum mechanics to information processing technology. Quantum computers are being researched and developed as next-generation high-speed computers.

History of AI

AI has become a word that everyone has heard once, but by the time we got here, it took many years to develop. Let’s take a look at the history and roots of AI.

The birth of AI and the first boom

The concept of AI has its roots in the book “Calculating Machines and Humans” published in 1950 by the English mathematician Alan Turing. In the same book, he asked the question, “Can machines think?” And for the first time at the Dartmouth Conference held in 1956, machines that think like humans were named “artificial intelligence.” This conference will make AI known to scientists.

The first AI boom was in the 1960s. What was researched in the first AI boom was the appearance of computers solving specific problems one after another, such as puzzles and games with clear rules, by inferring and searching using computers. .. However, when I realized that the rules were unclear and I couldn’t solve complicated problems, it gradually went down.

The second boom arrived in the 1980s

In the second AI boom that came in the 1980s, “expert systems” emerged. An expert system is a program that has knowledge in a specific specialized field and can infer and judge events like an expert. Expert systems seemed like a great approach, but the boom didn’t last long because AI at the time couldn’t handle every case accurately.

Third boom and future

In the third boom, which has continued from the 2000s to the present, the practical level of machine learning has greatly improved by utilizing big data. The existence of the research team at the University of Toronto, Canada, was the catalyst for the boom around this time. At the 2012 image recognition software competition, we used neural networks to win the championship by a large margin in second place. In the same year, a group of Google researchers published a paper on image discrimination of cats using neural networks, which is said to have triggered the third boom.

AI utilization case study

Currently, AI is widely used in various fields. Here, we will introduce familiar cases where AI is used.

Netflix recommendation feature

Netflix, a video distribution subscription service, has built its own recommendation system to increase the engagement rate of viewers, and displays thumbnails of works according to the tastes of viewers. Even for the same movie / content, what kind of image it reacts to depends on the viewer. In Netflix, the theme and actors to be emphasized are changed according to the user’s viewing history, and the most effective thumbnail is displayed, which leads to the improvement of the audience rating of the content.

Google search engine

AI technology is also used in the field of search. For example, Google, which is famous for its search engine, uses “Sematic Search”, a mechanism that analyzes the meaning of search terms and displays the optimum search results. In addition, various technologies are used, such as “entity search” that displays accurate search results even from ambiguous information, and “voice search” that supports voice search.

Image diagnosis of NTT DATA

At NTT DATA, the development of diagnostic imaging AI that streamlines the diagnosis of doctors is underway. By analyzing the medical image of the patient with AI technology and showing the potential part of the disease on the screen of the PACS system used for diagnosis, we support accurate diagnosis.

AI learning method

There are several ways to learn AI. This time, we will list three learning methods that have the highest study effect among AI learning methods.

Buy a reference book

Currently, there are many AI-related reference books out there. You can learn by yourself by using such books. By incorporating knowledge using reference books, you can feel the merits such as “information is covered”, “expressions are easy to understand even for beginners”, and “you can remember important parts while writing”. ..

Go to school

In order to acquire AI, a wide range of specialized knowledge such as machine learning, deep learning knowledge, mathematics and statistics is required. Therefore, for those who have difficulty studying on their own or who do not know what to start with, learning from a professional at school is one way to do it.
There are two types of schools, school type and online type. If you want to study more efficiently, you can use a school-based programming school, and if you want to study at home in your spare time, you can use an online school.

Introduce tools

There is also a way to introduce AI tools and learn systematically while actually operating the tools. By learning while experiencing, you can master AI in the shortest time, which also helps to improve work efficiency.

TRYETING’s “UMWELT” for business use

If you want to utilize AI for your own business, we recommend using “UMWELT” developed by TRYETING. UMWELT is a “no-code AI cloud tool” that allows you to easily analyze data and automate operations without programming. Since it is equipped with abundant algorithms, you can build your own original AI system in 3 steps: data collection, algorithm selection, and system integration. Another strength of UMWELT is that it is offered at the lowest price in the industry, so it can be introduced while keeping costs down.

summary

In this article, we introduced the outline and basic knowledge of AI, and familiar cases where AI is currently used. AI itself is still in the process of growth. Going forward, “evolution of machine learning and deep learning technology” and “further improvement of computer computing performance” will continue to solve social issues facing Japan and play a role in supporting sustainable economic growth. Why don’t you deepen your knowledge about AI in this article and use AI for your business?

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With the arrival of the full-scale AI era, it is an urgent task to develop AI human resources. I think there are many people in charge of companies who want to incorporate it into employee training as soon as possible. In this article, we will explain the content of AI training required to keep up with the accelerating wave of AI introduction.

The introduction of AI training is widespread

With the introduction of AI training spreading, let’s first look at the outline of AI and the specific content of AI training.

What is AI (artificial intelligence) in the first place?

AI (artificial intelligence) is an abbreviation for Artificial Intelligence, which is an artificial reproduction of a part of human intellectual action using software. Artificial intelligence that exists as of 2022 is either human-like or smarter artificial intelligence (specialized AI) for specific tasks, or a tool that reproduces a part of human intelligence (weak AI).
It is said that there is no conscious artificial intelligence (strong AI) that is as smart as humans or smarter than humans (general-purpose AI) or that human intelligence itself is precisely reproduced in every task. .. However, AI technology is expected to continue to evolve at an accelerating pace.

What is AI training?

AI training is literally training to learn about AI. There are various purposes and training contents for AI, from those who want to learn from the basics to those who want to deepen their knowledge. The specific content of the training will be described later.

What you can learn from AI training: Utilization

Now, I will explain the curriculum that you can actually learn in AI training.

Basic knowledge of AI

Learn about the history of AI from the first AI boom to the third AI boom, the current state of AI, and future forecasts of how AI will evolve in the future.
In addition, basic terms related to AI include machine learning, neural networks, big data, deep learning, and data mining. There are many terms that I have heard once, and even if I have a vague understanding of their contents, it is difficult to elaborate on them. So, first of all, you will learn the basic terms related to AI and how to use it.

How to use AI tools

Next, you will learn how to use AI tools that are suitable for your company and the steps and points that you should take when introducing AI to the actual site.
AI development tools have been released by many IT vendors, but in order to select the one that suits your company’s requirements, it is necessary to consider not only the cost but also the operation method and environment construction.

Business improvement utilizing AI

Think about the business content that will lead to improvement by utilizing AI. For example, in the manufacturing industry, the use of image recognition technology enables faster and more accurate object recognition, leading to improved manufacturing efficiency. If you are a customer center, you can use AI chatbots to respond quickly to frequently asked questions, which will improve user satisfaction and reduce human costs. We will learn these cases in a classroom lecture and think about how to utilize our own business.

What you can learn from AI training: Skills

From here, let’s take a closer look at the skills that can be learned in AI training.

Programming language

Learn the programming languages ​​needed for AI development. There are many different types of programming languages, but Python, which features simple code, is popular because it is in high demand and relatively easy to learn.

Data analysis

Once you have acquired data analysis skills, you will be able to utilize internal data. As a result, we can expect to contribute to improving operational efficiency and increasing sales.

Machine learning

Machine learning is a method of letting artificial intelligence learn knowledge by itself based on past experience and statistical data. You can discover rules and patterns in the data that humans cannot think of. In the training, you will learn the principles of machine learning and the differences in learning methods (supervised learning, unsupervised learning, enhanced learning, semi-supervised learning, deep enhanced learning).

Key points for successful AI training

Now that we’ve looked at the content of AI training, let’s consider three points for successful AI training.

Clarify the purpose

It is not limited to AI training, but the first is to clarify the purpose of the training. If the purpose remains ambiguous, the training will not be fruitful and cannot be put to practical use. It is necessary to clarify the future utilization method of AI in the company’s business and share the vision with the trainees.

Clarify the target person

Decide who will take the AI ​​training. If you are a complete amateur with regard to AI, it will take time and cost to develop as an AI human resource. We recommend that you select the target person in advance, considering the level of understanding and proficiency of each employee.

Emphasis on skill acquisition

Just understanding the basic knowledge and outline of AI will not lead to practical use. In order to effectively utilize AI in business, it is desirable to have training that emphasizes the acquisition of data analysis skills and programming skills that are useful in practice.

Issues after AI training

In order for AI training to be successful, it is necessary to keep the above points in mind, but let’s also look at the issues after the training.

Unable to formulate a training plan for AI human resources

As a preliminary step to AI training, many companies have not been able to systematize AI human resources development plans. It is necessary to understand in advance “how much AI human resources are needed in the company” and “whether education by training is enough”. On top of that, let’s create an effective AI human resources development system.

Incompatible with existing businesses

Many companies will introduce AI while continuing their existing businesses. If there is not enough human resources to have a dedicated AI introduction staff, there will be cases where you will be doing normal work while also performing AI introduction work. The person in charge may run out of time and spirit due to the work load.

I can’t improve my skills in practice

After AI training, it is necessary to utilize what we have learned to improve our skills in practice, but the problem is that we are not blessed with the opportunity. Especially for companies that have just started to introduce AI, it will be even more difficult to improve their skills in actual projects.

With UMWELT, you can develop AI without skills!

So far, I have explained about AI training. I think there are many companies that want to introduce AI as soon as possible, but are thinking that “there are no human resources with knowledge of AI” and “there is no time to train AI human resources through training”. Therefore, I would like to recommend TRYETING’s no-code AI tool “UMWELT”. With UMWELT, you can realize advanced AI development without programming, and you can build an AI system in-house without the need for specialized personnel. Currently, it is used by companies in a wide range of industries, from major companies to start-ups.

Summary

While you can learn a lot from AI training, it is also a fact that there are issues such as difficulty in planning AI human resource development. With UMWELT, our dedicated consultants run in parallel not only at the time of introduction but also after operation, so there is an advantage that AI human resources and DX human resources can be trained in your company. If you are interested in introducing UMWELT, please feel free to contact us.

Follow us on Facebook for updates and exclusive content! Click here: Each Techy

With the arrival of the full-scale AI era, it is an urgent task to develop AI human resources. I think there are many people in charge of companies who want to incorporate it into employee training as soon as possible. In this article, we will explain the content of AI training required to keep up with the accelerating wave of AI introduction.

The introduction of AI training is widespread

With the introduction of AI training spreading, let’s first look at the outline of AI and the specific content of AI training.

What is AI (artificial intelligence) in the first place?

AI (artificial intelligence) is an abbreviation for Artificial Intelligence, which is an artificial reproduction of a part of human intellectual action using software. Artificial intelligence that exists as of 2022 is either human-like or smarter artificial intelligence (specialized AI) for specific tasks, or a tool that reproduces a part of human intelligence (weak AI).
It is said that there is no conscious artificial intelligence (strong AI) that is as smart as humans or smarter than humans (general-purpose AI) or that human intelligence itself is precisely reproduced in every task. .. However, AI technology is expected to continue to evolve at an accelerating pace.

What is AI training?

AI training is literally training to learn about AI. There are various purposes and training contents for AI, from those who want to learn from the basics to those who want to deepen their knowledge. The specific content of the training will be described later.

What you can learn from AI training: Utilization

Now, I will explain the curriculum that you can actually learn in AI training.

Basic knowledge of AI

Learn about the history of AI from the first AI boom to the third AI boom, the current state of AI, and future forecasts of how AI will evolve in the future.
In addition, basic terms related to AI include machine learning, neural networks, big data, deep learning, and data mining. There are many terms that I have heard once, and even if I have a vague understanding of their contents, it is difficult to elaborate on them. So, first of all, you will learn the basic terms related to AI and how to use it.

How to use AI tools

Next, you will learn how to use AI tools that are suitable for your company and the steps and points that you should take when introducing AI to the actual site.
AI development tools have been released by many IT vendors, but in order to select the one that suits your company’s requirements, it is necessary to consider not only the cost but also the operation method and environment construction.

Business improvement utilizing AI

Think about the business content that will lead to improvement by utilizing AI. For example, in the manufacturing industry, the use of image recognition technology enables faster and more accurate object recognition, leading to improved manufacturing efficiency. If you are a customer center, you can use AI chatbots to respond quickly to frequently asked questions, which will improve user satisfaction and reduce human costs. We will learn these cases in a classroom lecture and think about how to utilize our own business.

What you can learn from AI training: Skills

From here, let’s take a closer look at the skills that can be learned in AI training.

Programming language

Learn the programming languages ​​needed for AI development. There are many different types of programming languages, but Python, which features simple code, is popular because it is in high demand and relatively easy to learn.

Data analysis

Once you have acquired data analysis skills, you will be able to utilize internal data. As a result, we can expect to contribute to improving operational efficiency and increasing sales.

Machine learning

Machine learning is a method of letting artificial intelligence learn knowledge by itself based on past experience and statistical data. You can discover rules and patterns in the data that humans cannot think of. In the training, you will learn the principles of machine learning and the differences in learning methods (supervised learning, unsupervised learning, enhanced learning, semi-supervised learning, deep enhanced learning).

Key points for successful AI training

Now that we’ve looked at the content of AI training, let’s consider three points for successful AI training.

Clarify the purpose

It is not limited to AI training, but the first is to clarify the purpose of the training. If the purpose remains ambiguous, the training will not be fruitful and cannot be put to practical use. It is necessary to clarify the future utilization method of AI in the company’s business and share the vision with the trainees.

Clarify the target person

Decide who will take the AI ​​training. If you are a complete amateur with regard to AI, it will take time and cost to develop as an AI human resource. We recommend that you select the target person in advance, considering the level of understanding and proficiency of each employee.

Emphasis on skill acquisition

Just understanding the basic knowledge and outline of AI will not lead to practical use. In order to effectively utilize AI in business, it is desirable to have training that emphasizes the acquisition of data analysis skills and programming skills that are useful in practice.

Issues after AI training

In order for AI training to be successful, it is necessary to keep the above points in mind, but let’s also look at the issues after the training.

Unable to formulate a training plan for AI human resources

As a preliminary step to AI training, many companies have not been able to systematize AI human resources development plans. It is necessary to understand in advance “how much AI human resources are needed in the company” and “whether education by training is enough”. On top of that, let’s create an effective AI human resources development system.

Incompatible with existing businesses

Many companies will introduce AI while continuing their existing businesses. If there is not enough human resources to have a dedicated AI introduction staff, there will be cases where you will be doing normal work while also performing AI introduction work. The person in charge may run out of time and spirit due to the work load.

I can’t improve my skills in practice

After AI training, it is necessary to utilize what we have learned to improve our skills in practice, but the problem is that we are not blessed with the opportunity. Especially for companies that have just started to introduce AI, it will be even more difficult to improve their skills in actual projects.

With UMWELT, you can develop AI without skills!

So far, I have explained about AI training. I think there are many companies that want to introduce AI as soon as possible, but are thinking that “there are no human resources with knowledge of AI” and “there is no time to train AI human resources through training”. Therefore, I would like to recommend TRYETING’s no-code AI tool “UMWELT”. With UMWELT, you can realize advanced AI development without programming, and you can build an AI system in-house without the need for specialized personnel. Currently, it is used by companies in a wide range of industries, from major companies to start-ups.

Summary

While you can learn a lot from AI training, it is also a fact that there are issues such as difficulty in planning AI human resource development. With UMWELT, our dedicated consultants run in parallel not only at the time of introduction but also after operation, so there is an advantage that AI human resources and DX human resources can be trained in your company. If you are interested in introducing UMWELT, please feel free to contact us.

Follow us on Facebook for updates and exclusive content! Click here: Each Techy

AI pattern recognition is an important function for proper data processing. What is the flow of processing and recognition by pattern recognition? In this article, we will explain AI pattern recognition in detail, including the mechanism of the algorithm. Please help us to improve our knowledge to utilize AI.

What is AI pattern recognition?

Pattern recognition is based on human behavior and is a feature of many of today’s widespread AIs. It has a lot to do with machine learning and algorithms, but let’s take a look at the details.

What is AI in the first place?

AI (artificial intelligence) is an artificial reproduction of the intellectual behavior of human beings using software. Computers acquire the ability to recognize patterns contained in large amounts of data, learn new patterns, and perform all tasks.

Relationship between AI and machine learning

Machine learning is one of the ways to analyze data to realize AI. The machine automatically learns the rules and patterns behind the data. In recent years, machine learning has placed the highest priority on prediction accuracy, and deep learning has become a typical analytical method. The characteristics of machine learning that learns and recognizes are the same as pattern recognition. In other words, pattern recognition is realized by utilizing machine learning.

What is pattern recognition?

Pattern recognition is the process of identifying and retrieving certain features and rules from data. The features and rules here are not logical information but are found from images and sounds, so they are generally supervised learning.

Even if we humans are not familiar with the word pattern recognition, we usually perform the same intellectual behavior. For example, when searching for a friend from many people at a meeting place, it is based on the characteristics (height, face, voice, etc.) of the friend, and this behavior corresponds to pattern recognition.

Process flow by AI pattern recognition

What is the flow of AI learning pattern recognition? From here, let’s deepen our understanding of the processing flow in pattern recognition.

Pre-process

First, in order to make it easier to extract features, we mainly perform processing such as digitizing the data signal and removing noise. If there is a problem with the data, it will not be possible to extract the appropriate data, so it cannot be used as it is. Pretreatment enables efficient feature extraction, which is the next process. In addition, equalize the variance of each scale.

Extract features

After the pre-processing is complete, the essential characteristics of the data are extracted in order to make a judgment based on the data. Depending on what you recognize, you need to clarify the features to be extracted. Features are expressed numerically, and the features that are picked up and arranged are called a feature vector.

Identify / classify

It identifies which class the feature vector existing in the feature space corresponds to, and classifies it by class. Machine learning is used because this identification / classification work has very high-dimensional features and is extremely difficult for humans to perform manually.

AI pattern recognition algorithm

There are many algorithms that show the calculation procedure for AI to perform pattern recognition. It is necessary to properly use the algorithm depending on the purpose of pattern recognition. From here, we will explain each algorithm.

Neural network

A neural network is a mathematical model of neurons, which are the human cranial nerve system. By enlarging and complicating this neural network, high performance is demonstrated in various tasks.

Naive Bayes

Naive Bayes is a model for solving classification problems. It is based on “Bayes’ theorem” which is the theorem of probability theory. The amount of calculation is small, the processing is fast, and it can handle large-scale data.

Logistic regression

A model for solving classification problems. When an input is given, it outputs which class the input is classified into and how likely it is to be classified. For example, in the two-class classification, the probability that a certain event will occur is predicted, and if the probability is greater than 50%, it is classified into the class of “a certain event will occur”, and if not, “a certain event will not occur”. Classify into classes.

Random forest

It is an algorithm that predicts each class in multiple different classification trees and decides which class to classify by majority vote. It is easy to handle because there are few parameters that must be determined in advance.

k-nearest neighbor method

It is a classification method that is frequently used in pattern recognition and is based on the closest training example in the feature space. It is a method of supervised learning of problems for which the answer is already known.

Support Vector Machine (SVM)

An algorithm that can be used for both classification and regression, it is an algorithm that uses supervised learning to find a linear function (hyperplane) that separates two classes on a feature space. A support vector is a data point that is closest to the data dividing line. It has the advantage of being easy to separate correctly even with a small amount of data.

What you can do with AI pattern recognition

The number of patterns that AI can recognize is innumerable, just like the patterns that humans can identify, and the number of pattern recognition is also very large. Among them, the following three are said to be characteristic. Let’s take a closer look at the technologies that are possible with each recognition.

Recognize images

Image recognition is a technique in which a computer determines what is in the target image. With the development of deep learning, it is used in a wide range of fields. The first popular technique for image recognition was barcodes in the 1940s. After that, a method of comparing the similarity of the target images appeared, but it was a difficult situation to put into practical use because of the great disadvantages. In the 2000s, as the accuracy of machine learning increased, pattern recognition using a large amount of image data became mainstream. Face recognition is a typical technique in image recognition. The number of scenes that are applied to face recognition when entering offices and large-scale event venues is increasing rapidly.

Recognize characters

Character recognition is widely known by OCR (Optical Character Recognition). It is a technology that reads handwritten characters and images with a scanner or digital camera, extracts them, and then converts them into digital character codes so that they can be used on a computer. In Japan, full-scale use began when Toshiba first commercialized domestic OCR in 1968. Currently, there are many companies that are promoting operational efficiency by introducing OCR, regardless of the size of the company.

The flow of utilizing OCR is to first capture the image of the manuscript for which character recognition is desired, and then perform layout analysis to determine the character part from the image. After that, the line is cut out line by line, the characters are further decomposed for each character, the characters are cut out, the characters are recognized, and then the output is performed in the format. In these steps, normalization to make characters a certain size, feature extraction to process characters so that they can be recognized accurately, standard patterns to register all the characters you want to recognize, etc. are performed.

Recognize voice

A technology that uses a computer to convert voice data into text data. With the spread of voice recognition services and smart speakers, the usage of voice recognition technology continues to expand. Speech recognition needs to go through four steps. By acoustic analysis, we perform an acoustic model that converts the data into data that is easy for the computer to recognize and calculates the success rate with the learning pattern. Next, the language model creates more accurate sentences, and the pronunciation dictionary words the combinations of sounds. By utilizing deep learning, it became possible to implement the process from the acoustic model to the language model in one, which led to a significant improvement in functionality.

If you want to use AI for your business, UMWELT!

We recommend “UMWELT” provided by TRYETING to those in charge of companies who want to utilize AI technology such as pattern recognition in their business. By utilizing UMWELT, you will be able to break through the barriers to introducing AI that you have felt difficult until then. Here, we will introduce the features of UMWELT.

No need for expertise

“UMWELT” is easy to operate, and you can build AI just by dragging and dropping the required functions. No advanced expertise such as machine learning algorithms is required, and anyone can easily introduce and utilize AI.

You can also train AI human resources

In “UMWELT”, our consultant will accompany the project from introduction to operation. With the option plan, it is also possible to create BI, set up system linkage, and hold seminars. By working on the project together, you can develop AI / DX human resources in-house without outsourcing.

Summary

The AI ​​pattern recognition function is expected to continue to improve in the future. As introduced in the section on what you can do with pattern recognition, the reality is that more and more companies are thinking that it is indispensable for improving operational efficiency. Please feel free to contact us if you are in charge of introducing AI technology into your internal business and aiming for further development.

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AI pattern recognition is an important function for proper data processing. What is the flow of processing and recognition by pattern recognition? In this article, we will explain AI pattern recognition in detail, including the mechanism of the algorithm. Please help us to improve our knowledge to utilize AI.

What is AI pattern recognition?

Pattern recognition is based on human behavior and is a feature of many of today’s widespread AIs. It has a lot to do with machine learning and algorithms, but let’s take a look at the details.

What is AI in the first place?

AI (artificial intelligence) is an artificial reproduction of the intellectual behavior of human beings using software. Computers acquire the ability to recognize patterns contained in large amounts of data, learn new patterns, and perform all tasks.

Relationship between AI and machine learning

Machine learning is one of the ways to analyze data to realize AI. The machine automatically learns the rules and patterns behind the data. In recent years, machine learning has placed the highest priority on prediction accuracy, and deep learning has become a typical analytical method. The characteristics of machine learning that learns and recognizes are the same as pattern recognition. In other words, pattern recognition is realized by utilizing machine learning.

What is pattern recognition?

Pattern recognition is the process of identifying and retrieving certain features and rules from data. The features and rules here are not logical information but are found from images and sounds, so they are generally supervised learning.

Even if we humans are not familiar with the word pattern recognition, we usually perform the same intellectual behavior. For example, when searching for a friend from many people at a meeting place, it is based on the characteristics (height, face, voice, etc.) of the friend, and this behavior corresponds to pattern recognition.

Process flow by AI pattern recognition

What is the flow of AI learning pattern recognition? From here, let’s deepen our understanding of the processing flow in pattern recognition.

Pre-process

First, in order to make it easier to extract features, we mainly perform processing such as digitizing the data signal and removing noise. If there is a problem with the data, it will not be possible to extract the appropriate data, so it cannot be used as it is. Pretreatment enables efficient feature extraction, which is the next process. In addition, equalize the variance of each scale.

Extract features

After the pre-processing is complete, the essential characteristics of the data are extracted in order to make a judgment based on the data. Depending on what you recognize, you need to clarify the features to be extracted. Features are expressed numerically, and the features that are picked up and arranged are called a feature vector.

Identify / classify

It identifies which class the feature vector existing in the feature space corresponds to, and classifies it by class. Machine learning is used because this identification / classification work has very high-dimensional features and is extremely difficult for humans to perform manually.

AI pattern recognition algorithm

There are many algorithms that show the calculation procedure for AI to perform pattern recognition. It is necessary to properly use the algorithm depending on the purpose of pattern recognition. From here, we will explain each algorithm.

Neural network

A neural network is a mathematical model of neurons, which are the human cranial nerve system. By enlarging and complicating this neural network, high performance is demonstrated in various tasks.

Naive Bayes

Naive Bayes is a model for solving classification problems. It is based on “Bayes’ theorem” which is the theorem of probability theory. The amount of calculation is small, the processing is fast, and it can handle large-scale data.

Logistic regression

A model for solving classification problems. When an input is given, it outputs which class the input is classified into and how likely it is to be classified. For example, in the two-class classification, the probability that a certain event will occur is predicted, and if the probability is greater than 50%, it is classified into the class of “a certain event will occur”, and if not, “a certain event will not occur”. Classify into classes.

Random forest

It is an algorithm that predicts each class in multiple different classification trees and decides which class to classify by majority vote. It is easy to handle because there are few parameters that must be determined in advance.

k-nearest neighbor method

It is a classification method that is frequently used in pattern recognition and is based on the closest training example in the feature space. It is a method of supervised learning of problems for which the answer is already known.

Support Vector Machine (SVM)

An algorithm that can be used for both classification and regression, it is an algorithm that uses supervised learning to find a linear function (hyperplane) that separates two classes on a feature space. A support vector is a data point that is closest to the data dividing line. It has the advantage of being easy to separate correctly even with a small amount of data.

What you can do with AI pattern recognition

The number of patterns that AI can recognize is innumerable, just like the patterns that humans can identify, and the number of pattern recognition is also very large. Among them, the following three are said to be characteristic. Let’s take a closer look at the technologies that are possible with each recognition.

Recognize images

Image recognition is a technique in which a computer determines what is in the target image. With the development of deep learning, it is used in a wide range of fields. The first popular technique for image recognition was barcodes in the 1940s. After that, a method of comparing the similarity of the target images appeared, but it was a difficult situation to put into practical use because of the great disadvantages. In the 2000s, as the accuracy of machine learning increased, pattern recognition using a large amount of image data became mainstream. Face recognition is a typical technique in image recognition. The number of scenes that are applied to face recognition when entering offices and large-scale event venues is increasing rapidly.

Recognize characters

Character recognition is widely known by OCR (Optical Character Recognition). It is a technology that reads handwritten characters and images with a scanner or digital camera, extracts them, and then converts them into digital character codes so that they can be used on a computer. In Japan, full-scale use began when Toshiba first commercialized domestic OCR in 1968. Currently, there are many companies that are promoting operational efficiency by introducing OCR, regardless of the size of the company.

The flow of utilizing OCR is to first capture the image of the manuscript for which character recognition is desired, and then perform layout analysis to determine the character part from the image. After that, the line is cut out line by line, the characters are further decomposed for each character, the characters are cut out, the characters are recognized, and then the output is performed in the format. In these steps, normalization to make characters a certain size, feature extraction to process characters so that they can be recognized accurately, standard patterns to register all the characters you want to recognize, etc. are performed.

Recognize voice

A technology that uses a computer to convert voice data into text data. With the spread of voice recognition services and smart speakers, the usage of voice recognition technology continues to expand. Speech recognition needs to go through four steps. By acoustic analysis, we perform an acoustic model that converts the data into data that is easy for the computer to recognize and calculates the success rate with the learning pattern. Next, the language model creates more accurate sentences, and the pronunciation dictionary words the combinations of sounds. By utilizing deep learning, it became possible to implement the process from the acoustic model to the language model in one, which led to a significant improvement in functionality.

If you want to use AI for your business, UMWELT!

We recommend “UMWELT” provided by TRYETING to those in charge of companies who want to utilize AI technology such as pattern recognition in their business. By utilizing UMWELT, you will be able to break through the barriers to introducing AI that you have felt difficult until then. Here, we will introduce the features of UMWELT.

No need for expertise

“UMWELT” is easy to operate, and you can build AI just by dragging and dropping the required functions. No advanced expertise such as machine learning algorithms is required, and anyone can easily introduce and utilize AI.

You can also train AI human resources

In “UMWELT”, our consultant will accompany the project from introduction to operation. With the option plan, it is also possible to create BI, set up system linkage, and hold seminars. By working on the project together, you can develop AI / DX human resources in-house without outsourcing.

Summary

The AI ​​pattern recognition function is expected to continue to improve in the future. As introduced in the section on what you can do with pattern recognition, the reality is that more and more companies are thinking that it is indispensable for improving operational efficiency. Please feel free to contact us if you are in charge of introducing AI technology into your internal business and aiming for further development.

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Did you know that AI, which has been applied in various fields in recent years, is also being used in investment trusts?

With investment trusts, investment targets are determined by experts, so you may be concerned about costs and reliability.

Therefore, by using AI, it analyzes all kinds of information and provides asset management advice, making it possible to use it with confidence.

What is AI investment trust?

An investment trust is a product that combines money collected from investors into one large fund and is invested and managed in stocks, bonds, etc. by asset management experts.

With investment trusts, experts decide where to invest based on the investor’s investment amount, and the investment results are distributed.

Therefore, there is no need for investors to decide where to invest.

When AI is utilized in the aforementioned investment trusts, AI will take the place of investment experts.

By using AI-powered investment trusts, you can manage your assets while referring to advice from AI.

Advantages of using AI investment trust

There are two advantages of AI investment trusts:

1. Able to make calm decisions
2. no cost

I will explain each point.

Able to make calm decisions

The first advantage of AI investment trusts is that they allow you to make calm decisions.

Emotional trading can be avoided as AI provides operational advice.

Using machine learning and deep learning , you can quickly analyze huge amounts of data and make predictions.

AI advice reflects large-scale and detailed analysis results that were impossible with traditional human analysis.

Therefore, you can notice things that humans might miss, so you can invest in investment trusts with confidence.

no cost

The second advantage of AI investment trusts is that there are no costs.

Generally, investment trusts incur fees such as purchase fees and audit fees.

However, some AI investment trust services charge only management fees.

Depending on the product and investment method you choose, there are no labor costs, and costs can be kept lower than investment trusts.

How to choose an investment destination

Now let’s talk about general investment.

There are two ways to choose a recommended investment destination:

1. Focus on familiar companies
2. Focus on the company’s track record and business content

Focus on familiar companies

First, let’s take a look at the companies whose products and services we often use.

Then, think about what you like about the company and what it will look like in the future, and decide whether to invest in it.

Focus on the company’s track record and business content

Next, let’s examine the company’s underlying business content and performance.

After researching them, it is important to know what their strengths are and what their future business plans and direction will be.

In the process of researching a company’s performance, it’s a good idea to check items such as trends in sales and profits, and the company’s position in the industry.

Examples of investment trusts using AI

As an example of an investment trust that utilizes AI, we would like to introduce “Deep AI”, which is set up and operated by Asset Management One.

In addition, based on the model analysis results, a portfolio is constructed by combining text analysis such as news flow and fundamental analysis of individual companies at the discretion of the fund manager.

The future of AI investment trusts

Currently, many AI investment trusts and related products are on sale in United State.

However, it is said that the penetration rate of AI investment trusts is low in United State compared to other countries.

This is related to the doubts about reliability due to the fact that AI is not perfect, which was mentioned in the section on the disadvantages of AI investment trusts.

From now on, AI will be used in a variety of fields, and it is expected that trust in AI will increase.

Investment trusts that collect AI companies

Apart from AI investment trusts, there are other general investment trusts that only deal with AI companies.

Why not consider an investment trust that specializes in AI companies that have gained momentum in recent years?

The following are two investment trusts that collect recommended AI companies.

1. Global AI Fund
2. Nomura Global AI Related Stock Fund A Course

Global AI Fund

First, we will introduce the “Global AI Fund” set up and managed by Sumitomo Mitsui DS Asset Management.

As of November 2021, it is an AI investment trust that has grown four times in five years and has high expectations.

A distinctive feature is that the group of companies related to AI ( artificial intelligence ) is not limited to the technology sector (information technology and communication services).

Therefore, as AI permeates industries, we are expanding our investment scope.

We are also taking flexible measures as industries such as travel, dining out, and entertainment, which have been suspended due to the coronavirus pandemic, are moving toward resumption.

Nomura Global AI Related Stock Fund A Course

Next, we will introduce the “Nomura Global AI-related Stock Fund A Course” set up and managed by Nomura Asset Management.

The main investment target here is AI ( artificial intelligence ) technology-related stocks from around the world, including emerging countries.

When selecting stocks, we focus on research results in advanced AI technology from a global perspective.

Stocks are selected with a focus on profit growth, with a focus on stocks in AI-related fields that are expected to become more attractive as investments as AI technology becomes more practical.

In addition, as a general rule, we aim to reduce exchange rate fluctuation risk through currency hedging (including alternative hedges using currencies of developed countries, etc.).

summary

In this article, we introduced investment trusts that utilize AI and general investment trusts that collect only AI companies.

Similarly, AI continues to gain momentum in other industries as well.

It is predicted that AI will be used for even more things in the future.

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