What is deep learning? Introducing the differences from conventional machine learning and the points of introduction to AI business.

1. Mechanism of AI Development and Deep Learning

1-1. What is Deep Learning?

Deep Learning is one of the machine learning methods used in the process of AI development. While it has become more widespread in recent years, its basic mechanism has existed for some time. In order to understand the differences from traditional machine learning, we will explain it from the perspective of AI development.
Note that this is the position of machine learning in AI development.

Learn more about the meaning of data annotation
>>What is data annotation? Explanation from its meaning to its relationship with AI and machine learning.

Click here for an article on teacher data used in machine learning.
>>What is teacher data? Explanation from the relationship with AI, machine learning, and annotation to how to create it.

　　

1-2. What is Machine Learning?

　　

Machine learning is a field of AI. It is a technology that allows machines to learn and classify/identify new data by giving them data and patterns in advance. It is used in various fields such as data analysis, image analysis, and natural language processing. Before the emergence of machine learning, it was necessary to manually design rules and algorithms. This method is called a rule-based system. In rule-based systems, predictions and analyses can be performed with high accuracy for data that fits specific rules, but they cannot handle data that deviates from those rules flexibly.

1-3. Types of Machine Learning

There are three main types of machine learning:
1. Supervised Learning This type of learning involves providing the machine with labeled data and teaching it the correct answers. Classification and regression algorithms are used, such as linear regression and random forest. 2. Unsupervised Learning This type of learning does not involve providing the machine with clear answers or labeled data. Instead, the machine automatically learns patterns and similarities in the data. Clustering and association analysis are commonly used algorithms. 3. Reinforcement Learning This type of learning does not require pre-prepared data. The machine learns through trial and error, with humans providing a score or goal for the machine to achieve. The machine then autonomously makes decisions to maximize the score, using algorithms such as Q-learning and SARSA.

These learning methods are types of machine learning, but with the emergence of a new technique called neural networks, machine learning has greatly evolved.

1-4. Neurons and Neural Networks

The human brain is composed of nerve cells called neurons. It is believed that information processing in the brain occurs through the complex connections between many neurons. The idea behind AI development is to replicate human intelligence by mimicking the structure of the human brain. This model, which imitates the mechanism of neurons, is called a neural network.

1-5. What is happening in deep learning

When input is entered into AI, in the neural network, each unit interacts with each other and performs calculations to adjust their respective biases. In machine learning through deep learning, by making the intermediate layer a multi-layer structure, calculations and judgments are performed by a huge number of neurons compared to traditional simple layer neural networks.

This is an image of a neural network for deep learning. The circular shapes represent each unit of a neuron, and the connecting arrows represent the decision paths of AI. As the intermediate layers are stacked, the variations of paths from the input layer to the output layer increase. This increase in paths contributes to the ability to handle complex calculations. In this image, there are three intermediate layers, but in actual deep learning, there may be more layers used.

1-6. Key Points for Introducing Deep Learning

To effectively achieve AI learning with its characteristics, deep learning requires a larger amount of data than conventional methods. High-performance hardware capable of processing vast amounts of data is also necessary. In cases where there is not enough data, other methods may yield better results. It is important to comprehensively consider the amount of data that can be prepared, annotation tools, and cost performance when determining whether to adopt deep learning, rather than assuming that it is suitable for all AI development projects.

1-7. Background of Deep Learning Development

The theory of deep learning first emerged in the 1980s. The recent development can be attributed to the improvement of computer performance and the availability of data. Deep learning requires a vast amount of labeled data. However, even preparing the raw materials before labeling was not easy due to the difficulty of obtaining a large quantity of data. With the recent improvement in computer processing power and the digitization of all types of data, especially images and videos, it can be said that the long-standing theory has become a reality.

1-8. Difference between Deep Learning and Machine Learning

Deep learning is positioned as one of the various technologies of machine learning. It can be said to be a development type of traditional machine learning. The biggest feature of deep learning, which is not found in traditional machine learning, is that the machine can learn automatically without human intervention. Human intervention refers to giving "*features" to the machine. In machine learning, humans provide "features" and the machine derives the correct answer, but in deep learning, the machine itself finds "features" and continues to learn.

*Features refer to the elements that affect predictions, or in other words, the variables that lead to predictions. For example, in sales forecasts, elements such as "price," "color," "brand," "shelf position," and "weather" fall under this category. By selecting these elements appropriately, accurate predictions can be made. When humans intervene, it is necessary to find the appropriate features, and if this is done incorrectly, accurate predictions cannot be obtained. In deep learning, the machine can reach the optimal solution by finding features without human intervention.

1-9. Differentiating between Deep Learning and Machine Learning

The use of deep learning and machine learning varies depending on the type and amount of data, as well as the problem or objective to be solved.

First, let's talk about the type and amount of data. In deep learning, as mentioned above, in order for machines to find their own features, a large amount of data is generally required. On the other hand, with machine learning, even with relatively small amounts of data, it is possible to achieve high accuracy by using appropriate algorithms.

Next, the usage varies depending on the challenges and objectives to be solved. Deep learning excels in complex pattern recognition and prediction problems. Examples include image recognition, speech recognition, and natural language processing. On the other hand, machine learning is suitable for relatively simple problems such as regression analysis, clustering, and classification, and can generally achieve high accuracy even with small amounts of data.

In addition, deep learning generally requires a large amount of computing resources, so it requires high-speed computing devices such as GPUs. On the other hand, machine learning can be executed on a general CPU as it requires relatively lightweight computing.

As mentioned above, the use of deep learning and machine learning varies depending on the type and amount of data, as well as the challenges and objectives to be solved. By choosing the appropriate method, we can provide efficient and accurate solutions.

2. Service Examples Utilizing Deep Learning

 

Most of the current AI is developed using deep learning. By introducing the method of deep learning in these fields where AI has been introduced since the past, the potential and accuracy that AI aims for have improved dramatically.

2-1. Medical Support

Using endoscopic and X-ray images as materials, AI identifies features and suspicious candidate areas. This helps prevent overlooking tumors and lesions and improves diagnostic accuracy.

2-2. Development of Autonomous Driving Technology

Using images and video materials from dashcams, we accurately recognize all elements such as traffic signs, pedestrians, and oncoming vehicles with AI to prevent accidents and near misses.

2-3. Improving Natural Language Processing Accuracy

This will help improve the accuracy of voice recognition and intent extraction, represented by "Hey Siri" and "OK, Google", used on smartphones and AI speakers.

2-4. Ensuring Safety in Production and Construction Sites

We capture the movements of workers engaged in production and construction on site, around manufacturing equipment and heavy machinery. When a worker enters a dangerous area, AI automatically detects it, contributing to the safety of the worker.

3. Recommended Books for Learning about Deep Learning

 

We have talked about an overview of deep learning so far. To gain a deeper understanding of its mechanism, it is best to deepen your knowledge through books. Here, we will introduce books on AI and machine learning, including deep learning.

The most beginner-friendly machine learning project textbook taught by a popular instructor on how to introduce AI into your work

You can learn about business growth know-how using machine learning, explained in a way that can be understood even without knowledge of IT or mathematics. From the mechanisms of AI and deep learning to project management, you can learn what you need to know as a project manager.

What AI Can and Cannot Do - Four Abilities for Surviving in the Business World

This is a book written with the concept of "Let's learn what AI can and cannot do in order to utilize AI!" It covers everything from the reality of AI to what you should know about deep learning, and even introduces real-life examples in a very easy-to-read manner.

How to Implement AI in Business

This is a book that focuses on the know-how when companies actually launch projects implementing AI. It introduces actual examples of before and after for companies that have introduced AI, targeting a wide range of industries. You can strategically learn how to utilize AI and deep learning in business.

Deep Learning Textbook Deep Learning G Certification (Generalist) Official Text

This is the official text for the G Certification (Generalist Certification), which is a certification exam for basic knowledge and business utilization ability of deep learning. It covers everything from the basics to the latest developments and includes practice problems, all in one book. Even if you do not plan on taking the certification exam, it is the perfect resource for systematically learning about deep learning.

4. Human Science's Data Annotation Outsourcing Service

4-1. Rich track record of creating 48 million pieces of teacher data

At Human Science, we are involved in AI development projects in various industries such as natural language processing, medical support, automotive, IT, manufacturing, and construction. Through direct transactions with many companies including GAFAM, we have provided over 48 million high-quality training data. We handle various annotation projects regardless of industry, from small-scale projects to large-scale projects with 150 annotators. If your company is interested in introducing AI but unsure where to start, please consult with us.

4-2. Resource Management without Using Crowdsourcing

At Human Science, we do not use crowdsourcing and instead directly contract with workers to manage projects. We carefully assess each member's practical experience and evaluations from previous projects to form a team that can perform to the best of their abilities.

4-3. Utilizing the Latest Data Annotation Tools

One of the annotation tools introduced by Human Science, AnnoFab, allows customers to check progress and provide feedback on the cloud even during project execution. By not allowing work data to be saved on local machines, we also consider security.

4-4. Equipped with a security room within the company

At Human Science, we have a security room that meets the ISMS standards in our Shinjuku office. We can handle highly confidential projects on-site. We consider ensuring confidentiality to be extremely important for all projects. We continuously provide security education to our staff and pay close attention to handling information and data, even for remote projects.