What is AI, machine learning, and deep learning?

AI refers to concepts and technologies that artificially imitate human intelligence. The concept was proposed in the 1950s to allow computers to provide solutions to specific problems. However, it was not possible to find a solution without clearly defined rules, that is, without an algorithm created by humans.

Machine learning is a technology that enables machines to experience and learn from humans. In other words, a machine analyzes a large amount of input information (=data), finds rules and judgment criteria from that data, and makes predictions (inferences) about unknown data. However, having a computer perform the “finding of data features” step in the learning process is unstable and prone to errors, and as a result, it was necessary to manually set the conditions for feature extraction.

Deep learning (deep learning) is a deep neural network (artificial neuron) in which many layers of structures that imitate human nerve cells are superimposed, in which a machine automatically extracts the feature value of data without human intervention. It is learning using a network). In other words, deep learning has made it possible for computers to learn the feature extraction process of machine learning on their own.

In recent years, IoT (Internet of Things), which collects large amounts of data, and GPUs (Graphic Processor Units), which process that data with high computing power, have spread, and machine learning algorithms have evolved further. has accelerated the evolution of deep learning, and is said to have surpassed the human eye in the field of image recognition in recent years.

How to utilize AI technology in business

An example of using machine learning is a recommendation system that combines purchase history and customer information, recommends products to customers who are likely to be interested, and encourages them to purchase. In addition, it can be used in the marketing field, such as a function that finds interests and preferences from data such as browsed articles and news and displays recommended articles.

As an example of utilizing deep learning, it has become possible to replace processes that used to rely on the five human senses, such as production, assembly, inspection, and equipment maintenance management at manufacturing sites, with robots equipped with deep learning. . A small improvement in one task/process on the manufacturing floor translates into a very large profit when converted to a yearly basis. Efforts are spreading around the world to shift the valuable human resources obtained from these improvements to processes with higher added value.

Face recognition technology has also become more sophisticated, making it possible to identify a person's characteristics even from a video of a person or a photograph of a person far away. It is expected to be used in all kinds of situations, such as finding wanted criminals even if they are wearing masks and sunglasses, estimating gender and age attributes, and using that data for marketing.

Start by setting goals for what you want to solve with AI

When using AI, it is important to set goals for what you want to achieve and what problems you want to solve with AI technology. It doesn't mean that "if you introduce AI, you can easily achieve this or that without the help of people". It is necessary to establish a path for how to solve specific business problems using AI technology.

The “AI model” is what specifically realizes “what you want AI to do”. There are several processes involved in creating an AI model, and specialized engineering skills are required.

Until the core AI model is created

Machine learning finds features from huge amounts of data and discovers rules for identifying them. This law is called a “learning model”. A learning model derives an output value from an input value, so to speak, it becomes the brain of AI. Generating a learning model involves performing calculations using a large amount of training data.

Also, this learning model is not completed once it is created. In order to improve accuracy, the data input to the learning model is also important. Prepare what kind of data and how many, adjust the parameters that require manual intervention, and repeat the creation of the learning model over and over. In addition, there are cases where only one learning model performs a task, but in reality, multiple learning models are combined to develop an "AI model" that solves the final problem.

Only when an AI model is created in this way will it be possible to achieve what you want the AI to do.

Can AI introduction and AI development be started immediately?

AI introduction and development can be roughly divided into three phases: planning, verification/main development, and operation. As I explained earlier, the important tasks when starting the introduction and development are goal setting and preparation for AI model development.

Target setting can be advanced if there is a place to extract internal issues and discuss how to use AI technology in business. On the other hand, even if you want to start AI model development immediately, you can't start without data for learning, and you can't do anything without an environment for AI development. From here, I will introduce how to prepare the necessary environment for AI development, and how to prepare in advance.

Environment required for AI development

The required environment is divided into hardware and software. High-speed processing is essential for AI development because it handles huge amounts of data, so a processor for high-speed arithmetic processing called a GPU (Graphic Processor Unit) is required as hardware. The price of the GPU varies greatly depending on its performance, so it is necessary to select a GPU that suits your environment.

On the other hand, as a software development environment, we have obtained the frameworks and libraries that are indispensable for AI model development, and recently, development using container technology, which makes it easy to create a complete set of environments, is becoming popular. It is necessary to learn and build the environment.

In order to avoid the situation where you want to proceed with AI development, but the development environment becomes a threshold and you can't start development for a long time, it is a wise choice to use a service that prepares the necessary environment according to each person. increase.

Inquiry

For companies that are considering digitizing their products and services and converting them to AI/IoT in order to develop new markets and promote the development of new products, we provide easy-to-understand explanations on everything from optimal product selection to introduction methods. Let me do it. Please feel free to contact us.

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