2023/08/09 | 4mins

We are encountering AI chatbots in our daily lives. When I wake up in the morning, I ask about today's weather, explore information I've been curious about, and plan a trip together. In this way, AI chatbots that permeate our daily lives are helping to make various activities more convenient and efficient. In this content, I would like to talk about AI chatbots, one of the technologies companies should pay attention to. We will look at what AI chatbots are, why they should be introduced, and examples of how AI chatbots are being used by industry.

WHAT IS AN AI CHATBOT?

An AI chatbot is an interactive software program that uses artificial intelligence and natural language processing (NLP) technology to understand users' questions or requests and provide appropriate interactions or answers . These chatbots work by using pretrained data and machine learning algorithms to understand user input and generate appropriate responses. Thanks to these characteristics, it can help users search for information related to a specific domain or ask questions and requests from users, and many companies are using it to more efficiently process tasks such as customer support, sales, and marketing activities.

TYPES OF AI CHATBOTS

1. Rules-based chatbot
   It is a chatbot that operates based on predefined rules and patterns . It is a method of constructing a set of questions and answers by learning a large amount of actual conversation data to handle mainly standardized tasks. These chatbots have the advantage of being simple to implement and useful in specific situations, but on the other hand, Relying on rules can make it difficult to respond to complex situations. Therefore, it is mainly used in areas where there are sequential tasks or where reliability is important.
   

2. machine learning chatbot
   Machine learning chatbots combine natural language processing and machine learning technologies to create natural conversations with users and provide information . It typically works text-based, learning from conversational data to identify patterns and handling conversations with users based on that.

   Machine learning chatbots capable of natural conversations can be used in a variety of fields. For example, it is used in an automatic response system of a customer service service or a virtual personal assistant or artificial intelligence speaker after learning based on various voice/video data.
   
   

3. Reinforcement Learning Chatbot
   Reinforcement learning chatbots are chatbots that learn and respond through interactions with users, and use techniques to select actions to maximize rewards . To do this, the chatbot selects a specific action in a given situation, i.e. what to say next, and receives positive or negative feedback from the user depending on the outcome. This is used to determine the probability that the chatbot chooses an action, and is designed to have a higher value when the conversation with the user is smooth and useful, allowing for a more appropriate response. Reinforcement learning chatbots are characterized by being able to provide natural conversations and useful responses as their performance is continuously improved through interactions and rewards.
   
   

4. Chatbots based on generative models
   A generative model-based chatbot is a chatbot that analyzes conversation data and creates a new conversation based on it . The working principle of this chatbot is divided into two main parts.

   • Conversation data analysis : Conversation data is analyzed through natural language processing technology, and sentence structure, keywords, and intentions are extracted. For this, one of the deep learning algorithms, such as RNN (Recurrent Neural Network) or LSTM (Long Short-Term Memory), is generally used.

   • Generative model training : The generative model is trained using the analyzed conversation data as training data. A generative model is a model used to generate a conversation, and a typical Seq2Seq (Sequence-to-Sequence) model is used. The Seq2Seq model consists of an encoder and a decoder. The encoder processes an input sentence and passes it to the decoder to generate an output sentence.

   Through this process, chatbots based on generative models have the ability to analyze large amounts of data and create new conversations freely. A use case is to implement a service that summarizes a long document given and presents the summarized document in a conversational format to help understand the information.
   
   

5. hybrid chatbot

   A hybrid chatbot is a chatbot that operates by converging various artificial intelligence technologies. It is mainly used in combination with rule-based and machine learning-based approaches, and its strength is that it can maximize the efficiency of functions while responding to various situations as it conducts conversations by utilizing the strengths of each. The rule-based approach used for this is to identify the intention of the conversation through predefined rules and recognize and respond to specific patterns, while the machine learning-based approach learns conversation data to create new conversations or predict responses. says
   Hybrid chatbots can be used in information search or product recommendation areas. They can identify user needs based on rules and provide optimal answers by understanding user intentions in detail through machine learning.

WHY BUSINESSES SHOULD ADOPT AI CHATBOTS

By introducing AI chatbots, companies can help the convenience of internal members and customers, and at the same time maximize efficiency and reduce business costs, thereby securing various benefits to strengthen corporate competitiveness.

• improve customer service
  AI chatbots have the advantage of being able to quickly respond to customer inquiries and requests 24/7, increasing customer satisfaction, and providing personalized service by improving user experience.

• cut down the money
  If you introduce an AI chatbot using artificial intelligence technology, you can reduce costs by eliminating labor costs and being able to process tasks in an automated process.

• Scalability and flexibility
  It is capable of handling large amounts of concurrent requests and can be easily scaled or updated as needed. Its advantage is that it can respond flexibly to the growth and change of the company.

• Consistent quality of service
  Service differences due to human error or emotional influence can be excluded, so a consistent level of service quality can be provided at all times.

• Analyzing customer data and deriving insights
  Through conversation data, insights on customer behavior, preferences, and needs can be derived and used for better product and service development or marketing strategies.

• Increased productivity
  It is also a great advantage to be able to automate repetitive and routine tasks and improve work efficiency. Businesses can create an environment where members can focus on more important tasks by automating tasks through chatbots.

• continuous learning and development
  AI chatbots continuously learn and evolve over time through data and conversations. Through this, services are gradually improved, and companies can benefit from sequentially improved performance.

AI CHATBOT USE CASES BY INDUSTRY

Then, how are companies applying AI chatbots to real services? Let's take a look at real-life examples that are being used in various industries.

1. Finance

From the left, personalization of DB Insurance chatbot service and chatbot service in KB Star Banking.

First, this is an example of the financial industry realizing digital transformation with AI chatbots. Now, as the number of people who visit the bank in person to perform tasks such as deposit and withdrawal, subscription to installment savings, and application for currency exchange has noticeably decreased, non-face-to-face processing has increased. Through the chatbot, financial consumers can use the service more conveniently.

In addition, securities, credit card, and insurance companies are investing heavily in advancing chatbots. Chatbots respond to various tasks such as insurance contract inquiry, card application and issuance, card recommendation by customer, fund recommendation, and stock item search, establishing themselves as a major communication channel with customers.

2. E-Commerce

From the left, chatbot Charlotte in the Lotte ON app and customer center talk in the SSG app.

The e-commerce industry is using AI chatbots for shopping advice or personalized services. According to the electronic newspaper (23.02), SSG.com is handling 25% of all customer service inquiries with chatbots, which improves customer service and increases work efficiency. Recently, in the e-commerce industry, 'personalization' is considered an essential factor for growth, and advancing interaction between chatbots and users based on natural language processing (NLP) engines is in the spotlight. There is also an active movement to implement customized product recommendations optimized for individuals based on data such as the user's previous purchase records, interests, and search history through chatbots.

3. Travel and Hotels

The travel industry is also improving users' search experience through conversational AI chatbots. It is based on ChatGPT so that users can answer complex or difficult questions. It plays an important role in various areas such as flight/accommodation reservations, travel information provision, customer service, and local guides. The interactive interface gives users access to relevant help at any time, and the advantage is that you can quickly get the information you need and plan your trip.

How to easily build your own chatbot, Private LLM

AI chatbots are also bringing revolutionary changes across a variety of industries. Businesses must unlock the full potential of AI chatbots in reimagining existing customer experiences and providing personalized services. To this end, it is most appropriate to create a 'Private LLM' that fits the company's circumstances and major products/services.

<Private LLM이 필요한 이유>

📍ChatGPT knows our company well

It is possible to create or fine-tune a specialized model suitable for the company's data and the corresponding domain.

📍 Cost Optimization / Efficiency

You can increase work productivity by building a model tailored to your business environment.

📍 Safe to use

It can be safely used without any security issues as the company's internal data is not exported to the outside.

Is there any way to easily create a private LLM, which is necessary to overcome the limitations of public LLM, but is difficult to build? In the 'Open LLM Leaderboard' evaluation run by Hugging Face, the world's largest machine learning platform, Upstage's LLM, which surpassed the performance of GPT-3.5, the base model of ChatGPT, and ranked No. 1 in the world, is the answer. Upstage's Private LLM is a specialized solution to prevent information leakage and hallucination by learning only the company's internal data. Through commercialization in the future, we plan to help many companies gain AI competitiveness without worrying about security. Be the first to meet Upstage's Private LLM, which will be released soon!

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