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Even though the relationship between AI and humans is interdependent and can be futile in helping I don't think we can determine the future with the present as collateral. It's just, you live today. I would like to judge the relationship between artificial intelligence and humans positively.

In other words, artificial intelligence improves human ability and productivity in interdependent relationships with humans Provide personalized experiences, promote creativity and collaboration, It is concluded that it can help share knowledge and education, Through this positive role, humans and artificial intelligence can work together to create a better world It is defined as follows.

1. Complementary relationships: Artificial intelligence can play a role in supplementing human limitations. Artificial intelligence can efficiently handle complex and repetitive tasks and analyze data to solve problems that humans may struggle with. For example, in the medical field, artificial intelligence can help with accurate diagnosis and support doctors' decision-making.

2. Productivity improvement: Artificial intelligence can realize automation of tasks and productivity improvement. By delegating tasks that generally require a lot of time and effort to artificial intelligence, humans can focus on more valuable tasks. For example, in the field of customer service, chatbots can be used to answer frequently asked questions or play a role in solving problems.

3. Providing personalized experiences: Artificial intelligence can provide personalized services by analyzing users' preferences, behavior patterns, feedback, etc. For example, the recommendation system analyzes the user's past purchase history or interests to provide customized recommendations, and the music streaming service can recommend music that suits the user's taste.

4. Creativity and cooperation: Artificial intelligence can promote human creativity and cooperation. Artificial intelligence can provide humans with new ideas or help them look at problems from a different perspective. The cooperation between humans and artificial intelligence can lead to new solutions and innovations.

5. Knowledge sharing and education: Artificial intelligence can help you share and educate knowledge and information. Online learning platforms or tutoring systems provide users with customized learning experiences, and artificial intelligence-based virtual assistants can provide and support the information they need.

1. Ideas for complementary relationships

It is important to discover new business ideas through creative thinking and market research, and to provide innovative solutions by utilizing the complementary relationship between artificial intelligence and humans.

1) Medical Aid Artificial Intelligence Systems: to develop a medical aid artificial intelligence system that works with human doctors to help with accurate diagnosis and treatment planning. The system supports decision-making by doctors and analyzes medical data to provide the latest research and guidance.

2) Automated customer service: to develop automated chat bots or voice recognition systems using artificial intelligence technology in customer service. This provides a 24-hour service to quickly respond to customer inquiries and resolve problems.

3) Smart Home System: Develop a smart home system using artificial intelligence technology. The system automates convenience functions and improves energy efficiency by learning user behavior patterns and preferences.

4) Educational AI tutoring: developing a personalized education platform using artificial intelligence. Establish an AI tutor system that provides customized guidance and improves the learning process according to the student's learning style and level.

5) Smart autonomous vehicles: developing smart cars that combine autonomous driving technology with artificial intelligence. The system ensures driver safety and optimizes traffic flow to create an efficient road environment.

6) Smart City Management System: Building a smart city management system using artificial intelligence and IoT technology. The system integrates infrastructure management, energy efficiency, traffic management, and environmental monitoring to improve the efficiency and sustainability of cities.

2. Ideas for Increase productivity

It maximizes work efficiency, reduces costs, and improves productivity. In order to develop a specific business model, it is necessary to investigate the requirements of the company and the bedlight of the customer, and to evaluate the technical implementability and marketability.

1) Process Automation Solutions: Develop solutions that improve productivity by automating various processes within the enterprise. Automate repetitive and time-consuming tasks such as task processing, data entry, and reporting to reduce staffing and increase efficiency.

2) Improving collaboration tools: developing tools for efficient collaboration. It provides a collaborative platform that integrates real-time chat, project management, document sharing and editing, and schedule management to facilitate communication and task management among team members.

3) Artificial intelligence-based automated manufacturing system: Build an automation system using artificial intelligence in the manufacturing sector. It integrates robots and artificial intelligence to automate and optimize production lines to increase productivity.

4) Data analytics and prediction solutions: develop solutions that support business decisions by collecting and analyzing large amounts of data. Maximize productivity and revenue by applying data analysis and prediction models to sales forecasting, inventory management, and marketing strategy development.

5) Remote Work and Collaboration Platform: Develop a platform that supports efficient work and collaboration in remote work environments. Improve productivity and collaboration by providing remote work tools, including non-face-to-face meetings, remote file sharing and editing, and task tracking.

3. Ideas related to providing personalized experience

Various steps such as user research, data collection and analysis, and artificial intelligence algorithm development are required to specify and implement an implementable business model. It is important to optimize the customer experience by providing user-centered personalized services and to develop services that are delivered to meet individual bedlights and needs.

1) Personalized music streaming: develop a music streaming platform to personalize music recommendations based on user preferences and preferences. It provides customized playlists by analyzing users' listening records, genre preferences, and moods using artificial intelligence algorithms.

2) Virtual reality shopping experience: Using virtual reality technology to provide a personalized shopping experience. Customers can simulate a product in a virtual reality environment and have a similar experience to actually wearing or using it.

3) Personal health care applications: develop applications that collect and analyze personal health data to provide customized health care and advice. Exercise planning, diet advice, and sleep management are provided according to individual health conditions and goals.

4) AI Personal Secretary: Developing a personal secretary based on artificial intelligence. This secretary personally supports the user's schedule management, work help, information provision, etc. It uses voice recognition technology and natural language processing to provide natural conversation and personalized services.

5) Personalized Learning Platform: Develop a platform that provides personalized learning experiences by identifying learners' learning styles, preferences, and learning levels. It utilizes artificial intelligence and data analysis to provide learners with optimized learning materials, problems, and feedback.

4. Ideas for creativity and cooperation

It is to promote individual and organizational creativity and improve cooperation by developing platforms or solutions that support creative idea discovery and collaboration. Specific business models should be developed in accordance with the Bedlights and requirements of the field.

1) Virtual space for collaboration: develop virtual or augmented reality space for collaboration. Geographically distant team members can collaborate and share ideas in real time. Use virtual reality to help discover creative ideas and solve problems.

2) Online creative platforms: developing online platforms for various creative fields. It provides a platform for creators such as graphic design, music composition, and video editing to gather to share ideas and cooperate to proceed with creation.

3) Creative Problem Solving Solutions: Develop solutions that help solve creative problems by utilizing artificial intelligence. It promotes creativity by developing algorithms that support idea generation and systems that recommend solutions to similar problems.

4) Education Platform for Creativity: Develop an online education platform to develop and enhance creativity. It provides various creative activities and tasks, and encourages cooperation and feedback among learners.

5) Culture and Arts Support Platform: Develop a platform that supports creativity and cooperation in culture and arts. It promotes interaction between artists and audiences and provides opportunities for collaboration on cultural events and exhibitions.

5. Ideas related to knowledge sharing and education

In order to specify and implement an implementable business model, various steps such as research and analysis according to user Bedlight and requirements, content creation and management, and user experience design are required. It is important to help learners improve their performance and develop the knowledge ecosystem by providing user-centered knowledge sharing and education.

1. Online education platform: to develop online education platforms for various fields of education. Experts provide online lectures and students can learn without being constrained by their time and place.

2. AI-based learning support system: Develop an AI-based system that provides customized learning support by collecting and analyzing students' learning data. It helps effective learning by providing customized content and individual guidance according to an individual's learning tendency, level, and performance.

3. Online Community Platform: Develop an online community platform to share and exchange knowledge of areas of interest or expertise. Users can share knowledge with each other and grow together through questions, answers, and discussions.

4. Digital libraries and content platforms: develop digital libraries and content platforms that collect and provide various forms of knowledge and content. It provides learners with a wide variety of knowledge, including e-books, papers, lectures, and videos.

5. In-company training and knowledge sharing platform: to develop a platform that promotes the training and knowledge sharing of employees within the enterprise. It strengthens the knowledge ecosystem within the company through internal educational content, discussion forums, and work experience sharing.

Here is 6 areas related to AI that we want to be actively involved in.

1. Automation and Robotics: AI enables automation in industries such as manufacturing, logistics, and transportation, enhancing productivity by integrating robotic technologies. For example, self-driving cars, drones, and robotic arms operate using AI technology.

2. Speech Recognition and Natural Language Processing: Speech recognition technology is used in applications like virtual assistants and voice-based interfaces to understand and execute voice commands. Natural language processing technology analyzes and understands text, enabling machine translation, text summarization, sentiment analysis, question-answering systems, and more.

3. Healthcare: AI is employed in medical diagnosis, drug development, patient monitoring, and more. For instance, AI is utilized in medical image analysis to detect tumors or predict a patient's health status.

4. Finance: AI is used in automating financial transactions, detecting fraudulent activities, credit scoring, investment recommendations, and more. Credit card companies, for example, employ AI technology to detect and prevent fraudulent behavior.

5. Education: AI is applied in personalized learning support, student assessment, and providing tailored educational content. It can analyze learners' styles and provide customized educational plans or support learners on online learning platforms.

6. Marketing and Advertising: AI is utilized in ad targeting, consumer behavior prediction, personalized marketing campaigns, and more. For instance, AI analyzes users' interests and actions to deliver relevant advertisements or target specific keywords in social media advertising.

1. K-Medical BERT system

1) Data Collection and Preprocessing: Collect and refine text data from Korean medical sources such as research papers, medical records, and medical terminologies. Preprocess the collected data by removing special characters, tokenizing, and normalizing the text.

2) Pretraining Data Generation: Generate a large-scale pretraining dataset for training the Medical BERT model. This process follows similar steps to training a general BERT model. Train a language model using a large text corpus from Korean medical literature, Korean medical textbooks, patient care charts that can be processed and disclosed for deletion of personal information such as public health data, real name and address, where the language model learns to predict the next word.

3) Designing the K-Medical BERT Architecture: Korean Medical BERT is based on the architecture of a general BERT model but may involve adjustments in the preprocessing steps or token embeddings to incorporate the unique characteristics of the medical domain. Domain-specific tokens related to medical terms, diseases, treatment methods, etc., may be added.

4) Training the K-Medical BERT Model: Train the Medical BERT model using the generated pretraining dataset. Train the final Medical BERT model using the pretraining data as input, where the language model predicts the next word. Large-scale training may require GPU or distributed computing systems.

5) Fine-tuning: Fine-tune the K-Medical BERT model to adapt it to specific medical tasks. For example, fine-tune the model for question-answering or named entity recognition tasks to address specific problems in the medical domain.

6) Performance Evaluation and Tuning: Evaluate the performance of the K-Medical BERT model and adjust it if necessary. Assess accuracy, recall, F1 scores, etc., using a test dataset, and tune the model's hyperparameters to improve its performance.

7. Energy indicating system that displays the calories expended during various activities and deducts them from the total energy you possess:

1) Measurement of activity-specific calorie expenditure: Research and derive methods to measure the calorie expenditure for different activities such as walking, running, swimming, etc. This can involve referencing existing studies or calorie measurement-related data, or using sensors or wearable devices to estimate real-time activity levels and calorie expenditure.

2) Measurement of energy reserves: Determine a method to measure the total amount of energy you possess. Typically, this involves estimating the Basal Metabolic Rate (BMR) considering factors such as body composition, age, gender, and activity level. BMR represents the amount of energy expended at rest or during static activities. Since BMR can vary based on individual characteristics and activity levels, accurate measurement considering individual traits is necessary.

3) System development: Develop a system based on activity-specific calorie expenditure and energy reserves. When the user selects an activity and inputs the duration, the system calculates the calorie expenditure for that activity and deducts it from the energy reserves. The results can be displayed to the user through a user interface (UI).

4) Data management and analysis: Add functionality to manage and analyze user activity records and changes in energy reserves. This allows users to track their activity patterns, energy expenditure, and adjust their diet or exercise plans according to their goals.

5) Personalization and real-time updates: Personalize the system and provide real-time updates to offer tailored information to the user. For example, considering the user's body information, goals, and achievements, the system can suggest personalized calorie targets and activity recommendations.

8. System that offers insights into the impact of normal activities on waste secretion on the skin. This can help users understand how their activities affect their skin health and guide them in making informed decisions to maintain or improve skin condition:

1) Research and Data Collection: Conduct research on the relationship between various activities and the secretion of waste materials on the skin. Collect relevant data and studies that explore how different activities, such as exercise, sweating, exposure to environmental factors, and lifestyle habits, can affect skin health and waste secretion.

2) Identification of Skin Markers: Identify specific skin markers or indicators that can be used to assess the impact of activities on waste secretion. This can include factors such as sebum production, sweat composition, pH levels, skin hydration, or the presence of specific waste products on the skin. Consult with dermatologists or experts in the field to determine the most relevant and reliable markers.

3) Sensor Integration: Explore the integration of sensors or wearable devices that can monitor and collect data related to skin health and waste secretion. These devices can include moisture sensors, pH sensors, sebum measurement tools, or other relevant technologies. Ensure that the selected sensors are accurate, non-invasive, and can provide real-time or continuous data.

4) Data Analysis and Machine Learning: Develop algorithms or machine learning models to analyze the collected data and identify patterns or correlations between activities and waste secretion. This can involve training models using labeled data to predict the impact of different activities on skin health or waste secretion. Consider utilizing techniques such as data clustering, classification, or regression to gain insights from the data.

5) User Interface and Recommendations: Design a user-friendly interface that displays the results and recommendations based on the analyzed data. The system can provide users with information on how their activities affect waste secretion, potential skin health risks, and suggestions for maintaining or improving skin health based on their specific skin markers and activity patterns.

6) Validation and Improvement: Validate the system's accuracy and reliability by comparing its predictions with expert opinions or existing studies. Continuously gather user feedback and update the system to enhance its performance and provide more personalized recommendations over time.

4. System that predicts the subsequent activity based on a person's previous static posture. This can have applications in areas such as motion analysis, human-computer interaction, or activity recognition, providing valuable insights into human behavior and enabling personalized assistance or feedback based on predicted activities:

1) Data Collection: Collect a dataset that includes recordings or motion capture data of various activities performed by individuals. Each data sample should consist of the person's initial static posture and the subsequent activity they engage in. The dataset should cover a wide range of activities to train the prediction model effectively.

2) Feature Extraction: Extract relevant features from the static posture data that can serve as inputs for the prediction model. These features may include joint angles, body positions, body segment orientations, or any other relevant parameters that capture the person's posture.

3) Model Training: Utilize machine learning techniques to train a prediction model. This can involve using algorithms such as decision trees, random forests, support vector machines, or deep learning models like recurrent neural networks (RNNs) or convolutional neural networks (CNNs). Train the model on the collected dataset, using the static posture features as input and the subsequent activity as the target output.

4) Evaluation and Validation: Assess the performance of the trained model using evaluation metrics such as accuracy, precision, recall, or F1 score. Split the dataset into training and testing sets to evaluate the model's ability to generalize and make accurate predictions on unseen data. Perform cross-validation or other validation techniques to ensure the reliability of the model.

5) Real-Time Prediction: Implement the trained model into a system that takes the static posture as input and predicts the subsequent activity in real-time. The system should process the static posture data, extract the relevant features, and feed them into the trained model to obtain the predicted activity. Provide a user-friendly interface to display the predicted activity to the user.

6) Continuous Improvement: Continuously gather feedback and update the system to improve its prediction accuracy. Collect additional data if needed to refine the model and expand the range of activities it can predict accurately.

Based on the above philosophy, the actual artificial intelligence projects

that we are currently involved in are as follows.

1. K-Medical Bert System

2. Medical illustration(Vectorizing Presentation) from stable diffusion

3. Extracting meaningful solution from unstructured data

4. Predicting the subsequent activity from a person's previous static posture

5. Designing a system to infer health status from specific word utterances

6. Visual localization and Mapping for smart phone

7. Indicator for current body energy

8. Secretion index of waste materials on the skin

9. Development of a global language dictionary by expanding quadruple vowels & consonants

10. Additional 351 items.

We intend to play an active role in finding and materializing solutions for projects related to the above five items.

The following are detailed ideas for the above five items.

Our current projects

5. Designing a system to infer health status from specific word utterances requires a comprehensive approach that incorporates voice analysis, machine learning, accurate data labeling, validation, and consideration of individual differences and external factors. Adequate data collection, privacy protection, and ensuring the reliability of results are also crucial aspects to consider:

1) Voice Feature Analysis: Analyzing the voice characteristics of the speaker is the starting point for inferring health status. Voice features such as pitch, timbre, intensity, prosody, and speech rate can be extracted and analyzed to identify patterns related to health. For instance, vocal hoarseness, limited pitch variation, or changes in timbre may indicate health issues.

2) Collection of Health-Related Data: To infer health status, it is necessary to collect health-related data from the speaker. This can include voice data as well as physiological signals related to physical health, medical history, physical activity, heart rate, respiration, etc. These data can be used to establish relationships between health status and voice characteristics.

3) Development of Machine Learning Models: Machine learning models need to be constructed using the collected voice and health-related data. These models learn the relationships between voice data and health status, enabling the prediction of health status based on specific word utterances. Various techniques such as voice signal processing, feature extraction, and classification algorithms can be employed.

4) Data Labeling and Validation: Labeled data with accurate diagnostic information about health status is required to train the machine learning models. Proper data labeling is essential, and a validation dataset should be used to evaluate the performance and improve the model.

5) Consideration of Individual Differences and External Factors: Health status can vary due to individual differences and external factors. These factors need to be taken into account during model development and result interpretation. For example, individuals may have different voice characteristics, and external factors such as environmental conditions or emotional state can influence voice patterns.

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