Your browser does not support JavaScript!

AI's Rising Influence Across Industries

General Report October 30, 2024
goover

TABLE OF CONTENTS

  1. Summary
  2. Programming Languages in Machine Learning
  3. AI Applications in Creative Industries
  4. AI and Machine Learning in Financial Services
  5. Healthcare Innovations through AI
  6. AI Education and Certifications
  7. Challenges and Future Directions
  8. Conclusion

1. Summary

  • Artificial Intelligence (AI) and Machine Learning (ML) technologies are revolutionizing various sectors by providing new tools and improving processes. With Python leading the pack, programming languages play a crucial role in the success of machine learning projects, owing to Python's user-friendly nature and robust libraries. Creative industries are seeing a surge in AI applications, such as realistic 3D avatar creation, which reduces costs and enhances storytelling. In financial services, Hana Bank has deployed AI-driven models for assessing technological capabilities, presenting a paradigm shift from traditional credit evaluations. Healthcare advancements are also notable, with domestic companies like Coreline Soft and Lunit making strides in the U.S. market, improving diagnostics and patient care. AI education and certification are becoming essential to equip professionals with relevant skills, underscoring the importance of continuous learning. Despite these advancements, organizations face challenges in adoption, including infrastructure requirements, ethical considerations, and workforce transformations.

2. Programming Languages in Machine Learning

  • 2-1. Most popular programming languages for machine learning

  • The current landscape in the machine learning (ML) domain reveals a wide range of programming languages available, with over 700 languages being utilized. Each programming language has its own advantages and is suited for different tasks. However, the most popular programming language for ML today is Python, with approximately 57% of data scientists and ML developers relying on it. This is followed by C/C++, with Java and JavaScript being less favored in comparison. Despite the presence of other languages like Julia, Scala, Ruby, MATLAB, Octave, and SAS, Python remains the leading language of choice due to its vast library ecosystem and ease of use for data manipulation and processing.

  • 2-2. Role of Python, R, C/C++, Java, and JavaScript

  • Python's dominance in machine learning is attributed to its comprehensive frameworks that have notably advanced in recent years, particularly for deep learning applications such as TensorFlow. C/C++ is recognized for its performance and is often used in game AI and robotics. R is increasingly acknowledged for its application in bioinformatics and biostatistics. Java, while less popular among ML practitioners, is preferred in enterprise settings, especially for applications involving network security and fraud detection. JavaScript, typically less prioritized, is occasionally used by front-end developers interested in integrating machine learning into web applications.

  • 2-3. Project-specific language selection for machine learning

  • Selecting the appropriate programming language for machine learning largely depends on the specific application or project type. For tasks such as sentiment analysis or natural language processing, Python or R are likely to be the best fit due to their extensive libraries. In contrast, Java may offer advantages in network security and fraud detection contexts given its prevalence in corporate environments. Ultimately, the choice of programming language in machine learning is influenced by the developer's background, the required capabilities for the project, and the efficacy of existing libraries and tools.

3. AI Applications in Creative Industries

  • 3-1. Development of realistic 3D avatars using AI

  • Recent advancements in artificial intelligence (AI) have led to the development of technologies that create realistic 3D avatars from 2D images. This innovative approach enables users to generate high-quality 3D representations of individuals without the need for extensive graphics expertise. The technology, developed by a research team led by Professor Jeon Hae-gon at Gwangju Institute of Science and Technology (GIST), allows for motion capture and realistic movements based on static 2D images. By utilizing depth information and skinning maps, the team can effectively create a dynamic avatar capable of various actions, thus revolutionizing the gaming, film, and entertainment industries. This new methodology significantly reduces costs and resource requirements, making it accessible for broader applications.

  • 3-2. AI's impact on the gaming and film industries

  • AI technologies are having a profound impact on both the gaming and film sectors. The ability to easily create realistic 3D avatars using AI opens new doors for creating characters that perform realistic actions, which can enhance the experience of games and movies. This innovation reduces the traditional reliance on skilled graphic designers and expensive equipment, allowing creators to focus on storytelling and engagement rather than technical limitations. Furthermore, the integration of AI in character design is expected to facilitate more dynamic, engaging, and accessible experiences for users in these industries.

  • 3-3. Integration of AI in content creation and visual arts

  • The integration of AI into content creation and visual arts has transformed how creators approach their projects. AI tools can automate complex tasks, suggest ideas, and assist in the production process, streamlining workflows for artists and designers. This progress is particularly notable in fields where creativity meets technology, enabling enhanced collaboration between human creativity and machine efficiency. As AI continues to evolve, it is expected to redefine traditional artistic practices, leading to innovative forms of expression and new disciplines within the realm of visual arts.

4. AI and Machine Learning in Financial Services

  • 4-1. Hana Bank's machine learning model for corporate evaluation

  • Hana Bank has developed a pioneering 'technology-based machine learning model' using artificial intelligence (AI) technology in collaboration with Hana Financial Group's Data Modelling Cell. The model was officially launched on the 8th and marks a significant step as the first of its kind in the banking sector. This machine learning model utilizes data accumulated since 2014 in technology credit assessment (TCB) to propose evaluation criteria for technological capabilities that were not considered in traditional credit evaluations. The insights gained from this model fulfill the foundational requirements of the Financial Services Commission's 'stepwise integration of credit models roadmap, phase one.' The model primarily draws upon technical-related information such as patents, technology certifications, skilled personnel, technology development status, and commercial capabilities. By shifting away from traditional financial statement-based evaluations, the model assesses the future growth potential of companies based on their current technological assets.

  • 4-2. AI's role in credit assessment and risk management

  • The technology-based machine learning model enhances risk assessment by quickly analyzing technical evaluation data. It automatically identifies items that can be explained from a risk perspective while ensuring the stability of evaluation results by training on variables that exhibit low volatility. This design enhances the discriminative power of technological indicators, allowing for a more tailored approach in presenting factors that positively or negatively impact a company's creditworthiness. Thus, the model provides greater transparency and reliability in the evaluation process.

  • 4-3. Adoption of AI technologies in banking

  • Hana Bank anticipates that the development of the technology-based machine learning model will facilitate greater financial benefits for small and medium enterprises (SMEs) possessing high technological capabilities. The bank expressed its commitment to contribute to the growth and development of SMEs through the creation of diverse financial products, indicating a proactive approach to integrating AI technologies in service offerings without forecasting future strategies.

5. Healthcare Innovations through AI

  • 5-1. Recent AI developments in medical diagnostics

  • There have been significant advancements in AI-based medical diagnostics. Notably, domestic AI healthcare companies have increasingly entered the U.S. market, which is recognized as the largest healthcare AI market globally. For instance, Coreline Soft's coronary artery calcification AI diagnostic solution received FDA approval in March 2024. Furthermore, Lunit completed the acquisition of Volpara Health Technologies in May 2024, a company that holds a 42% share in the U.S. mammography market. Additionally, Vuno launched its AI-based brain quantification medical device, 'Deep Brain,' in the U.S. last month. These developments indicate an uptick in AI utilization for diagnostics, leading to improved patient care.

  • 5-2. AI's influence on drug development and patient care

  • AI technologies are increasingly influencing drug development and patient care processes. AI diagnostic assistance solutions have been expanding in clinical settings, enabling medical staff to diagnose conditions more accurately and swiftly. The AI's ability to learn from vast datasets related to diseases allows it to identify deviations in patients’ imaging or test results, thereby supporting healthcare professionals in making informed decisions. This is particularly beneficial during drug development, as the predictive capabilities regarding patients' responses to medications enhance the efficiency of the new drug development process. The expectation is that as AI-based solutions penetrate the healthcare field, revenue generation will substantially increase.

  • 5-3. Growth of the AI healthcare market in the U.S.

  • The U.S. AI healthcare market is witnessing substantial growth. The stocks of companies involved in AI for healthcare diagnostics have been rising, driven by optimism regarding the imminent revenue potential as AI solutions are adopted more widely in medical practice. For example, during a recent five-day trading span, the stock of Lunit surged by 23.20%, while Vuno saw its stock rise by 14.85%. Other companies in the AI medical diagnostics sector, such as Selvas AI, Deep Noid, and Coreline Soft, also experienced gains. This growth in market value is encouraged by favorable conditions, including anticipated interest rate reductions, which facilitate continued investment in research and development.

6. AI Education and Certifications

  • 6-1. Importance of AI and ML education in modern careers

  • The necessity of education in artificial intelligence (AI) and machine learning (ML) is evident due to the increasing demand for professionals equipped with these skill sets. AI and ML courses prepare students for various industries that require knowledge in data analysis, predictive modeling, and algorithm development. These skills enhance analytical thinking and promote problem-solving abilities, ensuring that individuals are well-prepared to tackle real-world challenges. Certification in AI and ML can significantly improve career prospects, opening new opportunities for advancement in technology-driven fields.

  • 6-2. Current trends in AI certification programs

  • Recent trends in AI certification programs indicate a diverse array of offerings targeting different skill levels. Programs include basic to advanced courses that can be pursued flexibly online. The AICE (AI Certificate for Everyone) provides tiered assessments from basic to professional levels, accommodating students from various educational backgrounds. These certifications focus on essential skills such as coding, data preprocessing, and model development, emphasizing practicality by involving case studies relevant to workplace scenarios. As the industry evolves, the integration of these certification programs into professional development is pivotal.

  • 6-3. Role of online platforms in AI skill development

  • Online platforms are increasingly becoming the primary sources for AI education and skill development. They offer diverse courses that cover various aspects of AI, including machine learning, deep learning, and data management. Platforms like Simplilearn provide comprehensive learning experiences with real-time interaction, hackathons, and expert-led masterclasses. This flexibility allows students to learn at their own pace while acquiring the necessary skills to excel in AI and ML careers. The growing reliance on online education for AI training highlights its essential role in producing a skilled workforce capable of meeting industry demands.

7. Challenges and Future Directions

  • 7-1. Barriers to AI and ML adoption in organizations

  • According to the data extracted from the references, organizations face several barriers when it comes to adopting AI and ML technologies. Key challenges include a lack of understanding of AI capabilities, limited access to skilled workforce, high costs associated with implementation, and the complexity of integrating AI systems into existing workflows. These overwhelming factors prevent organizations from fully harnessing the potential benefits of AI and ML in corporate training and other areas of business.

  • 7-2. Concerns regarding AI's impact on employment

  • The adoption of AI and ML raises substantial concerns regarding its impact on employment. As outlined in the reports, many fear that these technologies will replace human jobs, particularly in lower-skilled roles. However, it is also suggested that instead of completely replacing jobs, AI could transform them, necessitating a shift in the skills required to remain relevant in the workforce. Organizations need to weigh these implications carefully as they explore AI integration.

  • 7-3. Need for robust AI infrastructure and ethical considerations

  • The documents highlight the pressing need for robust AI infrastructure to support the adoption of these technologies. A lack of adequate infrastructure could lead to disparities in access, where only wealthier organizations benefit from AI advancements. Furthermore, there are ethical considerations surrounding the use of AI, especially concerning data privacy and the potential for bias in AI algorithms. Ensuring that AI systems are designed and implemented ethically is critical for maintaining public trust and achieving sustainable benefits.

Conclusion

  • The findings from the report emphasize the transformative potential that AI and Machine Learning hold for various domains, from creative industries to healthcare and financial services. Programming languages, particularly Python, are pivotal to these advancements due to their adaptability and extensive library support, catering to a range of applications from data analysis to deep learning. Hana Bank's innovative AI model showcases how financial institutions can benefit from technology to refine risk management and credit assessment, accelerating the industry's evolution. Generative AI progresses the creative enterprise, enhancing tasks traditionally limited by human capability. However, the journey towards widespread AI adoption is laden with challenges such as ensuring robust infrastructure and addressing ethical concerns regarding bias and privacy. As the demand for AI-skilled professionals increases, comprehensive education and certification programs become vital. Moving forward, integrating AI ethically and effectively while balancing the economic and societal impacts will shape the future landscape, compelling industries to adapt dynamically to maintain a competitive edge and sustain growth. Practical applications will require organizations to focus on building supportive ecosystems that foster innovation while safeguarding ethical standards.

Glossary

  • Python [Programming Language]: Python is recognized as the most popular programming language in the field of machine learning, favored for its simplicity, readability, and extensive libraries. It plays a crucial role in data analysis, machine learning, and artificial intelligence, making it a preferred choice among data scientists and machine learning practitioners.
  • Hana Bank [Financial Institution]: Hana Bank has pioneered the development of a technology-driven machine learning model for corporate evaluation, leveraging AI to enhance credit assessments and risk management. This innovation positions the bank as a leader in integrating AI technologies into the financial services sector.
  • Generative AI [Technology]: Generative AI refers to algorithms that can generate new content, including text, images, and audio, based on learned patterns from existing data. Its applications are rapidly expanding in creative industries, enhancing the capabilities of artists, designers, and content creators.

Source Documents