Future of AI: Trends, Machine Learning, and Language Models

AI is transforming industries, making understanding its trends essential for future business planning.

### The Impact of AI on Industry Transformation
AI is set to bring **significant changes** to customer relationships and the industrial landscape, with its emergence in 2025 following a **15-year cycle**. Machine learning, often seen as an **old technology**, is still highly useful and complex, serving as the **foundation for AI advancements**. The integration of AI in various projects and companies is already happening, with its applications becoming a **basic requirement for success** in software development.

### The Role of L&M and Machine Learning in Technology
L&M is currently operating within existing standards but is expected to **evolve and do the unimaginable** in the future. The AI industry has experienced bubbles, with LLMs and ChatGPT causing recent noise, while the true breakthroughs in technology have come from the **World Wide Web and the iPhone**. Machine learning, often overlooked, plays a crucial role in the work done with computers, and L&M is an advanced version of machine learning, excelling at **understanding unstructured data and providing contextual insights**.

### The Process of Rule Discovery in Programming and Machine Learning
Traditional programming relies on inputting data and rules to produce an output, but this approach has limitations due to **exceptions and the complexity of rule-setting**. Reversing the process by using **data and output to derive rules** is a more effective method, similar to how both humans and machines can **discover rules through experimentation**. Machine learning further enhances this process by using **large amounts of data to find better rules** and allowing for **retraining when incorrect classifications occur**.

### The Role of Parameters in Mathematics and Machine Learning
Mathematical operations can involve one or two symbols, with different combinations leading to **varying outcomes**. The introduction of more parameters in a process can **improve classification and rule determination**. Machine learning models with a larger number of parameters can **address diverse issues**, and the transition from traditional algorithms to **self-learning models** has **significantly impacted artificial intelligence**.

### The Role of Data and Computing Power in Machine Learning
Machine learning relies on **vast amounts of data**, such as cat and dog photos, to train models effectively. The recent availability of **powerful GPUs and advanced learning algorithms** has accelerated the growth of machine learning, leading to the emergence of systems like **AlphaGo and ChatGPT**. The continuous improvement of **data collection, labeling, and digitization**, along with the development of **competitive computing products**, is expected to further advance machine learning and language models in the future.

### The Role of Machine Learning and Language Models
Machine Learning (ML) has laid the technological foundation for progress, but its **complex and manual nature** makes it less user-friendly than Language Models (LM). ML is still widely used in areas like **recommendation systems, time series forecasting, image recognition, and natural language processing**. While LM has dominated natural language processing and offers **convenience**, it can be **expensive and less adaptable** for tasks that require retraining or correction.

### The Role of AI and Machine Learning in Service Provision
AI and machine learning are essential for **fixing bugs and implementing functions** in expensive, convenient products. While AI can handle various tasks, it doesn't take on every job, with machine learning excelling in **personalized recommendations and image recognition**. Companies like Amazon and Google are providing ready-made APIs for these services, making it easier to **fine-tune and modify pre-trained models** for specific needs.

### The Continuous Learning of Recommendation Systems
Recommendation systems, like Coupang's, operate in **real-time**, learning from user behavior and adjusting recommendations accordingly. The system's effectiveness is influenced by **data accumulation and trend recognition**, but there are limitations in training with **excessive data**. Proper data organization and management are crucial for successful machine learning, as **messy data can lead to incorrect results and ineffective AI**.

### The Evolution of AI: Understanding Machine Learning and Language Models
* 🤖 In the next 30 years, significant industry changes driven by AI are expected to **mirror the mobile technology revolution** that occurred 15 years ago, emphasizing the **importance of understanding AI trends** for future business planning.
* 📈 The 2025 AI Trends Course has been developed to educate corporate members on the technological evolution of AI, highlighting the **foundational role of machine learning** in these advancements.
* 💡 Despite being considered an "old technology," machine learning remains **crucial and widely used**, although its complexity often leads to a **lack of awareness about its applications**.
* 🔍 The discussion will focus on how AI, particularly **machine learning and L&M (Language Models)**, operates, its **limitations**, and its **current and future utilization** in various industries.
* 🏫 The speaker emphasizes the need to **revisit the basics of machine learning** to better analyze and predict future developments, particularly as L&M continues to **evolve and impact various sectors**.
* 💻 Companies are already utilizing AI in practical applications, with examples including a university hackathon where **16 out of 20 teams incorporated AI**, showcasing its **current relevance and utility**.
* 🌍 The narrative draws parallels between the transformative impacts of the internet, mobile technology, and AI, suggesting that while AI is currently surrounded by **hype**, it is poised to bring about **real and substantial changes** in how we interact with technology.
* 🔄 The conversation concludes by stressing that **effective data management is essential** for successful machine learning and L&M applications, as **poor data organization can lead to significant operational challenges**.

"AI의 미래에 있어 LLM이 전부가 아니라는 것 아시죠?" (30년 개발자 박종천)

Technology(기술), Trend(트렌드), Transformation(혁신)에 관해 
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