2021年2月26日版
2021年2月26日版
| Category | Explanation | Typical Difficulties |
|---|---|---|
| Reliability and Safety | Difficulties in quality assurance of MLAS (reliability, safety, and performance). | Gaps between training data and operational data; Balancing model refinement and cost; No stop condition for model verification ; Integration of induction (machine learning) and deduction (logical rules); Dealing with malicious users. |
| Efficiency and Productivity | Difficulties related to the efficiency and productivity of MLAS development (cost and time) | Appropriate selection of models and algorithms; Tagging cost reduction; Model reuse and versioning. |
| Process Management | Difficulties in process management of MLAS implementation and operation. | Moving goals; Unpredictable model accuracy; Unpredictable implementation cost, Difficulties in collecting data from the field; Appropriate model maintenance during operation. |
| Relationship between Humans and AI | Difficulties arising from the immaturity of the human-AI relationship | Refusal of the field/customer to accept the results of AI; Diminished human ability due to relying too much on AI. |
| Business and Monetizing | Difficulties related to investment and return in MLAS | The effect of MLAS cannot be predicted in advance; Limitation of Proof of Concept (PoC); Difficulty in monetizing. |
| Standards and Guidelines | Difficulties caused by the lack of standards and guidelines recognized in society and industry | Necessity of safety standards and quality assurance guidelines; Liability and indemnification in case of failure and accident. |
| AI Awareness | Difficulties caused by stakeholders' misperceptions of AI | Phenomenon tossed around in the AI boom (the introduction of AI has become a goal); Lack of understanding of the limitations of AI. |
| AI Human Resource Development | Difficulties related to the talent shortage capable of leveraging AI | Shortage of executives, users, managers and system developers who can properly use AI; AI human resources education. |
| Data and Model Distribution | Difficulties related to the distribution and protection of data and models | Ownership of models and data; Distribution and protection of data and models; Elimination of data monopoly. |
| Policy and Social System | Difficulties due to the immaturity of the policy and social system for AI | National AI research support system; Tax system for AI industries. |
| Security and Privacy | Difficulties in ensuring security and protecting privacy | Hacking; Anti-attack strategies; Personal information and confidential information protection. |
| Legal Systems and Regulations | Difficulties due to the immaturity of the legal and regulatory system | Protection of AI users' rights; Gaps between current law and latest AI applications. |