Đề thi, bài tập trắc nghiệm online Trí tuệ nhân tạo trong kinh doanh – Đề 12

AI enhances risk management by analyzing large datasets to identify potential risks, assess their probability and potential impact, and recommend or automate mitigation strategies, thus improving a business's ability to anticipate and manage threats effectively.

AI adoption in business is generally associated with *reduced* manual labor and *increased* automation, leading to cost savings and efficiency gains. Option 2 is the opposite of a typical benefit.

AI-powered predictive maintenance uses sensor data and machine learning to predict when machinery might fail, allowing for proactive maintenance scheduling. This minimizes downtime, optimizes production schedules, and improves overall operational efficiency in manufacturing.

While AI is increasingly impacting creative fields, purely subjective artistic creation, especially those driven by deep personal expression without reliance on data-driven insights, is currently the function least likely to be significantly *replaced* or fundamentally altered by AI compared to functions heavily reliant on data processing and optimization.

AI augmentation focuses on using AI as a tool to assist and enhance human workers, making their jobs more efficient, productive, and fulfilling. It's about collaboration between humans and AI, not just replacement.

The 'black box' problem refers to the opacity of some AI models, where it's hard to understand *why* they arrive at a particular decision. This lack of transparency can be a concern in business, especially in regulated industries or when accountability and explainability are crucial.

Implementing AI effectively requires addressing several challenges including access to good quality data, hiring or training personnel with AI expertise, understanding the limitations of AI technology, and often dealing with integration complexities and significant initial investment.

The primary goal of AI in business is to improve business outcomes by making better decisions, automating tasks, and enhancing efficiency, not to replace humans completely or complicate operations unnecessarily.

The consensus view is that AI will transform, rather than simply eliminate, the job market. While automation may displace some routine jobs, it is also expected to create new jobs in AI-related fields and augment existing roles, shifting the focus towards skills that complement AI, such as creativity, critical thinking, and emotional intelligence.

Algorithmic bias refers to systematic and often unintentional biases embedded within AI systems. These biases can arise from biased training data, flawed algorithm design, or reflect existing societal biases, leading to unfair or discriminatory outcomes. It's not about intentional discrimination, but rather unintended consequences of data and design choices.

RPA involves using software robots or 'bots' to automate routine, rule-based tasks that are typically done by humans in digital systems. This increases efficiency and reduces errors for tasks like data entry, invoice processing, etc.

AI algorithms trained on biased historical data can perpetuate and even amplify existing biases in hiring, leading to unfair or discriminatory outcomes, even if unintentionally. This is a significant ethical consideration.

Chatbots are designed to automate the initial stages of customer interaction, addressing common questions and tasks efficiently, thereby allowing human agents to focus on more complex or sensitive issues that require human empathy and nuanced understanding.

Ethical AI implementation requires businesses to prioritize fairness (avoiding bias), transparency (making decision-making processes understandable), accountability (establishing responsibility for AI outcomes), and privacy (protecting data). These considerations are crucial for building trust and ensuring responsible AI adoption.

AI enhances decision-making by augmenting human capabilities, not replacing them entirely. It processes data efficiently, uncovers hidden patterns, and provides data-driven insights that humans can use to make better, more informed decisions.

While recommendation systems offer personalization benefits, a potential drawback is the 'filter bubble' effect. By constantly showing users items similar to their past preferences, these systems may limit exposure to new and diverse products or ideas, potentially narrowing user experiences.

Personalized marketing leverages AI to analyze individual customer data to tailor marketing messages and product recommendations. Recommending products based on past behavior is a prime example of this personalization.

Computer Vision is the field of AI that specializes in enabling computers to 'see' and interpret images and videos. This is crucial for applications like automated quality control where visual inspection is required.

Explainable AI (XAI) focuses on making AI decision-making processes transparent and understandable to humans. This is increasingly important for building trust in AI systems, ensuring accountability, complying with regulations (especially in sensitive sectors), and enabling effective human oversight and intervention.

AI can significantly enhance customer experience by enabling faster response times, more personalized interactions through tailored recommendations and service, and by proactively anticipating customer needs based on data analysis.

While AI is used in HR for recruitment, retention, and employee support, facial recognition for constant employee surveillance raises significant ethical and privacy concerns and is not a typical or ethically sound application of AI in HR. Options 1, 2, and 4 are common and accepted uses.

Predictive analytics uses AI to analyze historical and current data to forecast potential future trends and risks. This allows businesses to make more informed strategic decisions, but it's important to remember that predictions are not guarantees and should be used in conjunction with human expertise.

AI applications in supply chain management can significantly improve efficiency and resilience by optimizing logistics routes, accurately forecasting demand, and managing inventory levels dynamically, leading to cost savings and improved responsiveness.

AI-powered fraud detection systems excel at analyzing large datasets in real-time and identifying complex, evolving patterns that might signal fraudulent activity, going beyond the limitations of static, rule-based systems.

NLP is a branch of AI that deals with the interaction between computers and human language. In business, it's applied in areas like chatbots (understanding customer queries), sentiment analysis (analyzing customer feedback), and language translation.

Machine Learning is a subset of AI focused on enabling systems to learn from data and improve performance over time without being explicitly programmed for each specific task. This adaptability is key to its value in business.

Deep Learning is a more advanced form of Machine Learning that utilizes deep neural networks. These networks, with their multiple layers, are capable of learning highly complex patterns and representations from data, making them powerful for tasks like image recognition, NLP, and complex data analysis.

Sentiment analysis uses NLP to automatically analyze text data and determine the emotional tone. Businesses use it to understand customer opinions from reviews, social media, and surveys, providing valuable insights for product development, marketing, and customer service improvements.

Data is the fuel for AI. The quality, relevance, and quantity of data used to train AI models are critical determinants of their performance and effectiveness. Without good data, AI systems are unlikely to deliver valuable results.

Over-reliance on AI without sufficient human oversight and critical thinking can stifle innovation and reduce adaptability. If businesses become too dependent on AI systems and neglect to maintain human expertise and judgment, they may become less resilient and less able to respond to novel situations or unexpected changes.