Which technology is enabling better human-AI collaboration in the development of AI technologies?
- Augmented Reality (AR)
- Blockchain
- Cloud Computing
- Natural Language Processing (NLP)
Natural Language Processing (NLP) technology is facilitating better human-AI collaboration in AI development. NLP enables humans to communicate with AI systems using natural language, making it easier for non-technical users to interact with and contribute to AI projects.
Imagine an autonomous vehicle’s AI system misinterpreting traffic signals due to a lack of standardization in signal design. How would you modify the AI’s training to adapt to varied signal designs without compromising safety?
- Train the AI to ignore all traffic signals.
- Increase the vehicle's speed to minimize signal interpretation time.
- Collect diverse signal data and implement robust object recognition techniques.
- Remove the AI system from the vehicle.
Option C is the correct choice. To address this issue, the AI system should be trained on a wide variety of traffic signal designs and implement robust object recognition techniques to correctly interpret them. Options A and D are impractical, and option B is unsafe and does not address the core issue.
Which of the following ethical considerations deals with the transparency of AI decision-making?
- Accountability
- Explainability
- Fairness
- Privacy
The ethical consideration of "Explainability" in AI deals with the transparency of AI decision-making. It emphasizes the importance of making AI systems understandable and interpretable, enabling users to comprehend why a particular decision or recommendation was made by an AI system.
Which of the following is a primary concern regarding data privacy in AI?
- AI's ability to play chess.
- AI's energy consumption.
- AI's impact on the job market.
- Unauthorized access to personal data.
A primary concern in AI is unauthorized access to personal data. AI systems often handle sensitive information, and protecting this data from breaches or misuse is crucial to maintaining privacy.
In supervised learning, what is the output variable also referred to as?
- Control variable
- Dependent variable
- Independent variable
- Target variable
In supervised learning, the output variable is commonly referred to as the "target variable." It's the variable we aim to predict or understand based on the input data and features.
Which type of AI is Siri (Apple's virtual assistant) categorized under?
- AGI (Artificial General Intelligence)
- Machine Learning AI
- Narrow AI
- Superintelligent AI
Siri is an example of Narrow AI, which is designed for a specific task (voice recognition and assistance) and lacks the broad learning and understanding capabilities of AGI.
In the context of deploying a facial recognition system at a large scale (e.g., in airports), what technical challenges related to scalability and adaptability would you anticipate, and how would you plan to overcome them?
- Hardware limitations.
- Lighting and environmental variations.
- Privacy and ethical concerns.
- Security and data protection.
When deploying a facial recognition system at a large scale, scalability and adaptability challenges may include hardware limitations. To overcome this, you can consider using advanced hardware, parallel processing, and optimization techniques to ensure efficient operation.
What is often a critical factor to consider in ensuring the adaptability of an AI system across different domains or applications?
- Cloud Computing
- Data Privacy
- Model Complexity
- Transfer Learning
Transfer Learning is a critical factor in ensuring the adaptability of AI systems across different domains or applications. It allows models trained on one task or dataset to be fine-tuned or reused for another related task, reducing the need for extensive training data in each new domain.
Which of the following ethical frameworks prioritizes doing the most good for the most number of people in AI decision-making?
- Deontology
- Egoism
- Utilitarianism
- Virtue Ethics
Utilitarianism is an ethical framework that prioritizes doing the most good for the most number of people. In AI decision-making, this means optimizing algorithms and systems to benefit the broader society and maximize overall welfare.
How does AI contribute to predictive maintenance in transportation?
- Changing tires
- Identifying patterns in sensor data
- Refueling vehicles
- Scheduling driver breaks
AI contributes to predictive maintenance by analyzing sensor data from vehicles to identify patterns that may indicate potential breakdowns or maintenance needs. This proactive approach helps prevent costly unplanned downtime.