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 the context of AI, "perception" refers to the process of acquiring, interpreting, selecting, and organizing sensory _______.
- Data
- Information
- Inputs
- Signals
In AI, "perception" refers to the process of acquiring, interpreting, selecting, and organizing sensory signals from the environment. This includes tasks like computer vision and speech recognition, where machines interpret and understand sensory inputs.
How does the lack of interoperability among AI systems affect the integration of autonomous technologies in smart cities?
- It enhances efficiency and reduces costs.
- It hinders data sharing and collaboration among AI systems.
- It simplifies the integration process.
- It standardizes AI systems.
The lack of interoperability among AI systems in smart cities hinders data sharing and collaboration. In a smart city, various autonomous technologies need to work together and share data to function optimally. Without interoperability, these technologies can't communicate effectively, which limits the potential of smart cities.
What does the term 'Neurosymbolic AI' refer to in recent AI research?
- A hybrid approach combining symbolic reasoning with neural networks
- A type of AI that understands human emotions
- AI systems designed to mimic the human nervous system
- Advanced speech recognition technology
'Neurosymbolic AI' refers to a recent AI research approach that combines symbolic reasoning with neural networks. It aims to leverage the strengths of both symbolic AI (logical reasoning) and neural networks (pattern recognition) to build more powerful AI systems.
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.
In what way does the concept of "Explainable AI" (XAI) influence policy-making in AI governance?
- It enhances transparency, accountability, and trust in AI systems.
- It has no impact on policy-making decisions.
- It hinders innovation by revealing proprietary algorithms.
- It prioritizes speed and efficiency over transparency.
Explainable AI (XAI) plays a crucial role in policy-making by enhancing transparency, accountability, and trust in AI systems. It helps policymakers ensure that AI technologies are ethically and responsibly deployed, addressing concerns about bias and unfair decision-making.