In a factory where robots are used for assembly, a robot malfunctions and delays the production line. How would you approach the investigation to ensure the issue is resolved and doesn’t reoccur?
- Replace the malfunctioning robot.
- Conduct a thorough root cause analysis.
- Increase maintenance frequency for all robots.
- Hire additional technicians.
The correct approach is to conduct a thorough root cause analysis. This involves identifying the underlying reasons for the malfunction, such as software glitches, mechanical issues, or sensor failures. By addressing the root cause, you can prevent similar issues from reoccurring and optimize the production process.
Which of the following is not a type of machine learning?
- Supervised Learning
- Reinforcement Learning
- Neural Networking
- Natural Language Processing
Neural Networking is not a type of machine learning but rather a technology used in machine learning, particularly in deep learning. The other options are indeed types of machine learning techniques.
How does the trolley problem illustrate ethical dilemmas in AI and autonomous vehicles?
- It demonstrates the importance of algorithm efficiency.
- It emphasizes the role of AI in reducing traffic accidents.
- It highlights the challenge of real-time decision-making in emergencies.
- It showcases the need for advanced sensors in autonomous vehicles.
The trolley problem is a thought experiment that forces us to confront ethical dilemmas related to AI and autonomous vehicles. It raises questions about how AI systems should make moral decisions in life-or-death situations, highlighting the complexity of programming ethics into machines.
In agriculture, AI technologies like _______ are used for monitoring crop health and optimizing farm processes.
- Augmented Reality
- Chatbots
- Drones
- Virtual Reality
In agriculture, AI technologies such as drones are employed to monitor crop health, assess field conditions, and optimize farming operations. Drones equipped with cameras and sensors capture data that is then analyzed by AI systems to make informed decisions for crop management.
What role does transparency play in AI governance and policy-making?
- It ensures that AI models are kept secret from the public.
- It helps build trust, accountability, and fairness in AI systems.
- It is not relevant in AI governance.
- It speeds up AI development without oversight.
Transparency plays a crucial role in AI governance and policy-making. It helps build trust in AI systems by making their operations understandable and accountable. It ensures that decisions made by AI systems are not hidden and can be scrutinized for fairness and ethical considerations.
How does BERT differ from traditional embeddings in NLP?
- BERT is not suitable for text classification.
- BERT uses pre-trained word vectors, while traditional embeddings do not.
- Traditional embeddings are context-agnostic, while BERT captures contextual information.
- Traditional embeddings are more accurate for NLP tasks.
BERT (Bidirectional Encoder Representations from Transformers) differs from traditional embeddings by capturing contextual information. Traditional embeddings like Word2Vec or GloVe do not consider context, whereas BERT looks at both preceding and following words to understand a word's meaning in context.
The concept of _______ involves machines being able to learn from data without being explicitly programmed.
- Artificial Intelligence
- Deep Learning
- Machine Learning
- Reinforcement Learning
The concept of Machine Learning involves machines learning from data without explicit programming. This field of AI focuses on developing algorithms and models that allow systems to improve their performance through experience and data analysis.
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.