What is a potential risk associated with deploying General AI in multiple sectors without adequate safety precautions?
- Economic displacement of human workers.
- Enhanced collaboration with AI.
- Increased productivity and efficiency.
- Reduced energy consumption.
Deploying General AI without adequate safety precautions can lead to economic displacement of human workers. As General AI is highly capable and versatile, it has the potential to replace human workers across various industries, impacting employment opportunities.
You are designing a Human-AI collaborative system intended to assist doctors in diagnosing diseases. How would you ensure that the AI provides valuable input without overriding the expertise of the medical professionals?
- Make the AI's suggestions non-negotiable.
- Train the AI to always follow the doctor's decisions.
- Allow doctors to easily override AI recommendations.
- Keep the AI's suggestions hidden from doctors.
To ensure a valuable collaboration between AI and medical professionals, it's crucial to allow doctors to easily override AI recommendations. Making AI suggestions non-negotiable or hiding them from doctors can lead to conflicts and reduced trust in the system. The goal is to provide valuable input while respecting medical expertise.
Which principle is not commonly included in the guidelines for AI governance?
- Accountability
- Fairness
- Privacy
- Secrecy
The principle of secrecy is not commonly included in AI governance guidelines. In AI governance, transparency, accountability, fairness, and privacy are frequently emphasized to ensure responsible and ethical AI development and deployment. Secrecy contradicts the idea of transparency and openness, which are vital in AI governance.
Which of the following is a key component of an expert system in AI?
- Database Management System
- Knowledge Base
- Operating System
- Web Browser
A Knowledge Base is a fundamental component of an expert system in AI. It contains the domain-specific information and rules that the system uses to make decisions and provide expert advice.
Which algorithm is most suitable for dealing with non-linear data?
- Decision Trees
- Linear Regression
- Neural Networks
- Support Vector Machines
Neural Networks are the most suitable for dealing with non-linear data as they can model complex relationships and patterns in the data, making them highly flexible for various tasks.
Suppose an AI system responsible for credit scoring begins to exhibit erratic behavior, assigning seemingly random scores to individuals. What should be the initial step in addressing this issue, considering AI governance principles?
- Shut down the AI system immediately.
- Review the training data and model architecture.
- Ignore the issue as it might stabilize on its own.
- Reduce the complexity of the AI model.
The initial step should be to review the training data and model architecture to understand why the AI is behaving erratically. Shutting down the system might not be necessary at this stage, and ignoring it is not a responsible approach. Reducing complexity may not be the immediate solution.
How would you address the challenges of integrating autonomous vehicles into urban areas with complex and dynamic traffic conditions?
- Advanced sensor technology and real-time data analysis.
- Increase the speed limit for autonomous vehicles.
- Reduce the number of autonomous vehicles on the road.
- Implement manual traffic control.
Integrating autonomous vehicles into complex urban traffic conditions requires advanced sensor technology to perceive the environment and real-time data analysis to make informed decisions. The other options are not viable solutions and can be detrimental to safety.
Which of the following is considered a recent trend in AI research and technologies?
- Artificial General Intelligence (AGI)
- Expert Systems
- Explainable AI (XAI)
- Machine Learning
Explainable AI (XAI) is a recent trend in AI research and technologies, focusing on making AI systems more transparent and interpretable, allowing humans to understand the reasoning behind AI decisions, which is crucial for trust and accountability.
Which machine learning approach is commonly used for sentiment analysis in NLP?
- Convolutional Neural Networks (CNNs).
- Decision Trees.
- Logistic Regression.
- Recurrent Neural Networks (RNNs).
Recurrent Neural Networks (RNNs) are commonly used for sentiment analysis in Natural Language Processing (NLP). RNNs are well-suited to sequential data, making them effective for tasks like sentiment analysis that involve analyzing the context of words in a sentence.
In terms of safety, how does the application of reinforcement learning in autonomous vehicles pose potential risks?
- It enhances safety by improving decision-making.
- It introduces uncertainty in decision-making.
- It reduces the need for human intervention.
- It simplifies the learning process for autonomous vehicles.
The application of reinforcement learning in autonomous vehicles can pose potential risks because it introduces uncertainty in decision-making. Reinforcement learning relies on trial and error, and in real-world situations, this can lead to unexpected and unsafe behavior in autonomous vehicles.