Imagine a scenario where a General AI is deployed in healthcare to support diagnosis and treatment planning. How would you ensure that the AI adheres to ethical guidelines and provides reliable outputs?
- Avoid disclosing AI involvement to patients.
- Ignore ethical guidelines to speed up diagnosis.
- Regularly audit the AI's decision-making processes.
- Rely solely on AI for medical decisions.
To ensure that a General AI in healthcare adheres to ethical guidelines and provides reliable outputs, regular audits of the AI's decision-making processes are essential. This ensures transparency, accountability, and compliance with ethical standards.
_______ is a key application of AI in the medical field which assists radiologists in detecting abnormalities in imaging data.
- Computer Vision
- Natural Language Processing (NLP)
- Predictive Analytics
- Reinforcement Learning
Computer Vision is a key application of AI in the medical field that assists radiologists in detecting abnormalities in imaging data such as X-rays, MRIs, and CT scans. AI-powered computer vision systems can identify patterns and anomalies in medical images to aid in diagnosis.
How does dropout regularization work in neural networks?
- It increases the learning rate during training.
- It optimizes the weight initialization process.
- It randomly removes a fraction of neurons during each forward pass.
- It reduces the number of layers in the network.
Dropout regularization is a technique that randomly drops (sets to zero) a fraction of neurons during each forward pass. This helps prevent overfitting by forcing the network to learn more robust features. It doesn't affect the learning rate or layer count.
The _______ algorithm is used to optimize the weights in a neural network by minimizing the error in the output layer.
- Gradient Descent
- K-Means
- Principal Component Analysis
- Random Forest
Gradient Descent is the optimization algorithm commonly used to train neural networks. It minimizes the error (often measured by a loss function) in the output layer by iteratively adjusting the weights. This process helps the network learn and improve its predictions.
_______ learning allows model training on a decentralized network to enhance privacy and security.
- Federated
- Reinforcement
- Transfer
- Unsupervised
Federated learning is an approach that allows machine learning models to be trained across multiple decentralized devices or servers, preserving data privacy and security while still benefiting from collective learning.
In the development of a new AI technology, you are facing a challenge where the technology is highly efficient but demands an exorbitant amount of energy, thereby having a substantial carbon footprint. How would you mitigate the environmental impact while maintaining efficiency?
- Ignore the environmental impact for the sake of efficiency.
- Offset carbon emissions through donations.
- Optimize the AI algorithms and hardware for energy efficiency.
- Increase energy consumption to maximize efficiency.
To mitigate the environmental impact (option c), it's crucial to optimize AI algorithms and hardware for energy efficiency. This involves reducing unnecessary energy consumption while maintaining high efficiency, aligning with the growing importance of sustainable AI development.
You are tasked with developing an ML model to predict stock prices. Midway through the project, you notice the model performs well on training data but poorly on unseen data. What strategies would you implement to rectify this issue?
- Increase the size of the training dataset.
- Fine-tune the model hyperparameters.
- Implement regularization techniques like dropout.
- Use time series-specific features and cross-validation.
To improve the performance of an ML model for stock price prediction, it's crucial to incorporate time series-specific features and use cross-validation to evaluate the model's ability to generalize to unseen data. This will help address overfitting issues.
You are developing an AI system for loan approval and notice that the model is consistently giving lower approval rates for applicants from a particular demographic. How would you address this issue while adhering to ethical guidelines?
- Retrain the model on a more diverse dataset.
- Ignore the bias as it might be a statistical anomaly.
- Remove demographic data from the model's features.
- Continue with the existing model as is.
To address bias in AI models, it's essential to retrain the model on a more diverse dataset that includes sufficient representation from underrepresented demographics. This helps reduce bias and ensures fairness in decision-making.
_______ refers to a decentralized approach to machine learning where the model is trained across multiple decentralized devices.
- Centralized Learning
- Federated Learning
- Neural Networks
- Quantum Computing
Federated Learning is a decentralized approach to machine learning where the model is trained across multiple decentralized devices while keeping data localized. It is used to preserve data privacy while allowing for collaborative model training.
In the context of autonomous vehicles, what does "Level 5" automation represent?
- Driver Assistance
- Full Automation
- No Automation
- Partial Automation
"Level 5" automation in the context of autonomous vehicles represents Full Automation. It means the vehicle can operate without human intervention in all conditions and environments, including areas without a human driver.