In a scenario where an AI-driven trading system starts making high-risk trades, what steps would you take to mitigate financial losses while maintaining trading functionality?
- Halt all trading activities immediately.
- Adjust risk parameters and implement fail-safes.
- Ignore the issue and continue trading.
- Shut down the AI system permanently.
Option B is the correct choice. In such a scenario, it's important to adjust risk parameters, implement fail-safes, and closely monitor the AI system to prevent further high-risk trades while still allowing it to operate within predefined safety limits. Halting trading entirely or ignoring the issue would not be practical or responsible actions. Shutting down the AI permanently should only be considered as a last resort.
What is a key component of autonomous vehicles for perceiving their surroundings?
- Air conditioning system
- Lidar sensors
- Radio receiver
- Windshield wipers
Lidar sensors are a crucial component of autonomous vehicles. They use lasers to measure distances and create detailed 3D maps of their surroundings, helping vehicles detect and navigate obstacles.
Which technology is used to train machines to mimic human actions?
- Algorithm Optimization.
- Cloud Computing.
- Neural Networks.
- Quantum Computing.
Neural Networks, particularly deep learning neural networks, are at the core of many modern AI technologies, enabling machines to process data in a way that mimics the human brain. This allows machines to recognize patterns and learn from data.
What are the implications of using transformer models in NLP for real-time applications?
- Enhanced performance in real-time tasks.
- Increased computation time.
- Reduced memory usage.
- Simplified model training.
Transformer models, while powerful, are known for their increased computation time, which can be a drawback in real-time applications where quick responses are crucial. They often require substantial computational resources for processing.
Which of the following algorithms is particularly challenging to scale for large datasets?
- Decision Trees
- K-Nearest Neighbors
- Naive Bayes
- Support Vector Machines
Decision Trees can be challenging to scale for large datasets because they tend to create complex trees that require a lot of memory and computational resources. They can easily overfit, making them less suitable for big data scenarios.
How does AI enhance safety and security in public transportation systems?
- Autonomous Navigation
- Fare Collection
- Passenger Entertainment
- Predictive Maintenance
AI enhances safety and security in public transportation systems through predictive maintenance. By analyzing data from sensors and historical records, AI can predict when maintenance is needed, reducing the risk of accidents and ensuring reliable service.
The _______ is an example of an international collaboration aimed at researching and implementing ethical AI.
- GPT-3
- IEEE
- MIT
- NSA
The IEEE (Institute of Electrical and Electronics Engineers) is an international organization that has been involved in various initiatives related to ethical AI research and implementation. They have published guidelines and standards for ethical AI practices.
How does the k-Nearest Neighbors (k-NN) algorithm classify an unknown data point?
- By assigning the class label that the farthest neighbor belongs to.
- By assigning the class label that the majority of its k-nearest neighbors belong to.
- By calculating the mean of its k-nearest neighbors' features.
- By using a weighted average of the k-nearest neighbors' class labels.
The k-Nearest Neighbors algorithm classifies an unknown data point by assigning the class label that the majority of its k-nearest neighbors belong to. It's a simple and intuitive classification method.
How can reinforcement learning be used in optimizing pricing strategies in e-commerce?
- By outsourcing pricing decisions to human experts.
- By relying on historical pricing data.
- By training algorithms to adjust prices based on real-time market demand.
- By using AI to set fixed prices for all products.
Reinforcement learning in AI can optimize pricing strategies by allowing algorithms to adjust prices based on real-time market demand and customer behavior. It learns from interactions and aims to maximize long-term rewards, making it valuable for dynamic pricing in e-commerce.
In finance, robo-advisors utilize algorithms and _______ to create automated, personalized financial advice with minimal human intervention.
- Artificial Intelligence
- Data Analysis
- Deep Learning
- Machine Learning
In finance, robo-advisors leverage algorithms and artificial intelligence (AI) to generate automated and personalized financial advice. AI enables them to analyze large datasets and provide tailored recommendations to investors without extensive human involvement.