Which legislation is designed to protect the privacy and security of personal information in the European Union?
- CCPA (California Consumer Privacy Act)
- COPPA (Children's Online Privacy Protection Act)
- GDPR (General Data Protection Regulation)
- HIPAA (Health Insurance Portability and Accountability Act)
GDPR (General Data Protection Regulation) is a comprehensive European Union regulation that governs the privacy and security of personal data. It imposes strict requirements on how organizations handle and protect personal information.
Which of the following approaches helps in reducing the environmental impact of training large-scale AI models?
- Increasing the batch size during training.
- Training models on local devices only.
- Using more complex model architectures.
- Using renewable energy sources for model training.
Using renewable energy sources for model training is a sustainable approach to reduce the environmental impact. Large-scale AI model training can be energy-intensive, and switching to clean energy sources helps mitigate this impact.
How does AI contribute to fraud detection in online transactions?
- AI can't effectively detect fraud in online transactions.
- AI only helps in transaction processing.
- AI relies solely on human intervention for fraud detection.
- AI uses machine learning algorithms to identify unusual patterns and flag potentially fraudulent transactions.
AI contributes significantly to fraud detection in online transactions by using machine learning algorithms to detect unusual patterns in transactions. It can analyze vast amounts of data in real-time, identify potential fraud, and trigger alerts for further investigation.
Which encryption technique might be utilized to perform computations on data while it is still encrypted?
- AES (Advanced Encryption Standard)
- Homomorphic Encryption
- RSA (Rivest–Shamir–Adleman)
- SSL (Secure Sockets Layer)
Homomorphic encryption allows computations to be performed on encrypted data without needing to decrypt it first, thereby ensuring data privacy while enabling useful computations.
How does AI contribute to fraud detection in banking transactions?
- By analyzing transaction patterns and identifying anomalies.
- By encrypting all financial transactions.
- By focusing solely on high-value transactions.
- By replacing human bank tellers with AI bots.
AI contributes to fraud detection in banking by analyzing transaction patterns and identifying anomalies that might indicate fraudulent activity. Machine learning algorithms can learn from historical data to detect unusual behavior in real-time, helping to prevent and mitigate fraud.
What considerations are crucial for ensuring safety in human-robot interaction?
- Clear communication, risk assessment, and physical barriers.
- Robot aesthetics.
- Robot speed and agility.
- Sensor accuracy and redundancy.
Safety in human-robot interaction involves various considerations, including clear communication between humans and robots, risk assessment, and physical barriers when necessary. Sensor accuracy and redundancy also play a crucial role in detecting and avoiding potential hazards.
Which algorithm is typically used for credit scoring in the finance industry?
- Decision Trees
- K-Means Clustering
- Naive Bayes
- Support Vector Machines
Decision Trees are commonly used in credit scoring as they provide a clear and interpretable way to assess an individual's creditworthiness. They can evaluate different factors and make decisions based on a series of questions, making them suitable for this application.
In the context of NLP, what does "tokenization" refer to?
- Breaking down a text into words, phrases, symbols, or other meaningful elements (tokens).
- Compressing a text to reduce its size.
- Converting a text into a numeric representation.
- Encrypting a text for secure transmission.
Tokenization in NLP refers to the process of breaking down a text into its individual components, such as words or phrases, to facilitate analysis. This step is essential for various NLP tasks like text classification and language modeling.
How does a "language model" assist in the functionality of NLP?
- It extracts named entities from a text.
- It identifies the sentiment of a text.
- It provides a statistical model for understanding grammar and sentence structure.
- It translates text from one language to another.
A "language model" assists in NLP by providing a statistical framework for understanding the grammar, sentence structure, and context of a given text. Language models, like BERT or GPT-3, enable tasks like text generation, sentiment analysis, and language understanding.
How does deep learning contribute to high-frequency trading strategies?
- Deep learning is not applicable to high-frequency trading.
- Deep learning models analyze vast datasets, identify patterns, and execute high-frequency trades in milliseconds.
- It enables the use of simpler trading algorithms, leading to reduced risk.
- It requires significant manual intervention and human expertise.
Deep learning contributes to high-frequency trading by processing large amounts of data, identifying complex patterns, and executing trades at extremely high speeds. This technology can outperform human traders in milliseconds, making it crucial for high-frequency strategies.