What does it mean to call an AI system 'opaque'? It is based on symbolic AI principles. It is always transparent and easy to interpret. It uses predefined rules and symbols. Its decision-making process is not understandable by humans.

Its decision-making process is not understandable by humans.

What is the main difference between symbolic and subsymbolic approaches to AI? Symbolic AI uses predefined rules, while subsymbolic AI learns patterns from data. Symbolic AI is less powerful than subsymbolic AI. Symbolic AI is opaque, while subsymbolic AI is transparent. Symbolic AI is based on neural networks, while subsymbolic AI uses explicit rules.

Symbolic AI uses predefined rules, while subsymbolic AI learns patterns from data.

Why do subsymbolic approaches to AI often create difficulties with explanation? They are always transparent and easy to interpret. They use symbolic representations that are easy to understand. They don't use predefined rules and have complex neural connections. They are based on explicit rules and symbols.

They don't use predefined rules and have complex neural connections.

According to Kate Vredenburgh, when does the right to explanation arise? When people are in an involuntary, rule-governed hierarchy. When there is no need for explanations in any context. When symbolic AI is used in decision-making. When AI systems are always transparent.

When people are in an involuntary, rule-governed hierarchy.

What does Vredenburgh mean by 'informed self-advocacy'? Always accepting decisions made by AI systems. Relying solely on symbolic AI for decision-making. Having the knowledge to navigate and challenge rules effectively. Ignoring the rules and making decisions independently.

Having the knowledge to navigate and challenge rules effectively.

How does Alex London respond to the argument against using opaque AI in medicine? He believes that opaque AI should never be used in medicine. He argues that effectiveness is more important than explainability. He insists that all medical treatments must be fully explainable. He claims that symbolic AI is the only acceptable approach.

He argues that effectiveness is more important than explainability.

How does Alex London argue in favor of using opaque AI in medicine? He states that accuracy is more important than explainability. He claims that opaque AI should be avoided at all costs. He believes that symbolic AI is always preferable. He argues that all AI systems must be transparent.

He states that accuracy is more important than explainability.

According to Carissa Véliz, how does the modern data economy endanger privacy? By using symbolic AI to protect user data. By ensuring complete transparency in data collection. By incentivizing companies to collect excessive personal information. By eliminating the need for data privacy altogether.

By incentivizing companies to collect excessive personal information.

What is the difference between hard power and soft power? Soft power is more effective than hard power in all situations. Hard power forces outcomes; soft power influences choices. Soft power removes all options; hard power offers choices. Hard power is always preferable to soft power.

Hard power forces outcomes; soft power influences choices.

What are examples of big tech companies using hard power and soft power? Hard power: maximizing user engagement; Soft power: AI decisions. Hard power: storing location data; Soft power: targeted ads. Hard power: targeted ads; Soft power: storing location data. Hard power: anonymous searches; Soft power: data collection.

Hard power: storing location data; Soft power: targeted ads.

Which of the following are common uses for Artificial Intelligence (AI)? Data Entry, Manual Calculations, Traditional Programming Matching, Fraud Detection, Forecasting, Personalization, Face/Speech Recognition Simple Arithmetic, Basic Logic Operations, Manual Sorting Basic Text Editing, Manual Data Analysis, Simple Graph Plotting

Matching, Fraud Detection, Forecasting, Personalization, Face/Speech Recognition

What are the three primary ways in which AI can learn? Reinforcement, Semi-Supervised, and Supervised Supervised, Unsupervised, and Reinforcement Unsupervised, Semi-Supervised, and Reinforcement Supervised, Semi-Supervised, and Unsupervised

Supervised, Unsupervised, and Reinforcement

How many types of Supervised Learning models are there, and what are they? Four: Classification, Regression, Clustering, and Association Models Three: Classification, Regression, and Clustering Models Two: Classification and Regression Models One: Classification Model

Two: Classification and Regression Models

What are the types of Artificial Intelligence (AI) based on their capabilities? Artificial Beginner, Intermediate, and Expert Intelligence Artificial Narrow, General, and Super Intelligence Artificial Basic, Intermediate, and Advanced Intelligence Artificial Simple, Complex, and Advanced Intelligence

Artificial Narrow, General, and Super Intelligence

What is Narrow AI, and how does it differ from other types of AI? Narrow AI can perform multiple tasks and apply knowledge across domains. Narrow AI is capable of self-learning and improving over time. Narrow AI is a theoretical concept that does not exist yet. Narrow AI is designed to perform a single task and does not apply knowledge to other tasks.

Narrow AI is designed to perform a single task and does not apply knowledge to other tasks.

What is General AI, and what is its current status in AI research? General AI is a concept that has been fully realized and implemented. General AI is a type of AI that focuses on specific tasks. General AI aims to create software with human-like intelligence and does not exist yet. General AI is currently used in various industries for automation.

General AI aims to create software with human-like intelligence and does not exist yet.

What was the major limitation of early machine learning models such as the perceptron? Inability to solve non-linear problems High computational cost Lack of data for training Complex implementation

Inability to solve non-linear problems

What is a characteristic feature of the AdaBoost algorithm introduced in the 1990s? Combining weak classifiers to form a strong classifier Minimizing the error in unsupervised learning Utilizing genetic mutations to optimize predictions Implementing a single-layer neural network

Combining weak classifiers to form a strong classifier

Which conference, initiated in 1987, is one of the leading annual gatherings for machine learning research? Neural Information Processing Systems (NeurIPS) International Conference on Machine Learning (ICML) Conference on Computer Vision and Pattern Recognition (CVPR) Association for Computational Linguistics (ACL)

Neural Information Processing Systems (NeurIPS)

Why did the 'AI Winter' occur in the 1970s and 1980s? Failed predictions and lack of computing power Excessive funding leading to oversaturation Over-dependence on expert systems Rapid advancements outpacing understanding

Failed predictions and lack of computing power

What is significant about the ImageNet competition started in 2010? It focused on robotics It accelerated deep learning research It was the first ML conference It ended the AI winter

It accelerated deep learning research

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