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Topic 1: Part 1
The framework set forth in the White House Blueprint for an Al Bill of Rights addresses all of the following EXCEPT?
A. Human alternatives, consideration and fallback.
B. High-risk mitigation standards.
C. Safe and effective systems.
D. Data privacy.
Explanation: The White House Blueprint for an AI Bill of Rights focuses on protecting civil rights, privacy, and ensuring AI systems are safe and effective. It includes principles like data privacy (D), human alternatives (A), and safe and effective systems (C). However, it does not specifically address high-risk mitigation standards as a distinct category (B).
All of the following are common optimization techniques in deep learning to determine weights that represent the strength of the connection between artificial neurons EXCEPT?
A. Gradient descent, which initially sets weights arbitrary values, and then at each step changes them.
B. Momentum, which improves the convergence speed and stability of neural network training.
C. Autoregression, which analyzes and makes predictions about time-series data.
D. Backpropagation, which starts from the last layer working backwards.
Explanation: Autoregression is not a common optimization technique in deep learning to determine weights for artificial neurons. Common techniques include gradient descent, momentum, and backpropagation. Autoregression is more commonly associated with timeseries analysis and forecasting rather than neural network optimization. Reference: AIGP BODY OF KNOWLEDGE, which discusses common optimization techniques used in deep learning.
According to the EU Al Act, providers of what kind of machine learning systems will be required to register with an EU oversight agency before placing their systems in the EU market?
A. Al systems that are harmful based on a legal risk-utility calculation.
B. Al systems that are "strong" general intelligence.
C. Al systems trained on sensitive personal data.
D. Al systems that are high-risk.
Explanation: According to the EU AI Act, providers of high-risk AI systems are required to register with an EU oversight agency before these systems can be placed on the market. This requirement is part of the Act's framework to ensure that high-risk AI systems comply with stringent safety, transparency, and accountability standards. High-risk systems are those that pose significant risks to health, safety, or fundamental rights. Registration with oversight agencies helps facilitate ongoing monitoring and enforcement of compliance with the Act's provisions. Systems categorized under other criteria, such as those trained on sensitive personal data or exhibiting "strong" general intelligence, also fall under scrutiny but are primarily covered under different regulatory requirements or classifications.
If it is possible to provide a rationale for a specific output of an Al system, that system can best be described as?
A. Accountable.
B. Transparent.
C. Explainable.
D. Reliable.
Explanation: If it is possible to provide a rationale for a specific output of an AI system, that system can best be described as explainable. Explainability in AI refers to the ability to interpret and understand the decision-making process of the AI system. This involves being able to articulate the factors and logic that led to a particular output or decision. Explainability is critical for building trust, enabling users to understand and validate the AI system's actions, and ensuring compliance with ethical and regulatory standards. It also facilitates debugging and improving the system by providing insights into its behavior.
What is the 1956 Dartmouth summer research project on Al best known as?
A. A meeting focused on the impacts of the launch of the first mass-produced computer.
B. A research project on the impacts of technology on society.
C. A research project to create a test for machine intelligence.
D. A meeting focused on the founding of the Al field.
Explanation: The 1956 Dartmouth summer research project on AI is best known as a
meeting focused on the founding of the AI field. This conference is historically significant
because it marked the formal beginning of artificial intelligence as an academic discipline.
The term "artificial intelligence" was coined during this event, and it laid the foundation for
future research and development in AI.
Reference: The AIGP Body of Knowledge highlights the importance of the Dartmouth
Conference as a pivotal moment in the history of AI, which established AI as a distinct field
of study and research.
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