Introduction to AI

Question 1

Artificial Intelligence

Answer 1

Computer systems performing tasks that typically require human intelligence. Technically the discipline of computer science that’s dedicated to making computer systems that can think like a human.

Question 2

Narrow / Weak AI

Answer 2

Limited to a specialized task or set of tasks.

Voice assistants, image recognition, recommendation systems.

Question 3

General / Strong AI

Answer 3

Can accomplish a broad range of tasks.

Examples: none; theoretical concept.

Question 4

Data Dependence of Narrow vs. General AI

Answer 4

Narrow AI relies on large amounts of labeled data. General AI is capable of learning from minimal data and experiences.

Question 5

Autonomy of Narrow vs. General AI

Answer 5

Narrow AI can perform tasks based on predefined instructions. General AI can perform tasks autonomously and make independent decisions.

Question 6

5 Steps to General AI

According to OpenAI

Answer 6

1. Conversational AI - achieved by Open AI
2. Reasoners - OpenAI approaching
3. Agents
4. Innovators
5. Organizations

Question 7

Reasoning

Open AI’s Step 2 Toward AGI

Answer 7

Systems capable of solving complex problems with the proficiency/logic of human experts. Expected to perform problem-solving tasks as well as someone with a PhD-level education, without access to external resources.

Open AI is approaching this level.

Question 8

Agentic

OpenAI’s Step 3 toward AGI

Answer 8

Systems capable of operating autonomously for extended periods. May spend several days acting on a user’s behalf, taking on complex tasks, making decisions, and adapting to changing circumstances without constant human oversight.

May revolutionize industries by handling intricate operations.

Question 9

Innovative

OpenAI’s Step 4 to AGI

Answer 9

Systems capable of developing groundbreaking ideas and solutions across various fields.

Question 10

Organizational

OpenAI’s Step 5 to AGI

Answer 10

Systems capable of functioning as entire entities, possessing strategic thinking, operational efficiency, and adaptability to manage complex systems and achieve organizational goals.

Question 11

Sub-Fields of AI

Answer 11

• Machine Learning
• Natural Language Processing
• Computer Vision
• Robotics
• Expert Systems (reasoning)
• Fuzzy Logic (degrees of truth)
• Genetic Algorithms - optimization and search
• Speech Recognition
• Planning and Decision-making
• Knowledge Representation and Reasoning

Question 12

Inference

Answer 12

Process of using a trained model to make predictions or decisions based on new, unseen data. Essentially, it’s the model applying the knowledge it learned during the training phase to infer outcomes on inputs it hasn’t encountered before.

Question 13

Human-in-the-Loop

Answer 13

Human input is integrated into the machine learning process. Humans provide feedback, make decisions, and intervene when necessary, allowing the AI to learn from real-world scenarios and improve over time. Creates a continuous feedback loop that helps address uncertainties inherent in AI predictions, ensuring that the outcomes align more closely with human judgment and ethical standards.
Combines the strengths of both human expertise and machine efficiency to achieve results that neither could accomplish alone.

Question 14

Artificial General Intelligence (AGI)

Answer 14

Artificial intelligence that’s as smart or smarter than a human.

Question 15

Artificial Superintelligence (ASI)

Answer 15

Hypothetical software-based artificial intelligence system with an intellectual scope beyond human intelligence. At the most fundamental level, this superintelligent AI has cutting-edge cognitive functions and highly developed thinking skills more advanced than any human. Much smarter than a human.