Module Question 1
1. Narrow AI and General AI
Question:
How would you explain the difference between narrow AI and general AI to someone with no technical background?
Status:
Completed
Description:
Narrow AI is an artificial intelligence system designed to perform a specific task or a limited range of tasks. Examples include recommendation systems, voice assistants, and language translation tools. These systems can perform their assigned tasks effectively but cannot easily perform tasks outside their designed capabilities. In contrast, General AI (AGI) refers to a theoretical form of AI that would be able to understand, learn, and perform a wide range of intellectual tasks similar to humans. Currently, narrow AI is widely used, while true General AI has not yet been achieved. IBM also explains that Narrow AI is the type of AI that currently exists, while AGI remains a theoretical concept.
Evidence:
IBM – Understanding the Different Types of Artificial Intelligence
Full link:
https://www.ibm.com/think/topics/artificial-intelligence-types
Additional source:
https://www.ibm.com/think/topics/artificial-general-intelligence
2. Dartmouth Workshop in 1956
Question:
What was the significance of the Dartmouth Workshop in 1956?
Status:
Completed
Description:
The Dartmouth Workshop in 1956 was a significant event in the history of Artificial Intelligence. It brought together mathematicians and scientists to discuss whether human intelligence could be recreated in machines. The term “Artificial Intelligence” was coined and discussed during the workshop. The ideas developed during the workshop provided an important foundation for AI research and helped establish AI as a distinct scientific field. Dartmouth College itself describes the 1956 workshop as the birthplace of a new scientific field.
Evidence:
Dartmouth College – Our Story: Where AI Was Born
Full link:
https://ai.dartmouth.edu/our-story
3. Turing Test
Question:
In your opinion, is the Turing Test still a valid measure of machine intelligence? Justify your answer.
Status:
Completed
Description:
In my opinion, the Turing Test is still useful, but it should not be considered a complete measure of machine intelligence. The test evaluates whether a machine can communicate in a way that is difficult to distinguish from human communication. This remains relevant because modern AI systems can produce increasingly human-like conversations. However, passing the Turing Test does not necessarily prove that a machine truly understands information, reasons like a human, or possesses consciousness. Therefore, the Turing Test can be used as one measure of AI performance, but it should be combined with other evaluation methods to measure machine intelligence more comprehensively. Stanford HAI also notes that the Turing Test has become controversial because modern chatbots can imitate human conversation without necessarily demonstrating genuine understanding.
Evidence:
Stanford Institute for Human-Centered Artificial Intelligence (HAI) – What Is the Turing Test?
Full link:
https://hai.stanford.edu/ai-definitions/what-is-the-turing-test
4. The AI Effect
Question:
What is the “AI effect,” and can you provide a modern example?
Status:
Completed
Description:
The AI effect refers to the phenomenon in which a technology that was once considered artificial intelligence becomes viewed as ordinary technology after it becomes common and familiar to people. In other words, when AI successfully solves a particular problem, people may stop considering that ability to be “AI” and instead regard it as a normal technological feature. A modern example is navigation technology. Today, people commonly use applications that automatically calculate routes and provide real-time directions. Because this technology has become familiar and widely used, people may no longer think of it as AI, even though similar capabilities were once considered advanced AI problems. Stanford’s One Hundred Year Study on Artificial Intelligence describes this phenomenon as the “AI effect.”
Evidence:
Stanford University – One Hundred Year Study on Artificial Intelligence: Defining AI
Full link:
https://ai100.stanford.edu/2016-report/section-i-what-artificial-intelligence/defining-ai
Additional Stanford source:
https://ai100.stanford.edu/sites/g/files/sbiybj18871/files/media/file/AI100Report_MT_10.pdf
Module Question 2
1. Uninformed Search vs. Informed Search
Question:
Explain the primary difference between an uninformed and an informed search algorithm.
Status:
Completed
Description:
The primary difference between uninformed and informed search algorithms is the information they use to find a solution. Uninformed search algorithms, such as Breadth-First Search (BFS) and Depth-First Search (DFS), do not have additional information about which direction is closer to the goal. They explore the search space based only on the problem definition and available paths. In contrast, informed search algorithms use additional information, called a heuristic, to estimate which path is more likely to lead to the goal. Therefore, informed search can often find a solution more efficiently because it focuses on more promising paths.
Evidence:
University of California, Berkeley – Introduction to Artificial Intelligence: Informed Search
Full link:
https://inst.eecs.berkeley.edu/~cs188/textbook/search/informed.html
University of California, Berkeley – Uninformed Search
Full link:
https://inst.eecs.berkeley.edu/~cs188/textbook/search/uninformed.html
2. Depth-First Search vs. Breadth-First Search
Question:
In what scenario would Depth-First Search be more efficient than Breadth-First Search?
Status:
Completed
Description:
Depth-First Search (DFS) can be more efficient than Breadth-First Search (BFS) when the solution is likely to be located deep in the search tree and the available memory is limited. DFS explores one path as deeply as possible before backtracking, so it generally requires less memory than BFS. In comparison, BFS explores all nodes at one level before moving to the next level, which can require a large amount of memory. Therefore, DFS is useful for problems with deep search spaces where finding any solution is more important than finding the shortest solution.
Evidence:
Oregon State University – AI Search: Breadth-First Search and Depth-First Search
Full link:
https://web.engr.oregonstate.edu/~huanlian/teaching/ai100/2026winter/unit2-symbolic/ai-search.html
University of California, Berkeley – Uninformed Search
Full link:
https://inst.eecs.berkeley.edu/~cs188/textbook/search/uninformed.html
3. Heuristic Function and A* Algorithm
Question:
What is a heuristic function, and what role does it play in the A* algorithm?
Status:
Completed
Description:
A heuristic function is a method used to estimate the remaining cost or distance from a current state to the goal. It helps an AI search algorithm determine which path appears to be more promising. In the A* algorithm, the heuristic function is represented by h(n) and is combined with the actual cost of reaching the current node, represented by g(n). A* uses these values to determine which path should be explored next. This allows A* to search more efficiently while still being able to find an optimal path when an appropriate admissible heuristic is used.
Evidence:
University of British Columbia – Artificial Intelligence: Foundations of Computational Agents, A* Search
Full link:
https://www.cs.ubc.ca/~poole/aibook/3e/html/ArtInt3e.Ch3.S6.html
University of California, Berkeley – Informed Search and Heuristics
Full link:
https://inst.eecs.berkeley.edu/~cs188/textbook/search/informed.html
4. Real-World Application of Search Algorithms
Question:
Describe a real-world problem (e.g., GPS navigation) that can be solved using a search algorithm.
Status:
Completed
Description:
One real-world problem that can be solved using a search algorithm is GPS navigation. When a user enters a destination, the system needs to find a suitable route from the current location to the destination. The locations can be represented as nodes, while roads can be represented as connections between the nodes. A search algorithm such as A* can evaluate different routes by considering the actual distance or travel cost and estimating the remaining distance to the destination. This allows the system to find an efficient route instead of checking every possible road combination. GPS navigation is therefore a practical example of how search algorithms can be applied to solve route-finding problems.
Evidence:
Cornell University – A* Search
Full link:
https://www.cs.cornell.edu/courses/cs312/2007sp/recitations/rec26.html
University of British Columbia – A* Search
Full link:
https://www.cs.ubc.ca/~poole/aibook/3e/html/ArtInt3e.Ch3.S6.html