AI Problems

*

1

AI Problems

• Game playing and theorem proving share the property that people who do them well are displaying intelligence.
• Despite this it appeared that computers could perform well at those tasks by being fast at exploring a large number of solution paths and then selecting the best one.
• But no computer is fast enough to overcome the combinatorial explosion generated by most problems.
• AI focusing on the sort of problem solving we do every day for instance, when we decide to get to work in the morning, often called commonsense reasoning.
• In investigating this sort of reasoning Newell, Shaw, and Simon built the General Problem Solver (GPS), which they applied to several commonsense tasks as well performing symbolic manipulations of logical expression.
• However, no attempt was made to create a program with a large amount of knowledge about a particular problem domain. Only quite simple tasks were selected.

*

2

AI Problems (cont..)

• As AI research progressed and techniques for handling larger amounts of world knowledge were developed in dealing with problem solving in specialized domains such as medical diagnosis and chemical analysis.
• Perception (vision and speech) is another area for AI problems.
• Natural language understanding and problem solving in specialized domain are other areas related to AI problems.
• The problem of understanding spoken language is perceptual problem and is hard to solve from the fact that it is more analog related than digital related.
• Examples of such as tasks include engineering design, scientific discovery, medical diagnosis, and financial planning.
• Programs that can solve problems in these domains also fall under the aegis of Artificial Intelligence.

*

3

AI Problems (cont..)

*

4

AI Problems (cont..)

• When one is building an expert system, following questions need to be considered before one can progress further:
• What are the underlying assumptions about intelligence?
• What kinds of techniques will be useful for solving AI problems?
• At what level if at all can human intelligence be modelled?
• When will it be realized when an intelligent program has been built?

*

5

*

6

AI Problems (cont..)

Key Terminologies in Artificial Intelligence Problems: Before exploring the characteristics, let’s clarify some essential AI concepts:

• Problem-solving: Problem-solving is a process that is a solution provided to a complex problem or task. When dealing with AI, problem-solving involves creating algorithms and methods of artificial intelligence that will empower machines to imitate humans’ capabilities of logical and reasonable thinking in certain situations.
• Search Space: Searching space refers to the area where an agent involved in the problem-solving process can examine all the possible states or settings with the hope of discovering a solution. It covers a gamut of options that the agent might select for arriving at the same destination.
• State: An entity represents some unique and specific arrangement of elements in a problem-solving situation. States can be assigned to different locations, challenges, or dangers that the problem-solving agent faces while looking for a solution to the problem within the search space.

​

*

7

AI Problems (cont..)

• Search Algorithm: A search algorithm describes any process or method targeted for examining and exploring the given problem space to find a solution.

Algorithm decision-making has diverging levels of complexity and effectiveness. They are studied to help in the discovery of the most suitable results.

• Heuristic: Heuristic is a thumb rule or guiding principle that is used to make intelligent decisions or solve the problems that are encountered during the process.

Applying heuristics in AI is prevalent in prioritizing search paths or evaluating probable solutions based on their likelihood of finishing successfully.

• Optimization: The problem of optimization implies finding the best solution for process selection among the set of feasible alternatives submitted to some previously set objectives or criteria.

AI optimization approaches are employed to deal optimally with complex issues through performance and efficiency improvement.

*

8

AI Problems (cont..)

Characteristics of Artificial Intelligence Problems:

1. Learning and adaptation:�AI systems should be capable of learning from data or experiences and adapting their behavior accordingly. This enables them to improve performance over time and handle new situations more effectively.
2. Complexity:�AI problems often involve dealing with complex systems or large amounts of data. AI systems must be able to handle this complexity efficiently to produce meaningful results.
3. Uncertainty: �AI systems frequently operate in environments where outcomes are uncertain or incomplete information is available. They must be equipped to make decisions or predictions under such conditions.

​

*

9

AI Problems (cont..)

4. Dynamism: �Environments in which AI systems operate can change over time. These changes may occur unpredictably or according to specific rules, requiring AI systems to continually adjust their strategies or models.
5. Interactivity: �Many AI applications involve interaction with users or other agents. Effective AI systems should be able to perceive, interpret, and respond to these interactions in a meaningful way.
6. Context dependence:�The behavior or performance of AI systems may depend on the context in which they operate. Understanding and appropriately responding to different contexts is essential for achieving desired outcomes.

*

10

AI Problems (cont..)

7. Multi-disciplinary:�AI problems often require knowledge and techniques from multiple disciplines, including computer science, mathematics, statistics, psychology, and more. Integrating insights from these diverse fields is necessary for developing effective AI solutions.
8. Goal-oriented Design:�AI systems are typically designed to achieve specific objectives or goals.

Designing AI systems with clear objectives in mind helps guide the development process and ensures that the resulting systems are focused on achieving meaningful outcomes.

​

​

*

11

AI Problems (cont..)

Examples of AI Applications and Challenges Across Domains:

1. Robotics

Problem: A delivery robot navigating a busy warehouse to locate and retrieve a specific item.

Characteristics:

• Complexity: Industrial storage is networked, in the middle of things, with obstacles, and other robots and people moving unpredictably. This robot must process the visual scene, plan the route effectively, and detect and avoid possible collisions.
• Dynamism: A combination of outside factors leads to change, which is a constant inside the warehouse. Unpredictable system failures or spontaneous tasks can make the robot change its means and decision-making at the moment of need.
• Uncertainty: Sensor data (such as images obtained from a camera) might be noisy, incomplete, and unstable. The robot could be handling decisions based on fragmented or formless pieces of information.

​

*

12

AI Problems (cont..)

2. Natural Language Processing (NLP)

Problem: A sentiment analysis system in NLP classifying customer reviews as positive, negative, or neutral.

Characteristics:

• Subjectivity: Human language is nuanced. Sarcasm, irony, and figurative expressions can be difficult for machines to accurately interpret.
• Need for Context: Understanding sentiment may depend on cultural references, product-specific knowledge, or even the reviewer’s prior interactions with the company.
• Ambiguity: A single word or phrase could have multiple meanings, affecting the overall sentiment of the text.

​

*

13

AI Problems (cont..)

3. Computer Vision

Problem: A medical image recognition system in Computer Vision designed to detect tumors in X-rays or MRI scans.

Characteristics:

• Complexity: Medical images are highly detailed and can exhibit subtle variations. The system needs to distinguish between healthy tissue and potential abnormalities.
• Uncertainty: Images may contain noise or artifacts. The presence of a tumor might not be immediately obvious, requiring the system to handle ambiguity.
• Ethical Considerations: False positives or false negatives have serious consequences for patient health. Accuracy, transparency, and minimizing bias are crucial.

​

*

14