Introduction

Hi

 AI  

deals with problem solving. Where it gets  

knowledge, is it accountable. Because as AI 

continues to advance, its applications are likely 

to expand, offering innovative solutions 

to an even wider range of challenges.

From many ways to use artificial intelligence, 

the hope of finding the solution to currently nagging

us problems is the most known reason. We simply ask and the results appear. Today we will read more into it.

Today the transversal skill that AI has and we will discuss is: problem solving. In this lesson we will take a look at many aspects of that system and how ai develops it.

At the end of the lesson, you will:

• know how ai solves problems 
• gain an understanding of how AI uses critical thinking 
• identify solved problems by AI 
• learn how the process of problem solving goes “step by step”
• what constitutes the obtained results achieved by AI 
• also AI data processing process

During the lesson you must read the written explanations and follow the given instructions, at interactive elements. By doing that you will learn more effectively, because we used learning methods that are commonly known to help you study and design to achieve better and better result 

Here are the explained methods provided by AI (it may just so help learn easily in the future) :

Discussion: (to check concentration during the movie) 

• To what Bradley was referring to as: “remarkably capable and fairly intelligent autumns systems that can explain why and how they make decisions (that we already have)”?
• What need to be write to define rules and logic that help AI think and react?
• Did lady in a red square where wearing striped or checked pans? (humour)

In a world where information is so readily available, it is essential to use critical thinking skills to evaluate the validity of the information we receive. By questioning what we read, we can gain a deeper understanding of the subject matter and potentially discover new insights. Furthermore, critical thinking allows us to examine the information from various perspectives, which can help us better understand the underlying assumptions and biases that may be present.

In the future, as AI becomes more prominent, critical thinking will become even more essential. AI is designed to make decisions using algorithms based on data and patterns. However, these algorithms are only as good as the data they are trained on, and they can also be biased by the assumptions and biases of their designers. Therefore, using critical thinking skills to evaluate AI’s decisions and ensure that they align with our values and goals will be essential. Additionally, critical thinking will be necessary for solving complex problems that AI may not be able to handle on its own. We can create a more informed and equitable future by using critical thinking skills in conjunction with AI.

Critical thinking by AI is an emerging field that holds promise and potential challenges. Critical thinking

Critical thinking by AI is an emerging field that holds promise and potential challenges. Critical thinking is the ability to analyse, evaluate, and synthesize information to make reasoned judgments and decisions. When it comes to AI, critical thinking can be approached in several ways:

This is a learning exercise that I hope help you understand more about how AI deals with critical thinking. Don’t worry if you can’t come up with the key words, the task aims to help you remember and learn more, in an interesting way.

key aspects of Critical thinking

Down

1. AI systems can assist humans in decision-making by providing data-driven insights and recommendations.  2. AI critical thinking can be tailored to specific domains or industries.
2. Identifying the problems or challenges, breaking them down into manageable components, and exploring various solutions or hypotheses.  6. AI systems engage in deductive and inductive processes to draw conclusions from available information. 7. Ability to sift through vast amounts of data, identifying patterns, trends, and outliers, which means collection and processing  8. AI systems can learn from their experiences and improve their critical thinking. This can involve adjusting algorithms, updating knowledge bases, or incorporating feedback from users. 

Across

3. AI systems assess the quality, reliability, and relevance of the information, using the techniques like data validation, fact-checking, and source credibility analysis. 5. Conclusions or recommendations based on the analysis and reasoning. 9. Handling unsureness by assigning probabilities to different outcomes and considering uncertainty in their decision-making processes. 10. Examining the data, such as fairness, bias mitigation, and ethical thinking. 

In summary, AI has the potential to enhance critical thinking by automating certain aspects of analysis, evaluation, and decision-making. However, it is not a replacement for human critical thinking, and careful consideration of biases, ethics, and the need for human oversight is essential in developing and deploying AI systems for critical thinking tasks.

Artificial intelligence primarily deals with problem-solving. It refers to the theory and development of robust computer systems that can efficiently handle tasks that have historically required human intelligence. Moreover, this field builds on datasets, Machine Learning (ML), and neural networks to learn through trial and error and positive reinforcement. Additionally, AI is highly adept at finding obscure data patterns by considering thousands of data points from a variety of sources.

Human intelligence is a mental quality that comprises certain survival abilities. These include learning from experience, adapting to new situations, and utilizing knowledge to the individual’s

advantage. Additionally, it equips humans to handle abstract concepts and manipulate their surroundings to flourish as a species. Furthermore, although different investigators have prioritized diverse aspects of intelligence, adaptability is a key driving force behind human intelligence.

The links which were used to help create Block 1:

https://www.aier.org/article/ai-critical-thinking-and-the-future-of-freedom/

https://chat.openai.com/?model=text-davinci-002-render-sha

https://pdf.co/blog/problem-solving-techniques-in-artificial-intelligence-ai

More information connected to the topic, if you want to learn further 

about the process of critical thinking in AI:  

https://www.torch.ox.ac.uk/event/the-philosophy-and-critical-thinking-of-ai

https://www.aier.org/article/ai-critical-thinking-and-the-future-of-freedom/

Now with the use of already know paragraphs we will disgust the part of problem solving which is formulation and decomposition.

AI can be a valuable tool for problem formulation and decomposition by leveraging data analysis, NLP, optimization algorithms, and learning mechanisms to enhance the problem-solving process. It can assist in breaking down complex problems, finding optimal solutions, and continuously improving problem-solving strategies. Here is a small scenario to sum up the process of learning those AI advantages:

• Firstly the media element about what constitutes the problem solving process 

Then text content considering foundations of problem Formulation and Decomposition and use of it in practise.

•   Interactive task that encourages you to take part in a discussion, think about the use of AI that will turn out to be useful in the future when you come upon a problem

At the end are  Resources, please read into them because they were choose carefully for you to gain additional knowledge.

decomposition are 

important aspects of 

problem-solving that can

be enhanced with the use 

of AI. 

Here’s how AI can assist 

in problem formulation 

and decomposition:

Formulation and Decomposition

Problem Formulation and Decomposition

Formulation and Decomposition

Problem formulation decomposition in context of AI Prompt Engineering

Problem formulation and prompt engineering differ in their focus, core tasks,   

and underlying abilities. Prompt engineering focuses on crafting the optimal textual input by selecting the appropriate words, phrases, sentence structures, and punctuation. In contrast, problem formulation

emphasizes defining the problem by delineating its focus, scope, and boundaries. Prompt engineering requires a firm grasp of a specific AI tool and linguistic proficiency while problem formulation necessitates a comprehensive understanding of the problem domain and ability to distil real-world  

issues. The fact is, without a well-formulated problem, even the most sophisticated prompts will fall short. However, once a problem is clearly  

defined, the linguistics nuances of a prompt become tangential to the solution. 

Unfortunately, problem formulation is a widely overlooked and underdeveloped skill for most of us. One reason is the disproportionate emphasis given to problem-solving at the expense of formulation. This imbalance is perhaps best illustrated by the prevalent yet misguided management adage, “don’t bring me problems, bring me solutions.” It is therefore not surprising to see a recent survey revealing that 85% of C-suite executives consider their organizations bad at diagnosing problems.

How can you get better at problem formulation? By synthesizing insights from past research on problem formulation and job design, as well as my own experience and research on crowdsourcing platforms — where organizational challenges are regularly articulated and opened up to large audiences — I have identified four key components for effective problem formulation: problem diagnosis, decomposition, reframing, and constraint design.

Problem decomposition entails breaking down complex problems into smaller, manageable sub-problems. This is particularly important when you are tackling multifaceted problems, which are often too convoluted to generate useful solutions.

Take the InnoCentive Amyotrophic Lateral Sclerosis (ALS) challenge for example. Rather than seeking solutions for the broad problem of discovering a treatment for ALS, the challenge concentrated on a subcomponent of it: detecting and monitoring the progress of the disease. Consequently, an ALS biomarker was developed for the first time, providing a non-invasive and cost-efficient solution based on measuring electrical current flow through muscle tissue.

The links which were used to help create Block 1:

https://www.geeksforgeeks.org/what-is-problem-decomposition/

https://chat.openai.com/?model=text-davinci-002-render-sha

https://hbr.org/2023/06/ai-prompt-engineering-isnt-the-future

More information connected to the topic, if you want to learn further 

about the process of Problem Formulation and Decomposition by AI:

https://open.spotify.com/episode/2YKHZ1hgNpY8Z6YAnEaLOO?si=3ddd481b6d73471e

https://www.sciencedirect.com/science/article/abs/pii/0020025585900155

https://blog.google/technology/ai/9-ways-we-use-ai-in-our-products/

D:AIDesktopFundamentals od Artificial Intelligence.pdf

Summarise the time:

Learning outcomes:

In this lesson we have learned the critical thinking provided by artificial intelligence, the process of problem solving and the prices of Formulation and Decomposition which has to be done by AI to achieve desired results.

By completing this lesson you are able to achieve previously set up goals. 

(you can read them again and check each other in pairs by doing that)

• know how ai solves problems 
• gain an understanding of how AI uses critical thinking 
• identify solved problems by AI 
• learn how the process of problem solving goes “step by step”
• what constitutes the obtained results achieved by AI  
• also AI data processing process

The skills to use AI carefully and with understanding of its ability.

In the next block you will acquire new knowledge about

Data Analysis in the alternative intelligence system. AI analyses data through a combination of data pre-processing, machine learning algorithms, specialized techniques (NLP, computer vision, time series analysis), and tools for model evaluation and validation. It plays a crucial role in extracting insights, making predictions, automating tasks, and solving complex problems across various domains. So to sum up its important to know where AI takes its knowledge and what are the processes of doing that if we want to use it on a daily basis, for example in our education.