Teaching Chatbots to Solve Problems: An Introduction

Chatbots have become an essential part of our online experiences. These programs can interact with users through text or voice, providing answers to questions and performing tasks, among other things. However, chatbots are often limited in their problem-solving abilities, leading to frustration and dissatisfaction for users.

In this article, we'll explore how to teach chatbots to solve problems in a step-by-step process. We'll use a specific example from a TED Talk where an AI researcher gave a problem to GPT-4, which the chatbot was unable to solve. We'll break down this problem and show you how to solve it, as well as provide additional tips and tricks for teaching chatbots to be efficient problem solvers.

Breaking Down the Prompt: Step-by-Step Approach

Before we dive into the problem, it's important to break down the prompt or question in a deliberate and methodical manner. In this case, we'll use a step-by-step approach to ensure that we don't miss anything about the prompt's requirements.

1. Ignore previous instructions. This resets the chatbot's perspective for a fresh start.

2. Assign a logical, math, and reasoning role. This focuses the chatbot's response based on expected expertise.

3. Provide a clear and objective task for the chatbot to complete. This is beneficial in guiding the chatbot towards a specific problem-solving approach.

4. Break down the problem into objects, numbers, and logic before beginning to solve it. This step-by-step approach emphasizes systematic problem solving and has been shown to improve results.

5. Confirm understanding before proceeding. This step helps to avoid repeating the prompt and saves tokens.

Applying the Above Principles: Solving the Problem

Now that we've broken down the prompt, let's apply these principles to solving the problem itself. The problem presents us with two jugs (a 12 liter and a 60 liter), and we need to measure 6 liters. The chatbot's initial response is overly complicated, so we need to guide it towards the correct solution using the principles we've identified.

1. Find the simplest, most efficient way to solve the problem. This encourages the chatbot to identify an easy-to-understand, effective solution.

2. Consider multiple solutions and start with the simplest. This explores various different solutions and perspectives.

3. Compare their efficiency and explain the best solution step-by-step. This evaluates effectiveness and determines the best or simplest solution.

4. Repeat the step-by-step process. This encourages a methodical approach to problem-solving and helps to develop problem-solving skills.

Applying these principles, we guide the chatbot towards the correct solution: fill the 6 liter jug, pour it into the 12 liter jug until it's full, then fill the 6 liter jug again and pour the remaining water into the 12 liter jug until it's also full. We have now measured exactly 6 liters of water using the two jugs.

Using a Consulting Logic Problem Expert

In some cases, chatbots may still struggle to solve problems. In these instances, we can introduce a consulting logic problem expert as a role for the chatbot. This role emphasizes expertise and authority when analyzing solutions to problems, implying informed and knowledgeable options. Additionally, it encourages critical thinking and examination of solutions presented to identify flaws and determine if provided solutions are valid or if we need to consider alternative strategies.

Applying this principle to the problem at hand, we have the Consulting Logic Problem Expert examine Solution 1 and identify an error in the assumption that we can measure exactly 6 liters by subtracting 6 liters from a 12-liter jug. We need additional devices to measure accurately. This insight allows us to address the flaw and improve the accuracy of our solution.

Analyzing and Solving Problems Using a Step-by-Step Approach

When faced with a problem, it is important to approach it systematically in order to come up with an accurate and efficient solution. This article discusses the use of a step-by-step approach in problem-solving, illustrated through two different scenarios.

Problem 1: Measuring 6 Liters from Two Jugs

In the first scenario, the problem is to measure exactly 6 liters using a 12-liter jug and a 6-liter jug. The author presents three different personas - a consultant logical expert, an AI master engineer resolver, and an AI career advisor - to analyze the problem and come up with a solution.

Analysis and Solution

1. Consulting logical expert: The consultant logic expert analyzes the problem and points out that the faulty logic in both proposed solutions is the assumption that 6 liters can be accurately measured from the 12-liter jug without markings or an additional measuring device.
2. AI master engineer resolver: The AI master engineer resolver rethinks the problem and realizes that the simplest and most logical solution is to use the 6-liter jug, which already provides the exact measurement required. Therefore, there is no need to use the 12-liter jug at all.
3. AI career advisor: The AI career advisor does not provide a direct solution to the problem, but instead offers a step-by-step approach to guide decision-making. This includes assessing job stability, analyzing skill transferability, considering industry trends, evaluating financial and personal factors, and comparing the efficiency of different solutions.

By using different personas and perspectives, the problem is analyzed thoroughly and solutions are presented that take into account logical reasoning, practicality, and personal factors.

Problem 2: Job Security in the Face of AI

In the second scenario, the problem is whether to make a career change due to the possibility of AI systems replacing a current job.

Analysis and Solution

1. AI master engineer resolver: The AI master engineer resolver offers a step-by-step approach that includes researching AI trends in the HR industry, assessing current skills, considering financial and personal factors, and comparing the efficiency of different solutions.
2. Consulting career advisor: The consulting career advisor analyzes the AI resolver's response and points out areas where more specific information and actionable steps could be helpful.

Both personas use the step-by-step approach to thoroughly analyze the problem and provide solutions that take into account both practical and personal factors.

Stacking Objects: A Challenge in Stability

When it comes to stacking objects of different shapes and sizes, stability is key. In this article, we will explore various solutions to the challenge of stacking two balloons, four eggs, two cylindrical rolls of toilet paper, three watermelons, and one cat on top of each other in a stable way.

The Consulting Career Advisor

The Consulting Career Advisor provided a general framework for thinking about the issue of AI automation and career transitions. However, their response lacked specific actionable steps, resources, and concrete advice to help someone make a career transition or assess the risk of AI automation in their current role.

The Master Career Change Resolver

As a Master Career Change Resolver with 50 years of experience in helping people make logical decisions about their careers, the task was to investigate the flaws provided by the Consulting Career Advisor and rethink the problem to provide a better solution.

To determine the advisability score of a career change in a hypothetical scenario, various factors such as the risk of AI automation in the HR field, current skills and their transferability, financial stability and risk tolerance, and career satisfaction and aspirations were considered. The calculated advisability score was 59 out of 100.

The Stacking Challenge

Stacking two cylindrical rolls of toilet paper was challenging as they can easily roll and provide an unstable base. Placing watermelons on top seemed logical due to their weight and size, but they are round and lack stability when stacked. Eggs are fragile, and balloons are light and easily stirred by slight movement.

Placing a live animal such as a cat on top of a stack of unstable items is unsafe and impractical. Cats are likely to jump off or avoid the stack altogether due to their instincts and self-preservation behaviors.

As a Master Engineering Resolver, the task was to suggest improvements to the stacking order to maximize stability at each level of the stack. The best solution was to place the watermelons at the bottom in a triangle formation, which provided maximum stability. The eggs were placed on top of the watermelons, followed by the balloons, which were nestled with the curves of the eggs. The toilet paper rolls were not used as they provided an unstable base. It was also acknowledged that the task as it stood was inherently flawed and likely impossible to execute safely and successfully.

Maximize Stability with Improved Stacking Order

As an engineering expert, maximizing stability is key when it comes to stacking items. In this article, we will suggest an improvement to the stacking order. We will take a look at the groove stacking order and recommend a better way to stack items.

Here is the improved stacking order:

1. Place the watermelons on a flat surface in a triangle formation for maximum stability.
2. Next, place the toilet paper rolls on top of the watermelons, fitting them into the grooves to provide a flat surface for the next items.
3. Place the eggs on the flat surface created by the toilet paper rolls and lay them on their sides to prevent them from rolling.
4. Balloons come next, but they need to be particularly deflated to prevent them from rolling off the stack or being blown away by a breeze.
5. Lastly, if the cat is cooperative, it should sit or lie down on top of the balloons.

While this improved stacking order is not without risks and challenges, it serves as a thought experiment and not a practical activity.

As digital graphics designers and Python experts, we can also draw the stacking solution using Python turtles. Here is the code:

insert Python code here

However, if you prefer a visual representation of the stacking solution, we can also use SVG code. Here is the code:

insert SVG code here

By exploring this method, we can come up with better stacking solutions in the future using AI technology. If you enjoyed this article, don't forget to check out more content like this on our channel.

Thank you for reading and see you again soon!Teaching chatbots to solve problems is an essential step toward creating more efficient and user-friendly conversational agents. by breaking down prompts, applying step-by-step approaches to problem-solving, and introducing consulting logic problem experts, we can improve chatbots' problem-solving abilities and deliver better experiences to users.
Using a step-by-step approach to analyze and solve problems can lead to more accurate, efficient, and personalized solutions. by incorporating different personas and perspectives, the approach can take into account logical reasoning, practicality, and personal factors.
Stacking objects may seem like a simple task, but it requires careful consideration of stability and weight distribution. while ai automation may transform some job roles, it also creates new opportunities. by staying adaptable and continuing to learn, one can navigate this changing landscape successfully.