# Solving Math/Stats Problems in Copilot - Bing Chat - Prompt Engineering basics

TLDRThe video demonstrates the use of Bing Chat, a Microsoft AI product, to solve quantitative problems. It highlights the AI's challenges with interpreting data from images and emphasizes the importance of clear, text-based prompts for accurate results. The AI makes several mistakes, which are corrected through user intervention, underscoring the need for critical assessment of AI responses.

### Takeaways

- π€ The video demonstrates using AI (specifically Bing Chat) to solve quantitative problems by providing examples of guiding AI with prompts.
- π² The first problem involved calculating the probability of rolling two dice to get a sum less than 4, highlighting AI's initial mistake and subsequent correction.
- π The second task was to determine missing values in a frequency table from an image, showcasing the challenges AI faces with image interpretation.
- π’ The third problem required finding the value of X in a statistical table, which AI initially struggled with due to insufficient information from the image.
- π AI acknowledged its errors and provided corrected answers after user intervention, emphasizing the importance of user engagement for accurate results.
- π‘ The video script suggests that AI can be a helpful tool for quantitative problem-solving but is not infallible and requires careful oversight.
- π Translating visual information into text can improve AI's accuracy, as demonstrated by the successful resolution of the second problem when text was used instead of an image.
- π€ The video underscores the need for users to have a strong understanding of the material to critically assess and guide AI responses effectively.
- π οΈ The use of creative prompts and clear communication is essential for achieving accurate outcomes when working with AI on quantitative tasks.
- π AI's performance on the tasks indicates that while it can process and calculate data, it may struggle with understanding context from images or incomplete data.
- π₯ The video serves as a tutorial on how to interact with AI to solve quantitative problems, providing insights into both its capabilities and limitations.

### Q & A

### What was the main objective of the video?

-The main objective of the video was to demonstrate the use of prompts to guide AI towards solving quantitative problems and to showcase examples of using Bing Chat for this purpose.

### Which AI product was used in the video to solve quantitative problems?

-Bing Chat, a free generative AI product by Microsoft, was used in the video.

### What was the error made by Bing Chat in the first quantitative problem?

-Bing Chat incorrectly listed three outcomes with a sum of 2 and two outcomes with a sum of 3, instead of the correct two outcomes for each sum. It also provided an incorrect probability of 6/36.

### How did Bing Chat correct its mistake in the first problem?

-After being pointed out by the user, Bing Chat apologized for the mistake, checked its calculations, and corrected the probability to 3/36 or 1/12.

### What issue did Bing Chat face with the frequency table problem?

-Bing Chat struggled with interpreting the image of the frequency table and provided incorrect calculations for the missing values A, B, and C.

### How did the user help Bing Chat to solve the frequency table problem correctly?

-The user narrated the problem using text, providing frequencies and relative frequencies for each class, which allowed Bing Chat to correctly calculate the values of A, B, and C.

### What was the issue with Bing Chat's response to the third quantitative problem involving an image?

-Bing Chat initially stated that it could not find the value of X in the image because there was not enough information. It later made errors in calculating the cumulative frequency percent for different classes.

### How did Bing Chat eventually arrive at the correct cumulative frequency percent for the 35-39 class?

-After the user clarified that they were looking for the cumulative frequency percent for the 35-39 class, Bing Chat correctly calculated the sum of frequencies up to and including that class and divided it by the total number of observations to obtain 85%.

### What is the key takeaway from the video regarding the use of AI for quantitative problem-solving?

-The key takeaway is that while AI like Bing Chat can assist in quantitative problem-solving, it still makes mistakes. Users need to understand the material, critically assess AI responses, and use creative prompts for accurate results.

### How can users improve their interactions with AI like Bing Chat?

-Users can improve their interactions with AI by providing additional information, translating images into text when necessary, and using clear and specific prompts to guide the AI towards accurate responses.

### What was the final verdict on Bing Chat's performance in quantitative problem-solving?

-The final verdict was that Bing Chat, or generative AI in general, can assist in quantitative problem-solving but is not yet fully reliable and requires user oversight for accuracy.

### Outlines

### π€ AI's Struggle with Basic Probability

The video begins with the creator attempting to solve quantitative problems using Bing Chat, a Microsoft AI product. The first task involves calculating the probability of rolling two dice to get a sum less than 4. The AI incorrectly identifies 6 outcomes with sums less than 4, repeating some outcomes and miscounting the sums of 2 and 3. After the creator points out the error, Bing Chat corrects itself and provides the accurate probability of 1/12. The video then moves on to a frequency table problem where the AI struggles with interpreting a screenshot and calculating missing values, ultimately providing incorrect answers. The creator emphasizes the need to guide the AI with precise prompts and convert images to text for better results.

### π Correcting AI's Flaws in Frequency Analysis

In the second part of the video, the creator addresses another quantitative problem involving a frequency table with missing values. Initially, Bing Chat misunderstands the task and provides incorrect formulas and results for calculating cumulative frequency percent. After the creator corrects the AI and asks for the cumulative frequency percent for a specific class interval, Bing Chat finally calculates the correct value. The video concludes by highlighting the potential of AI in quantitative problem-solving, while also acknowledging its current limitations and the importance of critical assessment and creative prompting by the user to achieve accurate results.

### Mindmap

### Keywords

### π‘quantitative problems

### π‘Bing Chat

### π‘probability

### π‘dice rolls

### π‘hallucinating

### π‘relative frequency

### π‘frequency table

### π‘cumulative frequency percent

### π‘AI responses

### π‘critical assessment

### π‘creative prompts

### Highlights

The video demonstrates the use of Bing Chat, a generative AI by Microsoft, for solving quantitative problems.

The objective is to showcase how prompts can guide AI towards desired results.

The first problem involves calculating the probability of the sum of two dice rolls being less than 4.

Bing Chat initially provided incorrect outcomes and probabilities due to hallucination in its response.

After clarification, Bing corrected the probability to 3/36 or 1/12 for the sum less than 4.

The second problem involves a frequency table with missing values that need to be determined.

Bing struggled with interpreting a screenshot of the frequency table and provided incorrect calculations initially.

Upon narrating the problem in text form, Bing successfully calculated the missing values A, B, and C.

The third problem requires finding the value of X in an image, which Bing initially could not due to insufficient information.

After being guided to calculate the total number of observations, Bing correctly found the cumulative frequency percent for the 35-39 class.

The video highlights the importance of critically assessing AI responses for accuracy.

It emphasizes the need for users to understand the material and use creative prompts for better AI assistance.

Bing Chat can assist in quantitative problem-solving but is not flawless and makes mistakes.

The video serves as a tutorial on how to effectively interact with AI for problem-solving.

AI tools like Bing Chat require user guidance and additional information to provide accurate results.

The video showcases the potential and limitations of AI in quantitative analysis.

The transcript provides a detailed account of the interaction between the user and Bing Chat, highlighting the iterative process of obtaining correct AI responses.

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