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

Joshua Emmanuel
31 Oct 202306:27

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

  • πŸ€– The video demonstrates using AI (specifically Bing Chat) to solve quantitative problems by providing examples of guiding AI with prompts.
  • 🎲 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.
  • πŸ“Š The second task was to determine missing values in a frequency table from an image, showcasing the challenges AI faces with image interpretation.
  • πŸ”’ 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.
  • πŸ‘ AI acknowledged its errors and provided corrected answers after user intervention, emphasizing the importance of user engagement for accurate results.
  • πŸ’‘ The video script suggests that AI can be a helpful tool for quantitative problem-solving but is not infallible and requires careful oversight.
  • πŸ“ 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.
  • πŸ€” The video underscores the need for users to have a strong understanding of the material to critically assess and guide AI responses effectively.
  • πŸ› οΈ The use of creative prompts and clear communication is essential for achieving accurate outcomes when working with AI on quantitative tasks.
  • πŸ“ˆ 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.
  • πŸ₯‚ 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

00:00

πŸ€– 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.

05:04

πŸ“Š 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

πŸ’‘quantitative problems

Quantitative problems refer to mathematical or statistical questions that involve numerical data. In the video, the presenter attempts to solve three such problems using an AI tool, highlighting the challenges and potential errors that can occur when using AI for quantitative analysis.

πŸ’‘Bing Chat

Bing Chat is a generative AI product by Microsoft that can interact with users through text-based conversations. In the context of the video, the presenter uses Bing Chat to attempt to solve quantitative problems, showcasing its capabilities and limitations.

πŸ’‘probability

Probability is a measure of the likelihood that a particular event will occur, expressed as a number between 0 and 1. In the video, the presenter uses this concept to calculate the chance of getting a sum less than 4 when rolling two dice.

πŸ’‘dice rolls

Dice rolls are the act of casting dice, small cubes with numbers, to generate random numbers. In the video, the dice rolls are used to illustrate a probability problem where the sum of the numbers rolled is analyzed.

πŸ’‘hallucinating

In the context of AI, 'hallucinating' refers to the phenomenon where the AI generates incorrect or nonsensical outputs, often due to misunderstanding the input or context. In the video, Bing Chat is said to hallucinate when it repeats the same outcome three times for sums of 2 and 3.

πŸ’‘relative frequency

Relative frequency is the proportion of a particular outcome in relation to the total number of occurrences. It is used in the video to calculate the frequencies of different outcomes in a dataset.

πŸ’‘frequency table

A frequency table is a statistical tool that displays the number of occurrences, or frequency, of each value or category in a dataset. In the video, the presenter uses a frequency table with missing values to demonstrate how AI can assist in filling in these gaps.

πŸ’‘cumulative frequency percent

Cumulative frequency percent is the percentage of the total number of observations that fall within or below a certain value in a dataset. It is calculated by dividing the cumulative frequency by the total number of observations. In the video, this concept is used to find the percentage for a specific class interval.

πŸ’‘AI responses

AI responses refer to the output or answers generated by an artificial intelligence system in response to user input. The video critiques the accuracy of AI responses, particularly in the context of quantitative problem-solving, and emphasizes the need for users to critically assess these responses.

πŸ’‘critical assessment

Critical assessment involves a thorough and analytical examination to evaluate the quality, accuracy, or effectiveness of something, such as AI responses. In the video, the presenter critically assesses the AI's solutions to quantitative problems, identifying and correcting errors to ensure accurate results.

πŸ’‘creative prompts

Creative prompts are innovative or unconventional questions or statements designed to guide an AI towards a desired outcome. In the video, the presenter uses creative prompts to overcome the limitations of the AI and to achieve accurate solutions to the quantitative problems.

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.