PROMPT EVOLUTIVO. CHATGPT + Algoritmo GENÉTICO. INGRESSOS PARA CPBR15 SORTEIO. PARTICIPE

Introduction to Evolutionary Prompts

In this section, the speaker introduces the concept of evolutionary prompts in building prompts for problem-solving using chat GPT. The speaker mentions that they have combined genetic algorithms with prompt creation and will explain how it works.

Evolutionary Prompts

• Evolutionary prompts are a new concept in constructing prompts for problem-solving using chat GPT.

• The speaker has mixed genetic algorithms with prompt creation to develop evolutionary prompts.

• The algorithm is based on the principles of biological evolution, where a population of candidate solutions undergoes genetic operations such as crossover.

• Instead of a biological population, the population consists of solutions to a problem.

• The process involves generating new potential solutions through genetic algorithms and evaluating their success probability using an analyzer.

Giveaway Announcement for Campus Party Brasil 15

In this section, the speaker announces a giveaway for two tickets to Campus Party Brasil 15. They provide instructions on how to participate and mention that winners will be announced on their Instagram profile.

Giveaway Announcement

• The speaker is giving away two tickets to Campus Party Brasil 15.

• Viewers need to be subscribed to the channel, like the video, and leave a comment to participate.

• Winners will be selected based on comments and announced on the speaker's Instagram profile.

Introduction to Genetic Algorithms

This section provides an introduction to genetic algorithms as a technique for optimization. The speaker explains that genetic algorithms involve creating a new population by combining existing solutions through genetic operations.

Genetic Algorithms

• Genetic algorithms are optimization techniques inspired by biological evolution.

• A population of candidate solutions undergoes genetic operations such as crossover.

• In this context, instead of biological populations, the population consists of solutions to a problem.

• The process involves generating new potential solutions through genetic algorithms and evaluating their success probability using an analyzer.

Applying Genetic Algorithms to Chat GPT

In this section, the speaker explains how genetic algorithms can be applied to problem-solving using chat GPT. They describe the process of generating initial solutions, applying genetic operations, and evaluating the success probability of each solution.

Applying Genetic Algorithms to Chat GPT

• The speaker describes the process of applying genetic algorithms to problem-solving with chat GPT.

• The chat GPT provides an initial collection of solutions for a given problem.

• Genetic algorithms are used to generate new potential solutions and evaluate their success probability using an analyzer.

• The best solutions are ranked based on their success probability.

• This process is repeated multiple times, creating a new population in each cycle, until a champion solution is found.

Combining Solutions Using Genetic Operations

This section focuses on combining solutions using genetic operations in the context of genetic algorithms. The speaker explains how combinations are made and emphasizes that this step contributes to improving the success rate of solutions.

Combining Solutions Using Genetic Operations

• Solutions generated by chat GPT are combined in a laboratory using genetic operations.

• Genetic operations involve making small alterations and creating new individuals within the population.

• This step contributes to improving the success rate of solutions by combining stronger individuals from previous generations.

Iterative Process of Genetic Algorithms

In this section, the iterative nature of genetic algorithms is explained. The speaker highlights that repeating the process multiple times leads to finding a champion solution with increasing success rates.

Iterative Process of Genetic Algorithms

• The application of genetic algorithms is an iterative process.

• Each cycle generates a new population through mutation, combination, and evaluation.

• Repeating the process multiple times leads to finding a champion solution with increasing success rates.

Giveaway Announcement for Campus Party Brasil 15 (Continued)

This section continues the announcement of the giveaway for two tickets to Campus Party Brasil 15. The speaker provides additional details on how to participate and mentions the date of the draw.

Giveaway Announcement (Continued)

• The speaker reiterates the giveaway for two tickets to Campus Party Brasil 15.

• Viewers need to be subscribed, like the video, and leave a comment to participate.

• The draw will take place on Wednesday, June 21st, and winners will be announced on Instagram and YouTube.

Introduction to Prompt Creation

In this section, the speaker introduces prompt creation by explaining various steps involved in preparing prompts for problem-solving using chat GPT.

Prompt Creation

• The speaker discusses various steps involved in prompt creation.

• They mention clearing previous conversations from memory before starting.

• A global storage space called "problem" is simulated as a variable.

• Another global storage space called "solutions" is created to store generated solutions throughout the process.

• The best solutions are stored in a space called "best solutions" after each step of genetic algorithms.

Conclusion

Introduction and Problem Analysis

In this section, the speaker discusses the need for a simulation of storage space and introduces two profiles: Analyzer and Geneticist. The Analyzer profile acts as a consultant to identify and evaluate solutions, while the Geneticist profile combines and mutates the best solutions.

• The speaker explains that simulated storage space is necessary as physical memory cannot be used.

• Two profiles are introduced: Analyzer and Geneticist.

• The Analyzer profile serves as a problem consultant, evaluating solutions in descending order of success.

• Steps involved in the analysis include naming the solution, determining its probability of success, summarizing failures, evaluating pros and cons, transferring solutions with higher probabilities to better solutions.

Geneticist Profile

This section focuses on the role of the Geneticist profile in combining and mutating solutions.

• The Geneticist profile combines and mutates the best solutions.

• Similar to genetic mutation, this process strengthens the population of solutions.

• Mutation can be subtle or radical based on a random factor that determines similarity or creativity.

• Regular sequences should be avoided to maintain diversity among solutions.

Generating New Solutions

Here, the process of generating new solutions through combination and mutation is explained.

• Steps involved in generating new solutions include using existing best solutions, creating five new offspring through combination and genetic mutation.

• Each solution has an assigned level of randomness.

• Previous population information is removed before adding new offspring to ensure only the best remain.

Defining the Problem

This section covers defining a problem within the context of applying genetic algorithms to solve it using chat GPT.

• The speaker provides an example of defining a problem for a video on genetic algorithms.

• The problem is to come up with a short and creative title that stands out among other videos on the same topic.

• Three initial solutions are created and ranked by the Analyzer profile.

Loading Profiles and Identifying the Problem

This section explains loading profiles, identifying the problem, and choosing a name for it.

• The skills defined in the Analyzer profile are loaded into the Analyzer profile variable.

• The skills defined in the Geneticist profile are loaded into the Geneticist profile variable.

• The problem identified within the tags is stored as "problem" and displayed on-screen.

• Three extremely simple solutions are created for the identified problem.

Executing Steps

This section covers executing steps in the algorithmic process.

• The process involves repeatedly executing genetic algorithms until desired results are achieved.

Analyzing Solutions

In this section, the speaker explains how to analyze solutions using the profile analyzer. The character takes the current solutions and ranks them based on their probability of success. The best solutions are stored in a global storage called "best solutions".

• The character uses the profile analyzer to analyze the current solutions and rank them based on their probability of success.

• The best solutions are stored in a global storage called "best solutions".

• This process helps identify the most promising solutions for further genetic evolution.

Problem Introduction

The speaker introduces a new problem related to increasing female audience engagement on their YouTube channel.

• The problem is about increasing female audience engagement on the speaker's YouTube channel.

• They have been trying to address this issue by creating more opportunities for women in technology.

• A new problem scenario is introduced, focusing on increasing female audience engagement.

Proposed Solutions

The speaker presents three possible solutions generated by the chatbot for increasing female audience engagement.

• Three possible solutions are proposed by the chatbot:

• Creating content specifically targeted towards female viewers.

• Collaborating with popular creators who have a predominantly female audience.

• Investing in targeted advertising for women on YouTube and other social media platforms.

Probability Analysis

The speaker discusses how to calculate the probability of success for each solution using the profile analyzer.

• The profile analyzer calculates the probability of success for each solution:

• Creating content specifically targeted towards female viewers has a 70% chance of success.

• Collaborating with popular creators who have a predominantly female audience has a 60% chance of success.

• Investing in targeted advertising for women on YouTube and other social media platforms has a 50% chance of success.

Genetic Evolution

The speaker explains the process of genetic evolution using the geneticist profile.

• The geneticist profile is used to perform genetic evolution on the best solutions.

• The solutions undergo mutation and crossover to generate new solutions.

• The resulting solutions are stored in a new population for further analysis.

Analyzing New Solutions

The speaker analyzes the new solutions generated through genetic evolution using the profile analyzer.

• The profile analyzer is used to analyze the new solutions generated through genetic evolution.

• The best solutions from the previous population are combined with the newly generated solutions.

• This process results in a new population of solutions for further analysis.

Best Solutions Identified

The speaker identifies the best solutions from the newly generated population using the profile analyzer.

• The profile analyzer is used again to identify the best solutions from the newly generated population.

• Three solutions are identified as better than the initial ones, with a 72% probability of success.

• These improved solutions will be used for further genetic evolution.

Iterative Genetic Evolution

The speaker continues with another round of genetic evolution using both the geneticist and profile analyzer profiles simultaneously.

• Another round of genetic evolution is performed by combining both the geneticist and profile analyzer profiles.

• Eight new solutions are generated through mutation and crossover.

• These new solutions will be analyzed by the profile analyzer to determine their probability of success.

Improved Solution Identified

The speaker identifies an improved solution with a higher probability of success after the iterative genetic evolution process.

• An improved solution with a 75% probability of success is identified.

• The solution involves creating a mixed content strategy that combines collaborations with popular creators and targeted advertising for women.

Final Solution Determination

The speaker performs the final round of analysis by simultaneously using both the geneticist and profile analyzer profiles to determine the winning solution.

• The final round of analysis is performed by using both the geneticist and profile analyzer profiles simultaneously.

• The result of this analysis will be considered as the winning solution.

• The current best solution is a daughter solution called "Daughter 13" with a 77% probability of success.

Repeating the Process

The speaker discusses the option to repeat the process multiple times to improve the probability of success further.

• The process can be repeated multiple times to increase the probability of success.

• Currently, there is a 77% probability of success, but it can be further improved through iteration.

Introduction to Genetic Algorithms

The speaker introduces the concept of genetic algorithms and explains how they work, including the processes of crossover and mutation. They mention that repeating the algorithm multiple times can lead to better solutions.

Evolutionary Process

• Genetic algorithms involve a combination of crossover and mutation to create variations in solutions.

• Repeating the algorithm multiple times can improve the quality of solutions.

Implementing the Algorithm

The speaker discusses how to implement the genetic algorithm by using a loop structure. They explain that the loop will iterate a certain number of times, performing crossover and mutation operations.

Loop Structure

• Start with a geneticist (initial solution).

• Perform crossover operation.

• Pass the result to a finalizer (improved solution).

Implementation Steps

1. Define a loop structure.

2. Repeat actions defined within the loop for 20 iterations.

3. Ignore any commands that display information during iterations.

4. After completing all iterations, display only the best solutions with their respective percentages.

Displaying Best Solutions

The speaker explains how to display the best solutions after running 20 iterations of the genetic algorithm. They mention ignoring commands that display information during iterations and only showing the best solutions with their percentages.

Displaying Best Solutions

• Run 20 iterations of the algorithm.

• Ignore commands that display information during each iteration.

• Show only the best solutions with their respective percentages.

Evaluating Results

The speaker evaluates the results after running 20 iterations of the genetic algorithm. They highlight an improvement in one solution's percentage and discuss other solutions with different percentages.

Results Evaluation

• After 20 iterations, the algorithm shows an improvement in one solution's percentage (from 77% to 84%).

• Other solutions have percentages of 81%, 82%, and 84%.

Describing the Winning Solution

The speaker explains how to describe the winning solution in detail. They mention including implementation details, pros and cons, and conducting a thorough analysis.

Describing the Winning Solution

• Provide a detailed description of the winning solution.

• Include implementation details, pros and cons.

• Conduct a thorough analysis of the solution.

Finalizing the Solution

The speaker discusses finalizing the solution by writing down all the details found during the genetic algorithm process. They suggest requesting a title for the final solution and provide an example output.

Finalizing Steps

• Write down all details of the solution found through the genetic algorithm process.

• Request a title for the final solution.

• Example output: A detailed description of how to implement and analyze a solution with an 84% success rate.