WINAXE AI or WINAX AI
objective : -
archiving all type of wishes , issues & needs shortly known as WIN's
vision : -
the objective will be archived through a single logic and idea of vision , which is to , creating an ai which creates other ais for the text prompts , this idea of vision works with a logic which is nothing but , every ai , technology , field and humans and other species are for archiving WIN's ( all things and persons exist to archive WIN's )
methodology :-
1.completely replacing the programmers ( this helps to above vision archiving because this is a fundamental step of the vision and below step of below methodology )
2.completely replacing the AI engineers and Data Scientists through above vision
the second method is possible with automating below things through an ai
Define the problem: The first step in developing an AI system is to clearly define the problem or task that the AI system will be solving.
Gather data: Once the problem is defined, the next step is to gather relevant data to train the AI system. This data should be diverse, representative, and labeled.
Pre-process the data: The data collected needs to be pre-processed to ensure that it is clean, accurate, and ready for use in training an AI model.
Choose a suitable AI algorithm: There are many different AI algorithms to choose from, such as neural networks, decision trees, and reinforcement learning. The choice of algorithm depends on the problem being solved and the data available.
Train the AI model: The selected AI algorithm needs to be trained on the pre-processed data. The training process involves adjusting the parameters of the algorithm to minimize the difference between the predicted outputs and the actual outputs.
Validate the AI model: Once the AI model is trained, it needs to be validated on a separate set of data that it has not seen before. This helps to ensure that the AI model is generalizable and not just overfitting the training data.
Deploy the AI model: Once the AI model is validated, it can be deployed in a production environment. The deployment process involves integrating the AI model into a larger software system and ensuring that it can handle real-world data.
Monitor and maintain the AI model: The performance of the AI model needs to be monitored over time, and any issues need to be addressed promptly. This may involve re-training the model with new data or making adjustments to the algorithm.
Continuous learning: Finally, the AI model can be further improved through continuous learning. This involves feeding new data into the AI model and re-training it periodically to improve its accuracy and effectiveness.
How to create AI that can archive any wishes , issues , needs including possible and impossible wishes ,issues,needs
To create an ai that can that can archive any wishes,issues,needs we can create an ai that creates ai
When an ai create ai for prompted it basically acheive any wishes , issues , needs
So ,
How to create an AI that can create an AI
To create an AI that can create AI we have to develop software for each development steps involves in AI development
So to create an AI that can create an AI we need to automate software development
For AI based software development we have code generating LLM’s
But the problem is current LLM can’t create entire software it can help on generating few or few functions of software
So to make llm create entire software we have to see its strengths, we can use the strengths of a llm like gpt to make it create entire software
The strengths of llm is it can create few lines of code efficiently without bugs and errors
So , we can use a loop of self prompting strategy based few lines of code generation
I mean an application which gets input from user about what software they want then gpt will create prompt that can be used to create first few set of code and then the prompts given again to gpt to create the response for gpt created prompted likewise this process continuous upto the software is developed
So first using above idea , we have to create 7 software that automate 7 AI development steps such as data collection, data preprocessing , algorithm selection or creation , training, testing , fine tuning , deploying.
Combining these 7 software that was developed by my custom gpt will lead to AI that creates AI , AI that creates AI will lead to AGI because collection of ANI will be AGI , collection of AGI will be ASI , ASI will lead to achieving any wishes ,issues and needs even if it impossible
Brief overview on how my techniques works :-
print("created by tsg balakarthik")
from transformers import pipeline, set_seed
# Setting seed for reproducibility
set_seed(42)
# Define the generator pipeline
generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B')
# Function to get response from GPT model
def get_gpt_response(prompt, max_new_tokens=100):
print(f"Generating response for prompt: {prompt}")
response = generator(prompt, max_new_tokens=max_new_tokens, num_return_sequences=1, pad_token_id=50256)[0]['generated_text']
return response.strip()
# Take user input for the initial goal
goal = input("Please enter your goal: ")
# Automatically generate the initial prompt
initial_prompt = f"Given the goal to '{goal}', outline the key components and sub-goals required to achieve this goal."
print(f"Initial prompt generated: {initial_prompt}")
# Generate initial response and sub-goals
initial_response = get_gpt_response(initial_prompt)
print(f"Initial response generated: {initial_response}")
# Split the initial response into lines and create follow-up questions
questions = [f"What steps should be taken to achieve the sub-goal '{line.strip()}' in order to achieve the main goal '{goal}'?" for line in initial_response.split('\n') if line.strip()]
print(f"Initial questions generated: {questions}")
responses = []
# Loop for iterative question and response generation
for i in range(5): # Example iteration limit
new_responses = []
for question in questions:
print(f"Generating response for question: {question}")
response = get_gpt_response(question)
responses.append(response)
print(f"Response received: {response}")
# Generate follow-up questions based on the response
new_prompt = f"Based on the response to the question '{question}', what are the next steps to ensure the sub-goal '{response}' is achieved as part of the main goal '{goal}'?"
new_questions = get_gpt_response(new_prompt).split('\n')
new_responses.extend([q.strip() for q in new_questions if q.strip()])
# Update questions for the next iteration
questions = new_responses
# Break if no new questions are generated
if not questions:
break
# Combine responses to form the final output
final_output = "\n".join(responses)
print("\nFinal Output:")
print(final_output)
Self prompting and response generating Custom-GPT
Languages exists for communication purposes mostly it used to get something from the other end by saying it loud
Programming languages exists to communicate with computers so by using the Programming languages we can communicate with computer to make computer do something that a computer can do
So here currently our technology is at development of Generative AI's
So making a Generative Ai to prompt itself for a human need and respond to its own prompts will lead to communication with computers without a human programmers , it means computer will do what human is requiring by a computer command
This is the power of self prompting generative AIs
But the limitation is the self -prompts need to be as specific as possible because current Gen Ai's are only good at responding to specific prompts
So if we can create specific prompting gen AI then the prompts will be responded by normal gen AI and then it will lead to AI that creates AI because the responses include codes , AI that creates AI will lead to AGI , AGI lead to ASI , ASI lead to utopian state and WIN Archiver State .
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