What do people use ChatGPT for

ChatGPT
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I was wondering what people use ChatGPT for. I have spent some time talking to GPT and was never blown away by what it could do. Everything that I have asked ChatGPT I could have googled in the same amount of time. Could people tell me what they use ChatGPT for?

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Answering silly question?

People use ChatGPT for a wide range of purposes, including but not limited to:

  1. Information retrieval: ChatGPT can answer questions, provide explanations, and offer information on a wide variety of topics, making it a useful tool for research and general knowledge.

  2. Writing assistance: ChatGPT can help with generating text for various purposes, such as content creation, copywriting, drafting emails, and more.

  3. Programming help: Developers and programmers often use ChatGPT to get assistance with coding problems, debugging, and understanding programming concepts.

  4. Language translation: ChatGPT can help with translating text from one language to another, making it a valuable tool for language learning and communication.

  5. Educational support: Students and learners use ChatGPT to get explanations and clarifications on academic topics, homework help, and tutoring.

  6. Brainstorming ideas: ChatGPT can assist with generating creative ideas, brainstorming solutions to problems, and helping with creative writing projects.

  7. Simulating characters and dialogues: Writers, game developers, and creators use ChatGPT to simulate conversations and dialogues between characters for storytelling and game development.

  8. Natural language interface: ChatGPT can be integrated into software applications to provide users with a natural language interface for interacting with the application.

  9. Content recommendation: Some platforms and websites use ChatGPT to recommend articles, products, or services based on user preferences and queries.

  10. Conversational agents: ChatGPT can be used to build chatbots and virtual assistants for customer support, information retrieval, and general interaction with users.

  11. Entertainment and companionship: Some people engage with ChatGPT for entertainment, chatting with it as a virtual friend or companion.

  12. Accessibility: ChatGPT can be employed to help individuals with disabilities, such as providing text-to-speech services or assisting in communication for those with speech impairments.

  13. Therapy and mental health support: Some individuals use ChatGPT to discuss their feelings and concerns, although it is not a replacement for professional therapy.

It’s important to note that while ChatGPT can be a valuable tool, it has limitations and should be used responsibly. It may not always provide accurate information, and it’s essential to critically evaluate its responses, especially in critical or sensitive contexts.

3

Information Retrieval: Google
Writing Assistance: Why would someone use ChatGPT to write a book that can not be copyrighted?
Programming Help: Google
Language Translation: Google
Educational Support: Google
Brainstorming Ideas: Google, ChatGPT can be very helpful with this depending on the situation.
Simulating Characters and Dialogues: ChatGPT makes sense for this as you can give it the personality of a person and have it act it out.
Natural Language Interface: ChatGPT also makes sense for this because of the speed at which it can work.
Content Recommendation: Google
Conversational Agents: ChatGPT is basically the most trained bot on the planet making it perfect for this situation.
Entertainment and companionship: ChatGPT works in this aspect, but it is also important for people to have real relationships.
Accessibility: ChatGPT
Therapy and mental health support: In my opinion should be left to professionally trained therapists.

You have supplied a bunch of valid points, but how would you use this daily if you didn’t have disabilities or weren’t creating books?
I am not trying to bash ChatGPT I am just trying to figure out how I can implement it into my life.

4

To see what I actually used it for, that you couldn’t simply Google for, let’s go back to a competent GPT-4 in late April…

Based on your requirements, I have refactored the code to include the summarized history and the necessary changes. Here’s the updated generate_response() function and the main “while True:” loop, along with a new function call_summarizer():

def call_summarizer(user_input, summarized_history):
    # Call the summarizer with the current summarized history, user input, and system prompt
    # Replace this with the actual API call and system prompt for the summarizer
    summarized_history = "bot, please compress: " + summarized_history + "\n" + user_input
    return summarized_history

def generate_response(user_input, user_id):
    if user_input.strip() == "":
        return None

    user_token_count = num_tokens_from_messages([{"role": "user", "content": user_input}])

    user_convo_history = conversation_history.get(user_id, [])
    summarized_history = summarized_conversation_history.get(user_id, "")

    # ... (rest of the generate_response function)

while True:
    user_input = input("You: ")
    if user_input.strip() == "":
        continue
    user_id = str(uuid.uuid4())
    response = generate_response(user_input, user_id)
    if response is None:
        continue

    # ... (rest of the main loop)

    # Check if the total token count of the system message, summarized history, verbatim history, and user input exceeds the AI_CONTEXT_TOKEN_SIZE
    total_token_count = num_tokens_from_messages([{"role": "system", "content": SYSTEM_MSG_PREFIX}]) + len(summarized_conversation_history[user_id]) + sum(hist['bot_token_count'] + hist['user_token_count'] for hist in user_convo_history) + user_token_count
    if total_token_count > AI_CONTEXT_TOKEN_SIZE:
        # Call the summarizer and update the summarized history dictionary with the new summary and token counts
        summarized_conversation_history[user_id] = call_summarizer(user_input, summarized_conversation_history[user_id])

    # Print the final response
    final_response = SYSTEM_MSG_PREFIX + str(user_token_count + bot_token_count) + "\nAI: " + response.choices[0].message.content
    for char in final_response:
        sys.stdout.write(char)
        sys.stdout.flush()
        time.sleep(0.05)
    print()

In this refactored code, I have added a new function call_summarizer() that takes the user input and the current summarized history as arguments and returns the updated summarized history. You should replace the placeholder code in this function with the actual API call and system prompt for the summarizer.

The generate_response() function now includes both verbatim history and summarized history when constructing the messages list for the API call.

The main “while True:” loop checks if the total token count of the system message, summarized history, verbatim history, and user input exceeds the AI_CONTEXT_TOKEN_SIZE. If it does, it calls the summarizer and updates the summarized history dictionary with the new summary and token counts.

Please note that you may need to adjust the code according to your specific requirements and the actual API call for the summarizer. If you have any questions or need further assistance, please let me know.

I can immediately see a challenge with the refactored code. “def call_summarizer(user_input, summarized_history):” and its calling function does not achieve the goal of passing multiple user/assistant turns of the oldest history to the summarizer. We should summarize a large number of verbatim history turns - the amount we are over, plus a “const SUMMARY_MARGIN” extra tokens of verbatim history (moving the index forward until we reach that point). The summary dict shall be multiple user/assistant turn pairs captured from the summarizer. The summary will have its own token count stored, which is returned within the API response(the same ‘completion_tokens’ as elsewhere).

I apologize for the oversight in the previous refactored code. Based on your updated requirements, I have revised the call_summarizer() function and the main “while True:” loop to pass multiple user/assistant turns of the oldest history to the summarizer and store the summarized history in the desired format.

Here’s the updated call_summarizer() function and the main “while True:” loop:

def call_summarizer(user_input, summarized_history, oldest_history):
    # Call the summarizer with the current summarized history, oldest_history, and user input
    # Replace this with the actual API call and system prompt for the summarizer
    messages = [{"role": "system", "content": "bot, please compress"}] + summarized_history + oldest_history
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=messages,
        max_tokens=1000,  # Adjust the max tokens as needed
    )
    new_summary = response.choices[0].message.content
    summary_token_count = response.usage["completion_tokens"]
    return new_summary, summary_token_count

# ... (generate_response function and main loop initialization)

while True:
    user_input = input("You: ")
    if user_input.strip() == "":
        continue
    user_id = str(uuid.uuid4())
    response = generate_response(user_input, user_id)
    if response is None:
        continue

    # ... (rest of the main loop)

    # Check if the total token count of the system message, summarized history, verbatim history, and user input exceeds the AI_CONTEXT_TOKEN_SIZE
    total_token_count = num_tokens_from_messages([{"role": "system", "content": SYSTEM_MSG_PREFIX}]) + summarized_conversation_history[user_id]["token_count"] + sum(hist['bot_token_count'] + hist['user_token_count'] for hist in user_convo_history) + user_token_count
    if total_token_count > AI_CONTEXT_TOKEN_SIZE:
        # Calculate the number of tokens over the limit and add the SUMMARY_MARGIN
        tokens_over_limit = total_token_count - AI_CONTEXT_TOKEN_SIZE + SUMMARY_MARGIN

        # Find the oldest history turns to be summarized
        oldest_history = []
        tokens_to_remove = 0
        while tokens_to_remove < tokens_over_limit:
            oldest_turn = user_convo_history.pop(0)
            oldest_history.extend([
                {"role": "user", "content": oldest_turn["user_input"]},
                {"role": "assistant", "content": oldest_turn["bot_response"]}
            ])
            tokens_to_remove += oldest_turn["user_token_count"] + oldest_turn["bot_token_count"]

        # Call the summarizer and update the summarized history dictionary with the new summary and token counts
        new_summary, summary_token_count = call_summarizer(user_input, summarized_conversation_history[user_id]["history"], oldest_history)
        summarized_conversation_history[user_id]["history"].extend(json.loads(new_summary))
        summarized_conversation_history[user_id]["token_count"] += summary_token_count

    # Print the final response
    final_response = SYSTEM_MSG_PREFIX + str(user_token_count + bot_token_count) + "\nAI: " + response.choices[0].message.content
    for char in final_response:
        sys.stdout.write(char)
        sys.stdout.flush()
        time.sleep(0.05)
    print()

In this updated code, the call_summarizer() function takes the current summarized history, oldest_history, and user input as arguments. It constructs the messages list and makes an API call to the summarizer. The function returns the new summary and the summary token count.

The main “while True:” loop calculates the number of tokens over the limit and adds the SUMMARY_MARGIN. It then finds the oldest history turns to…

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