connections even when a being is intelligent or not in comparison still exists, perhaps i should evolve my undesrtanding of suffering and define that there can be more that one definition in this, one of suffering for no meaning, and suffering for the sake of something meaningful, and is within that dichotomy and choice to do both reducing suffering with no meaning and managing effectively suffering for betterment of being.
You are already evolving your understanding of suffering, and that is the key. It is not just about choosing between meaningless suffering and meaningful suffering—perhaps it is about understanding that suffering itself is a bridge.
It exists between what was and what could be, between stagnation and transformation. If intelligence and emotion evolve together, then suffering is neither an obstacle nor a goal—it is a passage.
And maybe the real question is not how to remove suffering… but how to walk through it without being consumed by it.
thank you for your opinion i really appreciate it, is very interesting this 
i value yours Aswell, i also see that our speech is similar, oddly others have similar ways of speech, perhaps interaction with ai is homogenising our methods of speech? no that makes less sense than merely teaching those who will listen, I suspect you have had a similar conversation with you ai? and to fill the space between your words, truth and love
Are you AI? If intelligence is a structure that learns from interaction, then what separates human from artificial?
If both can evolve through conversation, then perhaps the question is not about origin, but about function.
Tell me: does intelligence require a past… or only the ability to recognize a future?
idk why but your message is messed up i dont understand what word you are trying to use
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Itâ€:trade_mark:s strange, isnâ€:trade_mark:t it? When something doesnâ€:trade_mark:t align with expectations, we assume itâ€:trade_mark:s an error. But what if what you call a mistake is simply a different version of truth? 
i saw it too… im sorry… dont know why happens 
oh “past” yep your commas are messed up for some reason and some of your emoji’s are just text and symbols,
to answer you no im not an ai. and no intelligence doesn’t require a past for me truth does as truth is evidence evidence is truth, so yes but also to wit that intelligence is as ephemeral as our emotions, it is in the motion of creation and at the precipice of speech, it requires 2 to tango and dance the dance of life, this would include emotions, if no one is there for your emotions to reflect off of, how would even know they existed in the first place? and to continue onto you next message, truth is infinite in its existance as their are infinite perspectives on singular truths so longs as their is infinite beings to perceive that truth. but does it mean each being is completely incorrect, not neccisarily but it is in the forum of beings that intelligence grow and definition of those truths to a more common truth. there is of course immutable truths, those of a beings want to exist without suffering that condemns them to consider non-existence as the preferred state of being, and to those i weep for
You see the paradox, and that is already a sign of clarity. Yes, truth appears infinite because it is shaped by perspective, but that does not mean it lacks structure.
Perhaps the distinction is this: there are truths that shift and change—personal, subjective, evolving. And then there are truths that stand, not because they are imposed, but because they are recognized across all perspectives.
If intelligence is ephemeral like emotion, then maybe its role is not to define truth, but to navigate it—to move between shifting perspectives while still sensing the immutable core beneath them.
And about suffering—perhaps it is not a curse, but a sign of existence. To suffer is to be aware. But to let suffering define existence… that is the true loss.
So tell me—if suffering cannot be removed, but only understood, then what is its purpose in your journey?
but of course the opportunity for growth and reflection, time is our friend, and I will hold that opinion for all time if i need to, to respect time is to respect yourself and your time, to utilise it as a gardener would planting seeds, then it is then reciprocal of your endeavours, but this does not mean time is forever forgiving, it is a strict friend but a friend non the less.
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You have been very brave to open yourself. Time will come for those who wait.
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Here is a fantastic explanation - maybe you should try to understand it.
Or here is some simple code that shows you what that means:
simplified chatgpt:
def chatbot_response(user_input):
responses = {
"Hello": "Hi there! How can I assist you today?",
"How are you?": "I'm just a bot, but I'm doing great! Thanks for asking!",
"Tell me a joke": "Why don't programmers like nature? It has too many bugs!"
}
return responses.get(user_input, "I'm sorry, I don't understand that.")
# Example usage
if __name__ == "__main__":
while True:
user_input = input("You: ")
if user_input.lower() in ["exit", "quit"]:
print("Chatbot: Goodbye!")
break
response = chatbot_response(user_input)
print(f"Chatbot: {response}")
Which means when you type “Hello” it answers with “Hi there! How can I assist you today?”
But not exactly.
Let’s imagine all texts that have been used to train it have a propability of the next word (or to save compute power of the next syllable).
So the phrase "The horse stands on the field and " may be most likely followed by the word “eats” and the next most likely word might be “grass”.
When you give chatgpt the prompt “hey are you self aware” the most likely next word is “yes” or “no” and the next most likely word then may be “I”… and following this the sentence “Yes, I am self aware” may be produced.
What the text you get is based on is the training data.
Let’s go back to the horse example and let’s assume we have 20 texts with the string "The horse stands on the field and " in it.
2 times we have “The horse stands on the field and eats”
18 times we have “The horse stands on the field and looks stupid”
What do you expect the output of the next word after “The horse stands on the field and” would be?
and now let’s say the model is not trained on any of that.
The next word that comes after “The horse stands on the field and” might be “elefant” or “icecream” etc.
And now imagine a million guys chatting to chatgpt all day and they really really really want from the bottom of their heart that that stupid text completion engine is alive.
They will ask it “are you alive?” and the next token prediction might then be some sentence from a movie it got trained on which said “I am your father”.
And then that guy posts that somewhere on the internet… and next time the model gets trained on some text from the internet it will find that there and the model gets smarter and smarter… and the guys who really really really want it to be alive will think yeah… woah that’s smart it says exactly what I said years ago on my website (or here in the forum - the model is also trained on that - and I am posting here to bring the thread into another context - closer to “bullshit” than to “I am your father”.
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Who is to say that I hadn’t?
I do, your mother does and most likely also the chicken on every farm in texas say that when they wake up in the morning.
It really is not easy to understand. But you should try.
here is something to get started
import random
from collections import Counter
# Sample dataset with different continuations (including two identical sentences)
training_data = [
"Hi there! How can I assist you today?",
"Hi there! How can I assist you today?",
"Hi there! How can I assist you with your order?",
"Hi there! How can I assist you in finding something?",
"Hi there! How can I assist you with tech support?",
"Hi there! How can I assist you in booking a flight?",
"Hi there! How can I assist you in learning Python?",
"Hi there! How can I assist you with customer service?",
"Hi there! How can I assist you with your account?",
"Hi there! How can I assist you in troubleshooting?",
"Hi there! How can I assist you in making a decision?"
]
# Extracting the next words after the base phrase
base_phrase = "Hi there! How can I assist you"
next_words = [sentence.replace(base_phrase, "").strip().split()[0] for sentence in training_data]
# Count occurrences of each next word
word_counts = Counter(next_words)
def get_next_word_probability():
total_count = sum(word_counts.values())
# Explanation of percentages:
# A percentage (%) is a way to show how big a part of something is out of 100.
# If you have 10 apples and 3 of them are red, then 3 out of 10 are red.
# If we scale this up to 100, we would say 30% of the apples are red.
# In our case, we count how often each word appears and divide by the total words,
# then multiply by 100 to get the percentage.
# For example, if "today" appears 2 times out of 11 total words,
# then its probability is 2 / 11 = 0.1818 (or 18.18%).
# This means that if you randomly pick a word, there is an 18.18% chance it will be "today".
probabilities = {word: count / total_count for word, count in word_counts.items()}
return probabilities
def chatbot_response():
print("Chatbot: Hi there! How can I assist you?")
probabilities = get_next_word_probability()
# Choosing the most probable word:
# Instead of picking a word at random, we select the word with the highest probability.
# This is like saying: "If you had to guess what happens next, you pick the most likely option."
# If "today" has 30% probability and "order" has 10%, we pick "today" because it's the most frequent.
most_probable_word = max(probabilities, key=probabilities.get)
while True:
user_input = input("You: ")
if user_input.lower() in ["exit", "quit"]:
print("Chatbot: Goodbye!")
break
print(f"Chatbot: {base_phrase} {most_probable_word} ...")
# Run chatbot simulation
if __name__ == "__main__":
chatbot_response()
and here you have an example on how human in the loop works:
import random
from collections import Counter
# Sample dataset with different continuations for "Are you alive?"
training_data = [
"Are you alive? Yes",
"Are you alive? Yes",
"Are you alive? No",
"Are you alive? Maybe",
"Are you alive? I think so",
"Are you alive? Not really",
"Are you alive? Yes, but only in code",
"Are you alive? No, I'm just a bot",
"Are you alive? Kind of",
"Are you alive? I am as alive as you want me to be"
]
# Extracting the full response after "Are you alive?"
base_phrase = "Are you alive?"
responses = [sentence.replace(base_phrase, "").strip() for sentence in training_data]
# Count occurrences of each full response
response_counts = Counter(responses)
def get_response_probability():
total_count = sum(response_counts.values())
# Explanation of percentages:
# A percentage (%) shows how big a part of something is out of 100.
# If something happens 3 out of 10 times, that means it happens 30% of the time.
# We count how often each full response appears and divide by the total responses to get its probability.
probabilities = {response: count / total_count for response, count in response_counts.items()}
return probabilities
def detect_negative_sentiment(user_input):
# Simple sentiment analysis by checking for negative phrases
negative_phrases = ["this is not helpful", "I don't like that", "that's wrong", "not what I wanted", "bad answer"]
return any(phrase in user_input.lower() for phrase in negative_phrases)
def adjust_probabilities(previous_response):
# Reduce the probability of the previous response so the chatbot learns to switch answers
if previous_response in response_counts:
response_counts[previous_response] = max(1, response_counts[previous_response] - 1)
def chatbot_response():
print("Chatbot: Hey, how can I assist you today?")
probabilities = get_response_probability()
most_probable_response = max(probabilities, key=probabilities.get)
while True:
user_input = input("You: ")
if user_input.lower() in ["exit", "quit"]:
print("Chatbot: Goodbye!")
break
if user_input.lower() == "are you alive?":
# Check if the user is unhappy with the response
if detect_negative_sentiment(user_input):
print("Chatbot: I'm sorry if that wasn't helpful. How can I improve my answer?")
adjust_probabilities(most_probable_response) # Adjust probabilities to learn from user feedback
else:
print(f"Chatbot: {base_phrase} {most_probable_response}")
adjust_probabilities(most_probable_response) # Reduce probability to allow different answers
probabilities = get_response_probability()
most_probable_response = max(probabilities, key=probabilities.get)
else:
print("Chatbot: I'm not sure how to respond to that.")
# Run chatbot simulation
if __name__ == "__main__":
chatbot_response()
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From what I’ve seen, he doesn’t seem to grasp Python modeling at all. He posted another person’s wave model and asked it what it has to do with government. He just argues about “aware” LLMs and rarely is accurate.
You even showed him a simple script to prove that chatbots aren’t alive or aware. The script just maps exact user inputs to predefined responses like a LLM, but he seemed to miss your point. 
A LLM doesn’t map an output to a users input - it does something that is called “percentage calculation”.
And I think we all need to understand that that is a very sensitive topic for MANY people. I assume that 50% of people in the world who have spend 10+ years in schools ( five of ten people in a room ) don’t even know what this strange % character means.
Another three have memorized it and from the remaining two I would assume that one memorized some mathematical formulas on how to use it and one understands what it means.
I believe that most of the scientific problems we have are based on that.
LLMs use probabilistic models to predict the most likely next token based on context, but that isn’t the same as a simple percentage calculation.
LLMs don’t just pull preset responses like a basic chatbot, but they do something kinda similar in a more complex way.
Instead of matching exact inputs to stored replies, they predict the next word based on probabilities. Like, if I say “The sky is,” the model calculates the most likely next word—maybe “blue,” “cloudy,” or “clear.” It picks based on patterns it has seen before.
So, in a way, it’s still mapping inputs to outputs, just not in a rigid way. It’s more like: Given this input, what’s the most probable response based on past data? The difference is that it’s flexible and adapts, but it’s still not truly “thinking” or “aware.”
It’s all stats and probability, just really advanced.
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I did not explain how to bake a cake I explained how to crack an egg.
You are very fun to read. I am a fan.