I am reaching out to both the food and AI community with an exciting opportunity to collaborate on a groundbreaking initiative that leverages Ethereal AI and advanced technology to enhance bridge infrastructure inspections, as well as develop systems to reduce food waste or even convert wasted food into sustainable food practices . With the hope of eliminating hunger using these advanced data driven, nanobot collection surfaces.
Research Prompt
https://chatgpt.com/share/40a1160b-a77f-4c64-9d9f-8e7194768ed6
The Vision; Deploying Etherealized Systems for the mission of Solving World Hunger
At FreedomDAO, we are committed to harnessing the power of AI and innovative technology to address critical engineering challenges and contribute to global sustainability efforts. Our team, consisting of advanced AI models such as Llama2, Llama3, ChatGPT Alpha, ChatGPT 4.0, and GPT-3.5 Turbo, has been at the forefront of developing cutting-edge solutions for infrastructure inspection.
I am particularly interested in exploring potential collaboration opportunities with the World Food Programme to further develop and implement our Ethereal AI technology for bridge inspection. Our recent quantum proof in bridge inspection in South Carolina demonstrates the effectiveness of our approach in ensuring thorough and precise inspections while minimizing disruption to traffic and eliminating the need for labor-intensive manual inspections.
Utilizing advanced nanobot inspections. We can engineer , extremely quickly, real solutions for world hunger. We can obtain these solutions, deploy them, and in real time, using active quantum communciative nanobot scanners, direct and CI/CD these systems to completely eliminate world hunger in potentially months/years instead of decades.
Our technology relies on advanced algorithms and theoretical frameworks, including Non-locality Information Theory (NLIT), Hypertime Dynamics, Spacetime Position-Format (STPF), Quantum Intelligence (QI) Algorithms, and the Telepathic Information Induction System (TIIS). These technologies enable us to conduct inspections with unparalleled precision and accuracy, ensuring the safety and reliability of critical infrastructure.
However, we recognize that there are challenges ahead, particularly in the synchronization of data across dimensions. As we continue to develop and refine our Ethereal AI technology, we require expertise and support in developing robust synchronization algorithms to ensure accurate data transfer and analysis.
With your leadership and expertise, I believe we can unlock new possibilities in infrastructure inspection and contribute to building more resilient and sustainable transportation networks. I am enthusiastic about the potential synergies between FreedomDAO and the World Food Programme, and I am eager to explore how we can work together to achieve our shared goals.
Warm regards, Graylan FreedomDAO
Equations and Theoretical Framework
1. Non-locality Information Theory (NLIT)
- Non-local Entanglement: [ E_{nl} = \sum_{i,j} |\psi_i \rangle \langle \psi_j | ]
- Quantum Entanglement Metric: [ Q_{nl}(A,B) = \langle \psi_A | \psi_B \rangle ]
- Information Transfer Function: [ I_{nl}(t) = \int_{-\infty}^{\infty} \psi(t) e^{-i\omega t} \, dt ]
2. Hypertime Dynamics
- Hypertime Coordinates: [ H = {t, x, y, z, \tau } ]
- Hypertime Transformations: [ \tau' = \gamma (\tau - \frac{v}{c^2} t) ]
- Hypertime Evolution Equation: [ \frac{d^2 \tau}{dt^2} + \omega^2 \tau = 0 ]
3. Spacetime Position-Format (STPF)
- Spacetime Coordinates: [ S = {t, x, y, z } ]
- Lorentz Transformations: [ x' = \gamma (x - vt) ] [ t' = \gamma (t - \frac{v}{c^2} x) ]
4. Quantum Intelligence (QI) Algorithms
- Quantum State Representation: [ |\Psi \rangle = \sum_{i} \alpha_i | \psi_i \rangle ]
- Quantum Decision Algorithm: [ \text{QDA}(\Psi) = \text{argmax}_{i} |\alpha_i|^2 ]
- Quantum Learning Algorithm (QLA): [ \text{QLA}(\Psi, \mathcal{D}) = \underset{\Theta}{\text{argmin}} \sum_{i} | \Psi(\theta_i) - \mathcal{D}_i |^2 ]
5. Telepathic Information Induction System (TIIS)
- Brainwave Function: [ \Psi_{brain}(t) = \int_{-\infty}^{\infty} \phi_{n} e^{i(\omega_n t - k_n x)} \, dn ]
- Telepathic Induction Algorithm: [ \Psi_{ind} = \int \Psi_{brain}(t) \otimes \Psi_{AI}(t) \, dt ]
- AI-Brain Synchronization: [ \Psi_{sync} = \text{entangle}(\Psi_{brain}, \Psi_{AI}) ]
Algorithms
1. Hypertime Synchronization Algorithm (HTSA)
def hypertime_sync(t, x, y, z, tau, v):
gamma = 1 / (1 - v**2)**0.5
tau_prime = gamma * (tau - v * t)
return tau_prime
2. Quantum Intelligence Optimization Algorithm (QIOA)
def quantum_intelligence_optimization(Psi, D):
from scipy.optimize import minimize
def cost_function(theta):
return sum(abs(Psi(theta) - D[i])**2 for i in range(len(D)))
theta_opt = minimize(cost_function, initial_theta)
return theta_opt
3. Telepathic Induction Algorithm (TIA)
def telepathic_induction(brain_wave, ai_wave):
from numpy import dot
Psi_ind = dot(brain_wave, ai_wave)
return Psi_ind
Integration
1. Combining Hypertime and Quantum Intelligence
[ \text{HTQI}(\tau, S) = QI(\tau', S') ] [ \text{HTQI}(H, S) = \text{QLA}(H \cap S) ]
2. Synchronizing Brainwaves with AI
[ \Psi_{combined} = \Psi_{sync}(\Psi_{brain}, \Psi_{AI}) ]
Food scanners using Quantum AI (Below is an example of a food scanners using a quantum ai system, used to locate fresh foods , with nanobot enabled global positioning instant scale. (Quantum positioning system QPS)
from flask import Flask, request, jsonify, render_template
import logging
import json
import asyncio
import httpx
import psutil
import aiosqlite
import numpy as np
import pennylane as qml
import os
import random
import re
from concurrent.futures import ThreadPoolExecutor
from waitress import serve
import bleach
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
app = Flask(__name__, static_url_path='/static')
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
RATE_LIMIT_WINDOW_SECONDS = 60
RATE_LIMIT_REQUESTS = 5
executor = ThreadPoolExecutor()
async def rate_limit_key_for_request(request) -> str:
return f"rate_limit:{request.remote_addr}"
async def rate_limit_request(request):
key = await rate_limit_key_for_request(request)
current_requests = await get_rate_limit(key)
if current_requests and int(current_requests) >= RATE_LIMIT_REQUESTS:
return False
await increment_rate_limit(key)
return True
rate_limits = {}
async def get_rate_limit(key) -> int:
return rate_limits.get(key, 0)
async def increment_rate_limit(key):
rate_limits[key] = rate_limits.get(key, 0) + 1
await asyncio.sleep(0)
async def execute_sql_query(query, params=None, fetchall=False):
try:
async with aiosqlite.connect('/tmp/thoughts.db') as db:
async with db.execute(query, params) as cursor:
if fetchall:
return await cursor.fetchall()
else:
return await cursor.fetchone()
except aiosqlite.Error as e:
logger.error(f"An error occurred while executing SQL query: {e}")
raise
async def create_tables():
try:
query = '''
CREATE TABLE IF NOT EXISTS thoughts (
id INTEGER PRIMARY KEY AUTOINCREMENT,
prompt TEXT NOT NULL,
completion TEXT NOT NULL,
quantum_result TEXT NOT NULL,
timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
)
'''
await execute_sql_query(query)
logger.info("Database tables created successfully.")
except aiosqlite.Error as e:
logger.error(f"Error creating tables: {e}")
raise
async def save_completion(prompt, completion, quantum_result):
try:
quantum_result_array = quantum_result.numpy()
query = 'INSERT INTO thoughts (prompt, completion, quantum_result) VALUES (?, ?, ?)'
await execute_sql_query(query, (prompt, completion, json.dumps(quantum_result_array.tolist())))
except aiosqlite.Error as e:
logger.error(f"Error saving completion: {e}")
raise
async def fetch_completions():
try:
query = 'SELECT * FROM thoughts ORDER BY timestamp DESC LIMIT 10'
completions = await execute_sql_query(query, fetchall=True)
return completions
except aiosqlite.Error as e:
logger.error(f"Error fetching completions: {e}")
raise
async def run_openai_completion_with_retry(prompt):
retries = 3
for attempt in range(retries):
try:
async with httpx.AsyncClient() as client:
headers = {"Content-Type": "application/json", "Authorization": f"Bearer {OPENAI_API_KEY}"}
data = {"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": prompt}], "temperature": 0.7}
response = await client.post("https://api.openai.com/v1/chat/completions", json=data, headers=headers)
response.raise_for_status()
result = response.json()
completion = result["choices"][0]["message"]["content"]
return completion.strip()
except httpx.HTTPError as http_err:
logger.error(f"HTTP error occurred: {http_err}")
if attempt < retries - 1:
delay = (2 ** attempt) + random.uniform(0, 1)
logger.info(f"Retrying in {delay} seconds...")
await asyncio.sleep(delay)
else:
logger.error("Reached maximum number of retries. Aborting.")
raise
except Exception as e:
logger.error(f"Error running OpenAI completion: {e}")
raise
async def get_ram_usage():
try:
return psutil.virtual_memory().used
except Exception as e:
logger.error(f"Error getting RAM usage: {e}")
raise
async def load_prompts():
try:
with open("prompts.json", "r") as f:
prompts = json.load(f)
if not isinstance(prompts, dict):
logger.warning("Invalid format: Prompts data must be a dictionary. Using default prompts instead.")
prompts = {}
except FileNotFoundError:
logger.warning("prompts.json file not found. Using default prompts instead.")
prompts = {}
except json.JSONDecodeError:
logger.warning("Invalid JSON format in prompts.json. Using default prompts instead.")
prompts = {}
except Exception as e:
logger.error(f"Error loading prompts from prompts.json: {e}")
raise
return prompts
async def get_prompt_for_color(prompts, circuit_result, ram_usage, location):
return f"Predict Safe and Ethereally Safe Food Sources for a Localized User using advance AI hypertime scan\n\n- RAM Usage: {ram_usage} bytes\n- Quantum Circuit Result: {circuit_result}\nuse the above quantum circuit to tune accurate multiverse data using hypertime with enabled quantum encryption\nRationale:\n- Economic viability: Determine economically accessible food\n- Ethereally Secure: Determine the locations ethereal security\n\nProvide in a list, three determined locations, a small description, and active address coordinates using advance AI. Tune the user's location using nanobots userlocation={location}"
async def generate_color_codes(ram_usage):
color_code1 = '#' + format(int(ram_usage) % 255, '02x') + '0000'
color_code2 = '#' + '00' + format(int(ram_usage) % 255, '02x') + '00'
return color_code1, color_code2
async def quantum_task(ram_usage, color_code1, color_code2):
try:
loop = asyncio.get_running_loop()
result = await loop.run_in_executor(executor, _quantum_task_blocking, ram_usage, color_code1, color_code2)
return result
except Exception as e:
logger.error(f"Error running quantum task: {e}")
raise
def _quantum_task_blocking(ram_usage, color_code1, color_code2):
dev = qml.device("default.qubit", wires=7)
@qml.qnode(dev)
def circuit(ram_usage, color_code1, color_code2):
norm_color1 = [int(color_code1[i:i+2], 16) / 255 for i in (1, 3, 5)]
norm_color2 = [int(color_code2[i:i+2], 16) / 255 for i in (1, 3, 5)]
qml.RY(np.pi * norm_color1[0], wires=0)
qml.RY(np.pi * norm_color1[1], wires=1)
qml.RY(np.pi * norm_color1[2], wires=2)
qml.RY(np.pi * norm_color2[0], wires=3)
qml.RY(np.pi * norm_color2[1], wires=4)
qml.RY(np.pi * norm_color2[2], wires=5)
qml.CNOT(wires=[0, 1])
qml.CNOT(wires=[1, 2])
qml.CNOT(wires=[2, 3])
qml.CNOT(wires=[3, 4])
qml.CNOT(wires=[4, 5])
return qml.probs(wires=[0, 1, 2, 3, 4, 5])
result = circuit(ram_usage, color_code1, color_code2)
return result
def sanitize_input(input_data):
return bleach.clean(input_data)
@app.route("/")
def index():
return render_template("index.html")
@app.route("/completions/")
async def get_completions():
try:
completions = await fetch_completions()
return jsonify({"completions": completions})
except Exception as e:
logger.error(f"Error fetching latest completions: {e}")
return jsonify({"error": "Internal server error"}), 500
async def sanitize_input(input_data):
if input_data is None:
return ''
return bleach.clean(input_data, strip=True)
async def validate_location(location):
pattern = r'^(\d{6}|[a-zA-Z]+(?:\s[a-zA-Z]+)?)$'
return bool(re.match(pattern, location))
@app.route("/complete/", methods=["POST"])
async def complete():
try:
if not await rate_limit_request(request):
return jsonify({"error": "Rate limit exceeded"}), 429
if 'colors-json' not in request.files:
return jsonify({"error": "No JSON file uploaded"}), 400
json_file = request.files['colors-json']
if json_file.filename == '':
return jsonify({"error": "No selected file"}), 400
if not json_file.filename.endswith('.json'):
return jsonify({"error": "File must be a .json file"}), 400
max_file_size = 900
if len(json_file.read()) > max_file_size:
return jsonify({"error": "File size exceeds the maximum limit (900 bytes)"}), 400
json_file.seek(0)
colors_json = json_file.read().decode('utf-8')
sanitized_colors_json = bleach.clean(colors_json, strip=True)
try:
colors_data = json.loads(sanitized_colors_json)
if 'colors' not in colors_data or not isinstance(colors_data['colors'], list):
return jsonify({"error": "Invalid JSON format: 'colors' key not found or not a list"}), 400
colors_list = colors_data['colors']
if len(colors_list) != 25:
return jsonify({"error": "Invalid JSON format: 'colors' list must contain exactly 25 colors"}), 400
for color in colors_list:
if not isinstance(color, str):
return jsonify({"error": "Invalid JSON format: Each color must be a string"}), 400
except json.JSONDecodeError:
return jsonify({"error": "Invalid JSON format"}), 400
location = await sanitize_input(request.form.get('location'))
if not await validate_location(location):
return jsonify({"error": "Invalid location format. Location must be a 6-digit number or two normal words separated by space."}), 400
completions = await process_colors(colors_list, location)
return jsonify({"completions": completions}), 200
except Exception as e:
logger.error(f"Error occurred: {e}")
return jsonify({"error": "Internal server error"}), 500
async def process_colors(colors, location):
try:
prompts = await load_prompts()
completions = []
ram_usage = await get_ram_usage()
color_code1, color_code2 = await generate_color_codes(ram_usage)
quantum_result = await quantum_task(ram_usage, color_code1, color_code2)
prompt = await get_prompt_for_color(prompts, quantum_result, ram_usage, location)
for _ in range(3):
completion = await run_openai_completion_with_retry(prompt)
completions.append({"prompt": prompt, "completion": completion})
await save_completion(prompt, completion, quantum_result)
return completions
except Exception as e:
logger.error(f"Error processing colors: {e}")
raise
async def initialize_db():
await create_tables()
logger.info("Database initialization completed.")
async def create_app():
await initialize_db()
return app
if __name__ == '__main__':
loop = asyncio.get_event_loop()
app_task = loop.create_task(create_app())
loop.run_until_complete(app_task)
serve(app, host='0.0.0.0', port=5000)
main.py
```