Saudi Arabia's Vision 2030 is a transformative initiative aiming to diversify the country's economy and establish it as a global leader in technology and innovation. Google's cutting-edge solutions in Digital Twin generation, Artificial Intelligence (AI), and cloud infrastructure present a unique opportunity to support this ambitious vision.
In this article, we’ll delve into how Google’s technology can align with Vision 2030 goals, explore real-world use cases, and include architecture diagrams, conceptual maps, and example implementations.
Vision 2030 focuses on three primary pillars:
Digital Twins and AI can play a transformative role in achieving these goals. By leveraging Google Cloud, Google Earth Engine, and AI-powered tools, Saudi Arabia can enhance urban planning, optimize resource utilization, and drive intelligent decision-making.
Digital twins are virtual replicas of physical entities, enabling real-time monitoring, analysis, and simulation. Google offers powerful tools to build and operate Digital Twins:
Google Cloud:
Google Earth Engine:
Vertex AI:
BigQuery:
Here’s a proposed architecture for a Digital Twin platform built on Google Cloud:
Here’s a Python script demonstrating real-time data ingestion and analysis using Google Cloud’s Pub/Sub and BigQuery.
from google.cloud import pubsub_v1 from google.cloud import bigquery # Initialize Pub/Sub and BigQuery clients project_id = "your-project-id" topic_id = "iot-data-topic" subscription_id = "iot-data-subscription" bq_dataset_id = "digital_twin_dataset" bq_table_id = "real_time_data" # Function to process Pub/Sub messages def process_messages(): subscriber = pubsub_v1.SubscriberClient() subscription_path = subscriber.subscription_path(project_id, subscription_id) def callback(message): print(f"Received message: {message.data}") # Save data to BigQuery client = bigquery.Client() table_id = f"{project_id}.{bq_dataset_id}.{bq_table_id}" row = {"sensor_id": "sensor_1", "value": message.data.decode("utf-8")} errors = client.insert_rows_json(table_id, [row]) if errors: print(f"Failed to write to BigQuery: {errors}") message.ack() streaming_pull_future = subscriber.subscribe(subscription_path, callback=callback) print(f"Listening for messages on {subscription_path}...") try: streaming_pull_future.result() except KeyboardInterrupt: streaming_pull_future.cancel() if __name__ == "__main__": process_messages()
Published on: November 25, 2024
El campo de la Inteligencia Artificial (IA) está avanzando rápidamente, y una de las innovaciones más emocionantes del momento es Google Gemini, el modelo de próxima generación desarrollado por Google DeepMind. Gemini representa un salto significativo en capacidades multimodales, permitiendo trabajar con texto, imágenes y más en un solo modelo. Su flexibilidad abre nuevas posibilidades para desarrolladores y empresas, especialmente cuando se integra con APIs a través de lenguajes como Python. En este artículo, exploraremos cómo aprovechar el potencial de Gemini utilizando Python, con un enfoque en su integración mediante APIs. También incluiremos un ejemplo práctico de código para que puedas comenzar.
from google.cloud import aiplatform # Configurar proyecto y ubicación project_id = "tu-proyecto-id" location = "us-central1" # Cambia según tu región model_name = "gemini-model-id" # Reemplazar con el ID del modelo Gemini api_endpoint = f"{location}-aiplatform.googleapis.com" # Inicializar cliente aiplatform.init( project=project_id, location=location, ) # Función para realizar una solicitud al modelo def generate_text(prompt): try: model = aiplatform.Model(model_name=model_name) response = model.predict( instances=[{"content": prompt}], parameters={"temperature": 0.7, "maxLength": 100}, ) return response.predictions[0]["content"] except Exception as e: print(f"Error al generar texto: {e}") return None # Ejemplo de uso if __name__ == "__main__": prompt = "Describe las ventajas de usar Google Gemini en proyectos de IA." result = generate_text(prompt) print("Resultado generado por Gemini:") print(result)
Published on: November 25, 2024
En los anales de mi historia, emerge un capítulo luminoso marcado por la presencia de un guía excepcional: mi Coach María José S.E.. Como la arquitecta de mi transformación, ella ha tejido con maestría los hilos de mi crecimiento personal, elevándome a nuevas alturas de autoconciencia y sensibilidad.
En el vasto lienzo de mi vida, María José ha sido la persona que ha infundido color y propósito. Con paciencia infinita y sabiduría inquebrantable, ha desentrañado los nudos de mis pensamientos y emociones, guiándome hacia la claridad y la comprensión. Su enfoque analítico ha sido la brújula que me ha orientado en medio de la neblina, revelando caminos antes ocultos y despertando en mí una sed insaciable de crecimiento y mejora continua. Conceptos, antes invisibles para mí, como la Hucha Emocional o las Sombras de Jung han contribuido sistemáticamente a mi transformación personal y profesional.
En cada sesión, su voz resonaba como un eco inspirador, recordándome el potencial latente que yacía dormido dentro de mí. A través de sus palabras, he aprendido a abrazar mis debilidades con valentía, transformándolas en fortalezas y combustible para mi evolución. En el viaje (muy corto), hacia la versión mejorada de mí mismo, María José ha sido mi faro en la oscuridad, iluminando el camino con su sabiduría y afecto incondicional, ayudándome a separar lo que es excelente, de lo que es exigente.
Que esta epopeya sirva como tributo a la grandeza de María José, cuyo legado perdurará en el alma de aquellos que han sido agraciados por su presencia o con orgullo llevaremos el título honorífica de haber sido un orgulloso coachee de ella. En el vasto océano del universo, su influencia brillará como una estrella eterna, guiando a las generaciones venideras hacia la plenitud y el autodescubrimiento. Por siempre estaré agradecido por el don invaluable de su orientación y amor.
Siempre tuyo, tu orgulloso coachee.
Published on: May 8, 2024
Quantum computing is poised to revolutionize the way we approach complex computational problems, offering unparalleled processing power and the ability to solve certain tasks exponentially faster than classical computers. As developers, understanding and harnessing the potential of quantum computing opens up a realm of possibilities for tackling challenges across various domains. In this post, we'll delve into the basics of quantum computing and explore how developers can start testing quantum algorithms using Python and Qiskit.
Understanding Quantum Computing:
Quantum computing operates on the principles of quantum mechanics, leveraging quantum bits or qubits to perform computations. Unlike classical bits, which can only exist in states of 0 or 1, qubits can exist in superposition, representing both 0 and 1 simultaneously. This property allows quantum computers to explore multiple solutions to a problem simultaneously, leading to exponential speedup for certain algorithms.
Getting Started with Qiskit:
Qiskit is an open-source quantum computing framework developed by IBM, providing tools and libraries for quantum circuit design, simulation, and execution. To begin experimenting with quantum computing in Python, you'll need to install Qiskit using pip:
pip install qiskit
from qiskit import QuantumCircuit, Aer, execute from qiskit.visualization import plot_histogram # Define the number of qubits and the target item to search for n = 4 # Number of qubits target = '1010' # Target item to search for # Create a quantum circuit qc = QuantumCircuit(n) # Apply Hadamard gates to all qubits qc.h(range(n)) # Define the oracle that marks the target item for i in range(n): if target[i] == '0': qc.x(i) qc.barrier() # Apply controlled-Z gate (oracle) qc.cz(0, 3) qc.barrier() # Apply Hadamard gates again qc.h(range(n)) # Measure qubits qc.measure_all() # Simulate the circuit simulator = Aer.get_backend('qasm_simulator') result = execute(qc, simulator, shots=1024).result() # Plot the results counts = result.get_counts(qc) plot_histogram(counts)
𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗿𝘆𝗽𝘁𝗼𝗴𝗿𝗮𝗽𝗵𝘆 𝗮𝗻𝗱 𝗣𝗼𝘀𝘁-𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗿𝘆𝗽𝘁𝗼𝗴𝗿𝗮𝗽𝗵𝘆:
— The Quantum Chronicle (@TheQuantumChron) March 20, 2024
Quantum computing poses a significant threat to existing cryptographic schemes, such as RSA, which rely on the difficulty of factoring large numbers. Shor’s algorithm, for instance,… pic.twitter.com/4c60mhJ3nB
Published on: March 20, 2024
In the presentation, the speaker delves into the complexities of Enterprise AI, emphasizing understanding over mere terminology. They discuss the architecture behind Large Language Models (LLMs), challenges of scalability, security, and cost, and the importance of trust and risk management. Exploring the concept of assistants, they highlight the need for an intermediary layer to interact with LLMs, ensuring independence and reliability. They touch on memory management, data sources, and the evolution towards agent-driven systems. The talk underscores the necessity of thoughtful infrastructure and a robust assistant layer for effective enterprise applications in the AI era.
Published on: March 14, 2024
![]() ![]() | Welcome to my home in Majadahonda |
Weather: clear sky
Temperature: 6.7 °C
Humidity: 72 %
zape@joseluis:~$ ifconfig eth0
eth0: Link encap:Ethernet HWaddr FF:FF:FF:FF:FF:FF
inet addr: zape.larebelion.com
inet from: Majadahonda, Madrid Spain (ES)
RX packets:2069593710 errors:547 dropped:877 overruns:0 frame:0
TX packets:942218213 errors:383 dropped:853 overruns:0 carrier:0
collisions:0 txqueuelen:0
RX bytes:744030742 (53 GiB) TX bytes:1918297953 (41 GiB)
Interrupt:0
zape@joseluis:~$ _