Why Fujitsu introduced a power-saving AI application for Open RAN with Virtuora SMO?
Fujitsu introduced a power-saving AI application for Open RAN with Virtuora SMO #AI #ArtificialIntelligence #OpenRAN #VirtuoraSMO #Fujitsu #Telefónica #DeutscheTelekon #Huawei #Ericsson #ZTE #Nokia
In December 2023, a power-saving AI application was introduced by Fujitsu for Open RAN with Virtuora Service Management and Orchestration (SMO).
The application used AI technology for optimizing network capacity by switching it on or off as needed to reduce power consumption by over 20% compared to conventional methods.
What is Open RAN?
Open RAN (Radio Access Network), a new architecture for…
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HOW TO BUILD AN AI APPLICATION FROM SCRATCH
Building an AI application requires a step-by-step approach, starting with problem identification and data collection.
Here's a simplified breakdown:
1. Identify the Problem and Value Proposition
Clearly define the problem your AI will solve and the value it will offer users.
2. Collect and Clean Data
Gather high-quality data, categorized as structured (e.g., names, dates) or unstructured (e.g., images, text).
Clean and process the data to ensure its accuracy and usability.
3. Choose and Create Algorithms
Select or create algorithms (mathematical instructions) for the AI to learn from the data.
4. Train and Optimize the Model
Train the AI model on the prepared data, aiming for high accuracy.
Refine and optimize the model as needed.
5. Select a Platform and Tools
Choose an in-house or cloud-based platform for development and deployment.
Select programming languages like Python, suitable for machine learning tasks.
6. Deploy and Monitor
Deploy the AI application and continuously monitor its performance.
Python Code Snippet (POC):
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
# Load data
data = pd.read_csv("your_data.csv")
# Split data into features and target variable
X = data.drop("target_column", axis=1)
y = data["target_column"]
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Create and train a Logistic Regression model
model = LogisticRegression()
model.fit(X_train, y_train)
# Make predictions on the test set
predictions = model.predict(X_test)
# Evaluate model performance
accuracy = model.score(X_test, y_test)
print("Accuracy:", accuracy)
Here's a brief explanation of the Python code snippet:
Purpose: This code demonstrates training a simple machine learning model for binary classification using Logistic Regression.
Steps:
Imports libraries:
pandas for data manipulation.
sklearn.model_selection for splitting data into training and testing sets.
sklearn.linear_model for Logistic Regression.
Loads data: Assumes a CSV file named "your_data.csv" containing features and a target variable.
Splits data: Separates features (X) from the target variable (y). Further splits X and y into training and testing sets (80% training, 20% testing).
Creates and trains a model: Initializes a Logistic Regression model and trains it on the training data (X_train, y_train).
Makes predictions: Uses the trained model to predict on the testing data (X_test).
Evaluate performance: Calculates the model's accuracy on the testing data and prints it.
This is a simplified overview.
The specific steps and tools involved will vary depending on your project's complexity and requirements.
Also, in Real-world AI applications involve more complex models, data processing, and evaluation techniques.
RDIDINI PROMPT ENGINEER.
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I just got a call back from a job I applied for last month.
"Are you still interested in the position?"
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"..."
"..."
"What other positions are available?"
"That was our only opening. Thank you for your interest. Have a nice day." *click*
LADY, WHAT THE FUCK DID YOU EVEN CALL ME FOR?!?
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1.Could you tell us a bit about yourself and your career before iion?
Believe it or not, I am a qualified medical doctor. I found my true calling with digital media and the internet and switched careers almost 20 years ago. I was fortunate to meet one of my fellow co-founders Wout 17 years ago and have since been involved in a number of startups, a sale of our business, and an ASX listing, to boot.
2. What was the inspiration behind beginning your career in this industry?
While studying medicine in my early 20s, I was drawn to the internet and began learning SEO/PPC in my free time as a hobby. I also immersed myself in the earliest forms of display ads and started doing affiliate marketing, which helped me earn enough to pay for my master’s degree. This journey led me to the realisation that my true passion was not medicine, it was digital media. And I haven’t looked back since. It was truly the best decision I ever made.
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