AI Assistant For Test Assignment

 

Photo by Google DeepMind

Creating an AI application to assist school teachers with testing assignments and result analysis can greatly benefit teachers and students. Here's an overview of why such an application would be beneficial and how it can be developed cost-effectively:

Grading assignments for all students is time-consuming for teachers. AI can automate this process for certain types of assessments, freeing up teachers' time for more interactive learning experiences.

Let's see how it can help our teachers.

1. Teacher Workload: Primary school teachers often have a heavy workload, including preparing and grading assignments for multiple subjects and students. Automating some of these tasks can significantly reduce their workload.

2. Personalized Learning: AI-based applications can provide personalized feedback to students, helping them understand their strengths and weaknesses, leading to more effective learning outcomes.

3. Efficiency: By automating tasks like grading and analysis, teachers can focus more on teaching and providing individualized support to students.

Key Features of the Application:

1. Assignment Creation: Teachers can create assignments for various subjects easily within the application, including multiple-choice questions, short-answer questions, and essay-type questions.

2. OCR Integration: Integration with Azure OCR services allows teachers to scan and digitize handwritten test papers quickly, saving time and effort.

3. AI-Powered Grading: Utilize OpenAI's ChatGPT for grading essay-type questions and providing feedback. Implement algorithms for grading multiple-choice and short-answer questions.

4. Result Analysis: Generate detailed reports and analytics on student performance, including overall scores, subject-wise performance, and areas of improvement.

5. Personalized Feedback: Provide personalized feedback to students based on their performance, highlighting strengths and areas for improvement.

6. Accessibility: Ensure the application is user-friendly and accessible to teachers with varying levels of technical expertise.

Development Approach:

1. Prototype Development: Start with a small-scale prototype to validate the concept and gather feedback from teachers and students.

2. Iterative Development: Adopt an iterative development approach, gradually adding features and refining the application based on user feedback.

3. Cloud-Based Architecture: Utilize cloud-based services for scalability and cost-effectiveness. For example, deploy the application on platforms like Azure or AWS, leveraging serverless computing and managed services.

4. Open Source Libraries: Utilize open-source libraries and frameworks to minimize development costs and accelerate development, such as Flask for the backend, React for the frontend, and TensorFlow for machine learning tasks.

5. Data Security and Privacy: Ensure compliance with data security and privacy regulations, especially when handling student data. Implement encryption, access controls, and data anonymization techniques as needed.

6. User Training and Support: Provide comprehensive user training and ongoing support to teachers to ensure they can effectively use the application.

By following these guidelines, you can develop a cost-effective AI application that enhances the teaching and learning experience for primary school teachers and students.

Here is a Python script to find out how much it costs to use the OpenAI tool for the application above.

def calculate_cost(params):

    """

    Calculates the cost for using ChatGPT for a dynamic assignment application in a school.

    Parameters:

    params (dict): A dictionary containing parameters for the cost calculation.

    Returns:

    float: The total cost of the assignment application.

    Explanation:

    - Extract parameters from the input dictionary.

    - Calculate the number of tokens based on the number of words (assuming 750 words per 1000 tokens).

    - Define costs for different models, fine-tuning, and embedding.

    - Determine the model to be used, considering fine-tuning and embedding.

    - Calculate the cost based on the chosen model, fine-tuning, embedding, number of students, and assignment subjects.

    - Return the total cost.

    """

    words = params["words"]

    tokens = words * 1.25  # Assuming 750 words per 1000 tokens

    model = params["model"]  # Which model to use

    fine_tuning = params["fine_tuning"]  # Fine-tuning required or not

    embed_model = params["embed_model"]  # For embedding model

    students = params["students"]

    assignment_sub_count = params["assignment_sub_count"]

    # Costs for different models

    models = {

        "gpt4": {"8k": 0.03, "32k": 0.06},

        "chatgpt": {"8k": 0.002, "32k": 0.002},

        "instructgpt": {

            "8k": {"ada": 0.0004, "babbage": 0.0005, "curie": 0.0020, "davinci": 0.0200},

            "32k": {"ada": 0.0004, "babbage": 0.0005, "curie": 0.0020, "davinci": 0.0200},

        },

    }

    # Fine-tuning costs

    fine_tuning_cost = {

        "ada": {"training": 0.0004, "usage": 0.0016},

        "babbage": {"training": 0.0006, "usage": 0.0024},

        "curie": {"training": 0.0030, "usage": 0.0120},

        "davinci": {"training": 0.0300, "usage": 0.120},

    }

    # Embedding model costs

    embedding_model = {"ada": 0.0004, "babbage": 0.005, "curie": 0.020, "davinci": 0.20}

    total_cost = 0.0

    instructgpt_models = ["ada", "babbage", "curie", "davinci"]

    if model in instructgpt_models:

        sub_model = model

        model = "instructgpt"

    if model == "instructgpt":

        if tokens > 32000:

            price_model = models[model]["32k"].get(sub_model, {})

        else:

            price_model = models[model]["8k"].get(sub_model, {})

    else:

        if tokens > 32000:

            price_model = models[model]["32k"]

        else:

            price_model = models[model]["8k"]

    if fine_tuning:

        total_cost += (tokens * fine_tuning_cost[sub_model]["training"]) + (

            tokens * fine_tuning_cost[sub_model]["usage"]

        )

    if embed_model:

        total_cost += tokens * embedding_model[sub_model]

    total_cost += price_model * students * assignment_sub_count

    return total_cost

params = {

    "words": 10000,

    "model": "ada",

    "fine_tuning": True,

    "embed_model": "ada",

    "students": 200,

    "assignment_sub_count": 8,

}

print(params)

cost = calculate_cost(params)

print(

    f"The total cost of using ChatGPT for an assignment application with {params['students']} students and {params['assignment_sub_count']} subjects is: ${cost:.2f}"

)

 

Some useful links from Azure

https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/quickstarts-sdk/client-library?tabs=linux%2Cvisual-studio&pivots=programming-language-python

https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-ocr

https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/quickstarts-sdk/image-analysis-client-library-40?tabs=visual-studio%2Clinux&pivots=programming-language-python

https://microsoft.github.io/PartnerResources/skilling/ai-ml-academy/openai

https://azure.microsoft.com/en-us/products/ai-services/ai-document-intelligence

ETL with Python

 

Photo by Hyundai Motor Group

ETL System and Tools:

ETL (Extract, Transform, Load) systems are essential for data integration and analytics workflows. They facilitate the extraction of data from various sources, transformation of the data into a usable format, and loading it into a target system, such as a data warehouse or data lake. Here's a breakdown:

1. Extract: This phase involves retrieving data from different sources, including databases, files, APIs, web services, etc. The data is typically extracted in its raw form.

2. Transform: In this phase, the extracted data undergoes cleansing, filtering, restructuring, and other transformations to prepare it for analysis or storage. This step ensures data quality and consistency.

3. Load: Finally, the transformed data is loaded into the target destination, such as a data warehouse, data mart, or data lake. This enables querying, reporting, and analysis of the data.

ETL Tools:

There are numerous ETL tools available, both open-source and commercial, offering a range of features for data integration and processing. Some popular ETL tools include:

- Apache NiFi: An open-source data flow automation tool that provides a graphical interface for designing data pipelines.

- Talend: A comprehensive ETL tool suite with support for data integration, data quality, and big data processing.

- Informatica PowerCenter: A leading enterprise-grade ETL tool with advanced capabilities for data integration, transformation, and governance.

- AWS Glue: A fully managed ETL service on AWS that simplifies the process of building, running, and monitoring ETL workflows.

Cloud and ETL:

Cloud platforms like Azure, AWS, and Google Cloud offer scalable and flexible infrastructure for deploying ETL solutions. They provide managed services for storage, compute, and data processing, making it easier to build and manage ETL pipelines in the cloud. Azure, for example, offers services like Azure Data Factory for orchestrating ETL workflows, Azure Databricks for big data processing, and Azure Synapse Analytics for data warehousing and analytics.

Python ETL Example:

Here's a simple Python example using the `pandas` library for ETL:

```python

import pandas as pd

# Extract data from a CSV file

data = pd.read_csv("source_data.csv")

# Transform data (e.g., clean, filter, aggregate)

transformed_data = data.dropna()  # Drop rows with missing values

# Load transformed data into a new CSV file

transformed_data.to_csv("transformed_data.csv", index=False)

```

This example reads data from a CSV file, applies a transformation to remove rows with missing values, and then saves the transformed data to a new CSV file.

Deep Dive with Databricks and Azure Data Lake Storage (ADLS Gen2):

Databricks is a unified analytics platform that integrates with Azure services like Azure Data Lake Storage Gen2 (ADLS Gen2) for building and deploying big data and machine learning applications. 

Here's a high-level overview of using Databricks and ADLS Gen2 for ETL:

1. Data Ingestion: Ingest data from various sources into ADLS Gen2 using Azure Data Factory, Azure Event Hubs, or other data ingestion tools.

2. ETL Processing: Use Databricks notebooks to perform ETL processing on the data stored in ADLS Gen2. Databricks provides a distributed computing environment for processing large datasets using Apache Spark.

3. Data Loading: After processing, load the transformed data back into ADLS Gen2 or other target destinations for further analysis or reporting.

Here's a simplified example of ETL processing with Databricks and ADLS Gen2 using Python Pyspark:

```python

from pyspark.sql import SparkSession

# Initialize Spark session

spark = SparkSession.builder \

    .appName("ETL Example") \

    .getOrCreate()

# Read data from ADLS Gen2

df = spark.read.csv("adl://

account_name.dfs.core.windows.net/path/to/source_data.csv", header=True)

# Perform transformations

transformed_df = df.dropna()

# Write transformed data back to ADLS Gen2

transformed_df.write.csv("adl://account_name.dfs.core.windows.net/path/to/transformed_data", mode="overwrite")

# Stop Spark session

spark.stop()

```

In this example, we use the `pyspark` library to read data from ADLS Gen2, perform a transformation to drop null values, and then write the transformed data back to ADLS Gen2.

This is a simplified illustration of ETL processing with Python, Databricks, and ADLS Gen2. In a real-world scenario, you would handle more complex transformations, error handling, monitoring, and scaling considerations. Additionally, you might leverage other Azure services such as Azure Data Factory for orchestration and Azure Synapse Analytics for data warehousing and analytics.

LLM for Humanoid Robot

 

Photo by Tara Winstead

Let's consider a scenario where we aim to integrate Long-Term Memory (LLM) into a humanoid robot to enhance its ability to interact with humans in a social setting. The robot needs to understand and respond appropriately to human emotions expressed through facial expressions and gestures.

Case Study: Integrating LLM for Social Interaction

Objective: Enhance the humanoid robot's social interaction capabilities by integrating LLM to understand and respond to human emotions.

Steps:

1. Data Collection: Collect a dataset of human facial expressions and gestures along with corresponding emotions (e.g., happy, sad, angry).

2. Preprocessing: Preprocess the data to extract facial landmarks, features, and gestures using computer vision techniques.

3. LLM Training: Train an LLM model using the preprocessed data to recognize patterns in human emotions and gestures over time.

4. Robot Hardware Setup: Configure the hardware of the humanoid robot to include cameras and microphones for capturing human interactions.

5. Software Integration: Develop software to interface between the robot's hardware and the trained LLM model for real-time emotion and gesture recognition.

6. Behavior Generation: Implement behavior generation algorithms that interpret the output of the LLM model and generate appropriate responses from the robot, such as facial expressions, verbal responses, or gestures.

7. Testing and Evaluation: Test the integrated system in various social interaction scenarios with human participants. Evaluate the robot's ability to accurately recognize and respond to human emotions and gestures.

Code (Python - Using OpenCV and TensorFlow for LLM):

```python

import cv2

import tensorflow as tf

# Load pre-trained facial expression recognition model

model = tf.keras.models.load_model('facial_expression_model.h5')

# Function to preprocess image for input to the model

def preprocess_image(image):

    # Convert to grayscale

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Resize to model input size

    resized = cv2.resize(gray, (48, 48))

    # Normalize pixel values

    normalized = resized / 255.0

    # Expand dimensions to match model input shape

    preprocessed = normalized.reshape((1, 48, 48, 1))

    return preprocessed

# Function to recognize facial expressions using LLM

def recognize_emotion(image):

    preprocessed_image = preprocess_image(image)

    # Perform emotion recognition using the LLM model

    predictions = model.predict(preprocessed_image)

    # Get the index of the predicted emotion

    emotion_label = predictions.argmax(axis=1)[0]

    # Map index to corresponding emotion label

    emotion_mapping = {0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}

    return emotion_mapping[emotion_label]

# Main loop for real-time emotion recognition

cap = cv2.VideoCapture(0)  # Use default camera

while True:

    ret, frame = cap.read()  # Read frame from camera

    if not ret:

        break

    # Perform emotion recognition on the frame

    emotion = recognize_emotion(frame)

    # Display the detected emotion on the frame

    cv2.putText(frame, emotion, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

    # Display the frame

    cv2.imshow('Emotion Recognition', frame)

    # Break the loop if 'q' is pressed

    if cv2.waitKey(1) & 0xFF == ord('q'):

        break

# Release the camera and close all OpenCV windows

cap.release()

cv2.destroyAllWindows()

```

This code snippet demonstrates how to integrate LLM (facial expression recognition model) into a Python program using OpenCV and TensorFlow for real-time emotion recognition from a webcam feed on a humanoid robot. You would need to train the facial expression recognition model (`facial_expression_model.h5`) using a suitable dataset before using it in this code.

To integrate LLM into a humanoid robot:

1. Understand LLM: Learn about the Long-Term Memory (LLM) model you want to integrate. Understand its architecture, capabilities, and limitations.

2. Robot Platform: Choose a suitable humanoid robot platform with the necessary computational capabilities to support LLM integration.

3. Sensor Integration: Integrate sensors such as cameras, microphones, and other relevant sensors to enable the robot to perceive its environment.

4. Data Preprocessing: Preprocess sensor data to extract relevant features and convert them into a format suitable for input into the LLM model.

5. LLM Integration: Implement the LLM model on the chosen robot platform. This may involve adapting the model to run efficiently on the robot's hardware.

6. Training and Fine-Tuning: Train the LLM model using appropriate data and fine-tune it to perform tasks relevant to the robot's objectives.

7. Real-Time Inference: Implement real-time inference capabilities to enable the robot to use the LLM model for decision-making and action execution.

8. Integration Testing: Test the integrated system in different scenarios to ensure robustness and performance.

9. Iterative Improvement: Continuously refine and improve the integration based on feedback and real-world usage.

10. Deployment: Deploy the integrated LLM-powered humanoid robot in its intended environment for practical use.

Useful links

https://scholar.google.de/scholar?q=llm+into+humanoid+robot&hl=en&as_sdt=0&as_vis=1&oi=scholart

https://tnoinkwms.github.io/ALTER-LLM/

GraphQL with Graph Database

Graph theory is a branch of mathematics that studies graphs, which are mathematical structures that model relationships between objects. A graph is made up of vertices that are connected by edges.

You can find out more about graph theory here https://en.wikipedia.org/wiki/Graph_theory

A connected graph is a graph where every pair of vertices is connected, meaning there is a path between them. A graph is also called disconnected if it is not connected. A connected graph may have a minimum number of edges or vertices that need to be removed to separate the vertices. A graph that has vertices removed is called a vertex-connected graph, while a graph that has edges removed is called an edge-connected graph. 

GraphQL: The Flexible API Query Language

- What it is: GraphQL is a query language specifically designed for APIs that expose data structured as a graph (like knowledge graphs).

- Key Features:

    - Client-Driven: Clients specify the exact data they need, unlike traditional REST APIs that provide predefined endpoints with fixed data structures.

    - Nested Queries: Retrieve related data in a single request, eliminating the need for multiple API calls and complex joins.

    - Flexibility: Schema-based, allowing for evolution over time as data needs change.

Graph Databases: Optimized for Interconnected Data

- What they are: Graph databases store data in nodes (entities) and edges (relationships) between those nodes. This structure excels at managing interconnected information.

- Benefits:

    - Native Connectivity: Relationships are central, eliminating the need for complex joins in relational databases.

    - Scalability: Designed to handle large datasets with intricate relationships.

    - Flexibility: Schema can evolve over time to accommodate new data types and relationships.

The Perfect Match: GraphQL and Graph Databases

- Synergy: GraphQL shines at querying data stored in graph databases. It translates client requests into queries that the graph database understands, delivering the desired data efficiently.

- Benefits of the Combination:

    - Efficient Data Retrieval: Clients get only the data they need, improving performance.

    - Complex Queries Made Simple: Nested queries allow for retrieving related data in one go.

    - Ideal for Interconnected Data: Perfect for applications dealing with heavily connected data, like social networks or recommendation systems.

Key Points to Remember:

- GraphQL is a query language, not a database itself. It can work with various data sources, but it's particularly well-suited for graph databases.

- Graph databases provide a natural fit for GraphQL because they store data in a structure that aligns with how GraphQL queries data.

- This combination unlocks powerful capabilities for building applications that leverage complex, interconnected data.

You can find out more about GraphQL here https://graphql.org/

Knowledge Graphs: A Powerful Tool for Interconnected Data

A knowledge graph (KG) is a powerful way to store and manage interconnected information. It represents data as nodes (entities) and edges (relationships) between those entities. This structure allows for efficient querying and exploration of complex relationships within your data.

Here's a breakdown of the key components:

• Nodes: These represent real-world objects, concepts, or events. Examples include "customer," "product," "security threat," "vulnerability."
• Edges: These define the connections between nodes. They can be labeled to specify the nature of the relationship, such as "purchased," "mitigates," or "exploits."
• Properties: Nodes and edges can have additional attributes that provide more context. For instance, a "customer" node might have properties like "name," "email," and "purchase history."

Benefits of Knowledge Graphs

• Improved Data Integration: KGs excel at unifying data from disparate sources, enabling holistic views across your systems.
• Enhanced Querying: GraphQL, a query language specifically designed for KGs, allows you to fetch related data in a single request, streamlining complex information retrieval.
• Reasoning and Inference: KGs can support reasoning and inference capabilities, allowing you to uncover hidden connections and derive new insights from your data.

Example: Knowledge Graph in Action

Imagine a cybersecurity scenario where you're investigating a potential breach. A knowledge graph could connect:

• Employees (nodes): Names, roles, access levels.
• Systems (nodes): Servers, databases, applications.
• Vulnerabilities (nodes): CVE IDs, severity ratings.
• Access Attempts (edges): Employee, system, time, success/failure.

By querying this KG using GraphQL, you could efficiently discover:

• Which employees accessed vulnerable systems around the time of the breach attempt.
• Whether specific vulnerabilities could be exploited to gain access to critical data.

Cybersecurity Applications of Knowledge Graphs

KGs can be invaluable for various cybersecurity tasks:

• Threat Intelligence: By connecting threat actors, attack methods, vulnerabilities, and compromised systems, KGs can help predict and prevent future attacks.
• Incident Response: Quickly identify affected assets, understand the scope of a breach, and prioritize mitigation efforts using KG-powered querying.
• Security Awareness Training: Create personalized training modules that target employees based on their roles and access levels, leveraging knowledge graphs to tailor the learning experience.

GraphQL for Knowledge Graph Interactions

GraphQL provides a flexible and efficient way to query knowledge graphs. Here's a simplified example of a GraphQL query:

GraphQL

query {
  employee(id: 123) {
    name
    accessAttempts {
      system {
        name
      }
      vulnerability {
        id
        severity
      }
    }
  }
}

This query retrieves information about an employee (ID: 123) and their access attempts, including the accessed systems and associated vulnerabilities, facilitating security analysis.

In Conclusion

Knowledge graphs, combined with GraphQL's querying power, offer a compelling approach for managing and analyzing complex cybersecurity data. By connecting entities and relationships, you gain valuable insights to enhance threat prevention, incident response, and overall security posture.

Deep Dive into Graph QL and Graph Databases with Use Cases

Graph Databases and GraphQL: A Match Made in Data Heaven

While knowledge graphs leverage both graph databases and GraphQL, here's a closer look at each:

Graph Databases:

• Structure: Graph databases store data in nodes (entities) and edges (relationships) just like knowledge graphs. They are specifically designed to optimize querying and traversal of interconnected data.
• Benefits:
  • Native Connectivity: Relationships are first-class citizens, eliminating the need for complex joins in traditional relational databases.
  • Scalability: Designed for handling large datasets with intricate relationships.
  • Flexibility: Schema can evolve over time to accommodate new data types and relationships.

GraphQL:

• Query Language: Designed specifically for APIs that expose data structured as a graph.
• Power of Choice: Clients request only the exact data they need, improving efficiency and performance.
• Flexibility: Supports nested queries, allowing you to retrieve related data in one go.

The Synergy:

• GraphQL excels at querying data stored in graph databases. It translates client requests into queries that the graph database understands, delivering the desired data efficiently.
• This combination is ideal for applications dealing with highly interconnected data.

Beyond Cybersecurity: Use Cases for Graph QL and Graph Databases

General AI (Gen AI):

• Reasoning and Inference: By leveraging KG connections, Gen AI systems can build more comprehensive models of the world, improving their ability to reason and draw inferences.
• Knowledge Base Integration: KGs can serve as a knowledge base for Gen AI systems, providing them with a rich source of structured information to inform their learning and decision-making processes.

Other Use Cases:

• Social Networks: Efficiently connect users, messages, and groups based on relationships.
• Recommendation Systems: Personalize recommendations by understanding user interests and item relationships.
• Supply Chain Management: Track product movement across the supply chain based on connections between manufacturers, distributors, and retailers.
• Fraud Detection: Identify suspicious patterns by analyzing financial transactions and connections between entities.

In essence, graph databases and GraphQL provide a powerful toolkit for managing and querying complex, interconnected data, opening doors for innovative applications in various domains.