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

LLM, GenAI and Database

 

Modern Applications: The Trio of Power - LLMs, GENAI, and Databases

Imagine a world where applications understand your voice, generate personalized content, and anticipate your needs. This isn't science fiction, it's the reality brewing thanks to three vital technological advancements: Large Language Models (LLMs), Generative AI (GENAI), and powerful Databases.

Understanding the Players:

• LLMs: These are AI models trained on massive amounts of text data, enabling them to understand and generate human-like language. Think of them as super-advanced linguists, capable of summarizing documents, translating languages, and even writing creative text formats.
• GENAI: This branch of AI focuses on creating new content, not just analyzing it. GENAI models can generate images, music, code, and even new kinds of data itself. They're the artistic inventors, crafting experiences and solutions never seen before.
• Databases: These are the organized libraries of information, storing everything from user preferences to product details. They provide the raw material for both LLMs and GENAI to work with, ensuring their responses and creations are grounded in reality.

                               Image: Google Cloud

Why They're Essential:

1. Personalized Experiences: LLMs and GENAI personalize applications, tailoring content and interactions to individual users. Imagine a news app that summarizes articles based on your interests, or a shopping assistant that recommends products you'll actually love.
2. Enhanced Productivity: These technologies automate tasks and generate reports, freeing humans to focus on higher-level thinking. LLMs can write marketing copy, translate documents, and even code basic software functions.
3. Innovation and Creativity: GENAI pushes the boundaries of what's possible. It can create new marketing campaigns, design innovative products, and even compose personalized music or art.
4. Data-Driven Decisions: Powerful databases provide the fuel for all of this. They allow applications to track user behaviour, analyze trends, and make informed decisions based on real-world data.

Modern applications that don't leverage this trio are falling behind. They'll struggle to compete with personalized experiences, automated tasks, and the constant churn of innovative content. LLMs, GENAI, and databases are not just buzzwords, they're the foundation of the future, and applications that embrace them will stand out from the crowd.

        image: Google cloud & Deutsche bank

Remember, it's not just about technical prowess, but also about finding the right balance. Combining these technologies thoughtfully with human insight and ethical considerations will unlock the true potential of AI and create applications that are not only powerful but also beneficial and user-friendly.

So, whether you're building a revolutionary new app or revamping an existing one, consider the power of this dynamic trio. LLMs, GENAI, and databases are not just tools, they're keys to unlocking a future of personalized, efficient, and truly innovative applications.

I'm ready to provide you with some information on AloyDI, bridging the gap between LLMs/GENAI and databases, and leveraging GCP Vertex AI. However, I'm unable to access real-time data or perform actions directly. I'll offer my best understanding based on current knowledge and resources.

                     image: google cloud

Bridging the Gap between LLMs/GENAI and Databases:

• Challenge: LLMs (Large Language Models) and GENAI (Generative AI) excel at generating text, translating languages, writing different creative content formats, and answering questions. However, they often lack factual knowledge or the ability to directly interact with databases, limiting their potential for real-world applications.
• Solution: AloyDI (likely a database or knowledge base) aims to bridge this gap by providing LLMs/GENAI with access to structured and factual information. This integration enables them to generate more accurate and comprehensive responses, produce content grounded in real-world knowledge, and perform tasks that require database interactions.

AloyDI (Potential Features and Usage):

• Database of factual knowledge: Stores information on various topics, potentially structured in a machine-readable format.
• API for access: Provides a way for LLMs/GENAI to query and retrieve information from the database.
• Integration with LLM/GENAI models: Allows models to seamlessly incorporate knowledge from AloyDI into their responses and generation processes.

Potential Applications:

• Customer service chatbots: Provide more accurate and informative responses to customer queries.
• Content generation tools: Create articles, reports, and other content grounded in real-world facts and figures.
• Question-answering systems: Answer questions more comprehensively and accurately, even when factual knowledge is required.
• Task completion agents: Perform tasks that involve database interactions, such as booking appointments or retrieving specific information.

Leveraging GCP Vertex AI:

• Vertex AI: A platform for managing and deploying machine learning models, including LLMs and GENAI models.
• Integration with AloyDI: Potential to enable deployment of models that leverage AloyDI's knowledge base, facilitating development and scaling of applications that combine language generation with factual knowledge.

Specific usage and integration details for AloyDI and Vertex AI would require access to their documentation and functionality.

To provide more comprehensive information, I'd need access to:

• Official documentation or resources for AloyDI
• GCP Vertex AI documentation and features

Here's how vector indexing and embedding relate to bridging the gap between LLMs/GENAI and databases, potentially involving AloyDI:

Vector Indexing:

• Storage of knowledge: AloyDI likely employs vector indexing techniques to store and efficiently retrieve knowledge elements. This involves representing concepts, entities, or facts as numerical vectors in a high-dimensional space.
• Search and retrieval: When an LLM or GENAI model needs to access knowledge from AloyDI, it can query using relevant keywords or phrases. AloyDI's vector-based indexing system can efficiently find matching knowledge elements based on their vector representations.

Embeddings:

• Integration of knowledge: Embeddings, which are dense vector representations of words, phrases, or entities, play a crucial role in integrating knowledge from AloyDI into LLM/GENAI models.
• Semantic understanding: Models can encode knowledge elements from AloyDI as embeddings, allowing them to incorporate factual information and enhance their semantic understanding of language.
• Contextual relevance: Models can use embeddings to measure similarity between concepts and retrieve relevant knowledge from AloyDI during text generation or question-answering tasks, ensuring contextually appropriate responses.

Potential Implementation:

1. Knowledge embedding: AloyDI's knowledge elements are embedded into vector space.
2. Query embedding: Incoming queries from LLMs/GENAI models are also embedded.
3. Match retrieval: Vector similarity techniques (e.g., cosine similarity) are used to find matching knowledge elements from AloyDI's database.
4. Integration: Retrieved knowledge is integrated into the model's generation or reasoning processes, using appropriate techniques for the specific model architecture.

Vertex AI's Role:

• Model deployment: Vertex AI could facilitate the deployment and management of models that seamlessly integrate with AloyDI, enabling efficient knowledge retrieval and utilization.
• Monitoring and optimization: Vertex AI could provide tools to monitor model performance and optimize knowledge integration for better results.

                                 image: google cloud

Further Information:

• AloyDI documentation: To provide more specific details on its vector indexing and embedding techniques, access to AloyDI's documentation or resources is necessary.
• Vertex AI features: Understanding Vertex AI's specific functionalities related to knowledge integration is crucial for determining optimal model deployment and management strategies.

                            image: google cloud

As you already know GENAI, LLMs, vector embedding, and databases, focus on their interplay in knowledge-driven AI applications:

Users:

• Interact with AI systems: Provide input, receive responses, and utilize generated content or services.
• Benefit from knowledge integration: Experience more informative, accurate, and contextually relevant interactions.

GENAI (Generative AI):

• Creates new content: Generates text, images, audio, or other creative formats.
• Leverages LLMs and knowledge bases: Combines language capabilities with factual information for enhanced generation.

LLMs (Large Language Models):

• Process and generate human-like text: Trained on massive amounts of text data, capable of understanding and producing language.
• Benefit from knowledge integration: Produce more grounded, factual, and comprehensive responses.

Vector Embedding:

• Represents concepts as vectors: Encodes words, phrases, or entities into numerical vectors in high-dimensional space.
• Enables knowledge integration: Facilitates efficient storage, retrieval, and comparison of knowledge elements in databases and AI models.

Database:

• Stores structured knowledge: Contains factual information on various topics, organized for efficient access and retrieval.
• AloyDI example: A potential knowledge base designed for integration with LLMs and GENAI.

Interplay in Knowledge-Driven AI Applications:

1. User Query: A user interacts with a GENAI or LLM system, providing a query or prompt.
2. Model Processing: The model processes the input, generating an initial response or identifying knowledge gaps.
3. Knowledge Retrieval: If necessary, the model queries a knowledge base like AloyDI using vector-based search techniques.
4. Integration: Retrieved knowledge is integrated into the model's reasoning or generation process, often using vector embeddings to ensure semantic alignment.
5. Enhanced Response: The model produces a more comprehensive, informative, and contextually relevant response, benefiting from the integrated knowledge.

Vertex AI's Potential Role:

• Deployment and Management: Facilitates deployment and management of models that integrate with knowledge bases.
• Monitoring and Optimization: Provides tools to monitor performance and optimize knowledge integration strategies.

Key Takeaways:

• Knowledge integration is crucial: Enhances AI capabilities for real-world applications.
• Vector embedding is essential: Enables efficient knowledge representation and retrieval.
• Databases like AloyDI: Provide structured knowledge sources for AI models.
• Vertex AI: Offers potential for managing and optimizing knowledge-driven AI systems.

Here's a use case illustrating how these components can work together:

Use Case: Customer Service Chatbot

Problem: Traditional chatbots often struggle to provide accurate and comprehensive answers to factual queries, leading to user frustration and low satisfaction.

Solution: Integrate a GENAI model with AloyDI to create a knowledge-enhanced chatbot.

Implementation:

1. User interacts with chatbot: Asks a question about product specifications, history, or troubleshooting.
2. GENAI model processes query: Generates an initial response based on language understanding.
3. Knowledge gap identified: Model recognizes the need for factual information from AloyDI.
4. Vector-based query: Model constructs a vector representation of the query and searches AloyDI for relevant knowledge elements.
5. Knowledge retrieval: AloyDI retrieves matching facts and figures, potentially using vector similarity techniques.
6. Integration into response: Retrieved knowledge is seamlessly integrated into the chatbot's response, ensuring accuracy and comprehensiveness.
7. Enhanced user experience: User receives a more informative and helpful answer, building trust and satisfaction.

Vector Embedding's Role:

• Knowledge representation: Both AloyDI and the GENAI model use vector embeddings to efficiently represent and compare knowledge elements.
• Semantic understanding: Embeddings enable the model to grasp the meaning of concepts and retrieve relevant knowledge from AloyDI, ensuring contextually appropriate responses.

Verta AI's Potential Role:

• Deployment and management: Verta AI could facilitate the deployment and management of the knowledge-enhanced chatbot model, ensuring scalability and reliability.
• Monitoring and optimization: It could provide tools to monitor the model's performance, identify areas for improvement, and optimize knowledge integration strategies over time.

Additional Benefits:

• Personalization: Chatbot could leverage user data and preferences to tailor responses further, enhancing user engagement.
• Multilingual support: Potential to support multiple languages, expanding reach and accessibility.
• Continuous learning: The model could continuously learn from interactions and new knowledge sources, improving accuracy and relevance over time.

Another use case: A mobile app using advanced AI technology to deliver personalized content summaries on your lock screen while respecting user privacy and ethical considerations holds immense potential. Here's how you could explore your vision:

Concept:

• Name: Consider a catchy name that reflects the essence of personalized summaries, like "In a Glimpse," "Knowledge Spark," or "Unlock Insight."
• Function: The app would analyze user data like news feeds, subscriptions, bookmarks, and even calendar events to curate relevant snippets and deliver them concisely on the lock screen.
• Technology: Implement LLMs/GENAI models for text summarization and natural language processing, coupled with vector embedding techniques for efficient search and knowledge retrieval.
• Privacy Focus: Emphasize user control and data security. Allow users to choose the sources analyzed, set preferences for content categories, and ensure anonymization of sensitive information.

Features:

• Smart Summaries: AI-powered summaries of articles, news stories, unread emails, and upcoming events, presented in visually appealing formats like text snippets, bullet points, or even infographics.
• Personalization: Adapt summaries based on user interests, reading habits, and past interactions.
• Offline Functionality: Enable saving summaries for offline viewing, empowering users to learn even when disconnected.
• Customization: Allow users to choose the type and frequency of content summaries on their lock screens.
• Additional Features: Consider integrations with fitness trackers or scheduling apps for contextually relevant summaries like workout routines or meeting agendas.

Ethical Considerations:

• Fact-Checking: Utilize reliable sources and implement fact-checking mechanisms to prevent misinformation.
• Bias Mitigation: Train AI models on diverse datasets to minimize bias in content selection and summarization.
• Transparency: Clearly communicate data usage practices and provide users with control over their data.

Potential Applications:

• Busy professionals can stay informed on critical updates or industry trends.
• Students can prepare for exams or review lectures efficiently.
• News enthusiasts can catch up on current events without diving into full articles.

Remember, success lies in striking a balance between cutting-edge technology and user trust. By prioritizing ethical considerations, data privacy, and personalization, you can create a valuable tool that empowers users to stay informed and engaged with the world around them, all within the safe confines of their lock screens.