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Building the AI Model : Test Questions Bank

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https://coda.io/d/How-to-create-and-publish-a-Technical-Blog_dD8LB1EHP5z/How-to-create-and-publish-a-Technical-Blog_su1Gzfmq⁠

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https://www.linkedin.com/pulse/peters-first-law-peter-sigurdson-qivze/⁠

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https://www.linkedin.com/pulse/bayesian-oracle-peter-sigurdson-rqn8e/⁠

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w25 ; January 27 Session 4

A review of our January 20 class:

Software Model Architecture

Intersection with consumer demand and what people will pay for

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`AML3304`⁠
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Summary

Safety Guidelines for AI Models

0:01⁠

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Peter Sigurdson discusses the importance of safety guidelines for AI models, emphasizing that they should not promote violence, racism, or inappropriate content.

He explains that these guidelines are built into foundational models like Vicuna, ChatGPT, and others.

Additional guard rails can be applied based on specific needs, such as directing users to human experts for investment advice.

Sigurdson mentions the unified model engineering process, which he believes is unique to AI development and not widely discussed in software development methodologies.

Introduction to Unified Model Engineering Process

1:40⁠

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Sigurdson introduces the concept of unified model engineering process, which he believes is essential for AI development.

He criticizes the unified process for not fitting well with AI development but still recommends learning it for its broader applicability.

Sigurdson emphasizes the importance of understanding foundational concepts like gradient descent vectors and language embeddings.

He plans to teach these concepts procedurally rather than mathematically in-depth, focusing on intuitive understanding.

Emerging Technologies and AI Development

17:44⁠

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Sigurdson discusses emerging technologies like Cloud DevOps, CICD, and software build, which are relevant to AI development.

He mentions the importance of understanding concepts like gradient descent vectors and language embeddings, which are crucial for AI model training.

Sigurdson plans to teach these concepts in a way that provides a foundational understanding without delving into complex mathematical proofs.

He encourages students to read the first three chapters of his book to prepare for the course.

Assignment and Learning Outcomes

43:42⁠

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Sigurdson introduces the assignment, which involves building and deploying an AI language model using a foundational model like Vicuna or ChatGPT.

He explains that students can work in teams of up to four and should become familiar with Hugging Face spaces and the model garden.

The assignment will involve training the model on a chosen training corpus and deploying it to Hugging Face spaces.

Sigurdson emphasizes the importance of understanding the theory behind AI model building and deployment.

Hugging Face Spaces and Model Garden

53:25⁠

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Sigurdson instructs students to create accounts on Hugging Face spaces and explore the model garden.

He explains that Hugging Face spaces allow students to deploy their models and share them with others.

Students should familiarize themselves with the various models and datasets available in the model garden.

Sigurdson encourages students to use their personal emails for these accounts to maintain access even after graduation.

Training and Deployment of AI Models

1:00:41⁠

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Sigurdson explains the process of training AI models using methods like Bayesian training and creating embeddings.

He emphasizes the importance of understanding these concepts intuitively rather than in-depth mathematical understanding.

Students will need to create a GitHub repository to manage their code and deploy it to Hugging Face spaces.

Sigurdson plans to cover the technical details of using GitHub and Hugging Face spaces in future sessions.

Philosophical Background of AI Models

1:02:31⁠

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Sigurdson mentions the philosophical background of AI models, referencing historical figures like Leibniz who attempted to build a calculus of human thought.

He explains that modern AI models use similar concepts to embed and train language models.

Sigurdson encourages students to explore the philosophical aspects of AI development to gain a deeper understanding.

He plans to provide lecture notes and resources for students to read before the next session.

Assignment Instructions and Grading

1:04:23⁠

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Sigurdson provides detailed instructions for the assignment, including the use of foundational models and training datasets.

He explains the grading rubric and the importance of following the instructions closely.

Students are encouraged to work in teams and to deploy their models to Hugging Face spaces.

Sigurdson emphasizes the practical application of AI models in various domains, from art to religion.

Preparation for Next Session

1:04:46⁠

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Sigurdson instructs students to read the provided lecture notes on embeddings and Bayesian training before the next session.

He encourages students to explore the Hugging Face spaces and model garden to familiarize themselves with the tools and resources.

Sigurdson plans to cover the technical details of deploying models to Hugging Face spaces in the next session.

Students are reminded to complete their homework and prepare for the next session's activities.

Discussion question that covers the key topics discussed in the lecture:

What are the key considerations and challenges in developing appropriate software engineering methodologies and processes for building AI models, particularly in the context of generative language models? How does the unified model engineering process differ from traditional software development approaches, and what are the benefits and drawbacks of this new methodology? The key topics covered include:

The importance of safety guidelines and guard rails for AI models

The need for a specialized software development methodology for AI, rather than adapting traditional approaches

The role of foundational models, embeddings, and Bayesian training in building generative language models

The practical considerations of deploying AI models on platforms like Hugging Face

The philosophical and historical context of AI model development

This question encourages students to think critically about the unique challenges of AI development and how new methodologies like the unified model engineering process can address them, while also considering the tradeoffs and practical implementation details

Discussion question related to social and safety trust issues in AI development:

What are the key ethical and safety considerations that need to be addressed when developing and deploying generative AI language models for public-facing applications? How can AI developers build in appropriate safeguards and guard rails to ensure these models are used responsibly and do not cause harm? Some key points to consider:

The potential for AI models to generate content that promotes violence, hate, or other harmful narratives, and how to prevent this.

The challenge of defining appropriate boundaries and limitations for AI assistants to avoid giving advice on sensitive topics like investments or medical issues.

The need to be transparent about the capabilities and limitations of AI models to manage user expectations and build trust.

Strategies for monitoring AI model outputs and having human oversight to quickly identify and address problematic content.

The role of regulatory frameworks and industry standards in establishing safety guidelines for public-facing AI applications.

This question encourages students to think deeply about the social implications of their work and how to proactively address ethical concerns around the deployment of powerful language models. Developing responsible AI practices should be a core consideration in the unified model engineering process.

What are the key considerations and challenges in developing appropriate software engineering methodologies and processes for building AI models, particularly in the context of generative language models? How does the unified model engineering process differ from traditional software development approaches, and what are the benefits and drawbacks of this new methodology?

The key considerations and challenges in developing appropriate software engineering methodologies for building AI models, particularly generative language models, include:

Adaptability and Iterative Development: Traditional software engineering approaches like waterfall often struggle to keep pace with the rapid advancements in AI. The unified model engineering process emphasizes adaptability, allowing for more iterative development and testing of AI models as the technology evolves.

Handling Uncertainty and Unpredictability: AI models, especially generative language models, can exhibit unpredictable behaviors and outputs that are difficult to fully anticipate. The unified process focuses on building in safeguards and monitoring mechanisms to quickly identify and address issues.

Incorporating Domain Expertise: Effective AI development requires deep understanding of the target domain and business context. The unified process prioritizes upfront study of the problem space and close collaboration with subject matter experts, rather than relying solely on technical teams.

Balancing Automation and Human Oversight: While AI models can automate many tasks, there is a need for human oversight and intervention, particularly around ethical considerations and safety. The unified process aims to find the right balance between automation and human control.

Continuous Learning and Adaptation: Language models in particular require ongoing learning and fine-tuning as they are exposed to new data. The unified process accounts for this by building in mechanisms for model updates and retraining.

The key difference between the unified model engineering process and traditional software development is the emphasis on the unique challenges of AI. Rather than trying to fit AI into existing methodologies, the unified process is designed from the ground up to address the unpredictability, domain-specificity, and continuous learning needs of AI systems.

The benefits of this approach include greater adaptability, stronger safeguards, and more effective collaboration between technical and domain experts. However, the drawbacks may include a steeper learning curve for teams unfamiliar with AI-specific processes, as well as the need for more specialized skills and tools.

Overall, the unified model engineering process represents an important evolution in software development to meet the unique demands of building robust and trustworthy AI applications, particularly in high-stakes domains like generative language models.

Discussion question related to the architecture and operation of an AI model:

How do the core concepts of tokenization, embeddings, and neural network training work together to enable the generation of coherent and contextually-appropriate language by an AI model? What are the key architectural considerations and design choices that impact the performance and capabilities of a generative language model? Some key points to explore:

The role of tokenization in breaking down input text into discrete units that can be processed by the model

How embeddings represent these tokens as numerical vectors that capture semantic relationships

The function of neural network architectures like transformers in learning patterns and generating new text

The importance of the training dataset and Bayesian training techniques in shaping the model's knowledge and response generation

Factors like model size, depth, attention mechanisms, and other architectural choices that influence the model's fluency, coherence, and ability to understand context

This question dives into the technical underpinnings of how generative language models operate, encouraging students to think critically about the interplay of different components and how design decisions impact the model's performance. Understanding these architectural principles is crucial for building effective AI systems.

What specific steps can students take during this course and beyond to make themselves competitive candidates for entry-level AI development roles? How can they leverage the skills and experience gained here to stand out in the job market?

During this course, students can take several key steps to make themselves competitive candidates for AI development jobs:

Build a Portfolio of AI Projects: The course assignment to build and deploy a generative language model provides an excellent opportunity to create a showcase project. Students should put effort into making this project high-quality and well-documented to demonstrate their practical skills.

Gain Experience with AI Tools and Platforms: Becoming proficient with popular AI development tools like PyTorch, TensorFlow, and Hugging Face will make students more attractive to employers. Actively exploring and experimenting with these platforms during the course can give students a head start.

Develop Software Engineering Best Practices: In addition to AI-specific skills, employers value strong software engineering fundamentals like version control, testing, and deployment. Applying these practices to the course project will help students develop a well-rounded skill set.

Stay Informed on AI Trends and Research: Students should make a habit of following the latest AI news, research, and industry applications. This will help them understand emerging areas of focus and be able to speak knowledgeably in interviews.

Beyond the course, students can further strengthen their candidacy by:

Networking and Building Connections: Engaging with local AI meetups, online communities, and industry professionals can uncover job opportunities and provide valuable mentorship.

Continuing Self-Directed Learning: AI is a rapidly evolving field, so students should commit to ongoing skill development through online courses, tutorials, and personal projects.

Highlighting Transferable Skills: In addition to technical AI expertise, students should emphasize transferable skills like problem-solving, critical thinking, and communication that are highly valued in the industry.

By leveraging the hands-on experience, tool proficiency, and broader understanding of AI gained in this course, students can position themselves as attractive candidates for entry-level AI development roles. Demonstrating a combination of practical skills, industry awareness, and a proactive approach to professional development will help them stand out in the job market.

w25 : January 20 Session 3

Summarized Review Questions and Detailed Answers

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Question 1:

What are the key differences between object-oriented programming and probabilistic programming, and how do these differences impact the design and development of AI models?

Summary of the Question:

This question asks students to compare two programming paradigms—object-oriented programming (OOP) and probabilistic programming (PP)—and explain how their differences influence the way AI models are designed and developed. It also touches on the statistical and mathematical foundations of AI, the challenges of working with different types of data, and the practical considerations for deploying AI models.

Detailed Answer:

Object-Oriented Programming (OOP):

Definition: OOP is a programming paradigm based on the concept of "objects," which are instances of classes. Objects encapsulate data (attributes) and behavior (methods).

Key Features:

Encapsulation: Data and methods are bundled together.

Inheritance: Classes can inherit properties and methods from other classes.

Polymorphism: Objects can take on multiple forms.

Impact on AI Development:

OOP is useful for structuring large-scale AI projects, such as defining reusable components (e.g., data loaders, model architectures, and evaluation pipelines).

It provides a modular approach, making it easier to maintain and extend AI systems.

Probabilistic Programming (PP):

Definition: PP is a paradigm that focuses on defining probabilistic models and reasoning about uncertainty. It uses statistical methods to infer relationships between variables.

Key Features:

Bayesian Inference: Models are built using probability distributions, and inference is performed to update beliefs based on data.

Flexibility: PP allows for the representation of complex, uncertain systems.

Impact on AI Development:

PP is essential for tasks involving uncertainty, such as natural language processing, recommendation systems, and decision-making under uncertainty.

It enables the use of probabilistic models like Bayesian networks and Markov chains.

Key Differences and Their Impact:

Focus:

OOP focuses on structuring code and reusability.

PP focuses on modeling uncertainty and reasoning about data.

Mathematical Foundations:

OOP relies on deterministic logic.

PP relies on statistical methods, such as Bayesian statistics and gradient descent.

Data Handling:

OOP is better suited for structured data.

PP excels in handling unstructured data, such as text and images, by leveraging tokenization and embeddings.

AI Model Design:

OOP is used to organize the components of AI systems.

PP is used to define the probabilistic relationships and optimize model parameters.

Practical Implications:

OOP is commonly used in frameworks like TensorFlow and PyTorch to define neural networks and training pipelines.

PP is used in probabilistic programming libraries like Pyro and Stan for tasks requiring uncertainty modeling.

Tools like Hugging Face and cloud platforms integrate both paradigms to deploy AI models effectively.

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Question 2:

How do the concepts of tokens, weightings, and embeddings relate to the architecture and performance of AI language models? Explain the importance of these concepts and how they impact the training and deployment of AI applications.

Summary of the Question:

This question focuses on the fundamental building blocks of AI language models—tokens, weightings, and embeddings—and their role in model architecture and performance. It also asks students to explain how these concepts influence the training and deployment of AI applications.

Detailed Answer:

Tokens:

Definition: Tokens are the smallest units of text (e.g., words, subwords, or characters) that a language model processes.

Role in AI Models:

Tokenization splits text into manageable pieces for processing.

The choice of tokenization method (e.g., word-level, subword-level, or character-level) affects the model's ability to handle rare or unseen words.

Impact on Performance:

Proper tokenization ensures efficient representation of text and reduces the size of the vocabulary, improving training speed and accuracy.

Weightings:

Definition: Weightings are the numerical values assigned to the connections between neurons in a neural network. They determine the importance of each input feature or token.

Role in AI Models:

During training, the model adjusts weightings to minimize the loss function and improve predictions.

Weightings capture the relationships and affinities between tokens.

Impact on Performance:

Properly optimized weightings lead to better generalization and accuracy in language tasks.

Embeddings:

Definition: Embeddings are dense vector representations of tokens in a continuous space. They capture semantic and syntactic relationships between tokens.

Role in AI Models:

Embeddings map tokens to numerical vectors that can be processed by neural networks.

Pre-trained embeddings (e.g., Word2Vec, GloVe) provide a starting point for training, reducing the need for large datasets.

Impact on Performance:

High-quality embeddings improve the model's ability to understand context and relationships in text.

Architectural Implications:

Tokens, weightings, and embeddings are fundamental to the architecture of AI language models like transformers.

Neural networks use embeddings as input, process them through layers of neurons, and adjust weightings using gradient descent.

The architecture determines how well the model captures context and generates predictions.

Training and Deployment:

Training:

Tokenization and embeddings are critical for preparing data for training.

Weightings are optimized during training using algorithms like stochastic gradient descent.

Deployment:

Efficient tokenization and embeddings reduce the computational cost of inference.

Pre-trained models (e.g., BERT, GPT) leverage embeddings and weightings to provide state-of-the-art performance in real-world applications.

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Question 3 (New Question with "First Day on the Job" Flavor):

Imagine you are tasked with building a chatbot for a customer service application. How would you use the concepts of tokenization, embeddings, and neural networks to design and train the chatbot? What challenges might you face, and how would you address them?

Summary of the Question:

This question asks students to apply their understanding of tokenization, embeddings, and neural networks to a practical task: building a chatbot. It also encourages them to think about potential challenges and solutions.

Detailed Answer:

Designing the Chatbot:

Tokenization:

Split user input into tokens (e.g., words or subwords) for processing.

Use subword tokenization (e.g., Byte Pair Encoding) to handle rare or unknown words.

Embeddings:

Convert tokens into dense vector representations using pre-trained embeddings (e.g., GloVe, FastText) or train custom embeddings.

Use embeddings to capture the semantic meaning of user input.

Neural Networks:

Use a transformer-based architecture (e.g., GPT or BERT) to process embeddings and generate responses.

Fine-tune the model on a dataset of customer service interactions.

Training the Chatbot:

Prepare a dataset of customer queries and responses.

Train the model using supervised learning, optimizing weightings with gradient descent.

Evaluate the model's performance using metrics like accuracy and BLEU score.

Challenges and Solutions:

Challenge: Handling ambiguous or incomplete user input.

Solution: Use context-aware models like transformers to understand the conversation history.

Challenge: Deploying the chatbot efficiently.

Solution: Use tools like Hugging Face Transformers and cloud platforms for scalable deployment.

Challenge: Ensuring the chatbot provides accurate and helpful responses.

Solution: Continuously fine-tune the model using feedback from real interactions.

This question encourages students to think critically about the practical application of AI concepts in a real-world scenario.

August 2

Table 1

Table 1

Question

Correct Answer

Answer 1

Answer 2

Answer 3

Answer 4

Column 7

Explanation

Explanation 2

In the conceptual model for AI language models, which layer receives raw text?

Input Layer

Embedding Layer

Encoder Layer

Output Layer

The Input Layer is responsible for receiving raw text in the conceptual model.

What does the Embedding Layer do in an AI language model?

Generate output text

Convert words to numerical vectors

Extract patterns

Produce final text prediction

The Embedding Layer converts words to numerical vectors, allowing the model to process text mathematically.

Which layers are responsible for extracting patterns and relationships in the text?

Input Layers

Embedding Layers

Encoder Layers

Output Layers

Encoder Layers are responsible for extracting patterns and relationships from the input text.

In the provided code example, what type of neural network layer is used for the encoder and decoder?

Dense

Embedding

LSTM

Convolutional

The code example uses LSTM (Long Short-Term Memory) layers for both the encoder and decoder.

What is the purpose of the output layer in the LanguageModel class?

Receive input

Generate embeddings

Process sequences

Produce final text prediction

The output layer (Dense layer) in the LanguageModel class produces the final text prediction.

How is modular design in AI language models similar to Java programming?

Each layer is a self-contained unit

Layers are written in Java

Models use Java Virtual Machine

AI models are objects

Modular design in AI models is similar to Java as each layer is a self-contained unit with a specific purpose, like Java objects.

How does data flow in an AI language model compare to Java object interactions?

Through SQL queries

Via HTTP requests

Through the layers, similar to method calls

Using global variables

Data flows through the layers in AI models, similar to method calls between Java objects.

What concept in AI models is analogous to shared fields in Java objects?

The model's weights

The input data

The output predictions

The layer names

The model's weights are like shared fields in Java objects, updated during training.

How is the concept of composition applied in AI language models?

Layers are composed together to form the complete model

Models are composed of Java classes

Composition is not used in AI models

Models are composed of SQL tables

Layers are composed together to form the complete model, analogous to object composition in Java.

What OOP concept is similar to how the high-level AI model hides complex internal operations?

Inheritance

Polymorphism

Encapsulation

Overloading

The high-level model hiding complex internal operations is similar to encapsulation in OOP.

In the code example, what does the 'embedding_dim' parameter represent?

The size of the input vocabulary

The number of encoder layers

The dimension of word embeddings

The batch size for training

The 'embedding_dim' parameter represents the dimension of word embeddings, determining how words are represented numerically.

What is the purpose of the 'vocab_size' parameter in the LanguageModel class?

To set the number of layers

To determine the size of the input vocabulary

To set the embedding dimension

To set the batch size

The 'vocab_size' parameter determines the size of the input vocabulary, affecting the input and output layer sizes.

Which method in the LanguageModel class is responsible for the forward pass of data?

init

call

fit

predict

The 'call' method in the LanguageModel class is responsible for the forward pass of data through the model.

What type of layer is used for the final output in the LanguageModel class?

LSTM

Embedding

Dense

Flatten

A Dense layer is used for the final output in the LanguageModel class.

How many encoder layers are created in the example LanguageModel initialization?

The example initializes the LanguageModel with 3 encoder layers (num_layers = 3).

What is the purpose of the 'return_sequences=True' parameter in the encoder LSTM layers?

To return the model to initial state

To allow sequences to be input

To output sequences for the next layer

To reverse the input sequence

The 'return_sequences=True' parameter allows the LSTM layer to output sequences for the next layer, maintaining temporal information.

Which TensorFlow module is primarily used in the code example?

tf.data

tf.keras

tf.train

tf.estimator

The code example primarily uses the tf.keras module for building the neural network model.

What would be a suitable method name for preprocessing input text in the LanguageModel class?

process_input

encode_text

preprocess_text

tokenize_input

A suitable method name for preprocessing input text would be 'preprocess_text', clearly indicating its purpose.

In the exercise, what functionality is suggested to be added to the LanguageModel class?

A method to visualize the model

A method to save the model

A method to generate text given a starting prompt

A method to evaluate the model's performance

The exercise suggests adding a method to generate text given a starting prompt to the LanguageModel class.

How does the conceptual model of AI language models help in understanding the architecture?

It provides a visual metaphor similar to Java objects

It explains the mathematical formulas used

It shows the exact TensorFlow implementation

It provides the training data required

The conceptual model helps understand AI language model architecture by providing a visual metaphor similar to Java objects, making it easier to grasp for those familiar with OOP concepts.

There are no rows in this table

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JULY 30:

Describe the key components of the AI application development methodology that we covered in this class, including the use of tools like Trello, Overleaf, and synthetic personalities created with AI assistants. Explain how these elements work together to support the successful delivery of the class project.

The Development of an AI application involves several stages; the mentioned tools help to facilitate the process in every stage. For instance, for project planning:

- A Trello board helps us to manage the project. We can create a list of tasks such as "To do," "In Progress," and "Done."

- Overleaf: This tool helps us create project documentation collaboratively, such as architecture design, research papers, model selection rationale, etc.

- Synthetic personalities: Can help in the data collection, such as synthetic data generation

Train your thinking to lean into / work with the AI agents and personalities you will build to amplify your cognitive scope and bankwidth. In addition to just to answering questions: Ask your AI to advise you on concerns which you SHOULD be thinking about which you have not get thought about.

Exam Question: How does your AI KNOW what you need that you have not yet considered?

Discuss the importance of the video component in the class project, as emphasized by Peter Sigurdson. Explain how creating and presenting a video can help reinforce learning and develop valuable skills for future job interviews and presentations.

You build a short look reward cycle of speaking and presenting knowledgably about these topics and getting a dopamine rush of confidence and encourage which spills over into your other arena.

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Provide a comprehensive overview of the process for building the AI application that you are required to develop for the AML 3304 class, as described in our lectures. Include the following key elements: The software development methodology and tools used, such as the Unified Model Engineering Process, Trello for project management, and Overleaf for the LaTeX presentation document.

Video / read my materials on Unified Process:

https://www.dropbox.com/scl/fo/qd34uvrylm5yrx8jt3v2r/ALbvH-v8zY-Exq2ifU0GpoA?rlkey=theqn07agmgpwnqhbnfi7c1t5&dl=0⁠

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The steps involved in constructing the AI model, including the use of Google Colab notebooks, PyTorch, TensorFlow, and potentially deploying the model to platforms like Hugging Face Spaces. The importance of incorporating theoretical concepts and research questions into the project report to demonstrate an understanding of the underlying principles of AI and language models. The role of synthetic personalities created using tools like Claude AI to assist in the development and delivery of the project, including providing guidance and feedback to the students.

Mid Term Review Test Prep:

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https://share.mindmanager.com/#publish/ni87_MMTKSdQsSFgSjoiekoQy-d5le5Pof_r3sjZ⁠

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What are the differences between old-school methodologies like Waterfall and Agile compared with Unified Process software development processes? Discuss this in the context of the Software Crisis of the 1970s.

Modern software development methodologies put the User at the Center by gather user stories which is what Unified process does.

Modern software development methodologies are “agile” meaning, we update the plan continually as we acquire new knowledge:

Knowledge of how the Business Domain operates

Knowledge of how our development technology stack tools work: programming languages, database.

What are you giving to OPEN AI in exchange for “free” access?

Training Data.

The analysts say that the big problem with the emerging Chat GPT 5 is: There is not enough data in the world to train next Gen AI?

Synthetic Data: Perry the Stochastic Parrot

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https://www.linkedin.com/pulse/enhancing-ai-training-synthetic-data-diving-deeper-peter-sigurdson-y2xuc/⁠

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The problem with Synthetic Data? “The 2 Irish men problem”

July 12 Attendance Quiz Start of class : AI/ML engineering practices

Feature Engineering Teaching Question: Feature engineering is the process of creating new input features or transforming existing ones to improve model performance.

One common technique is binning, where continuous variables are grouped into discrete categories.

For example, age could be binned into "child", "adult", and "senior".

Q: What is the main purpose of feature binning in machine learning?

A: To convert continuous variables into categorical ones, potentially uncovering non-linear relationships and reducing noise.

CI/CD with GitHub Teaching Question:

Continuous Integration and Continuous Deployment (CI/CD) automate the building, testing, and deployment of code changes.

GitHub Actions allows you to create workflows that automatically run when certain events occur in your repository, like a push or pull request.

In a GitHub Actions workflow, what file extension is used for the configuration file? A: .yml or .yaml

(Yet Another Markup Language)

AI Model Architecture Teaching Question: The architecture of an AI model refers to its structure and components.

In a transformer model, a key component is the self-attention mechanism, which allows the model to weigh the importance of different parts of the input when processing each element.

What is the primary advantage of self-attention in transformer models?

A: It allows the model to consider the context and relationships between all parts of the input sequence.

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Building the AI Model : Test Questions Bank

⁠AML3304⁠⁠

Summary

Discussion question related to social and safety trust issues in AI development:

Discussion question related to the architecture and operation of an AI model:

Summarized Review Questions and Detailed Answers

Question 1:

Question 2:

Question 3 (New Question with "First Day on the Job" Flavor):

Discuss the importance of the video component in the class project, as emphasized by Peter Sigurdson. Explain how creating and presenting a video can help reinforce learning and develop valuable skills for future job interviews and presentations.

What are the differences between old-school methodologies like Waterfall and Agile compared with Unified Process software development processes? Discuss this in the context of the Software Crisis of the 1970s.

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`AML3304`⁠
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