This module includes a graded quiz to test and reinforce your understanding of concepts covered in the course. The module also includes a glossary to enhance your comprehension of generative AI-related terms. The module includes an optional project, which provides an opportunity to practice generating text, images, and code through generative AI. Finally, the module guides you about the next steps in your learning journey.
Learning Objectives
- Demonstrate understanding of the course concepts through a project and graded quiz.
- Plan for the next steps in your learning journey.
Glossary and Final Project
Glossary: Generative AI: Introduction and Applications
Glossary
| Term | Definition |
|---|---|
| Data augmentation | A technique commonly used in machine learning and deep learning to increase the diversity and amount of training data. |
| Deep learning | A subset of machine learning that focuses on training computers to perform tasks by learning from data. It uses artificial neural networks. |
| Diffusion model | A type of generative model that is popularly used for generating high-quality samples and performing various tasks, including image synthesis. They are trained by gradually adding noise to an image and then learning to remove the noise. This process is called diffusion. |
| Discriminative AI | A type of artificial intelligence that distinguishes between different classes of data. |
| Discriminative AI models | Models that identify and classify based on patterns they observe in training data. In general, they are used in prediction and classification tasks. |
| Foundational models | AI models with broad capabilities that can be adapted to create more specialized models or tools for specific use cases. |
| Generative adversarial network (GAN) | A type of generative model that includes two neural networks: generator and discriminator. The generator is trained on vast data sets to create samples like text and images. The discriminator tries to distinguish whether the sample is real or fake. |
| Generative AI | A type of artificial intelligence that can create new content, such as text, images, audio, and video. |
| Generative AI models | Models that can understand the context of input content to generate new content. In general, they are used for automated content creation and interactive communication. |
| Generative pre-trained transformer (GPT) | A series of large language models developed by OpenAI. They are designed to understand language by leveraging a combination of two concepts: training and transformers. |
| Large language models (LLMs) | A type of deep learning model trained on massive amounts of text data to learn the patterns and structures of language. They can perform language-related tasks, including text generation, translation, summarization, sentiment analysis, and more. |
| Machine learning | A type of artificial intelligence that focuses on creating algorithms and models that enable computers to learn and make predictions or decisions. It involves designing systems that can learn from training data. |
| Natural language processing (NLP) | A branch of artificial intelligence that enables computers to understand, manipulate and generate human language (natural language). |
| Neural networks | Computational models inspired by the structure and functioning of the human brain. They are a fundamental component of deep learning and artificial intelligence. |
| Prompt | Instructions or questions that are given to a generative AI model to generate new content. |
| Training data | Data (generally, large datasets that also have examples) used to teach a machine learning model. |
| Transformers | A deep learning architecture that uses an encoder-decoder mechanism. Transformers can generate coherent and contextually relevant text. |
| Variational autoencoder (VAE) | A type of generative model that is basically a neural network model designed to learn the efficient representation of input data by encoding it into a smaller space and decoding back to the original space. |
Final Project: Generating Text, Images, and Code
Graded Quiz and Wrap-up
Graded Quiz: Generative AI: Introduction and Applications
Ravi is debating which Generative AI tool he can use to improve and index his code. Can you guide him based on the tools listed here?
GitHub Copilot
Correct! GitHub Copilot and AlphaCode are popular Generative AI tools for code generation.
AI
While all the listed tools are interesting Generative AI options, they might not be the best fit for Ravi’s specific need of improving and indexing his code. Here’s a breakdown of why:
Synthesia: This tool focuses on generating realistic videos from text, not on code analysis or improvement.
Bard: While I can access and process information related to code, I am still under development for code-related tasks. I can potentially answer questions about specific functionalities or APIs, but not directly improve or index code.
GitHub Copilot: This is a strong contender for Ravi’s needs. It’s an AI-powered code completion tool that suggests code based on context and patterns, potentially helping Ravi write better and faster code. It also has features for indexing and searching code within a repository.
DALL-E: This tool specializes in generating images and artwork based on text descriptions. It wouldn’t be helpful for code improvement or indexing.
Therefore, based on the listed options, GitHub Copilot seems like the most suitable tool for Ravi’s goal of improving and indexing his code. However, it’s important to remember that even the best AI tools aren’t perfect, and some human expertise and judgment will still be necessary.
Here are some additional considerations for Ravi:
- Specific needs: Clearly define what aspects of code improvement and indexing are most important to him.
- Tool capabilities: Research each tool’s specific features and limitations to see if they align with his needs.
- User reviews and ratings: Read reviews and ratings from other developers to get insights into the tools’ strengths and weaknesses.
- Free trials: Many AI tools offer free trials, allowing Ravi to experiment before committing.
By carefully considering his needs and exploring the available options, Ravi can find the right Generative AI tool to enhance his coding workflow and achieve his goals.
Maria is a scientific visualizer. How can the image-generation capabilities of generative AI help her in her daily work?
Help her create comprehensive artwork from a simple sketch
Correct! Generative AI image generation tools, such as DeepArt.io, can help create comprehensive artwork from a simple sketch.
AI
Generative AI has the potential to significantly enhance Maria’s daily work as a scientific visualizer in several ways:
1. Image Generation from Sketches:
- Transforming rough ideas into detailed visuals: Maria can upload a basic sketch or outline and use AI to generate various high-resolution versions with different styles, color palettes, and compositions. This can help her quickly explore diverse visual interpretations of her scientific data.
- Creating complex scientific illustrations: If she needs to illustrate intricate cellular structures or molecules, AI can generate realistic and accurate visuals based on scientific descriptions or data sets. This saves time and ensures scientific accuracy.
2. Generating Diverse Datasets:
- Overcoming data limitations: Many scientific visualizations rely on limited data sets. AI can generate synthetic data that resembles real data, allowing Maria to create more diverse and representative visuals. This is especially helpful for exploring hypothetical scenarios or visualizing data with missing information.
- Ensuring data privacy: If working with sensitive data, AI can generate anonymized synthetic data for visualization, preserving privacy while enabling valuable insights.
3. Automating Repetitive Tasks:
- Creating color palettes and variations: AI can generate various color palettes based on a desired theme or data set, saving Maria time from manual selection.
- Formatting and styling visuals: Repetitive tasks like adding labels, scales, and formatting can be automated with AI, freeing up Maria’s time for more creative aspects.
4. Collaboration and Exploration:
- Interactive visualization tools: AI-powered tools can create interactive visualizations that allow viewers to explore the data dynamically, potentially revealing hidden patterns or relationships.
- Collaborative image generation: AI platforms can enable Maria to collaborate with other scientists or artists in real-time, generating and refining visuals iteratively based on shared input.
It’s important to remember that AI is a tool, not a replacement for scientific expertise. Maria’s knowledge and interpretation of the data remain crucial for creating meaningful and accurate scientific visualizations.
By effectively integrating generative AI into her workflow, Maria can:
- Increase her productivity and efficiency.
- Explore more creative and diverse visual representations of her data.
- Communicate complex scientific concepts more effectively.
This technology holds immense potential for the field of scientific visualization, and Maria can be at the forefront of its exploration and application.
Henri is a robotics engineer and has written a new code. Which Generative AI tool will most likely help him identify and fix bugs in his code?
IBM Watson Code Assistant
Correct! IBM Watson Code Assistant is an AI-based programming assistant that helps autocomplete code, accelerate hard tasks, and generate code for provided input.
AI
The most likely Generative AI tool to help Henri identify and fix bugs in his code is IBM Watson Code Assistant.
Here’s a breakdown of why the other options aren’t as well-suited for this task:
- Pathways Language Model: While it’s a powerful language model, it’s not specifically designed for code analysis or debugging. Its strengths lie in generating text, translating languages, and writing different kinds of creative content.
- WaveGAN: This tool is focused on generating audio waves, not on working with code. It’s used for creating realistic soundscapes and audio samples.
- DALL·E: This tool excels at generating images and artwork from text descriptions, but it doesn’t have capabilities for code analysis or debugging.
IBM Watson Code Assistant, on the other hand, is specifically designed to help developers with code:
- Identifies potential errors: It can scan code to detect potential bugs, syntax errors, and style issues using AI-powered analysis.
- Suggests fixes: It can provide suggested code snippets or alternative approaches to address identified problems.
- Offers explanations: It can explain the reasoning behind its suggestions, helping developers understand the logic and learn from the process.
- Learns from user feedback: It can adapt to individual coding styles and preferences over time, providing more personalized recommendations.
While it’s not a perfect solution, and human expertise is still crucial, IBM Watson Code Assistant can be a valuable tool for Henri to improve code quality, reduce debugging time, and potentially learn better coding practices.
Harsh needs to monitor and detect anomalies in the company software to reduce downtime and prevent critical failures. Which tool can he use to do that?
Watson AIOps
Correct! To monitor and detect anomalies in code, tools such as IBM’s Watson AIOps and Moogosft AIOps analyze system logs, metrics, and other data. This helps reduce downtime and prevent critical failures.
AI
Out of the listed options, Watson AIOps is the most likely tool to help Harsh monitor and detect anomalies in the company software for reducing downtime and preventing critical failures. Here’s why:
DataRobot: Primarily focused on building and deploying Machine Learning models, it might not be specifically designed for real-time monitoring and anomaly detection within software systems.
NOLEJ: This tool specializes in Natural Language Processing (NLP) and wouldn’t directly address software anomaly detection.
Watson AIOps: Developed by IBM specifically for AI-powered IT operations, it offers features like:
- Real-time monitoring: Continuously analyzes system metrics, logs, and events to identify deviations from normal behavior.
- Anomaly detection: Uses machine learning algorithms to detect unusual patterns that could indicate potential problems.
- Root cause analysis: Helps pinpoint the source of anomalies, enabling faster troubleshooting and resolution.
- Predictive maintenance: Can predict potential issues before they occur, allowing for proactive intervention and downtime prevention.
While AIO Logic also offers certain anomaly detection capabilities, its focus might be broader than Watson AIOps’ specialization in IT operations and software monitoring.
Therefore, considering Harsh’s specific needs, Watson AIOps seems like the most suitable tool for his software anomaly detection and prevention goals. However, it’s always advisable to research and compare available options based on specific features, compatibility, pricing, and user reviews before making a final decision.
Taesha is a second-grade teacher wondering how generative AI tools can help her students with special needs and learning disabilities. What do you think?
Track learner progress
Correct! Generative algorithms can track learner progress over time. Known as knowledge tracing, this helps provide the right pacing and content for individual needs.
AI
While the listed options are interesting applications of generative AI, they wouldn’t directly benefit Taesha’s students with special needs and learning disabilities in the classroom. Here are some ways generative AI can potentially help Taesha’s students:
Personalized Learning:
- Adaptive learning platforms: These platforms use AI to personalize learning content, difficulty levels, and pacing based on each student’s individual needs and progress. This can be especially helpful for students with learning disabilities who might need different approaches to grasp concepts.
- AI-powered tutors: Virtual tutors powered by AI can provide individualized instruction, answer questions in real-time, and offer immediate feedback, fostering a more personalized learning experience.
Accessibility and Engagement:
- Text-to-speech and speech-to-text tools: These tools can assist students with reading difficulties by reading text aloud or by transcribing their spoken words into text, promoting inclusion and participation.
- Interactive and gamified learning experiences: AI-powered platforms can create engaging and interactive learning experiences that cater to different learning styles and abilities, motivating students and making learning more enjoyable.
Additional Considerations:
- Assistive technology: Certain AI-powered tools can act as assistive technology, like providing voice commands for students with physical limitations or offering visual aids for those with visual impairments.
- Content creation: AI can assist in generating customized learning materials, worksheets, or even personalized stories catering to individual student needs and interests.
It’s important to remember that AI tools are not a replacement for teachers. Taesha’s expertise and understanding of her students’ specific needs remain crucial for effectively tailoring these tools and ensuring their successful implementation in the classroom.
By exploring and utilizing relevant generative AI tools responsibly, Taesha can create a more inclusive and engaging learning environment that caters to the diverse needs of her students with special needs and learning disabilities.
Nadia was using ChatGPT to debug and correct a code that she was working on. However, when she started to __________, she found this AI tool was struggling to execute the task.
Generate a complex code from scratch
Correct! While ChatGPT can generate code from images, it cannot generate large or complex code from scratch.
AI
The most likely task that ChatGPT would struggle with is generating a complex code from scratch.
Here’s why the other options are less likely to cause difficulties for ChatGPT:
- Asking for guidance on how to run the code: ChatGPT can access and process information from real-world sources, including code documentation and tutorials. It could potentially provide basic guidance on code execution or point to relevant resources.
- Creating a code from image prompts: While not its primary function, ChatGPT might be able to generate simple code snippets based on image descriptions, leveraging its language understanding and text generation capabilities.
- Translating the code into another language: ChatGPT excels at language translation and code is a language, so this task aligns with its strengths. It could translate code into different programming languages or human languages for better understanding.
However, generating complex code from scratch is a more challenging task that requires deeper understanding of programming logic, syntax, and problem-solving. While ChatGPT can generate text, translate languages, and write different creative text formats, it’s not designed for the intricate process of building code from the ground up. This task often involves understanding specific programming paradigms, algorithms, and data structures, which are beyond ChatGPT’s current capabilities.
Therefore, Nadia would likely encounter limitations when asking ChatGPT to generate complex code from scratch. It’s important to understand the strengths and limitations of AI tools to use them effectively and avoid frustration.
Ryan interns for a popular art gallery. He is looking for software to help him remove specific trees from a modernist landscape image. A friend suggested he use ______________ capabilities in DALL-E.
Inpainting
Correct! Inpainting is a technique that helps remove unwanted objects in images while preserving continuity and context. DALL-E and Stable Diffusion provide inpainting capabilities.
AI
Out of the listed options, the most suitable capability for Ryan’s need to remove specific trees from a modernist landscape image in DALL-E is Inpainting. Here’s why:
- Inpainting: This functionality allows users to select a specific area in an image and describe what they want to see filled in. Ryan could carefully select the trees he wants to remove and then use inpainting to replace them with the surrounding landscape texture, seamlessly blending the edit and maintaining the overall style of the artwork.
- Style transfer: While interesting, it wouldn’t directly address the task of removing the trees. It applies the artistic style of one image to another, not removing elements.
- Image-to-image translation: This feature translates an image into another style, like turning a photo into a painting. It wouldn’t handle precise object removal.
- Text-to-image generation: This capability creates entirely new images based on text descriptions. It wouldn’t work for modifying an existing image like Ryan needs.
Therefore, considering Ryan’s specific requirement of removing objects while preserving the image’s style, Inpainting emerges as the most appropriate functionality within DALL-E for his task.
It’s important to remember that using AI tools responsibly and ethically is crucial, especially when modifying artistic works. Ryan should ensure he has the rights to modify the image and be transparent about the use of AI editing tools to maintain artistic integrity.