In this module, you will learn about pre-trained models and platforms for AI application development. You will explore the ability of foundation models to generate text, images, and code using pre-trained models. You will also learn about the features, capabilities, and applications of different platforms, including IBM watsonx and Hugging Face.
Learning Objectives
- Explore the ability of foundation models to generate text, images, and code using pre-trained models.
- Describe the features, capabilities, and applications of IBM watsonx.
- Explain Hugging Face as a community for building AI models for everyone.
- Video: Pre-trained Models: Text-to-Text Generation
- Hands-on Lab: Develop AI Applications with the Foundation Models
- Video: Pre-trained Models: Text-to-Image Generation
- Video: Pre-trained Models: Text-to-Code Generation
- Hands-on Lab: Develop AI Applications for Code Generation
- Video: IBM watsonx.ai
- Reading: IBM watsonx.data and watsonx.governance
- Video: Hugging Face
- Reading: Lesson Summary
- Practice Assignment: Practice Quiz: Pre-trained Models and Platforms for AI Applications Development
- Graded Assignment: Graded Quiz: Platforms for Generative AI
Video: Pre-trained Models: Text-to-Text Generation
What is Text-to-Text Generation?
- A type of machine learning where models generate new text based on a given input.
- Models are trained on large amounts of text data to learn language patterns and structures.
Types of Text-to-Text Generation Models
- Statistical Models: Use statistical techniques (like Markov chains) for basic text generation.
- Neural Network Models: More advanced, using artificial neural networks to understand complex patterns and produce more human-like text.
Key Model Architectures
- Sequence-to-Sequence: Encodes input text, then decodes it into a new generated text. Used for tasks like summarization and translation.
- Transformer: Directly maps input to output, leading to more fluent and natural-sounding output. Emphasizes relationships between words using the concept of ‘attention’.
Popular Text-to-Text Generation Models
- GPT (OpenAI): A large language model skilled in generating various creative text formats.
- T5 (Google AI): Versatile model for summarization, translation, and question answering. Excels due to its ‘text-to-text’ framework.
- BART (Facebook AI): Combines aspects of BERT and GPT for tasks like sentiment analysis, question answering, language translation, and text generation.
Applications and Benefits
- Text Summarization: Condensing text without losing meaning.
- Conversational AI: Powering text-based chatbots and assistants.
- Content Creation: Drafting product descriptions, emails, etc.
- Productivity Enhancement: Automating tasks, reducing manual effort.
- Accuracy Improvement: Minimizing human errors in tasks like translation.
[MUSIC] Welcome to Pre-Trained Models:
Text-to-Text Generation. After watching this video, you’ll be able
to explore the different models used for text-to-text generation in generative AI. You’ll also be able to describe
the uses and benefits of these models. Summarize, code, translate, create
content, these are just some of the things that text-to-text generation
models can do for you. But what exactly are text-to-text
generation models? Text-to-text generation models
are a type of machine learning model used to generate text from a given input. They are trained on
a large corpus of text. These language models are trained to learn
patterns, grammar, and casual information. Using this input,
the models generate the new text and can generate a variety of text formats,
including code, scripts, musical pieces, emails,
letters, and so on. There are two main text-to-text
generation models, statistical and neural network models. Statistical models use statistical
techniques to generate text. One common statistical
model is the Markov chain. A Markov chain generates text by
starting with a seed state and then generating the next state
based on the previous state. For example, it can predict and generate the next character based on
previously observed language patterns. Markov chains can generate text for
several purposes, like speech recognition and journalism. Neural network models use artificial
neural networks to generate text. These models can represent complex
relationships between data. Neural network models are typically
trained on a large text corpus. They can then generate text similar
to the text they were trained on. Text-to-text generation models use
either sequence-to-sequence or transformer type of models. Sequence-to-sequence models first encode
the input text into a sequence of numbers, then decode the sequence into a new
one representing the generated text. Some examples of these tasks
are summarization, speech recognition, and machine translation. Transformer models directly map
the input text to the generated text, allowing the models to
generate more fluent and natural-sounding text than
sequence-to-sequence models. A key feature of transformer models is
how they leverage an AI concept called attention to emphasize
the weight of related words. This can help provide the context for
a specific word or token describing some other type of data,
for example, a section of an image. Let’s explore some of the most popular
text-to-text generation models. First, you have the GPT created by OpenAI. It is a substantial language model. Its development involved training
on an extensive corpus of text and code, equipping it with
the capability to produce text, perform language translation, generate
diverse forms of creative content, and provide informative
responses to user inquiries. Then there is T5. T5 is a text-to-text transfer transformer
model developed by Google AI. This model is also trained on
a substantial data set of code and text. It can be used for various tasks,
including summarization, translation, and question answering. What makes T5 popular? Generating text requires capabilities
accomplished using deep learning models based on natural language
processing, also called NLP. However, you need one model for
language translation and another for language auto-completion,
making the approach inefficient. It gives rise to the need for a unified
model capable of producing coherent and contextually appropriate text. This requires an architecture
that can convert all input and output data into text. The model can then learn
from diverse examples and apply the learning to a wide
range of tasks in NLP. T5 is the answer to this need. It is a universal model that uses
a standard encoder-decoder architecture to perform various tasks like
transformations and classifications. It also leverages transfer
learning capabilities. The model trained on the data for one
task can be fine-tuned to perform another downstream task in the same domain. Finally, you have BART. BART is a bidirectional autoregressive
transformer model developed by Facebook AI. BART is a deep neural network with
a sequence-to-sequence translation architecture with bidirectional
encoder representation like BERT and a left-to-right decoder like GPT. BERT stands for bidirectional encoder
representations from transformers. It refers to a family of language models
by Google that uses pretraining and fine-tuning to create models that
can accomplish several tasks. BART’s bidirectional nature
processes text forward and backward. This BERT and GPT decoder
combination allows BART to perform tasks like analyzing sentiment,
answering questions, NLP tasks like language translation,
and generating human-like text. Further, its autoregression nature
allows it to generate contextual text, taking feedback from previously
generated tokens to generate new ones. The BART capability also applies
to computer vision tasks. The versatility of the model makes
it useful across industries. I’m sure by now you can see how
text-to-text generation models can be powerful tools for task completion. Let’s look at some examples. There is text summarization, where these
models can process the input text to generate the summarized text without
changing the original text’s meaning. Text-to-text models can support
conversational intelligence by providing personalized assistance through
text-based query responses. Finally, did you know that text-to-text
models can produce digital text that helps create product descriptions,
write emails, create resumes, and so on? Now that’s something. Because of these versatile applications, text-to-text models
offer several benefits. They increase productivity
through automation. For example, they can generate marketing
copies or even create social media posts. They improve the accuracy of
tasks prone to human error. For example, a text-to-text generation
model can translate documents in context without mistakes. In this video, you learned the meaning of text-to-text
generation models in generative AI. You learned the two types of
text-to-text generation models, statistical and neural networks. Text-to-text generation models use
either sequence-to-sequence or transformer type of models. You also explored the most popular
text-to-text generation models, GPT, T5, and BART. Finally, you learned the several uses and
benefits of these models. [MUSIC]
Hands-on Lab: Develop AI Applications with the Foundation Models
Develop AI Applications with Foundation Models
Developing AI applications with generative AI foundation models
Given excellent natural language understanding and generation, the foundation models can serve as a base for you to develop applications for tasks like text completion, translation, summarization, etc. Their power and distinguishing features can directly impact the feasibility, effectiveness, and efficiency of your AI application.
Developing AI applications with generative models is an ongoing process that may require iteration and adaptation based on user needs and changing requirements. It involves several steps, beginning with understanding the use case and clearly defining the problem you aim to solve with your AI application.
For this, you must determine the generative AI foundation model that best suits your use case. This will help you make informed decisions about the direction of your AI application development.
In this lab, you will leverage foundation models’ capabilities that exhibit comprehensive and coherent content creation ability in the context of solutions to a specific problem.
Learning Objectives:
- Explore the vocabulary generation using gpt-3.5-turbo to develop an AI app like a dictionary
- Explore language learning capabilities using gpt-3.5-turbo to develop an educational tool or a language learning app
- Experiment with the translation capabilities of the foundation model gpt-3.5-turbo to create an AI-powered app for translation services
How can you develop AI applications with generative AI foundation models?
Exploring various foundation models serves as a starting point for various AI applications. Understanding and examining their capabilities will help you draw a roadmap for developing AI applications.
Using an appropriate foundation model ensures you choose the right tools and technologies, saving time, effort, and resources.
In this lab, you will explore various foundation models by finding answers to specific questions, such as:
How can you develop an AI app that creates a scenario or object-specific content?
You can leverage the text generation capability of generative AI foundation models to create moral stories, academic poems, social media content, product descriptions, and more that can extend the scope to fields like sentiment analysis.
Before you begin prompting
Before beginning the exercises, you must set up the AI classroom and know the Generative AI workspace.
- As a first step, you must set up your AI classroom for better learning experience.
- Name the chat
- Know your workspace to prompt the foundation model-based chatbot better.
- Use prompt instructions
How gpt-3.5-turbo can help develop AI applications
OpenAI’s gpt-3.5-turbo can help you generate text in different languages, assist in language translation, grasp language with detailed guides and explanations, and respond to your language-related queries.
Combining AI power with traditional learning methods, the model excels in language understanding tasks and can certainly aid you in developing AI applications around them.
It can be commercially used for various applications focused on NLP tasks, like:
- Language understanding and generation
- Vocabulary expansion
- Conversational and language practice
- Educational content and personalized learning
- Translation services
Let’s begin experimenting with the foundation model.
Exercise 1: Learn a language and expand your vocabulary
In this exercise, you will learn vocabulary generation and language learning using the foundation model gpt-3.5-turbo.
Step 1: Set your AI environment and select the foundation model
- As a first step, you can name the chat per your context.
- Select the gpt-3.5-turbo model from the dropdown in the upper section of the right pane for this exercise
Step 2: Enhance your vocabulary in a familiar language
- Consider a context to learn a language and enhance your vocabulary.
- Provide the instructions in the Prompt instructions field.
For example,
Act as a language expert and start a conversation in the language specified in the prompt. To keep the conversation going, you must provide an explanation and then pose another question for the prompt.
3. Write a prompt in Type your message field. Here, you can enter a language for which you want to enhance your vocabulary.
For example,
English
4. Click Start Chat to get the chatbot response as a result in the middle part of the right pane.
You will notice the generated response is similar to the screenshot below.
5. You can continue to converse with the system by responding to the query posed by the model and providing the context of the topic you want to learn about in the Type your message field.
For example,
Explain the meaning of serenity.
6. Next, you can continue to interact with the system and pose questions like
- Give me the synonyms of serenity.
- Give me the antonyms of serenity.
- When should one refrain from using the word serenity?
- Give me a fill in the blank or MCQ type question to test my knowledge of serenity word usage.
You will get a response followed by a question that you can utilize to learn and practice the target language.
The generated responses to some of the questions are shown below.
Step 3: Review results and responses
- Continue interacting with the chatbot to generate more and more responses. The responses followed by a question can be utilized to learn and practice the target language.
- Use the Regenerate response option to get another version of the responses to your latest question.
Exercise 2: Learn a foreign language using foundation model
Step 1: Set your AI environment and select the foundation model
- Name the chat and select the foundation model. You can create a new chat or continue with the same chat from exercise 1.
a. You can create a new chat and select gpt-3.5-turbo as the foundation model.
b. Alternatively, continue with the same chat under exercise 1 to use the same prompt instructions.
- You can choose Reset chat if you want to remove the previous conversation under the same chat window.
- You can also click Duplicate chat to create a copy of the existing chat and modify the prompt instructions.
Step 2: Prompt the model to learn a foreign language
- Think of a foreign language you want to learn.
- Provide the instructions in the Prompt instructions field.
For example,
Act as a language expert and start a conversation in the language specified in the prompt. To keep the conversation going, you must provide an explanation and then pose another question for the prompt.3. Write the language you want to learn in the Type your message field.
For example,
German4. Click Start Chat and observe the generated response, similar to the screenshot below.
5. Continue to converse with the system by responding to the query posed by the model and providing the context of the topic you want to learn about in the Type your message field.
For example,
What is serenity in German?6. Next, you can continue to interact with the system and ask questions like
- Tell me the synonyms and their usage of the word Gelassenheit.
- Give me the antonyms of serenity.
- Teach me German idioms or expressions related to emotions or feelings.
- Give me a fill-in-the-blank or MCQ-type question to test my knowledge of the word usage.
The generated responses to some of the questions are shown below.
Step 3: Review results and responses
- Continue interacting with the chatbot to generate more and more responses. The responses followed by a question can be utilized to learn and practice the target language.
- Use the Regenerate response option to get another version of the responses to your latest question.
Exercise 3: Translate text using a foundational model
Step 1: Set your AI environment and select the foundation model
- Create a new chat.
- Name the chat per the context.
- Select the foundation model gpt-3.5-turbo.
Step 2: Write text to translate from one language to another
- Consider the text you need to translate.
- Provide the instructions in the Prompt instructions field.
For example,
As a translation expert, help me translate [language1] text into [language2].Provide language1 and language2 as prompts in the Type your message field.
For example,
English, German4. Click Start chat to get a response like “Sure, I can help you with that. Please provide the text you would like to translate from English to German.” as shown below.
5. Now, write the text you want to translate from one language to the target language.
For example,
Please convert the poem “Nothing Gold Can Stay” given below: Nature’s first green is gold, Her hardest hue to hold. Her early leaf’s a flower; But only so an hour. Then leaf subsides to leaf. So Eden sank to grief, So dawn goes down to day. Nothing gold can stay.Step 3: Translate into a different target language
To translate text into another target language, reset the chat and enter the base language1 and the target language2 in the Type your message field.
For example,
English, French3. Click Start chat and review the generated response, as shown below.
Now, write the text you want to translate from one language to another.
For example,
Please translate the poem "The road not taken" as given below: Two roads diverged in a yellow wood, And sorry I could not travel both And be one traveler, long I stood And looked down one as far as I could To where it bent in the undergrowth; Then took the other, as just as fair, And having perhaps the better claim, Because it was grassy and wanted wear; Though as for that the passing there Had worn them really about the same And both that morning equally lay In leaves no step had trodden black. Oh, I kept the first for another day! Yet knowing how way leads on to way, I doubted if I should ever come back. I shall be telling this with a sigh Somewhere ages and ages hence: Two roads diverged in a wood, and I— I took the one less traveled by, And that has made all the difference. 4. Click Start chat and review the generated response shown below.
5. Now try with another text, such as:
Think globally, act locally.You will get a response similar to the screenshot shown below.
Step 3: Review results and responses
- Continue interacting with the chatbot to translate more and more text from one language to another.
- Use the Regenerate response option to get another version of the responses to your latest question
Summary:
Congratulations! You just completed the hands-on lab to develop AI applications using foundation models.
In this lab, you used foundation models of generative AI for vocabulary enhancement, language learning, and language translation. You used various prompts to experiment with various foundation models using the generative AI classroom lab. You also generated content for various natural language processing (NLP) tasks, including text and content generation, text translation, and code generation.
To leverage gpt-3.5-turbo for these purposes, you must use the OpenAI API and integrate it into your language learning or AI app development project. The API allows you to interact with the model and access its language generation capabilities programmatically, making it easier to create tailored language learning experiences or develop AI-powered language-related applications.
Video: Pre-trained Models: Text-to-Image Generation
What are Text-to-Image Generation Models?
- Machine learning models that create images based on text descriptions.
- Powered by generative AI, which interprets your words to build unique visuals.
Types of Models
- Generative Adversarial Networks (GANs):
- Use a generator (creates images) and a discriminator (distinguishes real from fake) in competition to improve image realism.
- Diffusion Models:
- Start with random noise and gradually add detail to match the text description.
- Often more creative and abstract than GAN-generated images.
Popular Diffusion Models
- DALL-E (OpenAI):
- Known for photorealism and ability to follow complex instructions.
- Incorporates inpainting (seamless image editing based on text descriptions).
- Valuable for understanding how AI interprets language and the world.
- Imagen (Google AI):
- Emphasizes unmatched photorealism.
- Leverages large language models for advanced text understanding, leading to high-quality images.
Benefits of Text-to-Image Models
- Creativity: Generate unique and novel images.
- Understanding AI: Help researchers understand how AI makes sense of text and visuals.
- Diverse Applications: Potential uses in design, art, education, and more.
Welcome to Pre-trained Models: Text-to-Image Generation. After watching this video, you’ll be able to explore the
different models used for text-to-image generation
in generative AI. You will also be
able to describe the uses and benefits
of these models. Text-to-image
generation models are a type of machine
learning model. They are used to generate
images from text descriptions. They use generative AI
to make meaning out of your words and turn
them into unique images. Text-to-image
generation models are trained on a large data set of text and images
and can be used to generate various
types of images. These can be realistic images, abstract images, or even images that do not exist
in the real world. There are two types of
text-to-image generation models, generative adversarial networks, also called GANs, and
diffusion models. Let’s look at each. GANs are a type of
machine-learning model that can be used to
generate realistic images. They work by training two
models against each other. A generator model that
generates images, and a discriminator model that distinguishes between
real and fake images. The generator model is gradually
improved over time until it can generate images that are indistinguishable
from real images. Diffusion models are also a type of machine
learning model. They are used to generate
images from text descriptions. They work by starting with a
random image and gradually adding detail to the image until it matches the
text description. Diffusion models are typically more efficient than GANs and can be used to generate more creative and
abstract images. Let’s explore the most popular text-to-image generation
diffusion models, DALL-E and Imagen. DALL-E is a text-to-image
generation model developed by OpenAI. DALL-E is trained on a massive data set of
texts and images, and can be used to generate realistic images from
various text descriptions. It also incorporates an
evaluation mechanism to determine whether the
final picture is accurate. To achieve this, DALL-E uses a combination of
its core elements, natural language
processing, also known as NLP, machine learning, and computer vision,
for example, it can take a
simple description, like a panda juggling a ball, and turn it into a
photo-realistic image that hasn’t been created before. Not only that, but DALL-E can also edit photographs based on a simple text description
such that the edit blends in seamlessly with the original image. It’s
called inpainting. DALL-E is special because
deep learning allows it to understand individual objects
such as pandas and balls, and how things are related. The latest version of
DALL-E is a large model, but not nearly as large as GPT-3 and interestingly, smaller
than its predecessor. Despite its size, this
newer version generates four times better
resolution images than the former
version of DALL-E. So, what are the benefits of DALL-E? An image generated
by DALL-E tells us if the system understands
what we’ve communicated, or merely repeats what
it has been taught. Further, DALL-E plays a
crucial role in developing useful and safe AI
by enabling us to understand how AI makes
sense of our world. The second popular
model is Imagen. Imagen is a text to image generation model
developed by Google AI. Just like DALL-E, Imagen is also trained on a massive data
set of text and images. Imagen is used to generate realistic images from a wide variety of
text descriptions. Imagen leverages the power of large transformer models and deep language understanding to generate high-fidelity images. An important discovery has been that large language models, like T-5 pre-trained on text, are pretty effective at encoding text to
generate images. Hence, it would follow that increasing the language model size
in Imagen would also augment image
fidelity rather than increasing the size of the
image diffusion model. Imagen claims to
be able to produce an unprecedented degree
of photorealism, which is one of its
key advantages. This is how it works,
the model picks up texts such as “two white umbrellas
rising into the sky. Clouds are moving,
it is raining,” and converts it into an
image depicting just that. The image could be either photo realistic or an artistic
interpretation. In this video, you learned how text-to-image
generation models work. These models are
classified into two types, GANs and diffusion models. Within diffusion
models, you explored the most popular text to image generation models,
DALL-E and Imagen. Finally, you learned
the benefits of each of these models.
Video: Pre-trained Models: Text-to-Code Generation
What are Text-to-Code Generation Models?
- Machine learning models that translate natural language descriptions into computer code.
- Powered by generative AI and neural code generation, which is loosely modeled on how the human brain processes information.
Types of Models
- Seq2seq: Translates between domains (natural language to code).
- Transformer: Learns complex relationships between words, better for code generation tasks.
Popular Models
- CodeT5 (Google AI): Versatile, supporting code understanding and generation tasks across multiple programming languages.
- code2seq (OpenAI): Leverages code structure, useful for code summarization, documentation, and retrieval.
- PanGu-Coder (Microsoft): Decoder-only model focused on natural language descriptions to generate code.
Uses and Benefits
- Auto-completion: Suggests code snippets, improving efficiency.
- Code Generation: Creates code from natural language descriptions.
- Debugging: Helps identify and fix errors, potentially offering solutions.
- Code Translation: Converts code between programming languages.
- Code Refactoring: Suggests optimizations and modernizations to code.
- Library Recommendations: Provides suggestions based on project needs.
- Test Data Generation: Automates creating realistic test cases.
- Code Documentation: Creates explanations for code, making it easier to understand and maintain.
Key Takeaway: Text-to-code generation models streamline development processes, saving time, reducing errors, and making coding more accessible.
Welcome to Pre-trained Models:
Text-to-Code Generation. After watching this video, you’ll be able to explain how text-to-code generation
models work in generative AI. You’ll also be able to
identify the different text-to-code models and describe
their uses and benefits. Text-to-code generation
models are a type of machine learning model
used to generate code from natural
language descriptions. The models use generative AI to write code through
neural code generation. Neural code generation
is a process that uses artificial neural
networks loosely based on neural networks
in the human brain. These neural networks are trained on a
substantial data set of code examples and
then fine-tuned to generate code snippets, functions, and
complete applications. The generated code is
similar in structure and function to the examples
it has been trained on. There are two main text-to-code
generation models, seq2seq and transformer. Seq2seq models are a type of machine learning model that can translate from one
domain to another. In the case of
text-to-code generation, the model can translate natural language descriptions
into a code sequence. Transformer models
are also a type of machine learning
model that can learn long-range dependencies
between words. In the case of
text-to-code generation, the model is trained to understand the
relationships between the words in a natural
language description and the corresponding code. Several popular
text-to-code generation models exist within the seq2seq and transformer
model classifications. Let’s look at some of them. The first model is CodeT5, a seq2seq model
developed by Google AI. CodeT5 is the first pre-trained programming
language model that is code-aware and
encoder-decoder-based. Like most models, CodeT5 is trained on a large
data set of text and code. It can enable a wide range of code intelligence
applications. These include code understanding
and generation tasks. Some examples of these tasks
are text summarization, question answering, and
language translation. The second popular
model is code2seq. Code2seq is a seq2seq
model developed by OpenAI. It is an alternative
approach that leverages the syntactic structure of programming languages to encode
source code even better. Having been trained on a substantial text
and code data set, code2seq can generate
code for several tasks such as code summarization,
documentation, and retrieval. For example, the
code-to-sequence approach can caption
code snippets. That is, it can assign a
natural language caption to the task performed
by the snippet. Finally, you have
the PanGu-Coder. PanGu-Coder is a
transformer model developed by Microsoft Research. PanGu-Coder is a pre-trained
decoder-only language model that generates code from
natural language descriptions. It’s built on the PanGu
Alpha architecture, a large-scale neural
network used for natural language processing,
also called NLP. This model can generate code for tasks like
function definition, class definition, and
program synthesis. For example, PanGu-Coder
can successfully synthesize running code to solve a problem based on a natural
language description. In addition, several other general-purpose
foundation models can be used for
text-to-code generation, such as OpenAI’s GPT
and code Llama by Meta. GPT excels in human-like
text generation and demonstrates impressive
capabilities in code creation. Code Llama can
generate and explain code in natural language,
specifically English. Similarly, some universal
generative AI tools for text-to-code generation
are GitHub Copilot and IBM Watson Code Assistant. GitHub Copilot, powered
by OpenAI Codex, can generate code based on various programming
languages and frameworks. IBM Watson Code Assistant
is built on IBM Watsonx.ai and enables developers
to write code accurately and efficiently with real-time recommendations, auto-complete features, and code restructuring
assistants. There are several
uses and benefits of text-to-code generation
models and tools. Let’s look at some of them. Text-to-code generation plays a crucial role in
auto-completion, helping developers
by suggesting and completing code snippets or
statements as they type. For example, as a
developer types a code comment in Python or
any programming language, auto-completion suggests
relevant keywords, variable names,
or function names based on the context
of the comment. This helps the developer quickly reference and document
parts of the code, improving code documentation
and readability. Text-to-code generation
models can automatically generate code based on natural
language descriptions. Here’s an example
to illustrate how. The task is, generating Python code from natural
language description. The natural language
description is, create a Python
function that can calculate the factorial
of a given integer. Here, you can see the
generated Python code. Furthermore, the stable
underlying architecture of text-to-code generation
tools reduces the time spent on debugging. Debugging is when errors in software programs and systems
are identified and fixed. For example, suppose a developer working on a chatbot using a generative AI model
encounters a problem where the chatbot generates irrelevant responses
to user queries. The developer states
the chatbot gives irrelevant responses
to user queries and need to fine-tune the model. The text-to-code generation model generates the code
snippet shown here. In this case, the text-to-code
generation model suggests the code snippet for fine-tuning the chatbot model using
user feedback data. This snippet assists
the developer in quickly addressing
the issue of irrelevant responses
and improving the chatbot’s performance.
That’s not all. Text-to-code generation
models can also facilitate code translation
by automatically translating code between
different programming languages, facilitating cross-platform
compatibility, and migration. They assist with
code refactoring and application modernization
by automating the identification of outdated or inefficient code segments and suggesting
optimized replacements, streamlining the
modernization process. Text-to-code generation
models can even provide recommendations
for libraries and frameworks based on
project requirements, helping developers
make informed choices. Text-to-code generation
models can generate test data by automatically
creating code snippets that populate databases
or data structures with diverse and realistic test cases saving time in the
testing process. Finally, text-to-code
models can automatically generate code documentation
by generating comments, function descriptions,
and documentation based on code functionality, making codebases more
understandable and maintainable. In this video, you explored the various
text-to-code generation models and learned
how they work. There are two types of
text-to-code generation models, seq2seq, and transformer. You also learned about
the most popular text-to-code generation models, CodeT5, code2seq, and PanGu-Coder and their uses. Finally, you learned the
various uses and benefits of text-to-code
generation models and tools for auto-completion,
debugging, code translation,
code refactoring, and application modernization, providing recommendations for
libraries and frameworks, test data generation,
and code documentation.
Hands-on Lab: Develop AI Applications for Code Generation
Develop AI Applications for Code Generation
Learning Objectives
Generate code for the desired programming language using gpt-3.5-turbo foundation model to develop simple AI-powered programming apps
How can you develop AI applications with generative AI foundation models?
Exploring various foundation models serves as a starting point for various AI applications. Understanding and examining their capabilities will help you draw a roadmap for developing AI applications.
Using an appropriate foundation model ensures you choose the right tools and technologies, saving time, effort, and resources.
In this lab, you will explore foundation models by finding answers to specific questions, such as:
- How can you develop an AI app that accomplishes a specific task with a specific code language?You can leverage the code generation capability of generative AI foundation models to write code in the C language that sorts elements of an array in ascending order or Python code that identifies whether a given number is prime.
Let’s get started prompting and experimenting with foundation models.
Before you begin prompting
Before beginning the exercise, you must set up the AI classroom and know the Generative AI workspace.
- As a first step, you must set up your AI classroom for better learning experience.
- Name the chat
- Choose the foundation model
- Know your workspace to prompt the foundation model-based chatbot better.
- Use prompt instructions
- Type your message
- Manage your chat
- Delete the chat from history
Exercise: Generate code using the foundation model to develop a simple AI app
Step 1: Set your AI environment and select the foundation model
- Create a new chat
- Name the chat per the context
- Select the foundation model gpt-3.5-turbo
Step 2: Write a relevant prompt for code generation
- Consider the text you need to translate.
- Provide the instructions in the Prompt instructions field.
For example,
Act as a coding expert and provide the code in the [programming language]. - Write the programming language as a prompt in Type your message field.
For example,
Python4. Click Start Chat and observe the response generated, as shown below.
5. Now provide the prompt to generate the code for the desired context.
For example,
Write complete Python code to create a Fibonacci series up to 10. Note: The versatility of Fibonacci numbers and the Fibonacci sequence makes them a fascinating topic in mathematics and its applications across various fields. You can use the Fibonacci series to develop AI applications related to coding education, automated code generation, algorithmic problem-solving, and many more.
6. Click Start Chat and observe the response generated, as shown below.
Step 3: Test the generated code
Testing the generated code helps you validate the accuracy and reliability of the code. You can use any accessible online programming language compiler tool to test the code. Here, CodeTester has been used to test and validate the generated code.
CodeTester is a free online code testing tool that can help you run the code, assess it, and execute it on your site.
- Click CodeTester to launch it.
- Once launched, you will reach the CodeTester home page, as shown below.
- To reach the code testing page, you must follow any of the steps below.
- i. Select the Run Some Code option from the home page of CodeTester, as shown below.
- ii. Alternatively, click CodeTester runner page.
- On the CodeTester testing page, you can see the test pane, the optional input box, and the grey color output box, as shown below.
- Select the Python language from the language dropdown option, as shown below.
- Now, go to the lab and copy only the code characters from the generated response in Step 2.
For example,
def fibonacci(n):
fib_series = [0, 1] # Initialize the first two numbers of the Fibonacci series
for i in range(2, n):
fib_series.append(fib_series[i-1] + fib_series[i-2]) # Calculate the next number in the series return fib_series
fibonacci_series = fibonacci(10)
print(fibonacci_series)7. Go to the CodeTester testing page and delete the default code as highlighted in the screenshot below.
8. Paste the copied code in the code test pane, as shown below.
9. Click Run next to the language dropdown at the top to test and validate the code.
10. Review the results displayed in the lower box on the screen, as shown below.
Step 4: Review the scope and applications
You can use the gpt-3.5-turbo foundation model interface to generate more and more code for your programming requirements.
Note: You must validate the code generated through ChatGPT for factual accuracy and use the technology ethically and responsibly. You can also learn coding and programming as the model provides step-by-step explanations and guidance. However, you cannot generate large and complex code from scratch, as their training data set based on 2021 libraries may limit their capability.
Video: IBM watsonx.ai
What is IBM watsonx.ai?
- An integrated platform within the larger IBM watsonx family, designed to help businesses build and manage AI applications responsibly.
- Focused on AI creators, offering tools for working with AI models, especially large language models and generative AI.
Key Capabilities of watsonx.ai
- Foundation Models: Provides access to pre-trained foundation models (both open-source and IBM-developed) that can be adapted and customized.
- Experimentation with Prompts: The Prompt Lab enables users to experiment with different prompts to guide foundation models for tasks like question answering, text generation, and more.
- Model Customization: The Tuning Studio allows users to fine-tune models with their own data for better performance on business-specific use cases.
- Model Deployment and Management: The Pipeline tool helps automate model deployment processes and management, streamlining the path to production.
- Security: Emphasizes security and privacy, keeping your data and models encrypted and accessible only to you.
Common Tools in watsonx.ai
- Prompt Lab: For building and refining prompts to interact with AI models effectively.
- Tuning Studio: For customizing and fine-tuning models with your own data.
- Pipeline Tool: For automating model lifecycle management and deployment.
Overall Goals watsonx.ai Helps Achieve:
- Efficiently building machine learning models
- Experimenting with and customizing foundation models
- Streamlining the entire AI lifecycle (data preparation, training, deployment, management)
[MUSIC] Welcome to IBM watsonx.ai. After watching this video, you’ll be
able to explain the capabilities and features of IBM watsonx.ai, and identify
the common tools available in Watsonx.ai. Generating poem, art, or
a song through AI is fun, but when AI is applied to business,
you need to think bigger. AI for businesses need to be
built based on requirements, based on higher standards. When you build AI in the core of your
business, it needs to be trusted, secured, scalable, and adaptable. Here is a platform that helps
businesses leverage AI IBM watsonx. IBM watsonx is an integrated AI and
data platform for AI builders. The watsonx platform
comprises three products. The first product is watsonx.ai,
a studio for new foundation models,
generative AI, and machine learning. The second product is watsonx.data,
which is a data store. And the third product is
watsonx.governance, which is a toolkit for monitoring and governance. In this video, we’ll focus on watsonx.ai. watsonx.ai is a studio of integrated
tools powered by foundation models for working with generative AI and
building machine learning models. With watsonx.ai, you can train,
tune, deploy, and manage foundation models easily. This helps you build AI applications
in a fraction of the time and with a fraction of the data. With watsonx.ai, these are some
prominent goals you can achieve, build machine learning models,
experiment with foundation models, and manage the AI lifecycle. Based on your goal, you can select
the tasks offered by watsonx.ai. These tasks can be performed using
the tools provided on the platform. The tasks and tools in watsonx.ai align
with the AI lifecycle of a model. You prepare your data,
build an experiment, and train models and solutions. Then you deploy your models and
start building your applications. Subsequently, you manage these models and
repetitive processes. watsonx.ai offers access to IBM’s selected
open-source models from Hugging Face, as well as a family of IBM-trained
foundation models of different sizes and architectures. As an AI value creator, you can also
bring your models and data to watsonx.ai. With tools like Prompt Lab, AI builders
can experiment with foundation models and build prompts that meet their needs. Prompt Lab enables users to experiment
with prompts to support a range of natural language processing or
NLP tasks, including question answering, content generation, and summarization,
text classification and extraction. As an AI creator, you may also want to
customize models based on your data and for your business use cases. watsonx.ai enables you to do so
using the Tuning Studio tool. With this tool, subsequent versions of
watsonx.ai will include tuning methods and examples for prompt tuning and
fine-tuning foundation models for better performance and accuracy. Putting a model into a product
is a multistep process. For managing and
automating a model lifecycle, watsonx.ai provides the Pipeline tool. The Pipeline tool can be used to automate
the steps involved in loading data, training models, deploying models and
evaluating models. This can reduce the time to get
a model into production and improve the accuracy and
reliability of models. Let’s learn from this video from IBM how
different tools in watsonx.ai help to create a collaborative environment to
streamline workflow for AI models. Until recently, AI models had to be trained to
perform a very specific task. But now, with the power of foundation
models, you can build powerful AI applications in a fraction of
the time with a fraction of the data. In our Prompt Laboratory, you can guide
models to meet your needs with easy to use tools for building and refining performant
prompts to achieve your desired result. [MUSIC] If you want to further customize, you can tailor models to be even more
accurate for your business’s use case. In our tuning studio,
bring in datasets and tune your model with as
few as 100 examples. We provide state of the art tuning methods that you can set up
with just a few clicks. Now it’s time to put your model to work, create an enterprise grade deployment,
and start building your application. That’s it. With watsonx.ai,
your team is empowered by a collaborative environment that streamlines workflows
throughout the AI lifecycle. Multiply the power of AI for
your enterprise. Practical, efficient and easy to use. watsonx.ai. IBM watsonx.ai ensures the security
of the data and models you work on. Your data and the models you
create are accessible only to you. Your data is stored in
an encrypted format. The models that you create
are also private to your account. IBM does not have access to your data or
models, and they will never be used by IBM or any other person
or organization without your permission. In this video, you learned
about IBM watsonx.ai, an AI and data platform that helps businesses create
AI responsibly and with transparency. One of the products of
watsonx is watsonx.ai. watsonx.ai is a studio of integrated
tools to train, tune, and deploy generative AI models. Prominent tools offered in watsonx.ai
include Prompt Lab, tuning studio, and Pipeline tool. [MUSIC]
Reading: IBM watsonx.data and watsonx.governance
Reading
Objectives
After completing this reading, you will be able to:
- Describe the features, capabilities, and applications of IBM watsonx.data and IBM watsonx.governance
Introduction
IBM watsonx, the AI and data platform designed for businesses, aims to enhance the impact of AI. It comprises three core components. The first component, watsonx.ai, combines new generative artificial intelligence (AI) capabilities, driven by foundation models and traditional machine learning, into a powerful studio spanning the AI lifecycle. The second component, watsonx.data, is a fit-for-purpose datastore, and the third component, watsonx.governance, is a powerful toolkit for monitoring and AI governance.
As a cloud-based platform, IBM watsonx offers accessibility from anywhere and is suitable for businesses of all sizes. It ensures scalability, facilitating AI integration across your organization and enabling businesses to adapt to evolving needs. IBM watsonx aims to advance trustworthy AI by enhancing data accessibility, implementing governance, reducing costs, and expediting the deployment of high-quality AI models.
In this reading, you’ll learn about watsonx.data and watsonx.governance.
Watsonx.data
Watsonx.data is an extensive, carefully curated data repository equipped with a state-of-the-art data management system. It is built on an open lakehouse architecture, which combines elements of data lakes and data warehouses.
Watsonx.data can be used to train and fine-tune AI models. It allows you to connect to your data effortlessly while enhancing data reliability through built-in governance, security, and automation features.
Watsonx.data provides fast and efficient data processing through multiple query engines. The pairing of multiple storage tiers and query engines also helps reduce the costs associated with data warehouses through workload optimization.
Watsonx.data leverages the foundation models of watsonx.ai to streamline and accelerate how users interact with data. Through the watsonx.ai conversational user interface, users can quickly generate responses to retrieve, explore, and augment data and metadata.
Watsonx.governance
AI is often considered a black box that raises more questions than it resolves. With watsonx.governance, you can govern your AI, scale responsibly, and shed light on the world of AI.
Watsonx.governance is a powerful toolkit that allows you to direct, manage, and monitor your organization’s AI activities. With watsonx.governance, you can design AI based on responsibility, transparency, and trust.
It enables you to trace datasets, models, and pipelines, ensuring you can consistently explain your AI’s decisions. Watsonx.governance also allows you to monitor your AI models for fairness, bias, and drift, facilitating necessary corrective actions. Additionally, you can translate regulatory requirements into enforceable policies to effectively manage compliance.
Watsonx.governance provides the right level of visibility into your AI models and processes through customizable dashboards, reports, and charts.
You can utilize watsonx.governance in several ways:
- Credit risk: Watsonx.governance enables you to optimize profitability by implementing fair, unbiased, and transparent approval processes.
- Health monitoring and diagnosis: In the health sector, watsonx.governance ensures precision by preventing inaccurate and harmful recommendations for diagnosis and treatment.
- Supply chain management: Watsonx.governance contributes to improved demand forecasting, enhancing accuracy and process efficiency, thereby increasing productivity.
- Recruitment: Watsonx.governance aids in establishing unprejudiced hiring practices by ensuring fair decision-making, actively detecting and mitigating bias, and providing documentation to support auditing efforts.
Summary
In this reading, you learned that IBM watsonx comprises three core components: Watsonx.ai, watsonx.data, and watsonx.governance.
Watsonx.ai combines new generative AI capabilities, driven by foundation models and traditional machine learning, into a powerful studio spanning the AI lifecycle.
Watsonx.data is a massive, curated data repository that can be used to train and fine-tune models. It uses watsonx.ai foundation models to simplify and accelerate user interactions with data.
Watsonx.governance is a powerful toolkit that allows you to direct, manage, and monitor your organization’s AI activities. It provides the right level of visibility into your AI models and processes through customizable dashboards, reports, and charts.
Video: Hugging Face
What is Hugging Face?
- A central platform where AI developers, scientists, and businesses collaborate to build and share machine learning models, datasets, and tools.
- Focused on democratizing AI, making it accessible even without huge budgets or in-house developer teams.
- Originally known for Natural Language Processing (NLP) models, now offers image, audio, video, and more.
Key Features
- Massive Library: 250K+ open-source models, 50K+ datasets, 1 million+ demos.
- Transformers Library: Pretrained models for PyTorch, TensorFlow, Google Jax, etc.
- Spaces: A place to host interactive demos of AI applications.
How Businesses Benefit
- Reduce Development Cost: Access pre-trained models and datasets instead of starting from scratch.
- Customize & Fine-Tune: Adapt models to specific business needs and data.
- Create Multimodal Apps: Combine capabilities (like text + image generation)
- Enterprise Support: Hugging Face helps businesses with model optimization and addressing bias.
Hugging Face + IBM Watsonx.ai Partnership
- Watsonx.ai: IBM’s AI studio incorporates select Hugging Face models.
- Hugging Face: Provides open-source versions of IBM’s Large Language Models (LLMs).
- Synergy: Combines the vast community and libraries of Hugging Face with IBM’s enterprise focus.
Why Hugging Face is Important:
- Collaborative innovation within the open-source AI community can be a powerful force.
- Smaller businesses and organizations can leverage powerful AI capabilities without the overhead of the tech giants.
Welcome to Hugging Face. After watching this video, you’ll be able to explain the purpose of the
Hugging Face platform, list the tools and capabilities it offers, and understand how Hugging Face and Watsonx.ai
jointly help businesses. Hugging Face is an open source artificial intelligence
platform where scientists, developers and
businesses collaborate to build personalized
machine learning tools. The platform was built with the purpose of
creating a hub for the open source AI community to share models, data sets
and applications. This way AI becomes accessible
to all types of users, even those who do not have
the budget or bandwidth to build machine learning
applications independently. Hugging Face is
therefore credited with democratizing AI
as everyone comes together to benefit from
smaller, curated models. Challenging the general one
model to rule assumption. At first the hugging
phase community focused on creating transformer-based models to leverage the capabilities of natural
language processing, or NLP. However, today the
platform offers various machine learning
tools for generating text, images, audio, and video. Currently, the Hugging
Face platform hosts over 250,000 open models, 50,000 data sets, and one
million open demos. This list keeps growing. Scientists and developers
use Hugging Face to build, train and deploy
their AI models. They have access
to the platform’s open source transformer library, which has over 25,000
pretrained models for PyTorch, Tensorflow, and Google Jax. PyTorch is a deep
learning library. Tensorflow is a machine
learning platform and Google Jax is a machine
learning framework. The models in the library
perform varied tasks, such as text generation,
question answering, summarization, automatic
speech recognition, and image segmentation, just to name a few. Users can filter these
models by name to find an existing model or share their own model
with the library. Developers can
also host demos of generative AI applications
on the Spaces tab, allowing users to interact
and validate them. How do businesses benefit
from the platform? Hugging Face offers
businesses the enterprise hub from where they can access pre trained models and data sets. This allows businesses
to leverage existing infrastructure
rather than build models from scratch. Not only does this reduce
their carbon footprint, time, and cost to scale, it also allows
businesses to train the models on proprietary
data and relevant use cases. Additionally, Hugging
Face helps businesses A, add or remove features to improve the efficiency
of their models. B, evaluate their
generative AI models to filter biased data. C, create multimodal
applications with text, image, audio, and video
generation capabilities. More than 50,000 large and small companies actively
use Hugging Face. For example, Writer, a
generative AI solution provider, hosts its Palmera
Large Language Models or LLMs on Hugging Face. Intel has officially joined Hugging Face Hardware
Partner program and is collaborating with Hugging Face to build state-
of-the-art machine learning hardware and end-to-end
machine learning workflows. Even universities
and non-profits are part of Hugging Face. Among other services. Hugging Face offers an expert
acceleration program to guide non-developers on
machine learning models. HuggingChat is the first
open source alternative to ChatGPT. To protect its users, Hugging Face complies with service organization
control type 2 regulation. This means ensuring
user data security, availability,
processing integrity, confidentiality, and privacy. Taking its collaborative
efforts one step further, Hugging Face has entered into a unique partnership
with watsonx.ai, IBM’s next generation enterprise
studio for AI builders. Watsonx.ai offers
select Hugging Face models in its studio to help its community
of builders train, test and deploy all types of machine learning and
generative AI applications. This way, the studio
leverages the diversity of data community strength and open source libraries that
Hugging Face provides. On its end, Hugging Face
creates open source versions of IBM’s LLMs and makes
them available on a hub. Both entities believe in
open source technology, both bet on the community to create value in the AI space. As proprietary AI models can
quickly become obsolete, Hugging Face may develop an
edge over the big five in AI, namely, Google, Open AI, Meta, IBM, and Microsoft. This is because it supports
and is supported by the open source AI community
that keeps innovating. In this video, you learned
all about Hugging Face. This AI platform demonstrates the collaborative power of
the open-source AI community. It creates space for
businesses to build customized proprietary models at a reduced cost in an
accelerated timeframe, incurring a lower
carbon footprint. Organizations, universities, and non-profits leverage
the platform’s tools and services to benefit from natural
language processing. In short, you do not have to be a big business to benefit
from generative AI.
Reading: Lesson Summary
Reading
Congratulations! You have completed this lesson.
At this point, you have learned about the pre-trained models and platforms used for generative AI application development.
You explored the ability of foundation models to generate text, images, and code using pre-trained models. You learned about the features and capabilities of different platforms, including IBM watsonx and Hugging Face. You even had the opportunity to experience AI application development and watsonx.ai in action through hands-on lab experiences. You were privy to what experts from the field have to say about application development in AI.
Specifically, you learned that:
- Text-to-text generation models are a type of machine learning model used to generate text from a given input.
- There are two types of text-to-text generation models: statistical and neural networks. Further, these models use seq2seq or transformer type of models.
- The most popular text-to-text generation models are GPT, T5, and BART.
- Text-to-text generation models increase the efficiency of tasks like writing scripts, musical pieces, emails, letters, and so on.
- Text-to-image generation models generate images from text descriptions. They make meaning out of your words and turn them into unique images.
- These models are classified into two types: GANs and diffusion models. The most popular text-to-image generation models are DALL-E and Imagen.
- Text-to-image generation models can generate various types of realistic or abstract images.
- Text-to-code generation models are a type of machine learning model used to generate code from natural language descriptions.
- There are two types of text-to-code generation models: Seq2Seq and transformer. The most popular models within this classification are CodeT5, Code2Seq, and PanGu-Coder.
- Text-to-code generation models use automatic coding, which is more efficient, accurate, and consistent than manual coding.
- IBM watsonx is an AI and data platform that helps businesses create AI responsibly and with transparency. It is a family of three products: watsonx.ai, watsonx.data, and watsonx.governance.
- With its three products, IBM watsonx can multiply the impact of AI across businesses.
- Hugging Face is an open-source AI platform where scientists, entrepreneurs, developers, and individuals collaborate to build personalized machine learning tools and models at a reduced cost, in an accelerated timeframe, and with a lower carbon footprint.
Practice Assignment: Practice Quiz: Pre-trained Models and Platforms for AI Applications Development
Which type of text-to-text generation model directly maps input text to the generated text?
Transformer-based model
Gemini
Here’s the correct answer and explanation:
- Sequence-to-Sequence model
Here’s why:
- Direct Mapping: Sequence-to-sequence models are designed to take an input sequence (e.g., a sentence in English) and directly produce an output sequence (e.g., a translation into French). This aligns with the concept of directly mapping input to output.
Why the other options are less accurate:
- Transformer-based model: While powerful, this is a broad category. Many types of models (including sequence-to-sequence) use transformers as their core architecture.
- Diffusion model: These are primarily used for image generation, not direct text-to-text tasks.
- Statistical model: This is too general. Many text processing models have a statistical foundation, but not all perform direct text-to-text generation.
Let me know if you’d like more details on how sequence-to-sequence models work!
ChatGPT
The type of text-to-text generation model that directly maps input text to the generated text is:
Transformer-based model
Claude
The type of text-to-text generation model that directly maps input text to the generated text is the Sequence-to-Sequence (Seq2Seq) model.
Sequence-to-Sequence models are a type of neural network architecture designed for tasks that involve transforming one sequence (e.g., a sentence in one language) into another sequence (e.g., the same sentence in another language). They consist of two main components:
- An encoder that processes the input sequence and encodes it into a fixed-length vector representation.
- A decoder that takes this vector representation and generates the output sequence one token at a time.
The encoder and decoder are typically implemented using recurrent neural networks (RNNs) or transformer models, with the transformer architecture being more prevalent in modern Seq2Seq models due to its ability to better capture long-range dependencies.
Seq2Seq models are widely used for tasks such as machine translation, text summarization, dialogue systems, and other natural language processing applications where the input and output are sequences of text.
To generate code from natural language descriptions, text-to-code generation models use a process known as _____________.
Neural code generation
Gemini
The correct answer is:
- Program synthesis
Here’s why:
- Program Synthesis: This field of computer science specifically focuses on the automatic generation of code from higher-level specifications, such as natural language descriptions.
Let’s break down why the other options aren’t as accurate:
- Neural code generation: This refers to the use of neural networks within the program synthesis process, but it’s not the process itself.
- Debugging: This is about finding and fixing errors in existing code, not generating new code.
- Code intelligence: This is a broader term encompassing tools and techniques that help developers understand and work with code, including code completion and navigation features.
ChatGPT
To generate code from natural language descriptions, text-to-code generation models use a process known as:
Program synthesis
Claude
To generate code from natural language descriptions, text-to-code generation models use a process known as program synthesis.
Program synthesis refers to the task of automatically generating executable code from some higher-level specification or description, such as natural language instructions or input-output examples. In the context of text-to-code generation, program synthesis involves taking natural language descriptions or requirements as input and producing the corresponding source code in a programming language as output.
This process typically involves techniques like machine translation, where the natural language description is treated as the “source language” and the programming language is treated as the “target language.” Neural sequence-to-sequence models, which are commonly used for machine translation tasks, are often employed for program synthesis as well.
The generated code should ideally match the specified requirements or behavior described in the natural language input. Program synthesis is a challenging task that requires understanding the semantics of both natural language and programming languages, as well as the ability to map between the two domains.
Which IBM watsonx.ai tool can help automate the steps for training, deploying, and evaluating models?
Pipeline tool
Gemini
The correct answer is:
- Pipeline tool
Here’s why:
- Automation Focus: The Pipeline tool in watsonx.ai is specifically designed to automate the entire model development lifecycle. This includes steps like data loading, model training, deployment, and evaluation.
Why the other options are less suitable:
- Prompt Lab: This helps experiment with foundation models and build prompts for text-based tasks.
- Tuning Studio: This assists in fine-tuning foundation models for better performance and accuracy.
- watsonx.data: This is a data store component, not focused on model creation automation.
ChatGPT
The IBM Watsonx.ai tool that can help automate the steps for training, deploying, and evaluating models is:
Pipeline tool
Graded Assignment: Graded Quiz: Platforms for Generative AI
__________________ are able to generate a random image based on a text prompt and then add details to the image until it matches the text description.
Diffusion models
Correct! Diffusion models generate a random image based on a text prompt and then add details to the image until it matches the text description.
Gemini
The correct answer is:
- Diffusion models
Here’s why:
- The Process: Diffusion models work by gradually adding noise to an image and then learning to reverse that process. This allows them to start from a random image and iteratively refine it until it closely matches the provided text description.
Why the other options are less accurate:
- Generative adversarial networks (GANs): While powerful for image generation, they don’t have the same iterative refinement process that diffusion models use for aligning closely with text descriptions.
- Transformer-based models: This is a broad architecture used in many NLP tasks, but not the primary mechanism for this type of image generation.
- Sequence-to-Sequence models: These are designed for text-to-text tasks, not image generation.
ChatGPT
Generative adversarial networks are able to generate a random image based on a text prompt and then add details to the image until it matches the text description.
Claude
Diffusion models are able to generate a random image based on a text prompt and then add details to the image until it matches the text description.
Diffusion models, such as Stable Diffusion, DALL-E 2, and Imagen, are a type of generative model that can create high-quality images from natural language prompts. They work by starting with pure noise (a random image) and then iteratively adding and removing noise in a carefully controlled way to slowly transform the random noise into a coherent image that matches the text prompt.
The process involves two main steps:
- A diffusion process that gradually adds noise to an initial image until it becomes pure noise.
- A reverse diffusion process that starts with pure noise and gradually removes the noise, guided by the text prompt, to generate the final image.
Diffusion models are trained on large datasets of images and their corresponding text descriptions, allowing them to learn the relationship between text and visual representations. During the reverse diffusion process, the model uses the text prompt to guide the denoising process, gradually adding details and refining the image until it matches the desired description.
This iterative process of adding and removing noise allows diffusion models to generate highly detailed and coherent images from text prompts, making them suitable for tasks like text-to-image generation, image editing, and image manipulation.
Text-to-text generation models can be used successfully to _____________.
Translate a poem into multiple languages
Correct! Text-to-text generation models can perform language translation with efficiency and accuracy.
Gemini
The most accurate answer here is:
- Translate a poem into multiple languages
Here’s why:
- Text-to-text Strength: Text-to-text generation models excel at tasks that involve transforming text while preserving its meaning or structure. Translation is a prime example.
Why other options are less suitable:
- Identify relationships between objects in an image: This requires image understanding, not just text processing.
- Forecast weather based on natural language descriptions: This involves complex prediction and modeling, beyond the core capability of text-to-text models.
- Generate programming scripts from natural language descriptions: While possible with specialized models, this task is more aligned with the field of program synthesis.
ChatGPT
Text-to-text generation models can be used successfully to:
Generate programming scripts from natural language descriptions.
Claude
Text-to-text generation models can be used successfully to generate programming scripts from natural language descriptions.
This task falls under the category of program synthesis or code generation, where the goal is to automatically generate executable code (e.g., programming scripts, functions, or entire programs) from natural language specifications or requirements.
Text-to-text generation models, particularly sequence-to-sequence (Seq2Seq) models and transformer-based architectures, are well-suited for this task. These models can be trained on parallel datasets of natural language descriptions and corresponding code snippets, learning to map the natural language input to the desired programming language output.
During inference, the natural language description is provided as input to the model, and it generates the corresponding code as output. This process can be quite useful in various scenarios, such as:
- Automating certain programming tasks based on natural language instructions.
- Assisting developers by generating boilerplate code or code skeletons from high-level descriptions.
- Enabling non-technical users to create simple programs or scripts by providing instructions in natural language.
- Prototyping and rapid development of software based on natural language specifications.
While the generated code may not be perfect and may require manual review and refinement, text-to-text generation models can significantly accelerate the process of transforming natural language requirements into executable code, making programming more accessible and efficient.
Which one aspect of Hugging Face’s AI platform differentiates it from other machine learning platforms? It’s ability to ______________.
Engage the open-source AI community
Correct! Hugging Face’s ability to engage the open-source AI community differentiates it from other machine learning projects.
Gemini
Here’s the answer and the reason why:
- Engage the open-source AI community
This is the key differentiator for Hugging Face. Here’s why:
- Collaboration Focus: Hugging Face actively fosters a collaborative community where users share models, datasets, and knowledge, democratizing access to AI tools.
- Not One Model to Rule: They embrace the idea of different models specialized for different purposes, contrasting with the “one model” approach of some tech giants.
While the other options are relevant to AI platforms in general:
- Create innovative generative AI applications: Many platforms support this, not just Hugging Face.
- Leverage natural language processing capabilities: This is a core feature of many AI platforms, not unique to Hugging Face.
- Pursue the general “one model to rule” assumption: This is more aligned with the focus of some large tech companies, in contrast to Hugging Face’s community-driven approach.
ChatGPT
The one aspect of Hugging Face’s AI platform that differentiates it from other machine learning platforms is its ability to:
Engage the open-source AI community
Claude
Hugging Face’s key differentiating aspect from other machine learning platforms is its ability to engage the open-source AI community.
Hugging Face is known for its commitment to open-source principles and fostering a vibrant ecosystem of researchers, developers, and practitioners collaborating on machine learning and natural language processing projects. Some of the ways in which Hugging Face engages the open-source AI community include:
- Hugging Face Hub: An open platform for sharing and discovering pre-trained models, datasets, and other resources contributed by the community.
- Open-source libraries: Hugging Face maintains popular open-source libraries like Transformers (for building and fine-tuning transformer models) and Datasets (for managing and processing datasets), which are widely used and contributed to by the community.
- Community forums and documentation: Hugging Face provides extensive documentation, tutorials, and active community forums where users can ask questions, share knowledge, and collaborate on projects.
- Hosting and sponsoring events: Hugging Face organizes and sponsors conferences, meetups, and hackathons, bringing together researchers, developers, and enthusiasts from around the world.
- Open governance: Hugging Face follows an open governance model, with many of its projects and initiatives driven by community contributions and feedback.
By fostering an inclusive and collaborative open-source ecosystem, Hugging Face has attracted a large and diverse community of contributors, which helps accelerate the development of new models, tools, and applications. This community-driven approach sets Hugging Face apart from more closed or proprietary machine learning platforms and enables rapid innovation and knowledge sharing in the field of AI.
Which of the following is the correct benefit of IBM watsonx.ai?
You can customize models based on your data and for your business use cases.
Correct! IBM watsonx.ai enables you to customize models based on your data and for your business use cases.
Gemini
The most accurate answer here is:
- You can customize models based on your data and for your business use cases.
Here’s why:
- Customization Focus: watsonx.ai emphasizes tailoring AI models to specific business needs using your own data. This is crucial for real-world problem-solving.
While the other options have some connection to data or AI, they’re not the primary benefit offered by watsonx.ai:
- You can analyze AI models and processes through dashboards, charts, and dimensional reporting. This describes model monitoring and explainability features, which are important but not the core reason to use the platform.
- You can process data efficiently through multiple query engines. This relates to data handling capabilities, not the unique value proposition of watsonx.ai.
- You can view and access the models and data of other users. watsonx.ai prioritizes security and privacy; your data and models remain accessible only to you.
ChatGPT
The correct benefit of IBM Watsonx.ai is:
You can customize models based on your data and for your business use cases.
Claude
The correct benefit of IBM watsonx.ai is:
You can customize models based on your data and for your business use cases.
IBM watsonx.ai is a platform that allows users to build, deploy, and manage AI models and applications tailored to their specific data and business requirements. One of its key benefits is the ability to customize pre-trained AI models by fine-tuning them on an organization’s proprietary data and use cases.
This customization capability enables organizations to leverage the power of pre-trained models while adapting them to their unique data distributions, domain-specific terminology, and specific business workflows. By fine-tuning the models, organizations can achieve better performance and more accurate results compared to using generic, off-the-shelf models.
Additionally, watsonx.ai provides tools and resources to streamline the entire AI lifecycle, from data preparation and model training to deployment and monitoring. However, analyzing models through dashboards and charts, processing data through query engines, and accessing other users’ models and data are not explicitly stated as benefits of the watsonx.ai platform.