This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.
Learning Objectives
- Define generative AI.
- Explain how generative AI works.
Video: Introduction to Generative AI
This comprehensive guide provides an in-depth introduction to Generative AI (Gen AI), covering its definition, workings, model types, and applications.
What is Gen AI?
Gen AI, a subset of deep learning, leverages artificial neural networks to create new content based on learned patterns from existing data. Unlike traditional programming that relies on hard-coded rules, Gen AI learns from data, enabling it to generate text, images, audio, video, and more.
Key Concepts:
- AI vs. Machine Learning vs. Deep Learning vs. Gen AI: The guide clearly distinguishes between these related fields, positioning Gen AI as a specialized area within deep learning.
- Supervised vs. Unsupervised Learning: These core machine learning concepts are illustrated through examples, emphasizing their relevance to Gen AI.
- Discriminative vs. Generative Models: The fundamental difference between these model types is explained, highlighting how generative models create new data instances.
- Transformers and Hallucinations: The guide delves into transformers, a key architecture in Gen AI, and discusses hallucinations, a potential issue where models generate nonsensical or incorrect outputs.
Model Types and Applications:
Various Gen AI model types are covered, including text-to-text, text-to-image, text-to-video, text-to-3D, and text-to-task. Real-world examples, such as code generation and sentiment analysis, demonstrate the practical applications of these models.
Google Cloud Tools:
The guide highlights Google Cloud’s offerings for Gen AI, including:
- Vertex AI Studio: For exploring and customizing Gen AI models.
- Vertex AI: For building Gen AI applications with minimal coding.
- PaLM API: For experimenting with Google’s large language models.
- Gemini: Google’s multimodal AI model capable of processing text, images, audio, and code.
Overall, this comprehensive guide provides a solid foundation for understanding Gen AI, its capabilities, and its potential applications.
- Hi, and welcome to
“Introduction to Generative AI.” Don’t know what that is? Then you’re in the perfect place. I’m Roger Martinez and I am a Developer Relations
Engineer at Google Cloud, and it’s my job to help developers learn to use Google Cloud. In this course, I’ll
teach you four things, how to define generative AI, explain how generative AI works, describe generative AI model types, describe generative AI applications. But let’s not get swept
away with all of that yet, let’s start by defining
what generative AI is first. Generative AI has become a
buzzword, but what is it? Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery,
audio, and synthetic data. But what is artificial intelligence? Since we are going to explore generative artificial intelligence, let’s provide a bit of context. Two very common questions asked are: What is artificial intelligence? And what is the difference
between AI and machine learning? Let’s get into it. So one way to think about it
is that AI is a discipline, like how physics is a
discipline of science. AI is a branch of computer science that deals with the creation
of intelligent agents and are systems that can reason, learn, and act autonomously. Are you with me so far? Essentially, AI has to do
with the theory and methods to build machines that
think and act like humans. Pretty simple, right? Now, let’s talk about machine learning. Machine learning is a subfield of AI. It is a program or system that trains a model from input data. The trained model can
make useful predictions from new, never before seen data drawn from the same one
used to train the model. This means that machine
learning gives the computer the ability to learn without
explicit programming. So what do these machine
learning models look like? Two of the most common classes
of machine learning models are unsupervised and supervised ML models. The key difference between the two is that with supervised
models, we have labels. Labeled data is data
that comes with a tag, like a name, a type, or a number. Unlabeled data is data
that comes with no tag. So what can you do with supervised
and unsupervised models? This graph is an example
of the sort of problem a supervised model might try to solve. For example, let’s say you’re
the owner of a restaurant. What type of food do they serve? Let’s say pizza or dumplings. No, let’s say pizza. I like pizza. Anyway, you have historical
data of the bill amount and how much different people tipped based on the order type,
pick up or delivery. In supervised learning, the
model learns from past examples to predict future values. Here, the model uses the
total bill amount data to predict the future tip amount based on whether an order
was picked up or delivered. Also, people, tip your delivery drivers. They work really hard. This is an example of a sort of problem that an unsupervised
model might try to solve. Here, you wanna look at tenure and income, and then group or cluster employees to see whether someone
is on the fast track. Nice work, Blue Shirt. Unsupervised problems
are all about discovery, about looking at the raw data and seeing if it naturally
falls into groups. This is a good start, but
let’s go a little deeper to show this difference graphically, because understanding these concepts is the foundation for your
understanding of generative AI. In supervised learning,
testing data values, X, are input into the model. The model outputs a prediction and compares it to the training data used to train the model. If the predicted test data values and actual training data
values are far apart, that is called “Error.” The model tries to reduce this error until the predicted and actual
values are closer together. This is a classic optimization problem. So, let’s check-in. So far, we’ve explored differences between artificial intelligence
and machine learning and supervised and unsupervised learning. That’s a good start, but what’s next? Let’s briefly explore
where deep learning fits as a subset of machine learning methods, and then, I promise, we’ll
start talking about Gen AI. While machine learning is a broad field that encompasses many
different techniques, deep learning is a type
of machine learning that uses artificial neural networks, allowing them to process
more complex patterns than machine learning. Artificial neural networks are
inspired by the human brain. Pretty cool, huh? Like your brain, they are made up of many
interconnected nodes or neurons that can learn to perform tasks by processing data and making predictions. Deep learning models typically
have many layers of neurons, which allows them to learn
more complex patterns than traditional machine learning models. Neural networks can use both
labeled and unlabeled data. This is called semi-supervised learning. In semi-supervised learning,
a neural network is trained on a small amount of labeled data and a large amount of unlabeled data. The labeled data helps the
neural network to learn the basic concepts of the tasks, while the unlabeled data
helps the neural network to generalize to new examples. Now we finally get to
where generative AI fits into this AI discipline. Gen AI is a subset of deep learning, which means it uses
artificial neural networks, can process both labeled
and unlabeled data using supervised, unsupervised,
and semi-supervised methods. Large language models are also
a subset of deep learning. See, I told you I’d bring
it all back to Gen AI. Good job, me. Deep learning models or machine
learning models in general can be divided into two types, generative and discriminative. A discriminative model is a type of model that is used to classify or
predict labels for data points. Discriminative models
are typically trained on the dataset of labeled data points, and they learn the relationship between the features of the data
points and the labels. Once a discriminative model is trained, it can be used to predict the
label for new data points. A generative model
generates new data instances based on a learned
probability distribution of existing data. Generative models generate new content. Take this example: Here, the discriminative model learns the conditional
probability distribution, or the probability of Y, our
output, given X, our input, that this is a dog, and classifies it as a dog and not a cat, which is great because
I’m allergic to cats. The generative model learns the joint probability distribution or the probability of X and Y, P of XY, and predicts the conditional
probability that this is a dog, and can then generate a picture of a dog. Good boy. I’m gonna name him Fred. To summarize, generative models can generate new data instances, and discriminative models discriminate between different
kinds of data instances. One more quick example. The top image shows a traditional
machine learning model which attempts to learn
the relationship between the data and the label, or
what you want to predict. The bottom image shows
a generative AI model which attempts to learn
patterns on content so that it can generate new content. So what if someone challenges you to a game of “Is It Gen AI or Not?” I’ve got your back. This illustration shows
a good way to distinguish between what is Gen AI and what is not. It is not Gen AI when the
output, or Y, or label, is a number or a class, for example, spam or not
spam, or a probability. It is Gen AI when the
output is natural language like speech or text, audio, or an image like Fred
from before, for example. Let’s get a little mathy to
really show the difference. Visualizing this mathematically
would look like this: If you haven’t seen this for a while, the y = f(x) equation
calculates the dependent output of a process given different inputs. The Y stands for the model output, the F embodies a function used
in the calculation or model, and the X represents the input or inputs used for the formula. As a reminder, inputs are the data, like comma separated value
files, text files, audio files, or image files, like Fred. So the model output is a
function of all the inputs. If the Y is a number,
like predicted sales, it is not generative AI. If Y is a sentence, like
define sales, it is generative, as the question would
elicit a text response. The response we base on
all the massive large data the model was already trained on. So the traditional ML
supervised learning process takes training code and
labeled data to build a model. Depending on the use case or problem, the model can give you a
prediction, classify something, or cluster something. Now, let’s check out how much more robust the generative AI
process is in comparison. The generative AI process
can take training code, labeled data and unlabeled
data of all data types and build a foundation model. The foundation model can
then generate new content, it can generate text, code, images, audio, video, and more. We’ve come a long way from
traditional programming, to neural networks, to generative models. In traditional programming,
we used to have to hard code the rules for distinguishing a cat, type, animal, legs, four, ears, two, fur, yes, likes, yarn,
catnip, dislikes, Fred. In the wave of neural networks, we could give the networks
pictures of cats and dogs and ask, “Is this a cat?” And it would predict,
“A cat,” or “Not a cat.” What’s really cool is that
in the generative wave, we as users can generate our own content, whether it be text, images,
audio, video, or more. For example, models like PaLM,
or Pathways Language Model, or LaMDA, Language Model
for Dialogue Applications, and just very, very large data from multiple sources across the internet and build foundation language models we can use simply by asking a question, whether typing it into a prompt or verbally talking
into the prompt itself. So when you ask it, “What’s a cat?” It can give you everything
it’s learned about a cat. Now, let’s make things
a little more formal with an official definition. What is generative AI? Gen AI is a type of
artificial intelligence that creates new content based on what it has learned
from existing content. The process of learning
from existing content is called training, and results in the creation
of a statistical model. When given a prompt, gen
AI uses a statistical model to predict what an
expected response might be, and this generates new content. It learns the underlying
structure of the data and can then generate new samples that are similar to the
data it was trained on. Like I mentioned earlier,
a generative language model can take what it has learned from the examples it’s been shown and create something entirely
new based on that information. That’s why we use the word “generative”. But large language models which generate novel combinations of texts in the form of natural sounding language are only one type of generative AI. A generative image model
takes an image’s input and can output text,
another image, or video. For example, under the output text, you can get visual question and answering, while under output image,
image completion is generated, and under output video,
animation is generated. A generative language
model takes text as input and can output more text, an
image, audio, or decisions. For example, under the output text, question and answering is generated, and under output image,
a video is generated. I mentioned that
generative language models learn about patterns in
language through training data. Check out this example: Based on things learned
from its training data, it offers predictions of how
to complete this sentence. I’m making a sandwich
with peanut butter and… Jelly. Pretty simple, right? So given some text, it can
predict what comes next. Thus, generative language models are pattern matching systems. They learn about patterns based on the data that you provide. Here is the same example using Gemini, which is trained on a
massive amount of text data and is able to communicate
and generate human-like text in response to a wide range
of prompts and questions. See how detailed the response can be? Here is another example that’s just a little more complicated than peanut butter and jelly sandwiches. The meaning of life is… And even with a more ambiguous question, Gemini gives you a contextual answer and then shows the highest
probability response. The power of generative AI comes from the use of transformers. Transformers produced the 2018 revolution in natural language processing. At a high level, a transformer model consists of an encoder and a decoder. The encoder encodes the input sequence and passes it to the decoder, which learns how to
decode the representations for a relevant task. Sometimes, transformers
run into issues though. Hallucinations are words or phrases that are generated by the model that are often nonsensical
or grammatically incorrect. See, not great. Hallucinations can be caused
by a number of factors, like when the model is not
trained on enough data, is trained on noisy or dirty data, is not given enough context, or is not given enough constraints. Hallucinations can be a
problem for transformers because they can make the output text difficult to understand. They can also make the model
more likely to generate incorrect or misleading information. So put simply, hallucinations are bad. Let’s pivot slightly
and talk about prompts. A prompt is a short piece of text that is given to a large
language model, or LLM, as input, and it can be used to control
the output of the model in a variety of ways. Prompt design is the
process of creating a prompt that will generate a
desired output from an LLM. Like I mentioned earlier,
generative AI depends a lot on the training data that
you have fed into it. It analyzes the patterns and
structures of the input data and thus, learns. But with access to a browser based prompt, you, the user, can
generate your own content. So, let’s talk a little bit about the model types available to
us when text is our input, and how they can be helpful
in solving problems, like never being able
to understand my friends when they talk about soccer. The first is text-to-text. Text-to-text models take
a natural language input and produce text output. These models are trained
to learn the mapping between a pair of text, for example, translating
from one language to others. Next, we have text-to-image. Text-to-image models are trained
on a large set of images, each captioned with a
short text description. Diffusion is one method
used to achieve this. There’s also text-to-video and text-to-3D. Text-to-video models aim to
generate a video representation from text input. The input text can be anything
from a single sentence to a full script, and
the output is a video that corresponds to the input text. Similarly, text-to-3D models generate three dimensional objects that correspond to a
user’s text description, for use in games or other 3D worlds. And finally, there’s text-to-task. Text-to-task models are trained
to perform a defined task or action based on text input. This task can be a wide range of actions, such as answering a question,
performing a search, making a prediction, or
taking some sort of action. For example, a text-to-task model could be trained to navigate
a web user interface or make changes to a doc through a graphical user interface. See, with these models,
I can actually understand what my friends are talking
about when the game is on. Another model that’s larger
than those I mentioned is a foundation model, which is a large AI model pre-trained on a vast quantity of data
designed to be adapted or fine-tuned to a wide
range of downstream tasks, such as sentiment
analysis, image captioning, and object recognition. Foundation models have the potential to revolutionize many industries, including healthcare,
finance, and customer service. They can even be used to detect fraud and provide personalized customer support. If you’re looking for foundation models, Vertex AI offers a Model Garden that includes foundation models. The language foundation
models include PaLM API for chat and text. The vision foundation models
include stable diffusion, which have been shown to be effective at generating high quality
images from text inscriptions. Let’s say you have a use case where you need to gather sentiments about how your customers feel
about your product or service. You can use the classification task sentiment analysis task model. Same for vision tasks, if you need to perform
occupancy analytics, there is a task-specific
model for your use case. So those are some examples of
foundation models we can use, but can Gen AI help
with code for your apps? Absolutely. Shown here, are generative
AI applications. You can see there’s quite a lot. Let’s look at an example
of code generation shown in the second block
under the code at the top. In this example, I’ve input a
code file conversion problem, converting from Python to JSON. I use Gemini and insert
into the prompt box, “I have a Pandas Dataframe
with two columns – one with a file name and one with the hour in
which it is generated: I am trying to convert it into a JSON file in the format shown on screen:” Gemini returns the
steps I need to do this. And here, my output is in a JSON format. Pretty cool, huh? Well, get ready, it gets even better. I happen to be using Google’s free browser
based Jupyter notebook and can simply export the
Python code to Google’s CoLab. So to summarize, Gemini
code generation can help you debug your lines of source code, explain your code to you line-by-line, craft SQL queries for your database, translate code from one
language to another, generate documentation and
tutorials for source code. I’m gonna tell you about
three other ways Google Cloud can help you get more
out of generative AI. The first is Vertex AI Studio. Vertex AI Studio lets you quickly explore and customize generative AI models that you can leverage in your
applications on Google Cloud. Vertex AI Studio helps developers create and deploy generative AI models by providing a variety
of tools and resources that make it easy to get started. For example, there is a
library of pre-trained models, a tool for fine-tuning models, a tool for deploying models to production, and a community forum for developers to share ideas and collaborate. Next, we have Vertex AI,
which is particularly helpful for all of you who don’t
have much coding experience. You can build generative
AI search and conversations for customers and employees with Vertex AI Search and Conversation, formerly Gen AI App Builder. Build with little or no coding and no prior machine learning experience. Vertex AI can help you
create your own chatbots, digital assistants, custom search engines, knowledge bases, training
applications, and more. And lastly, we have PaLM API. PaLM API lets you test and experiment with Google’s large language
models and Gen AI tools. To make prototyping quick
and more accessible, developers can integrate
PaLM API with Maker Suite, and use it to access the API using a graphical user interface. The suite includes a
number of different tools, such as a model training
tool, a model deployment tool, and a model monitoring tool. And what do these tools
do? I’m so glad you asked. The model training tool helps
developers train ML models on their data using different algorithms. The model deployment tool helps developers deploy
ML models to production with a number of different
deployment options. The model monitoring tool
helps developers monitor the performance of their
ML models in production using a dashboard and a
number of different metrics. Lastly, there is Gemini,
a multimodal AI model. Unlike traditional language models, it’s not limited to
understanding text alone, it can analyze images,
understand the nuances of audio, and even interpret programming code. This allows Gemini to
perform complex tasks that were previously impossible for AI. Due to its advanced architecture, Gemini is incredibly
adaptable and scalable, making it suitable for
diverse applications. Model Garden is continuously
updated to include new models. And now you know absolutely
everything about generative AI. Okay, maybe you don’t know everything, but you definitely know the basics. Thank you for watching our course and make sure to check
out our other videos if you wanna learn more
about how you can use AI.
Quiz: Introduction to Generative AI: Quiz
What is Generative AI?:
Generative AI is a type of artificial intelligence (AI) that is designed to generate unbiased opinions in the content it produces.
Generative AI is a type of artificial intelligence (AI) that can create new content, such as text, images, audio, and video.
Generative AI is a type of artificial intelligence (AI) that can create new content which guarantees accuracy and truthfulness.
Generative AI is a type of artificial intelligence (AI) that can generate new and original ideas or solutions without being trained on existing data.
Generative AI is a type of artificial intelligence (AI) that can create new content, such as text, images, audio, and video.
Correct! Generative AI is a type of artificial intelligence (AI) that can create new content, such as text, images, audio, and video. It does this by learning from existing data and then using that knowledge to generate new and unique outputs.
Gemini
Out of the options you provided, the most accurate definition of Generative AI is:
- Generative AI is a type of artificial intelligence (AI) that can create new content, such as text, images, audio, and video.
Here’s why the other options are not entirely accurate:
- Unbiased opinions: While Generative AI can be a powerful tool for creative exploration, it doesn’t inherently guarantee unbiased outputs. The training data and prompts used can influence the generated content.
- Accuracy and truthfulness: Similar to unbiased opinions, generative models can’t guarantee factual accuracy. They are very good at mimicking patterns in their training data, but the generated content might not always be truthful, especially if the training data itself has biases or factual errors.
- Without existing data: Generative AI models are trained on massive amounts of existing data. This data helps them learn the patterns and relationships that they use to create new content.
gemini-advanced-0514
The correct answer is:
Generative AI is a type of artificial intelligence (AI) that can create new content, such as text, images, audio, and video.
Here’s why the other options are incorrect:
- Generative AI is a type of artificial intelligence (AI) that is designed to generate unbiased opinions in the content it produces. While AI can be trained to reduce bias, generative AI itself doesn’t inherently generate unbiased opinions. The content it produces reflects the data it was trained on and the prompts it receives.
- Generative AI is a type of artificial intelligence (AI) that can create new content which guarantees accuracy and truthfulness. Generative AI models are powerful, but they can also produce inaccurate or misleading information. It’s important to critically evaluate the content they produce.
- Generative AI is a type of artificial intelligence (AI) that can generate new and original ideas or solutions without being trained on existing data. Generative AI models learn patterns from existing data to generate new content. They don’t create truly original ideas in the way humans do.
llama-3-70b-instruct
The correct answer is:
- Generative AI is a type of artificial intelligence (AI) that can create new content, such as text, images, audio, and video.
Generative AI refers to a class of AI models that can generate new, original content, such as text, images, audio, or video, based on patterns and structures learned from existing data. This type of AI has the ability to create new content that is similar in style and format to the training data, but is not simply a copy or reproduction of it.
What is an example of both a generative AI model and a discriminative AI model?
A generative AI model could be trained on a dataset of images of cats and then used to cluster images of cats. A discriminative AI model could be trained on a dataset of images of cats and dogs and then used to predict as either cats or dogs.
A generative AI model could be trained on a dataset of images of cats and dogs and then used to classify new images as either cats or dogs. A discriminative AI model could be trained on a dataset of images of cats and then used to generate new images of cats.
A generative AI model could be trained on a dataset of images of cats and then used to generate new images of cats. A discriminative AI model could be trained on a dataset of images of cats and dogs and then used to classify new images as either cats or dogs.
A generative AI model could be trained on a dataset of images of cats and then used to classify new images of cats. A discriminative AI model could be trained on a dataset of images of cats and dogs and then used to predict new images as either cats or dogs.
A generative AI model could be trained on a dataset of images of cats and then used to generate new images of cats. A discriminative AI model could be trained on a dataset of images of cats and dogs and then used to classify new images as either cats or dogs.
Correct! A generative AI model could be trained on a dataset of images of cats and then used to generate new images of cats. A discriminative AI model could be trained on a dataset of images of cats and dogs and then used to classify new images as either cats or dogs.
gpt-4o-2024-05-13
The correct example of both a generative AI model and a discriminative AI model is:
A generative AI model could be trained on a dataset of images of cats and then used to generate new images of cats. A discriminative AI model could be trained on a dataset of images of cats and dogs and then used to classify new images as either cats or dogs.
This example accurately reflects the primary functions of generative and discriminative models:
- Generative AI Model: These models learn the underlying distribution of the data and can generate new data points that resemble the original dataset. In this case, the generative model is trained on images of cats and can generate new images of cats.
- Discriminative AI Model: These models learn the boundary between different classes in the data and are used for classification tasks. In this example, the discriminative model is trained on a dataset of images of cats and dogs and is used to classify new images as either cats or dogs.
claude-3-sonnet-20240229
The correct example is:
A generative AI model could be trained on a dataset of images of cats and then used to generate new images of cats. A discriminative AI model could be trained on a dataset of images of cats and dogs and then used to classify new images as either cats or dogs.
Explanation:
Generative models learn the underlying distribution of the training data and can generate new samples from that learned distribution. For example, a generative model trained on cat images can generate new, synthetic images of cats.
Discriminative models learn to distinguish between different classes or categories in the data. For example, a discriminative model trained on images of cats and dogs can classify a new image as either a cat or a dog.
So in the example I provided:
- The generative model is trained on just cat images and can generate new, artificial cat images.
- The discriminative model is trained on both cat and dog images and can classify new images into those two categories.
The other examples you provided had the generative and discriminative model roles reversed incorrectly.
llama3
The correct answer is:
A generative AI model could be trained on a dataset of images of cats and then used to generate new images of cats. A discriminative AI model could be trained on a dataset of images of cats and dogs and then used to classify new images as either cats or dogs.
Here’s why:
- A generative AI model is trained on a dataset and then generates new, original data that resembles the training data. In this case, a generative AI model is trained on images of cats and then generates new images of cats that are similar to the training data.
- A discriminative AI model, on the other hand, is trained on a dataset and then makes predictions or classifications on new, unseen data. In this case, a discriminative AI model is trained on images of cats and dogs and then classifies new images as either cats or dogs.
The other options are not accurate examples of generative and discriminative AI models:
- Option 1 is incorrect because a generative AI model would not be used for clustering, and a discriminative AI model would not be used for classification in this scenario.
- Option 2 is incorrect because a generative AI model would not be used for classification, and a discriminative AI model would not be used for generation.
- Option 4 is incorrect because a generative AI model would not be used for classification, and a discriminative AI model would not be used for prediction in this scenario.
It’s worth noting that generative AI models are often used for tasks like image synthesis, data augmentation, and style transfer, while discriminative AI models are often used for tasks like image classification, object detection, and sentiment analysis.
What are foundation models in Generative AI?
A foundation model is a large AI model pretrained on a vast quantity of data that was “designed to be adapted” (or fine-tuned) to a wide range of downstream tasks, such as sentiment analysis, image captioning, and object recognition.
A foundation model is used to create new types of software that can generate different types of content.
A foundation model is the first step in the generative AI process, responsible for creating the basic building blocks that are used to create more complex models.
A foundation model is used to improve the performance of other models by fine-tuning other models, which can help them to learn how to generate more accurate and realistic content.
A foundation model is a large AI model pretrained on a vast quantity of data that was “designed to be adapted” (or fine-tuned) to a wide range of downstream tasks, such as sentiment analysis, image captioning, and object recognition.
Correct! A foundation model is a large AI model pre-trained on a vast quantity of data that is “designed to be adapted” (or fine-tuned) to a wide range of downstream tasks, such as sentiment analysis, image captioning, and object recognition.
phi-3-medium-4k-instruct
A foundation model in the context of Generative AI refers to a large-scale model that is pre-trained on a massive and diverse dataset. These models are designed to be highly adaptable and can be fine-tuned for a variety of downstream tasks, such as natural language processing, image generation, and more. The adaptability comes from the model’s ability to generalize from the data it was trained on to understand and generate content in different domains.
The most common example of foundation models in Generative AI are models like GPT (Generative Pretrained Transformer) for language tasks, and DALL-E, VQ-VAE-2, and Imagen for image generation tasks. These models learn to predict or generate sequences of data (like text or pixels) and can be adapted to generate new content by fine-tuning them on specific datasets.
Here are key points about foundation models:
- Pre-training: Before being adapted for specific tasks, foundation models are pre-trained on large, diverse datasets. This pre-training helps the models to learn a wide range of features and patterns that can be useful across various applications.
- Adaptability: Foundation models are designed to be fine-tuned with additional data. This means that after the initial pre-training, the model can be further trained (or fine-tuned) on a smaller, task-specific dataset to perform particular tasks like translation, summarization, image generation, etc.
- Generative Capabilities: In the context of Generative AI, foundation models are capable of generating new content that is similar to the data they were trained on. For example, a foundation model trained on a dataset of images can be fine-tuned to generate new images that resemble those in the dataset.
- Transfer Learning: Foundation models are a cornerstone of transfer learning, where knowledge gained during pre-training on one task is transferred to a different but related task. This makes it possible to achieve high performance with less data for the downstream tasks.
- Efficiency and Scalability: By using foundation models, researchers and developers can leverage the pre-trained weights and architecture to quickly prototype and deploy AI solutions for various tasks without starting from scratch.
- Ethical Considerations: With the power of foundation models comes the responsibility to ensure they are used ethically. There are concerns about bias, misuse, and the environmental impact of training such large models.
In summary, foundation models in Generative AI are sophisticated tools that serve as a starting point for creating a variety of AI applications. Their strength lies in their pre-trained knowledge, adaptability, and generative capabilities, which can be harnessed to produce content across different domains.
llama3
The correct answer is:
A foundation model is a large AI model pretrained on a vast quantity of data that was “designed to be adapted” (or fine-tuned) to a wide range of downstream tasks, such as sentiment analysis, image captioning, and object recognition.
Foundation models are a type of artificial intelligence (AI) model that are:
- Large in scale, often with hundreds of millions or even billions of parameters
- Pretrained on vast amounts of data, such as text, images, or audio
- Designed to be adaptable to a wide range of downstream tasks, such as natural language processing, computer vision, and multimodal tasks
- Fine-tuned for specific tasks, allowing them to learn and adapt to new tasks with minimal additional training data
Foundation models are considered “foundational” because they provide a common foundation for a wide range of AI applications, allowing developers to build upon them and adapt them to specific use cases. Examples of foundation models include language models like BERT and RoBERTa, and vision models like CLIP and DALL-E.
The other options are not accurate definitions of foundation models:
- Option 2 is incorrect because foundation models are not used to create new types of software, but rather to provide a foundation for a wide range of AI applications.
- Option 3 is incorrect because foundation models are not the first step in the generative AI process, but rather a type of model that can be used as a starting point for a wide range of AI applications.
- Option 4 is incorrect because foundation models are not used to improve the performance of other models by fine-tuning them, but rather provide a foundation for a wide range of AI applications.
Hallucinations are words or phrases that are generated by the model that are often nonsensical or grammatically incorrect. What are some factors that can cause hallucinations? Select three options.
The model is trained on noisy or dirty data.
The model is not given enough context.
The model is trained on too much data.
The model is not trained on enough data
- The model is trained on noisy or dirty data.
- The model is not given enough context.
- The model is not trained on enough data
gpt-4-1106-preview
Hallucinations in the context of language models like me refer to instances where the model generates incorrect, nonsensical, or irrelevant information. Here are three factors from the options provided that can cause hallucinations:
- The model is trained on noisy or dirty data. If the training data contains a lot of errors, inconsistencies, or irrelevant information, the model might learn to replicate these issues in its outputs. This can lead to the generation of hallucinations as the model mimics the noise in the data.
- The model is not given enough context. When a model does not have sufficient context for generating a response, it might fill in the gaps with information that seems plausible to it based on the patterns it has learned, which can sometimes be incorrect or nonsensical.
- The model is not trained on enough data. If a model is trained on a limited dataset, it may not have been exposed to a wide variety of language use cases and information. This can result in the model not having a comprehensive understanding of language or the subject matter, leading to the generation of incorrect or irrelevant content.
The option “The model is trained on too much data” is generally not a direct cause of hallucinations. In fact, training on a large and varied dataset typically helps the model to better understand language and generate more accurate and coherent responses. However, the quality of the data is crucial; simply having a large quantity of data is not helpful if that data is noisy or of poor quality.
yi-large-preview
Hallucinations in the context of language models can indeed be caused by various factors. The three options that can cause hallucinations are:
- The model is trained on noisy or dirty data. If the training data contains a lot of errors, inconsistencies, or nonsensical content, the model may learn these patterns and reproduce them, leading to hallucinations in its outputs.
- The model is not given enough context. Language models rely on context to generate appropriate responses. If the input prompt does not provide sufficient context, the model might generate responses that are not grounded in the provided information, potentially leading to nonsensical or irrelevant outputs.
- The model is not trained on enough data. While it might seem counterintuitive given the second option, training on too little data can also be problematic. If the model is undertrained, it may not learn the full range of linguistic patterns and rules, which can result in grammatically incorrect or nonsensical outputs.
The option “The model is trained on too much data” is generally not a direct cause of hallucinations. In fact, training on a large and diverse dataset can help reduce the likelihood of hallucinations by allowing the model to learn a wide variety of linguistic patterns and contexts. However, if the additional data is of poor quality (noisy or dirty), then it could contribute to the problem.
llama3
The correct answers are:
- The model is trained on noisy or dirty data.
- The model is not given enough context.
- The model is not trained on enough data.
Hallucinations in generative models can occur due to various factors. Here’s why these three options are correct:
- Noisy or dirty data: If the training data contains errors, inconsistencies, or irrelevant information, the model may learn to generate nonsensical or incorrect text. Noisy data can lead to hallucinations, as the model tries to make sense of the flawed input.
- Not given enough context: When the model lacks sufficient context or information about the topic or task, it may generate text that is unrelated or nonsensical. Insufficient context can cause the model to “hallucinate” and produce incorrect or irrelevant text.
- Not trained on enough data: If the model is not trained on a sufficient amount of data, it may not have learned enough patterns and relationships to generate coherent text. This can lead to hallucinations, as the model tries to fill in gaps in its knowledge with incorrect or nonsensical text.
The option “The model is trained on too much data” is not a correct cause of hallucinations. While overfitting can occur when a model is trained on too much data, this is not typically a direct cause of hallucinations.
It’s worth noting that hallucinations can also be caused by other factors, such as:
- Model architecture or design flaws
- Poor hyperparameter tuning
- Lack of regularization or constraints
- Adversarial attacks or intentional manipulation of the model
What is a prompt?
A prompt is a long piece of text that is used to debug the large language model.
A prompt is a short piece of text that is used to train the large language model.
A prompt is a pre-trained generative AI model that serves as a starting point for fine-tuning and customizing the generation of specific types of content.
A prompt is a short piece of text that is given to the large language model as input, and it can be used to control the output of the model in many ways.
A prompt is a short piece of text that is given to the large language model as input, and it can be used to control the output of the model in many ways.
claude-3-opus-20240229
A prompt is a short piece of text that is given to a large language model (like myself) as input. The prompt can be used to control the output of the model in various ways, such as:
- Specifying the topic or type of content to generate
- Providing context or background information
- Giving instructions on the desired format, style, or tone of the response
- Asking the model to answer a question, complete a task, or engage in a dialogue
The model processes the prompt and uses it as a starting point to generate a relevant and coherent continuation or response, drawing upon its training data and capabilities. By carefully crafting the prompt, humans can guide and shape the model’s output to suit their needs and goals.
Prompts are a key mechanism for interacting with and controlling the behavior of large language models like myself. They allow humans to harness the broad knowledge and generative abilities of the model in flexible and targeted ways.
qwen1.5-110b-chat
The most accurate description among the options provided is:
A prompt is a short piece of text that is given to the large language model as input, and it can be used to control the output of the model in many ways.
This definition captures the essence of how prompts are used in the context of large language models. They serve as a starting point for the model to generate responses, allowing users to guide the conversation or task the model with specific inquiries or tasks. The other options either overcomplicate the definition or inaccurately describe the nature of a prompt in relation to language models.
llama3
The correct answer is:
A prompt is a short piece of text that is given to the large language model as input, and it can be used to control the output of the model in many ways.
A prompt is a short piece of text that is input into a large language model to generate a response. The prompt can be a question, a statement, a phrase, or even a single word. The model processes the prompt and generates a response based on its training data and algorithms. The prompt can influence the tone, style, and content of the generated response.
Here’s why the other options are incorrect:
- Option 1 is incorrect because a prompt is typically a short piece of text, not a long piece of text used for debugging.
- Option 2 is incorrect because a prompt is not used to train the large language model, but rather to generate a response from the model.
- Option 3 is incorrect because a prompt is not a pre-trained generative AI model, but rather a short piece of text input into a model to generate a response.
Prompts can be used in various ways to control the output of a large language model, such as:
- Generating text on a specific topic or theme
- Controlling the tone or style of the generated text
- Providing context or background information for the generated text
- Specifying the format or structure of the generated text
By crafting a well-designed prompt, users can influence the output of a large language model and generate high-quality, relevant, and informative text.