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Generative AI Flashcards
Bard
Googles
Jupyter Notebooks
Vertex AI Studio
LLM
Large Language Model
Vertex AI
Vertex AI Search and Conversation
PaLM
LaMDA
Pathways Language Model or LaMDA (or Language Model for Dialogue Applications) ingest 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.
MakerSuite
Gemini
Model Garden
Temperature
https://lukesalamone.github.io/posts/what-is-temperature/
Transformer
Pegasus
NLU
Natural language understanding
NLG
Natural language generation
Hallucinations
words or phrases that are generated by the model that are often nonsensical or grammatically incorrect
Because…
The model is not trained on enough data
The model is trained on noisy or dirty data.
The model is not given enough context.
A prompt
a natural language text that requests the generative AI to perform a specific task.
A prompt is a short piece of text that is given to the 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 the desired output from a large language model (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.”
Foundation Model
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.
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.
Define Generative AI
Generative AI is a type of artificial intelligence technology that can produce various types of content- including text, imagery, audio and synthetic data.
What is Generative AI? GenAI 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, GenAI uses this 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.
Explain how generative AI works
A generative AI model starts by efficiently encoding a representation of what you want to generate. For example, a generative AI model for text might begin by finding a way to represent the words as vectors that characterize the similarity between words often used in the same sentence or that mean similar things.
Describe General AI model types
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.
LLMs are also a subset of Deep Learning.
Describe General AI applications
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.
What is AI?
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.
Deep Learning
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.
Supervised Learning
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.
Unsupervised Learning
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.
Discriminative Model
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 a 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.
Generative Model
A generative model generates new data instances based on a learned probability distribution of existing data. Generative models generate new content.
Deep Learning Model Types
Discriminative, Generative
Generative language model
Generative language models are pattern-matching systems. They learn about patterns based on the data you provide.
Transformer
Transformers produced the 2018 revolution in Natural Language Processing. At a high-level, a Transformer model consists of an encoder and decoder.
Hallucinations can be a problem for Transformers because they can make the output text difficult to understand.
Encoder
The encoder encodes the input sequence and passes it to the decoder, which learns how to decode the representations for a relevant task.
Decoder
decode the representations for a relevant task.
Gemini
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 the filename 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 the code snippet! And here my output is in a JSON format. Pretty cool huh? Well get ready, it gets even better. It gets better. I happen to be using Google’s free-browser based Jupyter Notebook and can simple 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
