IBM Generative AI Fundamentals Specialization

Prompt engineering patterns

1. Interview Pattern. 2. Train of thought. 3. Tree of thought.

The four core building blocks of generative AI models

1. Variational auto-encoders that rapidly reduce the dimensionality of samples. 2. Generative adversarial networks use competing networks to produce realistic samples. 3. Transformer-based models use attention mechanisms to model long-term text dependencies. 4. Diffusion models address information decay by removing noise in the latent space.

Artificial neural networks (ANNs)

A collection of smaller computing units called neurons which are modeled in a manner similar to how a human brain processes information.

Chatbot

A computer program that simulates human conversation with an end user. Though not all chatbots are equipped with artificial intelligence (AI), modern chatbots increasingly use conversational AI techniques like natural language processing (NLP) to make sense of the user's questions and automate their responses.

Open lakehouse architecture

A data lakehouse architecture that combines elements of data lakes and data warehouses.

Transformers

A deep learning architecture that uses an encoder-decoder mechanism. Transformers can generate coherent and contextually relevant text.

Large language models (LLMs)

A deep learning model trained on substantial text data to learn the patterns and structures of language. They can perform language-related tasks, including text generation, translation, summarization, sentiment analysis, and more.

Bidirectional encoder representations from transformers (BERT)

A family of language models by Google that uses pre-training and fine-tuning to create models that can accomplish several tasks.

TensorFlow

A free and open-source software library used for machine learning and artificial intelligence.

Variational autoencoder (VAE)

A generative model that is a neural network model designed to learn the efficient representation of input data by encoding it into a smaller space and decoding it back to the original space.

Falcon

A large language model developed by the Technology Institute of Innovation (TII). Its variant, falcon-7b-instruct, is a 7-billion-parameter model based on the decoder-only model.

Llama

A large language model from Meta AI.

Google JAX

A machine learning framework used for transforming numerical functions that combines autograd (automatic obtaining of the gradient function through differentiation of a function) as well as TensorFlow's XLA (accelerated linear algebra).

Pre-trained models

A machine learning model trained on an extensive data set before being fine-tuned or adapted for a specific task or application. These models are a type of transfer learning where the knowledge gained from one task (the pre-training task) is leveraged to perform another task (the fine-tuning task).

watsonx.data

A massive, curated data repository that can be used to train and fine-tune models with a state-of-the-art data management system.

watsonx.governance

A powerful toolkit to direct, manage, and monitor your organization's AI activities.

Neural code generation

A process that uses artificial neural networks like neural networks work in the human brain.

What is a prompt?

A prompt is any input you provide to a generative model to produce a desired output. You can think of it as an instruction you provide to the model. These optimize the response of generative AI models. Direct and effective prompt makes it powerful

Generative pre-trained transformer (GPT)

A series of large language models developed by OpenAI designed to understand language by leveraging a combination of two concepts: Training and transformers.

watsonx.ai

A studio of integrated tools for working with generative AI capabilities powered by foundational models and building machine learning models.

Supervised learning

A subset of AI and machine learning that uses labeled data sets to train algorithms to classify data or predict outcomes accurately.

Natural language processing (NLP)

A subset of artificial intelligence that enables computers to understand, manipulate, and generate human language (natural language).

Unsupervised learning

A subset of machine learning and artificial intelligence that uses algorithms based on machine learning to analyze and cluster unlabeled data sets. These algorithms can discover hidden patterns or data groupings without human intervention.

Pre-training

A technique in which unsupervised algorithms are repeatedly given the liberty to make connections between diverse pieces of information.

CodeT5

A text-to-code seq2seq model developed by Google AI trained on a large data set of text and code. CodeT5 is the first pre-trained programming language model that is code-aware and encoder-decoder based.

Code2Seq

A text-to-code seq2seq model developed by OpenAI trained on a substantial text and code data set. It leverages the syntactic structure of programming languages to encode source code.

PanGu-Coder

A text-to-code transformer model developed by Microsoft Research. It is a pre-trained decoder-only language model that generates code from natural language descriptions.

Imagen

A text-to-image generation model developed by Google AI trained on a large data set of text and images. Imagen is used to generate realistic images from various text descriptions.

DALL-E

A text-to-image generation model developed by OpenAI that is trained on a large data set of text and images and can be used to generate realistic images from various text descriptions.

Seq2seq model

A text-to-text generation model that first encodes the input text into a sequence of numbers and then decodes this sequence into a new one, representing the generated text.

Bidirectional autoregressive transformer model (BART)

A text-to-text transfer transformer model developed by Facebook AI with a seq2seq translation architecture with bidirectional encoder representation like BERT and a left-to-right decoder like GPT.

T5

A text-to-text transfer transformer model developed by Google AI trained on a substantial data set of code and text. It can be used for various tasks, including summarization, translation, and question-answering.

Generative adversarial network (GAN)

A type of generative model that includes two neural networks: Generator and discriminator. The generator is trained on vast data sets to create samples like text and images. The discriminator tries to distinguish whether the sample is real or fake.

Diffusion model

A type of generative model that is popularly used for generating high-quality samples and performing various tasks, including image synthesis. It is trained by gradually adding noise to an image and then learning to remove the noise. This process is called diffusion.

Deep learning

A type of machine learning focused on training computers to perform tasks through learning from data. It uses artificial neural networks.

Text-to-code generation model

A type of machine learning model used to generate code from natural language descriptions. It uses generative AI to write code through neural code generation.

Text-to-image generation model

A type of machine learning model used to generate images from text descriptions. It uses generative AI to make meaning out of words and turn them into unique images.

Text-to-text generation model

A type of machine learning model used to generate text from a given input. It is trained on a large text corpus and is taught to learn patterns, grammar, and causal information. Using the given input, the models generate the new text.

Neural network model

A type of text-to-text generation model that uses artificial neural networks to generate text.

Statistical model

A type of text-to-text generation model that uses statistical techniques to generate text.

Foundational models

AI models with broad capabilities that can be adapted to create more specialized models or tools for specific use cases.

Hugging Face

An AI platform that allows open-source scientists, entrepreneurs, developers, and individuals to collaborate and build personalized machine learning tools and models.

Dimensionality reduction

An application of unsupervised learning wherein the algorithms capture the most essential data features while discarding redundant or less informative ones.

Clustering

An application of unsupervised learning wherein the algorithms group similar instances together based on their inherent properties.

Google flan

An encoder-decoder foundation model based on the T5 architecture.

Prompt

An instruction or question given to a generative AI model to generate new content.

IBM watsonx

An integrated AI and data platform with a set of AI assistants designed to scale and accelerate the impact of AI with trusted data across businesses.

PyTorch

An open-source machine learning framework based on the Torch library. This framework is used for applications such as computer vision and natural language processing.

Neural networks

Computational models inspired by the human brain's structure and functioning. They are a fundamental component of deep learning and artificial intelligence.

Training data

Data (generally, large data sets that also have examples) used to teach a machine learning model.

Recurrent neural networks (RNNs)

Deep learning architecture designed to handle sequences of data by maintaining hidden states that capture information from previous steps in the sequence.

Convolutional neural networks (CNNs)

Deep learning architecture networks that contain a series of layers, each conducting a convolution or mathematical operation on a previous layer.

Clustering

Group similar instances together based on their inherent properties

Generative AI models

Models that can understand the context of input content to generate new content. In general, they are used for automated content creation and interactive communication.

IBM Granite

Multi-size foundation models that are specially designed for businesses. These models use a decoder architecture to apply generative AI to both language and code.

Good prompt

Provides relevant context, proper structure and is comprehensible. It has 4 building blocks. 1. Instructions. 2. Context. 3. Input data. 4. Output Indicator- benchmarking assessing attributes of output

Prompt engineering

The skillful design of input prompts for LLMs to produce high-quality, coherent outputs. High quality output is Relevant, contextual, imaginative, nuanced, and linguistically accurate

Unsupervised Learning

When an AI system can look at data on its own and build rules for deciding what it is seeing. It isa category of data-mining techniques in which an algorithm explains relationships without an outcome variable to guide the process.

Dimensionality Reduction

the process of reducing the number of random variables under consideration by obtaining a set of principal variables. Set of techniques that reduce the number of random variables (or dimensions) under consideration. Capture the most important features and discard the redundant or less informative ones