AI Practitioners Exam - Abbrev and Terms

 

AI Practitioner Exam Prep

Abbrev. and Terms

Abbreviations in AI

ART = Automatic Reasoning and Tool-use

ATLAS = Adversarial Threat Landscape for AI Systems

BERT score metric = Bidirectional Encoder Representations from Transformers

BLEU metric = Bilingual Evaluation Understudy metric

CNN = Convolutional Neural Networks

CoT = Chain-Of-Thought prompting

GANs = Generative Adversarial Networks

GLUE benchmark = General Language Understanding Evaluation benchmark

GPT = Generative Pre-trained Transformers

FM = Foundational Models

IDP = Intelligent Data Processing

LLM = Large Language Models

NL or NLP = Natural Language Processing

NTM = Neural Topic Modeling algorithm 

PDP = Partial dependence plots

PRA = Privacy Reference Architecture

RAG = Retrieval-Augmented Generation 

RL = Reinforcement Learning 

RLHF = Reinforcement Learning from Human Feedback

ROUGE metric = Recall-Oriented Understudy for Gisting Evaluation metric

RWK metric = Real World Knowledge score

SLM = Small Language Model

SQuAD benchmark = Stanford Question Answering Dataset benchmark

SSL = Self-Supervised Learning

ToT = Tree of thoughts prompting

VAEs = Variational Auto Encoders

WMT benchmark= Workshop on Machine Translation benchmark

AI Term Definitions

Amplified Decision Making = helps humans in decisions in stressful times

Augmentation of Dataset = create more data for underrepresented. Purpose: fixes bias.

Batch Size = Number of training samples processed in one forward or backward pass before updating the model parameters.  Small batches give faster iterations and generalization, while large batches improve stability and GPU efficiency.

Batch Inferencing = start with lots of data and time.  Focus on all data. For slow decisions with historical understanding.  good for data analysis reports.

BERT score metric = Scores semantic similarity (using cosine) between two sentences. Good for text generation, summarization, translation, and filling in missing words.

BLEU metric = score for how well the AI translation did against human translation samples.

CNN = deep learning method to process images into grids using neutral network by using filters (3 x 3 block of pixels) that deal with pattern recognition. filters increasingly become of larger parts of the picture and more abstract.  good for computer vision tasks, including image classification, OCR, object detection, and medical imaging. 

CoT prompts = user prompts for answers to steps from AI.

Curating a Dataset = labeling the dataset

Data efficiency = could start with little data and create a lot of data

Diffusion models = in LLM. types: forward and reverse diffusion. 

"Embeddings" = are numbers for tokens, where semantically similar tokens have similar vectors. Numerical representations of data, such as words, images, or items, in a lower dimensional vector space. Purpose: Captures semantic relationships and patterns in the data, enabling models to process and compare inputs efficiently.

"Embedding"/"Transformation"/"Vectorization" process = the algorithm that creates the math vector.

Emergent = at large scales, these models develop skills that are not explicitly programmed into them.

Epochs = Neural networks. One epoch = every training sample was processed by model once. 

Exposure in prompt = PII or privacy issues in the output

"Feature" in ML = dataset property or characteristic used as ML models input to make predictions. Almost the same as "dimension". ex = square feet, actual price, asking price, etc.

Few-shot prompts = user prompts for something that AI is given a little context.

"Fine tuning" = improve pre-trained language model using labeled data (ex: industry-specific data). Most important task for fine tuning is labeling with accurate and relevant labels. Types are instruction tuning, RHLF, adapting models for specific domains, transfer learning, and continuous pretraining. 

Forward Diffusion Model = hiding by adding noise till unrecognizable. Why? A: Maybe showing transform frame by frame. Similar to encryption.

FMs = large data model that is pre-trained, lots of possible types of data, and general purpose. adapted to do multiple tasks.

GANs = generator and discriminator compete against each other in zero-sum game. creates synthetic data.

"Generalization" = model's ability to apply knowledge from training on new unseen data. 

Generative Pre-trained Transformers = start with NLP text and can go to SQL or other.

Hijacking a Prompt = Enter AI prompt that gives bad suggestions then publish about AI's responses.

Hyperparameters = are human-defined settings that make an AI model tick.

IDP = extracts and classifies unstructured data in docs. gives summaries and actionable insights.

Image Processing = processes computer vision (image or video) and time series (video frames, satellite photos, etc.). Areas are image classification, object detection, and semantic segmentation.

Inference = process of when model analyzes new data to decide the new output.

"Jailbreaking" a Prompt = Enter AI prompt that gives criminal or evil suggestions.

"Labeling" = id and tag data with meaningful labels of the contents of each data piece 

"Learning Rate" = compares multiple trials to see improvement rate.

LLM = large language models are a subset of FM. ex: Claude, Chat GPT, etc.

Logistic regression = Algorithm to classify. Estimates probability that input is in category.

"Masking" of Input = intentionally hiding parts of the input, forces models to understand context

"Model" in AI = a trained software program or algorithm designed to recognize patterns, categorize, make predictions, or generate outputs by analyzing data. Created by humans and computers. Humans select data to show it, tell the purpose, send in rule parameters, tag some of the labeled data, and validate the output. Computers take the untrained algorithm to find patterns in the data, creates the learning parameters/weights, and self optimize in ML.

Multi-Modal = uses multiple data types such as text, images, audio, video, and computer code.

Multi-Modal Embedding = uses multiple data types embedding them into a shared space. search focus.

Multi-Modal Generation = uses multiple types to create new content.

Neural networks = like brain. takes input then runs through hidden layer and outputs the answer.

Parameters = are the learned (by computer refining) settings that make an AI model tick.

PDP = visualize the plot of one thing like age affects a second thing like income. good for transparency.

Perplexity metric = metric for language models, but not for text summaries.

"Point" in AI = exact coordinates on array with a number for each of the dimensions.

Prompt Chaining = breaking prompt into multiple tasks.

Prompt Engineering = improving the prompt with strategies such as ART, CoT, few-shot, RAG, ReAct, self-consistency, ToT, zero-shot.

Prompt Leaking = having the prompt ask about the model's instructions

RAG = searches few relevant docs (such as company-specific data or inventory levels) to get answer. Great to pair with language model. Parts are retrieval system and generative AI.  ex1: chatbots that look up company data. ex2: AI agents using CRM employee data to arrive at answer. ex3: legal analysis. ex4: health care answers. Offline pieces could be creation of: a) content embeddings, and b) search index. 

Real-Time Inferencing = given real-time interactive data (such as self-driving cars or missile defense systems). Focus on recent data that arrived. For fast decisions to new conditions. Older data just for background.

Residual Neural Network = uses images and tries to "skip connections" method instead of CNN.

Reverse Diffusion Model = revealing. remove noise until clear image. Similar to decryption.

ROUGE metric = Uses F1, precision, and recall compared to a reference text. Good for text.

R-Squared score = calcs the variance proportion in the dependent variable explained by the model. 1 is perfect prediction, 0 is worst, rest is between.

RWK metric = measures a model's ability to understand and apply facts about the world, rather than just recognize patterns in the data. high score is accurate to real world.

Self-Attention = enables a model to weigh the importance of different words in a sequence relative to a specific word. For context and long-range dependencies. Ex: "bank" in "river bank" versus "bank deposit".

SLM = Rare. Used for edge devices.

SSL = What is it? A1: models learn from raw data by predicting masked(hidden) parts of sentence or image. A2: makes use of the structure within the raw data to autogenerate labels.

Support Vector Machine = classifies tasks on tabular data. good for high dimensions on limited or small datasets.

Temperature of AI Output = randomness or creativity of the AI output. High temp = more random or creative.

Text Analysis = processes text and speech.  Areas are text classification, Word2Vec, machine translation, and topic modeling. Example algorithms are BlazingText, Sequence to Sequence, LDA,  and NTM. 

TF-IDF = statistical measurement of importance of a word within a document. Lessens filler words like "the" or "an".

"Token" in AI = chopped up smallest pieces. main factor in overall cost. ex: words in a sentence. If error with language model, check max context size that limits tokens they can process at once. If a book's length exceeds this limit, the model cannot handle the full input, leading to failure in summarization.

Top K of AI Output = limits output to top K of probability words.

Top P Sampling/Nucleus Sampling of AI Output = limits output to top percentage of probability words.

Training in ML = iterative teaching a ML model to find patterns, make decisions, or generate content.

Transfer Learning = takes existing pre-trained model on supervised task and then fine tunes.

Transformer-based language models = neural networks. revolutionized AI by parallel processing using self-attention, rather than word-by-word. Good for context and long-range dependencies between words. Good for NLP, text generation, translation, and summarization tasks. Examples BERT and GPT.

Zero-shot prompts = user prompts for something that the AI does not know is coming.