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Anki Deck for Learning Artificial Intelligence

Anki deck cards to help with learning listed at Artificial Intelligence (AI) - Artificial Intelligence (AI) Some cards were generated with the help of Microsoft Copilot Artificial Intelligence (AI) and are being updated or corrected.

Artificial intelligence

Section titled “Artificial intelligence”

Areas of artificial intelligence

Section titled “Areas of artificial intelligence”

Front

Section titled “Front”

What are the 6 areas of AI use cases and functions

Hint: they are like human capabilities and senses

Back

Section titled “Back”
  1. Machine learning - like human learning
  2. Generative AI - like human creativity and pattern matching
  3. Computer Vision - like human sight
  4. Speech - like human voice and hearing
  5. Natural Language Processing (NLP) - like human content analysis
  6. Extract data and insights - like human ability to follow a process and analysis

Bonus: Responsible AI - human ability for audit, monitoring, checking, and inclusion is required in application of AI.

Power of Machine Learning

Section titled “Power of Machine Learning”

Front

Section titled “Front”

What human capability is machine learning like and how might it be used and compared to traditional computer programming?

Back

Section titled “Back”

Machine learning is like human learning where a computer can be trained/learn using an algorithm to predict the correct human capability.

In comparison to programming a computer to do human functions precisely like coding all shapes of an orange for a computer to recognize. With machine learning, the machine can be trained to do any tasks using predictions as long as there is data of the input and what the task completed looks like.

The data are features like what an orange looks like and labels like whether it is an orange or type of oranges.

Introduction to Artificial Intelligence Microsoft Azure AI-900

Section titled “Introduction to Artificial Intelligence Microsoft Azure AI-900”

Machine Learning machine learning

Section titled “Machine Learning machine learning”

Goal of Machine Learning

Section titled “Goal of Machine Learning”

  1. Front

    What is the goal of Machine Learning (ML)?

  2. Back

    To create a predictive model by combining data science and software engineering, enabling software to make predictions using past data.

ML Model as a Function

Section titled “ML Model as a Function”

  1. Front

    How is a Machine Learning model structured as a function?

  2. Back

    An ML model is a function y = f(x), where x is a vector of input features and y is the predicted output. After training, the model generalizes this function to make predictions.

Supervised vs Unsupervised Learning

Section titled “Supervised vs Unsupervised Learning”

  1. Front

    What is the difference between supervised and unsupervised learning?

  2. Back

    Supervised learning uses labeled data to train models to predict known outcomes. Unsupervised learning uses unlabeled data to find patterns or groupings without predefined labels.

Types of Machine Learning

Section titled “Types of Machine Learning”

  1. Front

    What are the main types of Machine Learning?

  2. Back

    • Supervised Learning: uses labeled data ([x1, x2, x3], y)
      • Regression: numeric labels
      • Classification: categorical labels (binary or multiclass)
    • Unsupervised Learning: uses unlabeled data ([x1, x2, x3])
      • Clustering: groups data based on feature similarity

    Deep Learning (subset of ML using neural networks)

What is inferencing in machine learning?

Section titled “What is inferencing in machine learning?”

  1. Front

    What is inferencing in machine learning?

  2. Back

    Inferencing is the process of using a trained machine learning model to make predictions on new data by applying the model’s function to input features.

What is the difference between regression and classification?

Section titled “What is the difference between regression and classification?”

  1. Front

    What is the difference between regression and classification?

  2. Back

    Regression predicts numeric values (e.g., ice cream sales), while classification predicts categorical labels (e.g., disease risk).

What is a confusion matrix used for?

Section titled “What is a confusion matrix used for?”

  1. Front

    What is a confusion matrix used for?

  2. Back

    A confusion matrix visualizes the accuracy of a classification model with a matrix of the number of correct and incorrect predictions for each possible class label like:

    y-haty-hat
    y01
    020
    113

    This visualization is called a confusion matrix showing prediction totals of: In the matrix, true predictions are on the diagonal line from top left to bottom right.

    • ŷ=0 and y=0: True negatives (TN)
    • ŷ=1 and y=0: False positives (FP)
    • ŷ=0 and y=1: False negatives (FN)
    • ŷ=1 and y=1: True positives (TP)

What does the R² (coefficient of determination) metric indicate?

Section titled “What does the R² (coefficient of determination) metric indicate?”

  1. Front

    What does the R² (coefficient of determination) metric indicate?

  2. Back

    The coefficient of determination (more commonly referred to as R2 or R-Squared) is a metric that measures the proportion of variance in the validation results that can be explained by the model, as opposed to some odd aspect of the validation data (for example, a day with a highly unusual number of ice creams sales because of a local festival).

    R2 is more complex then previous options and compares the sum of squared differences between predicted and actual labels with the sum of squared differences between the actual label values and the mean of actual label values, like this:

    R2 = 1- ∑(y-ŷ)2 ÷ ∑(y-ȳ)2

    The result is a value between 0 and 1 that describes the proportion of variance explained by the model:

    • Closer to 1 this value is, means the better the model fits validation data
    • In example ice cream regression model, the R2 calculated from the validation data is 0.95.

What is the purpose of the softmax function in multiclass classification?

Section titled “What is the purpose of the softmax function in multiclass classification?”

  1. Front

    What is the purpose of the softmax function in multiclass classification?

  2. Back

    Softmax converts raw scores into probabilities for each class, ensuring they sum to 1. The class with the highest probability is selected.

    Example is a model predicts these scores:

    • Cat: 2.0
    • Dog: 1.0
    • Bird: 0.1

    Using the Softmax function, these scores will be transformed into probabilities:

    • Cat: 0.73 (73% chance)
    • Dog: 0.25 (25% chance)
    • Bird: 0.02 (2% chance)

What is the role of centroids in K-Means clustering?

Section titled “What is the role of centroids in K-Means clustering?”

  1. Front

    What is the role of centroids in K-Means clustering?

  2. Back

    Centroids are the center points of clusters that features vectors are organized close to. Data points are assigned to the nearest centroid, which is updated iteratively to minimize distance.

    • Features (x) are vectorized to n-dimensional coordinates (n is the number of features). In the example, we use leaves (x1) and petals (x2) and would have coordinates in 2 dimensional space [x1, x2]
    • Each data point (example observed flower) is assign to its nearest centroid
    • The centroids are moved to the centre of the data points assigned to it based on mean (average) distance between the points
    • After the centroid moves, the data points are reassigned to clusters based on the new closest centroid.
    • The centroid movement and cluster reassignment can be repeated until becoming stable or set number of iterations

What are ways to evaluate a binary classification model in ML

Section titled “What are ways to evaluate a binary classification model in ML”

  1. Front

    What are ways to evaluate a binary classification model in ML?

    List at least 3 ways and summaries of them.

  2. Back

    • Accuracy uses the confusion matrix to check proportion of predictions the model get right
    • Precision measures how many predicted positives are true positives
    • Recall measures how many actual positives were correctly predicted
    • F1-score is a combination of recall and precision
    • Area Under the Curve (AUC) uses the true positive rate and false positive rate to determine how often to expect the model to be correct.

    Bonus: multiclass classification models can be evaluated using a confusion matrix with multiple labels against features and then the above methods can be used for each class.

What are the steps in designing a machine learning solution in Azure?

Section titled “What are the steps in designing a machine learning solution in Azure?”

  1. Front

    What are the steps in designing a machine learning solution in Azure?

  2. Back

    1. Define problem and decide on type of model
    2. Get data
    3. Prepare data - explore, clean, and transform data for the model
    4. Train model - choose algorithm, hyperparameter values and experiment.
    5. Integrate the model - deploy to an endpoint
    6. Monitor - check the model’s performance

Azure ML solutions

Section titled “Azure ML solutions”

  1. Front

    Following the problem > data > train model > use model > monitor steps of an ML solution in Azure, what are possible Azure services used for:

    1. Data management and loading
    2. Model training, including data management, pipelines
    3. Integrations with model, including visualizations and reports
    4. Using prebuilt models and APIs for them and customizing models

  2. Back

    1. Data management and loading: Azure Databricks, Synapse Analytics
    2. Model training, including data management, pipelines: Azure Machine Learning, and its Studio UI, Azure Automated Machine Learning
    3. Integrations with model, including visualizations and reports: Microsoft Fabric
    4. Using prebuilt models and APIs for them and customizing models: Azure AI Services

What is the function of the encoder and decoder in transformer models?

Section titled “What is the function of the encoder and decoder in transformer models?”

  1. Front

    What is the function of the encoder and decoder in transformer models?

  2. Back

    Encoder processes input and creates contextual representations. Decoder generates output by predicting the next token using encoder’s context.

    • Encoder block: processing input and create a representation with context of tokens, creates semantic representation of the training vocabulary
    • Decoder block: generates new language sequences, generative output by looking at the encoder’s representation and predicting the next token in the sequence

Predicting use cases

Section titled “Predicting use cases”

  1. Front

    What type of machine learning should be used for these use cases?

    1. If you need to predict the sea level in meter for the next 10 years
    2. If you need to predict the hours of overtime for an office worker given the number of incidents they receive?

    Examples: classification, regression, clustering

  2. Back

    Regression because the values predicted will be numeric values in meters for #1 and hours in #2.

    The trained model uses a labeled dataset and regression gets a relationship between feature variables like the sea level in meters in the training data, allowing it to make predictions.

Automated ML

Section titled “Automated ML”

  1. Front

    Which of the following statements are true about automated machine learning?

    1. It can automate iterative tasks of model development
    2. It can automatically infer training data from the use case
    3. It runs multiple training iterations that are scored and ranked with user provided metrics
    4. It allows setting a dataset and understanding which labels to predict

  2. Back

    1. True - AutoML can do iterations for model development
    2. F
    3. True - Azure ML creates pipelines for running in parallel and tries different algorithms and parameters
    4. F - Automated ML uses target metrics you specify

Banking system predictions

Section titled “Banking system predictions”

  1. Front

    A banking system can use the _ type of machine learning to predict whether a loan will be repaid.

  2. Back

    Classification - The bank can use features like information about the customer to predict yes or no classes for loan repayment.

Model training and setup

Section titled “Model training and setup”

  1. Front

    Which of the following statements about models and training is true?

    1. Labelling is tagging training data with known values
    2. Data used for validation should be the same data used to train the model
    3. Accuracy is always the primary metric to measure models

  2. Back

    1. T - Labelled data does mean data is marked and annotated with the targets for predictions
    2. F - a separate set of data should be set aside before training to be used for validation later
    3. F - Accuracy is a metric for classification but may not be appropriate for unbalanced data where many data points are in one class or being correct in one class is more important than others.

Accessing Azure Machine Learning

Section titled “Accessing Azure Machine Learning”

  1. Front

    After creating a web service with Azure Machine Learning for inference, what two items of the following are required for accessing it?

    1. Model name
    2. Training endpoint
    3. Authentication key
    4. REST endpoint

  2. Back

    C and D: The (D) REST endpoint can be accessed by an application using an (C) authentication key.

    SDKs are available for some services to make application development easier.

Azure ML Designer first step

Section titled “Azure ML Designer first step”

  1. Front

    You need to use the Azure Machine Learning designer to train a machine learning model.

    What should you do first in the Machine Learning designer?

  2. Back

    Before you can start training a machine learning model, you must first create a pipeline in the Machine Learning designer. This is followed by adding a dataset, adding training modules, and eventually deploying a service.

Generative AI generative ai

Section titled “Generative AI generative ai”

What is Generative AI?

Section titled “What is Generative AI?”

  1. Front

    What is Generative AI and what kind of outputs can it produce?

  2. Back

    Generative AI refers to artificial intelligence models trained to generate new, original content from natural language input. It can produce outputs such as text, images, code, and media.

What are Large Language Models (LLMs)?

Section titled “What are Large Language Models (LLMs)?”

  1. Front

    What are Large Language Models (LLMs) and what tasks are they used for?

  2. Back

    LLMs are specialized machine learning models designed for natural language processing (NLP) tasks. They support generative AI by processing and generating human-like language.

What is tokenization in NLP?

Section titled “What is tokenization in NLP?”

  1. Front

    What is tokenization and why is it important in training language models?

  2. Back

    Tokenization is to put training text in tokens to identify unique text values, could be words, partial words, and works and punctuation. With tokens, statistics can be used with computers to find patterns and apply rules.

What are word embeddings?

Section titled “What are word embeddings?”

  1. Front

    What are word embeddings and how do they represent semantic relationships?

  2. Back

    Word embeddings are vectors representing words in a multidimensional space, created during training. They capture semantic relationships based on word usage patterns.

    A vocabulary is developed that holds semantic relationships between the tokens. The vocabulary has contextual vectors for the relationships known as embeddings.

    The semantic relationship is the distance between vectors

    For example, bike and car are used in same patterns of words and are used like driving, buying, or repairing them. As a result, the model will put the bike and car words close in vector space.

How do transformers improve upon RNNs?

Section titled “How do transformers improve upon RNNs?”

  1. Front

    How do transformers improve upon RNNs in language modeling?

  2. Back

    Transformers use attention mechanisms instead of sequential processing, allowing parallel processing of tokens and better context handling, improving efficiency and scalability.

What is the role of attention in transformers?

Section titled “What is the role of attention in transformers?”

  1. Front

    What is attention in transformers and how does it work?

  2. Back

    Attention maps new information to learned data using queries, keys, and values. It computes similarity scores to determine relevance and enables parallel processing of tokens.

What is prompt engineering?

Section titled “What is prompt engineering?”

  1. Front

    What is prompt engineering and why is it important in generative AI?

  2. Back

    Prompt engineering involves refining prompts to improve AI responses. It includes setting goals, context, expectations, and iterating to enhance relevance and quality.

How do transformers improve upon RNNs?

Section titled “How do transformers improve upon RNNs?”

  1. Front

    How do transformers improve upon RNNs?

  2. Back

    Transformers use attention mechanisms to process words in parallel, capturing context more efficiently than RNNs which process sequentially.

What is Retrieval-Augmented Generation (RAG)?

Section titled “What is Retrieval-Augmented Generation (RAG)?”

  1. Front

    What is Retrieval-Augmented Generation (RAG)?

  2. Back

    RAG enhances model responses by retrieving relevant data from external sources, grounding outputs in factual and contextual information.

What is Azure AI Foundry?

Section titled “What is Azure AI Foundry?”

  1. Front

    What is Azure AI Foundry?

  2. Back

    Azure AI Foundry is a platform for building, customizing, and deploying generative AI models and applications, including tools like model catalogues and playgrounds.

Azure AI Foundry Components

Section titled “Azure AI Foundry Components”

  1. Front

    List parts of the AI Foundry and their uses

  2. Back

    • Azure AI Foundry model catalog: for generative AI model selection and use
    • Playgrounds: Ready-to-use environments for testing ideas, trying out models, and exploring services
    • Azure AI services: build, test, see demos, and deploy Azure AI services
    • Solutions: build agents and customize models
    • Observability: monitor usage and performance of models

    Bonus: Microsoft Copilot Studio is used for low code development approach.

Natural Language Processing (NLP) nlp

Section titled “Natural Language Processing (NLP) nlp”

What is Natural Language Processing (NLP)?

Section titled “What is Natural Language Processing (NLP)?”

  1. Front

    What is Natural Language Processing (NLP)?

  2. Back

    NLP is the ability of computers to understand, interpret, and respond to human language, both written and spoken.

What are common NLP use cases?

Section titled “What are common NLP use cases?”

  1. Front

    What are common NLP use cases?

  2. Back

    • Speech-to-text and text-to-speech conversion. For example, generate subtitles for videos.
    • Machine translation. For example, translate text from English to Japanese.
    • Text classification. For example, label an email as spam or not spam.
    • Entity extraction. For example, extract keywords or names from a document.
    • Question answering. For example, provide answers to questions like “What is the capital of France?”
    • Text summary. For example, generate a short one-paragraph summary from a multi-page document.

What is tokenization in NLP?

Section titled “What is tokenization in NLP?”

  1. Front

    What is tokenization in NLP?

  2. Back

    Tokenization is the process of breaking text into smaller units called tokens, which can be words, phrases, or symbols.

    For example, “we choose to go to the moon”, the phrase is broken down into the following tokens, with numeric identifiers:

    1. we
    2. choose
    3. to
    4. go
    5. the
    6. moon

    The word “to” is token number 3. The phrase “we choose to go to the moon” can be represented by the tokens {1,2,3,4,3,5,6}.

What is TF-IDF and what is it used for?

Section titled “What is TF-IDF and what is it used for?”

  1. Front

    What is TF-IDF and what is it used for?

  2. Back

    Term Frequency-Inverse Document Frequency (TF-IDF) is a statistical technique used to evaluate the importance of a word in a document relative to a corpus. It’s used for information retrieval and text classification.

    TF-IDF calculates scores based on how often a word or term appears in one documents compared to its frequency across the collection of documents. The technique assumes a high relevance for words that appear frequently in a documents but relatively infrequently in the other documents.

What is sentiment analysis?

Section titled “What is sentiment analysis?”

  1. Front

    What is sentiment analysis?

  2. Back

    Sentiment analysis is the process of determining whether a piece of text expresses a positive, negative, or neutral sentiment.

What is the difference between extractive and abstractive summarization?

Section titled “What is the difference between extractive and abstractive summarization?”

  1. Front

    What is the difference between extractive and abstractive summarization?

  2. Back

    • Extractive summarization selects key sentences directly from the text.
    • Abstractive summarization rewrites key ideas using new phrasing.

What does Azure AI Language service provide?

Section titled “What does Azure AI Language service provide?”

  1. Front

    What does Azure AI Language service provide?

  2. Back

    Azure AI Language service: understanding and analyzing text like support sentiment analysis, key phrase identification, text summarization, and conversational language understanding.

Azure AI Translator service

Section titled “Azure AI Translator service”

  1. Front

    What does Azure AI Translator service provide?

  2. Back

    Azure AI Translator service: uses Neural Machine Translation (NMT) for translation, which analyzes the semantic context of the text and renders a more accurate and complete translation as a result.

    It does text, document, and custom translation.

Azure NLP Services

Section titled “Azure NLP Services”

  1. Front

    Where in Azure are NLP services like Azure AI Language and Translator services?

  2. Back

    Azure AI Language and Translator can be used in solutions and created in:

    • Azure AI Foundry portal
    • Software development kit (SDK) or REST API

Azure Conversational Language Understanding (CLU) and Question Answering

Section titled “Azure Conversational Language Understanding (CLU) and Question Answering”

  1. Front

    What is Conversational Language Understanding (CLU) and Question Answering Services in Azure?

  2. Back

    Question Answering allows Azure Language Services to create a knowledge base of question and answer pairs that can be queried using natural language input.

    CLU builds language models that interpret meaning of phrases in a conversation.

    Example: commands like “turn on lights”

    • Intent: Turn on
    • Entity: Lights
    • Entity recognition: action

What is the difference between NLP and Generative AI?

Section titled “What is the difference between NLP and Generative AI?”

  1. Front

    What is the difference between NLP and Generative AI?

  2. Back

    NLP focuses on understanding and analyzing language, while Generative AI builds on NLP to create new content such as text, images, or code.

Tokenization for NLP

Section titled “Tokenization for NLP”

  1. Front

    What are 4 concepts in NLP tokenization?

  2. Back

    • Text normalization: Before generating tokens, text is normalized by removing punctuation and making all words lower case.
      • Good for analysis purely on word frequency with high performance.
      • Some semantic meaning may be lost like example, “Mr Banks has worked in many banks.” where the sentence and punctuation has meaning for Mr. Banks and bank.
    • Stop word removal: Stop words are words that should be excluded from the analysis. For example, “the”, “a”, or “it” make text easier for people to read but add little semantic meaning. Excluding these words, allows better identification of important words.
    • n-grams: Multi-term phrases such as “I have” or “he walked”. A single word phrase is a unigram, a two-word phrase is a bi-gram, a three-word phrase is a tri-gram, and so on. Considering words as groups allows a model to make better sense of the text.
    • Stemming: algorithms are applied to consolidate words before counting them, so that words with the same root, like “power”, “powered”, and “powerful”, are interpreted as the same token.

Computer Vision computer vision

Section titled “Computer Vision computer vision”

What is computer vision (CV)?

Section titled “What is computer vision (CV)?”

  1. Front

    What is computer vision (CV)?

  2. Back

    Computer vision is a field of AI that enables machines to interpret and understand visual information from the world, such as images and videos.

What are the main capabilities of computer vision?

Section titled “What are the main capabilities of computer vision?”

  1. Front

    What are the main capabilities of computer vision?

  2. Back

    • Image analysis
    • Spatial analysis
    • Facial recognition
    • Optical character recognition (OCR)

What is a convolutional neural network (CNN)?

Section titled “What is a convolutional neural network (CNN)?”

  1. Front

    What is a convolutional neural network (CNN)?

    Image as a data in a computer program. An image is an array of numeric pixel values.

     0   0   0   0   0   0   0
     0   0   0   0   0   0   0
     0   0  255 255 255  0   0
     0   0  255 255 255  0   0
     0   0  255 255 255  0   0
     0   0   0   0   0   0   0
     0   0   0   0   0   0   0
    

    The array consists of seven rows and seven columns, representing the pixel values for a 7x7 pixel image (which is known as the image's resolution). Each pixel has a value between 0 (black) and 255 (white); with values between these bounds representing shades of gray.

  2. Back

    A CNN is a deep learning architecture that uses filters to extract features from images and predict labels, commonly used in image classification.

What is convolutional filtering in image processing?

Section titled “What is convolutional filtering in image processing?”

  1. Front

    What is convolutional filtering in image processing?

  2. Back

    Convolutional filtering applies filter kernels to an image to transform pixel values, often used for edge detection, blurring, or sharpening.

What is OCR and how is it used in computer vision?

Section titled “What is OCR and how is it used in computer vision?”

  1. Front

    What is OCR and how is it used in computer vision?

  2. Back

    Optical Character Recognition (OCR) extracts printed or handwritten text from images, enabling text analysis and digitization.

What are multi-modal models in computer vision?

Section titled “What are multi-modal models in computer vision?”

  1. Front

    What are multi-modal models in computer vision?

  2. Back

    Multi-modal models combine image and language data, training on captioned images to perform tasks like classification, detection, and captioning.

What does Azure AI Vision Image Analysis service do?

Section titled “What does Azure AI Vision Image Analysis service do?”

  1. Front

    What does Azure AI Vision Image Analysis service do?

  2. Back

    It detects objects, tags features, creates captions, and performs OCR on images without requiring custom training.

What is the purpose of Azure AI Face service?

Section titled “What is the purpose of Azure AI Face service?”

  1. Front

    What is the purpose of Azure AI Face service?

  2. Back

    It detects, recognizes, and analyzes human faces, including attributes like head pose, glasses, mask, and facial landmarks.

What are common use cases for computer vision in industry?

Section titled “What are common use cases for computer vision in industry?”

  1. Front

    What are common use cases for computer vision in industry? Name at least 3

  2. Back

    • Defect detection in manufacturing, anomaly detection in any industry
    • Medical imaging analysis
    • Retail shelf monitoring
    • Autonomous vehicle navigation
    • Object detection and analysis in transportation, security

What is the role of feature maps in CNNs?

Section titled “What is the role of feature maps in CNNs?”

  1. Front

    What is the role of feature maps in CNNs?

  2. Back

    Images –> Feature maps –> Visual features in classification

    Feature maps are arrays of numeric values extracted from images using filters, representing visual features used for classification.

Pixel Analysis

Section titled “Pixel Analysis”

  1. Front

    Which type of artificial intelligence (AI) workload provides the ability to classify individual pixels in an image depending on the object that they represent?

  2. Back

    Semantic segmentation is advanced object detection where in addition to drawing a box around the object’s location, it can identify pixels that belong to the object.

AI Speech ai speech

Section titled “AI Speech ai speech”

What are the two main capabilities of AI speech?

Section titled “What are the two main capabilities of AI speech?”

  1. Front

    What are the two main capabilities of AI speech?

  2. Back

    • Speech recognition: interpreting spoken input
    • Speech synthesis: generating spoken output

What is speech recognition?

Section titled “What is speech recognition?”

  1. Front

    What is speech recognition?

  2. Back

    Speech recognition converts spoken words into data, typically transcribing audio into text using acoustic and language models.

What is speech synthesis?

Section titled “What is speech synthesis?”

  1. Front

    What is speech synthesis?

  2. Back

    Speech synthesis converts text into spoken audio using phonetic transcription and prosodic units, with customizable voice settings.

What does a language and acoustic model do in speech recognition and synthesis?

Section titled “What does a language and acoustic model do in speech recognition and synthesis?”

  1. Front

    What does a language and acoustic model do in speech recognition?

  2. Back

    • Acoustic model that converts the audio signal into representations of specific sounds, called phonemes
    • Language model maps sounds (phonemes) to words, usually using statistics to predicts the most probable sequence of words based on phonemes.

What is Azure AI Speech used for?

Section titled “What is Azure AI Speech used for?”

  1. Front

    What is Azure AI Speech used for?

  2. Back

    Azure AI Speech provides capabilities for speech recognition, speech synthesis, and translation, accessible via Studio, CLI, REST APIs, and SDKs.

What is speech-to-text (STT)?

Section titled “What is speech-to-text (STT)?”

  1. Front

    What is speech-to-text (STT)?

  2. Back

    STT converts spoken audio into text, supporting real-time and batch transcription, with options for custom acoustic and language models.

What is text-to-speech (TTS)?

Section titled “What is text-to-speech (TTS)?”

  1. Front

    What is text-to-speech (TTS)?

  2. Back

    TTS converts text into audible speech using selected voices, including neural voices for natural intonation and regional pronunciation.

What are neural voices in Azure AI Speech?

Section titled “What are neural voices in Azure AI Speech?”

  1. Front

    What are neural voices in Azure AI Speech?

  2. Back

    Neural voices use deep learning to produce more natural-sounding speech with realistic intonation and expression.

What resources are needed to use Azure AI Speech?

Section titled “What resources are needed to use Azure AI Speech?”

  1. Front

    What resources are needed to use Azure AI Speech?

  2. Back

    You need either a dedicated Azure AI Speech resource or a general Azure AI services resource to access speech capabilities.

Information Extraction info extraction

Section titled “Information Extraction info extraction”

What is AI-powered information extraction?

Section titled “What is AI-powered information extraction?”

  1. Front

    What is AI-powered information extraction?

  2. Back

    It is the process of identifying and extracting meaningful data from various content types like documents, images, audio, and video using AI techniques.

What are the four steps in the information extraction process?

Section titled “What are the four steps in the information extraction process?”

  1. Front

    What are the four steps in the information extraction process?

  2. Back

    1. Source Identification
    2. Extraction
    3. Transformation & Structuring
    4. Storage & Integration

What is OCR and how is it used in information extraction?

Section titled “What is OCR and how is it used in information extraction?”

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    What is OCR and how is it used in information extraction?

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    Optical Character Recognition (OCR) detects and extracts printed or handwritten text from images using machine learning models.

What is Document Intelligence in Azure?

Section titled “What is Document Intelligence in Azure?”

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    What is Document Intelligence in Azure?

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    Document Intelligence extracts structured data from forms and documents using OCR and machine learning, identifying fields and values with confidence scores.

What is multi-modal data extraction?

Section titled “What is multi-modal data extraction?”

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    What is multi-modal data extraction?

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    It involves using multiple AI services (e.g., vision, NLP, speech) to extract information from diverse content types like documents, images, and audio.

What is knowledge mining in AI?

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    What is knowledge mining in AI?

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    Knowledge mining automates extraction from large volumes of unstructured data, enriching it with AI and storing it for search and analysis.

Section titled “What is a search index schema in Azure AI Search?”

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    What is a search index schema in Azure AI Search?

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    It defines the structure of searchable data, including fields, data types, and attributes used for filtering and sorting.

What does Azure AI Vision Image Analysis provide?

Section titled “What does Azure AI Vision Image Analysis provide?”

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    What does Azure AI Vision Image Analysis provide?

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    It offers automated captioning, object detection, tagging, and OCR for images and scanned documents.

What is the role of Azure AI Content Understanding?

Section titled “What is the role of Azure AI Content Understanding?”

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    What is the role of Azure AI Content Understanding?

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    It performs multimodal analysis using generative AI to extract insights from text, images, audio, and video based on custom schemas.

Section titled “What is the function of an indexer in Azure AI Search?”

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    What is the function of an indexer in Azure AI Search?

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    An indexer ingests data, extracts content using AI skills, and stores it in a searchable index for querying and analysis.

Scanned documents and information extraction

Section titled “Scanned documents and information extraction”

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    Which service can be used to extract data like text, field/data pairs, and tables from scanned documents?

    Which service can be used to extract totals and subtotals from an invoice?

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    Azure AI Content Understanding can extract data from documents with structures and use schemas, for example scanning invoices and analyze their data.

Responsible AI responsible ai

Section titled “Responsible AI responsible ai”

What are Microsoft’s principles for responsible AI?

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    What are the key principles for responsible AI according to Microsoft?

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    RiskResponsible AI Principle
    BiasFairness
    ErrorReliability & Safety
    Data exposurePrivacy & Security
    Not for everyoneInclusion
    Trust?Transparency
    Who is responsibleAccountability
    • Fairness - treat all people fairly. Example: models make predictions without incorporating bias
    • Reliability and safety - Example: an autonomous vehicle or patient diagnoses must be safe to use related to human life
    • Privacy and security - training data may have personal details and must be kept private, even after model deployment, sensitive data must be protected
    • Inclusiveness - AI must empower everyone and benefit all parts of society, regardless of physical ability, gender, sexual orientation, ethnicity, or other factors. Example: have a diverse group of people test applications
    • Transparency - systems are understandable and users know the purpose of the system, how it works, and what limitations may be expected. Example: a system communicates to users about its accuracy, training data, and features that affect predictions. When personal data like faces are use in a system, the system makes clear how the personal data is handled, retained, and accessed.
    • Accountability - although many AI systems seem to operate autonomously, it is the responsibility of the developers who trained and validated the models they use, and defined the logic that bases decisions on model predictions to ensure that the system meets requirements. Example: solutions use a framework of governance and principles to meet responsible and legal standards that are defined.

Healthcare and responsible AI

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    Which two principles of responsible artificial intelligence (AI) are most important when designing an AI system to manage healthcare data? Each correct answer presents part of the solution.

    Example: reliability and safety, accountability, transparency, inclusiveness, privacy & security

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    1. Privacy & security
    2. Accountability

    The accountability principle states that AI systems are designed to meet any ethical and legal standards that are applicable. The system must be designed to ensure that privacy of the healthcare data is of the highest importance, including anonymizing data where applicable.

Responsible AI Limits

Section titled “Responsible AI Limits”

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    Which principle of responsible artificial intelligence (AI) raises awareness about the limitations of AI-based solutions?

    Example: reliability and safety, accountability, transparency

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    Transparency so users understand how an AI came up with its predictions and limits of the model.