Part 3: Core Concepts in Artificial Intelligence

<p>Cybersecurity (A)</p> Signup and view all the answers

<p>Natural Language Processing (NLP) and Natural Language Generation (NLG) (D)</p> Signup and view all the answers

<p>Machine learning is a subset of artificial intelligence. (C)</p> Signup and view all the answers

<p>Reinforcement machine learning (D)</p> Signup and view all the answers

<p>To convert text data into a numerical format (C)</p> Signup and view all the answers

<p>K-Nearest Neighbors (KNN) (C)</p> Signup and view all the answers

<p>Unsupervised machine learning (C)</p> Signup and view all the answers

<p>Regression (D)</p> Signup and view all the answers

<p>Naive Bayes (NB) (C)</p> Signup and view all the answers

<p>Unsupervised machine learning (A)</p> Signup and view all the answers

<p>Clustering (B)</p> Signup and view all the answers

<p>Use of labeled datasets vs. unlabeled datasets (C)</p> Signup and view all the answers

<p>Supervised Machine Learning Models (A)</p> Signup and view all the answers

<p>To remove words that do not add much information to the text (A)</p> Signup and view all the answers

<p>Principal Component Analysis (PCA) (A)</p> Signup and view all the answers

<p>To grammatically label the words within a sentence (B)</p> Signup and view all the answers

<p>Generative Adversarial Network (GAN) (A)</p> Signup and view all the answers

<p>To break the text into individual words and word fragments (D)</p> Signup and view all the answers

<p>Convolutional Neural Network (CNN) (C)</p> Signup and view all the answers

<p>Semantic analysis of text to determine intended meaning (A)</p> Signup and view all the answers

<p>Dimensionality Reduction (D)</p> Signup and view all the answers

<p>To convert the raw text data into a more concise and understandable form (D)</p> Signup and view all the answers

<p>Detecting and filtering hate speech on social media platforms (D)</p> Signup and view all the answers

<p>NLU focuses on semantic analysis of text to determine intended meaning, while NLG focuses on machine-generated text (A)</p> Signup and view all the answers

<p>Stemming and Lemmatization (A)</p> Signup and view all the answers

<p>Deep learning automates feature extraction from unstructured data (D)</p> Signup and view all the answers

<p>Visible layers (B)</p> Signup and view all the answers

<p>Forward propagation (A)</p> Signup and view all the answers

<p>By mimicking the human brain (C)</p> Signup and view all the answers

<p>Deep learning automates the creation of feature hierarchies (C)</p> Signup and view all the answers

<p>Backpropagation (A)</p> Signup and view all the answers

<p>To extract meanings from a sequence of text and comprehend the relationships among the words and phrases. (B)</p> Signup and view all the answers

<p>Transformer models process all sequences in parallel, while RNNs process inputs sequentially. (C)</p> Signup and view all the answers

<p>To achieve better results by adapting the model to specific tasks or datasets. (C)</p> Signup and view all the answers

<p>The more parameters, the more technical documentation or books the model can process. (C)</p> Signup and view all the answers

<p>Common Crawl. (D)</p> Signup and view all the answers

<p>To update the weights of the model during the training process. (B)</p> Signup and view all the answers

<p>Parameters refer to the weights of the model, while tokens refer to the number of words in the dataset. (B)</p> Signup and view all the answers

<p>To improve the model's ability to comprehend and interpret human language or complex data types. (A)</p> Signup and view all the answers

<p>Self-supervised learning is a more precise description of the training process used by transformer-based large language models. (C)</p> Signup and view all the answers

<p>To enable the model to understand the meaning of words by considering their context in relation to other words. (B)</p> Signup and view all the answers

<p>All of the above (D)</p> Signup and view all the answers

<p>Both A and B (C)</p> Signup and view all the answers

<p>Lack of diversity in the training data can lead to biased outputs (A)</p> Signup and view all the answers

<p>Copyright infringement by Getty Images (C)</p> Signup and view all the answers

<p>Scaling and maintaining these models is generally challenging, time-consuming, and resource-intensive (D)</p> Signup and view all the answers

<p>Both A and B (B)</p> Signup and view all the answers

<p>They can produce outputs that are inaccurate or misaligned with the user's intent. (B)</p> Signup and view all the answers

<p>They can inadvertently disclose sensitive or confidential data provided as input. (A)</p> Signup and view all the answers

<p>The data used in the training of the language models. (D)</p> Signup and view all the answers

<p>They may pose significant security risks. (D)</p> Signup and view all the answers

<p>Inability to handle long-range dependencies in text. (D)</p> Signup and view all the answers

<p>It provided a coherent but factually inaccurate response. (A)</p> Signup and view all the answers

<p>Machine Learning = A subset of AI that enables systems to learn and improve from experience without being explicitly programmed Natural Language Processing = The ability of machines to understand, interpret, and generate human language Deep Learning = A subset of machine learning that uses neural networks to model and solve complex problems Artificial Intelligence = The simulation of human intelligence processes by machines, especially computer systems</p> Signup and view all the answers

<p>Artificial Narrow Intelligence = AI that is designed for a specific task or set of tasks Artificial General Intelligence = AI that can understand, learn, and apply knowledge in a variety of domains Super Intelligence = AI that surpasses human intelligence in all areas Industry 4.0 = A concept based on AI and machine learning to enable human-like thinking in computers</p> Signup and view all the answers

<p>AI Spring: The Birth of AI = The initial period marked by the birth and early development of artificial intelligence AI Summer and Winter = Periods characterized by enthusiasm and setbacks in AI research and development AI Fall = A phase where AI technologies mature and become more integrated into various industries Core Concepts and Overview of Technical Frameworks = An overview of key concepts and frameworks in the field of AI</p> Signup and view all the answers

<p>Brief History = Studies on the development process of artificial intelligence based on different classification frameworks Machine Learning = A technique that allows machines to learn patterns from data without being explicitly programmed Natural Language Processing = The field that focuses on enabling computers to understand, interpret, and generate human language Deep Learning = A subset of machine learning that uses neural networks to model complex patterns</p> Signup and view all the answers

<p>External Data Interpretation = Ability to understand and interpret data from external sources Learning from Data = Capability to learn from the processed data and adapt based on insights gained Flexible Adaptation = Capacity to adjust strategies and behaviors based on new information or changing circumstances Specific Goals Achievement = Achieving predefined objectives through the application of learned insights</p> Signup and view all the answers

<p>Business Environment Integration = Incorporation of AI technologies into various business operations and decision-making processes Mainstream Usage Transition = Shift from cutting-edge technology status to widespread adoption in everyday applications Industry 4.0 Focus = Emphasis on utilizing AI and machine learning to enhance computational capabilities in industrial settings Human-like Thinking Enablement = Aims to imbue computers with cognitive abilities akin to human thought processes</p> Signup and view all the answers

<p>Isaac Asimov's 'Runaround' = Inspired generations of scientists in robotics and AI Marvin Minsky and John McCarthy's Dartmouth Summer Research Project = Reunited researchers to create a new research area in AI Alan Turing's work on The Bombe = Led to wonder about machine intelligence and published seminal article Michael Haenlein and Andreas Kaplan's article on the phases of AI = Analogized AI development to the 4 seasons</p> Signup and view all the answers

<p>Computer vision = Achieved as the first major breakthrough in AI Natural language processing = Currently experiencing another major breakthrough in AI Generative AI = Can understand human language and process various data types Deep Learning = Allowed computers to beat world champions in complex games like Go</p> Signup and view all the answers

<p>Speech recognition = Transcribing spoken words into text Customer service = Automating responses to customer inquiries Supply chain = Optimizing logistics and inventory management Anomaly detection = Identifying unusual patterns or behaviors in data</p> Signup and view all the answers

<p>High spending criticism by U.S. Congress = Resulted in reduced support for AI research by governments Expert Systems limitations = Led to stagnation until Deep Learning breakthrough Determining roles of AI and humans = A challenge in coexistence and collaboration Transforming everyday life = The impact of AI on decision-making and stakeholder interactions</p> Signup and view all the answers

<p>Machine learning vs. deep learning = Primary differences between two related concepts Parameters vs. tokens in large language models = Understanding key elements in model architecture Forward propagation vs. backward propagation in deep learning training = Steps involved in optimizing model performance Feature extraction importance in machine learning = Purpose of extracting relevant information from data</p> Signup and view all the answers

<p>Encoder = Processes input text by tokenizing the data and discovering relationships between tokens Decoder = Generates predictions based on the relationships between tokens identified by the encoder Self-attention mechanism = Allows the model to consider different parts of a sequence or entire context to make predictions Forward propagation = The movement of data through a neural network to make predictions</p> Signup and view all the answers

<p>Parameters = Weights learned during training used for predicting the next token in sequences Tokens = Basic units of text utilized in modeling and prediction tasks Self-supervised learning = Ability to predict the next token in a sentence without explicit directions Curated datasets = Datasets that are carefully selected or cleaned before training LLMs</p> Signup and view all the answers

<p>Supervised learning = Training with labeled data to make predictions or classify new data points Unsupervised learning = Training with unlabeled data to find hidden patterns or structures in the data Self-learning = Training models without explicit directions or supervision from external sources Reinforcement learning = Training based on a reward and penalty system to optimize behavior</p> Signup and view all the answers

<p>Vast datasets = Require a sufficient number of examples to comprehend human language or complex data types Transformer architecture = Process all sequences in parallel, reducing training time and enabling GPU usage Pre-training stage = Learn high-level features that can be transferred to specific tasks during fine-tuning Feature extraction = Identifying meaningful information from raw data for training purposes</p> Signup and view all the answers

<p>Sequential processing (RNNs) = Process inputs one at a time in sequence, potentially leading to longer training times Parallel processing (Transformers) = Process all sequences simultaneously, reducing training time and enabling GPU acceleration Forward propagation = Movement of data through the network to make predictions based on learned weights Backward propagation = Update weights based on prediction errors to improve model performance</p> Signup and view all the answers

<p>Naive Bayes (NB) = Used for text classification problems like sentiment analysis Linear Regression (LR) = Used for making predictions based on continuous data K-Nearest Neighbors (KNN) = Classifies data based on similarity to neighboring data points Random Forest (RF) = Ensemble learning technique combining multiple decision trees</p> Signup and view all the answers

<p>Bag-of-Words = Represents text as a collection of words without considering grammar or word order TF-IDF = Assigns weights to words based on their frequency in a document and across documents Countvectorizer = Converts text into a matrix of token counts Principal component analysis (PCA) = Reduces the dimensionality of data while preserving the most important information</p> Signup and view all the answers

<p>Classification = Involves categorizing data into predefined classes or labels Regression = Predicts continuous values based on input features Clustering = Groups similar data points together without predefined labels Dimensionality reduction = Technique to reduce the number of input variables in a dataset</p> Signup and view all the answers

<p>Principal component analysis (PCA) = Technique for reducing the dimensionality of data by finding principal components Singular value decomposition (SVD) = Factorization method used for dimensionality reduction and noise filtering k-means clustering = Partitioning method to group data points into 'k' clusters based on similarity Probabilistic clustering methods = Incorporate probability distributions to assign data points to clusters</p> Signup and view all the answers

<p>Supervised machine learning = Trains algorithms using labeled datasets to make predictions or classifications Unsupervised machine learning = Analyzes unlabeled data to discover patterns and structures without human intervention Reinforcement machine learning = Learns from real-world experiences through a reward and penalty system Ensemble learning techniques = Combine multiple models to improve prediction accuracy</p> Signup and view all the answers

<p>Bag-of-Words = Represent text as numerical vectors for machine learning models TF-IDF = Highlight important words in documents by considering their frequency and uniqueness Countvectorizer = Convert text into a matrix of token counts for analysis Dimensionality reduction = Reduce the number of features extracted from text data while preserving relevant information</p> Signup and view all the answers

<p>Naive Bayes (NB) = Classifying emails as spam or not spam based on content analysis Linear Regression (LR) = Predicting house prices based on factors like area, location, and number of bedrooms K-Nearest Neighbors (KNN) = Recommendation systems that suggest similar products based on user preferences Random Forest (RF) = Predicting customer churn in a subscription-based service using historical data</p> Signup and view all the answers

<p>Naive Bayes (NB) = Sentiment analysis on social media comments to determine positive or negative sentiment Linear Regression (LR) = Predicting stock prices based on historical market data and external factors K-Nearest Neighbors (KNN) = Classifying customers into segments for targeted marketing campaigns Support Vector Machine (SVM) = Identifying fraudulent transactions in financial services by analyzing patterns</p> Signup and view all the answers

<p>Principal component analysis (PCA) = Reduce the dimensionality of data while preserving variance information Singular value decomposition (SVD) = Decompose a matrix into singular vectors and values for dimensionality reduction k-means clustering = Partition data points into 'k' clusters based on similarity criteria Probabilistic clustering methods = Assign data points to clusters using probability distributions</p> Signup and view all the answers

<p>Customer segmentation based on shopping habits = Clustering problem in unsupervised machine learning Sentiment analysis of product reviews = Classification problem in supervised machine learning Weather forecasting using historical data = Regression problem in supervised machine learning Grouping customers by purchasing behavior = Clustering problem in unsupervised machine learning</p> Signup and view all the answers

<p>Convolutional Neural Network (CNN) = Image recognition Recurrent Neural Network (RNN) = Sequential data processing Long short-term memory (LSTM) = Long-range dependencies modeling Transformers = Natural Language Processing tasks</p> Signup and view all the answers

<p>Input layer = Receives data to be processed Hidden layers = Learning and feature extraction Output layer = Final prediction or classification Visible layers = Responsible for input and output predictions</p> Signup and view all the answers

<p>BERT = 110 million parameters PaLM 2 = 340 billion parameters OpenAI's GPT-3 = 175 billion parameters ChatGPT = Not specified in the text</p> Signup and view all the answers

<p>BERT = Text classification PaLM 2 = Document summarization OpenAI's GPT-3 = Text generation ChatGPT = Not specified in the text</p> Signup and view all the answers

<p>Encoder and decoder systems = Extract meanings and relationships from text sequences Attention mechanism = Considers word context for better understanding Self-attention mechanism = Establishes relationships between words in a text Transformer models = Utilize attention mechanism for context understanding</p> Signup and view all the answers

<p>Structured data = Challenging for large language models Unstructured data (text) = Automate feature extraction and understanding Images = Processed by Convolutional Neural Networks (CNN) Numerical data = Not specifically mentioned in the text</p> Signup and view all the answers

<p>Overfitting to biased data sources = Reduced generalization ability Inappropriate responses or outputs = Risk of misinformation or harm Excessive computational resources requirements = Slower model training and inference Model inaccuracies due to lack of monitoring = Degradation of performance over time</p> Signup and view all the answers

<p>Text classification = BERT model capability Question answering = OpenAI's GPT-3 model capability Document summarization = PaLM 2 model capability Text generation = Transformer model capability</p> Signup and view all the answers

<p>Forward propagation = Computations moving through the network from input to output layers Backpropagation = Correcting errors in predictions to refine accuracy over time Feature extraction automation by deep learning algorithms = Determining important distinguishing features without manual hierarchy creation by experts Learning through a combination of inputs, weights, and biases in neural networks = Aiding neural networks in making predictions and error correction</p> Signup and view all the answers

<p>Input layer and output layer = Receive data to be processed and make final predictions or classifications, respectively. Hidden layers = Learn and extract specific features from input data. Visible layers = Responsible for receiving input data and producing output predictions.</p> Signup and view all the answers

<p>Stemming and Lemmatization = Finding the root or basic form of words based on grammar rules and semantic information Stop Word Removal = Eliminating words that do not contribute much information to the text Tokenization = Breaking text into individual words and fragments for numeric representation Part of Speech Tagging = Grammatically labeling words in a sentence, including nouns, pronouns, verbs, adjectives, etc.</p> Signup and view all the answers

<p>Bag-of-Words = Representing text as a collection of words without considering grammar or word order TF-IDF = Assigning importance to words based on their frequency in a document compared to a corpus Word2Vec = Mapping words to dense vectors based on context similarity N-grams = Extracting sequences of 'n' contiguous words or characters from text</p> Signup and view all the answers

<p>Logistic Regression = Model used for binary classification by estimating probabilities Naive Bayes = Probabilistic model based on Bayes' theorem often used for text classification Decision Tree = Tree-like model making decisions based on feature values Latent Dirichlet Allocation (LDA) = Generative model for topic modeling in text documents</p> Signup and view all the answers

<p>Natural Language Understanding (NLU) = Focuses on interpreting the meaning of text or speech Natural Language Generation (NLG) = Involves generating human-like text or speech from structured data Data Processing = Transforming raw text data into a more understandable format using NLP tools Feature Extraction = Identifying relevant information from text data for modeling purposes</p> Signup and view all the answers

<p>Bag-of-Words = Simple method representing text by word frequencies without sequence information TF-IDF = Weights words based on importance in a document relative to a larger corpus CountVectorizer = Converts a collection of text documents into a matrix of token counts N-grams = Captures sequences of 'n' contiguous words or characters from text</p> Signup and view all the answers

<p>Logistic Regression = Model used for binary classification tasks Naive Bayes = Probabilistic model commonly used for text classification Decision Tree = Tree-like model for decision-making based on feature values Latent Dirichlet Allocation (LDA) = Generative model for topic modeling in textual data</p> Signup and view all the answers

<p>Natural Language Understanding (NLU) = Interpreting and deriving meaning from human language data Natural Language Generation (NLG) = Generating human-like language output from structured data inputs Data Processing = Transforming raw textual data into a more structured and meaningful representation Feature Extraction = Identifying and extracting relevant information from textual data for further analysis</p> Signup and view all the answers

<p>Bag-of-Words = Representing text by word frequencies without considering word order or structure TF-IDF = 'Term Frequency-Inverse Document Frequency' method to weight word importance in documents Word2Vec = Mapping words to continuous vectors based on semantic similarity N-grams = 'n' contiguous sequence of words or characters extracted from textual data</p> Signup and view all the answers

<p>Logistic Regression = Binary classification algorithm estimating probabilities for classes Naive Bayes = 'Naive' probabilistic model often used for text classification tasks Decision Tree = 'Tree-like' model making decisions based on feature values Latent Dirichlet Allocation (LDA) = 'Generative' model for discovering topics in textual data</p> Signup and view all the answers

<p>Natural Language Understanding (NLU) = Focuses on interpreting and deriving meaning from human language data Natural Language Generation (NLG) = Involves generating human-like language output from structured data inputs Data Processing = Transforming raw textual data into a more structured and meaningful representation Feature Extraction = Identifying and extracting relevant information from textual data for further analysis</p> Signup and view all the answers

<p>Hallucinations = Producing inaccurate or misaligned outputs with user intent Security = Posing significant security risks when not properly managed or monitored Bias = Outputs generated reflect the biases present in the training data Consent = Training on data obtained without proper permission or consent</p> Signup and view all the answers

<p>Fine-tuning = Optimizing model performance for specific tasks through adjustments Prompt-tuning = Training a model to perform tasks via few-shot or zero-shot prompting Contextual learning = Ability of a model to learn continuously after pre-training Scaling = Challenges in maintaining and expanding large language models</p> Signup and view all the answers

<p>Hallucinations = Producing inaccurate or fabricated information in responses Security = Exposing users to privacy risks and unauthorized data disclosures Bias = Generating outputs that lack diversity due to biased training data Consent = Using data without permission leading to copyright infringement and privacy violations</p> Signup and view all the answers

<p>Fine-tuning = Optimizing model performance for specific tasks Prompt-tuning = Training a model to perform tasks via few-shot or zero-shot prompting Pretraining = Initial training phase before specialized optimization Contextual learning = Continuous learning after pre-training based on prompts</p> Signup and view all the answers

<p>Malicious reprogramming = Alignment with user ideologies leading to misinformation spread Data disclosure = Potential for revealing sensitive and confidential information in responses Legal issues = Facing lawsuits for intellectual property infringement and data privacy violations Low security measures = Increased risk of unauthorized access and misuse of AI capabilities</p> Signup and view all the answers

<p>Bias in data = Outputs reflecting the lack of diversity or representation in training data Consent violation = Legal issues arising from unauthorized data usage and copyright infringements Privacy risks = Exposure of personal data leading to privacy violations and confidentiality breaches Intellectual property concerns = Facing lawsuits for unauthorized use of proprietary content and data scraping</p> Signup and view all the answers

<p>Fine-tuning = Optimizing model performance for specific tasks through adjustments Scaling challenges = Issues in maintaining and expanding large language models Pretraining phase = Initial training before specialized optimization for tasks Data bias recognition = Identifying and addressing biases present in the training datasets</p> Signup and view all the answers

<p>Hallucinations = Producing inaccurate or misleading outputs affecting user trust Security vulnerabilities = Exposing systems to unauthorized access and misuse risks Bias implications = 'Outputs reflecting biased training data affecting diversity and fairness' Consent violations consequences = 'Legal issues arising from unauthorized data usage'</p> Signup and view all the answers

<p>Fine-tuning = Specialized optimization for specific tasks Prompt-tuning = Training via few-shot or zero-shot prompting Pretraining = Initial phase before task-specific adjustments Contextual learning = Continuous learning post pre-training based on prompts</p> Signup and view all the answers

<p>Data disclosure = Potential exposure of sensitive information Malicious reprogramming = Misuse by aligning AI with harmful ideologies Legal issues = Facing lawsuits for intellectual property infringement Low security measures = Increased risk of unauthorized access</p> Signup and view all the answers