Intro to AI

  1. What is supervised Learning (Learn By Example)?

    • data is trained on labeled data -maps between input to labeled output.

    • acts as a teacher → goal is to train the model or machine to make predictions and decisions based on the data that is given.

  2. What is classification?

    • sorts data into predefined classes and apply learned characteristics to them.

    • predicts discrete label or category for given input

    • Ex: Spam detection, disease detection, image classification, bank loan prediction

  3. What is regression?

    • used to predict a continuous numerical value of a given input

    • Ex: house price, stock price

  4. What is unsupervised Learning (learn By Observations)?

    • trained on unlabeled data

    • model learns by finding patterns - model learns through observations and finds structure in the data.

  5. What is clustering?

    • groups data points into clusters where data points in the clusters are similar to each other

    • Ex: search engine, face recognition, targetted marketing, recommender system

  6. What is Association Rule Mining?

    • used to discover interesting relationships or patterns between variables in large datasets (often in the form of if-then rules)

    • Ex: Market Basket: identifying which products are mainly purchased tgt, recommendation system, census data, medical diagnosis

  7. What is reinforcement learning (Learn from Mistakes)?

    • agent is rewarded or penalized based on correct or wrong actions - the agent interacts with the environment and finds the best outcome, leading to less mistakes overtime

    • model trains itself based on the reward points it gets

  8. What are the 3 steps for ML?

    1. Train - train model with data

    2. Validate - evaluating model’s performance on another data set and fins tuning the hyperparameters without affecting final result

    3. Test - final test, use on test set (not involved in training or validating)

      • we test to avoid overfitting

        • model performs well for specific trained set and not for unseeded data (too specific to that set)

      • the model should provide generalization

        • should be applicable to different data

      • retrain if model does not work anymore

  9. What are neural networks?

    • aims to mimic how the human brain works + its structure

    • consists of layers of interconnected nodes (neurons) that signal e/o

    • good for complex tasks like facial recognition, nlp, speech recognition

    • In human brain, dendrites: input, cell body: processor, synapse: link, axon: output

      • Neural Network:

        • Input Layer: takes in raw data as input - node represents feature of data

        • Hidden Layer: process input through series of transformation

          • they learn and extract patterns - no. of hidden noes represent model’s complexity

        • Output Layer: produces final result

          • e.g. class label or classification or number for regression

  • Simple Neural network with bias calc. and no hidden layers

  • more complex with hidden layers

  • In facial recognition, features are transformed into matrix of numbers that ML models can use to identify faces

  • Autoencoders: NN that encodes input data into lower dimensional rep. and converts it back to og dimension

    • dimension reduction, anomaly detection

    • learn compressed rep. (encoding) while preserving important features

  1. How do you evaluate the performance of your model?

    • Split data into 3 sets (not so small data)

      • 1. Training Set: contains majority of data used to teach/train models to recognize patterns

        • Ex: perfect weight in MLP: learns weights between neurons to best fit data

      • 2. Validating Set: tune hyperparameters and avoid overfitting

        • Finding right parameters (fine tunning)

          • Ex: best hidden unit in MLP

      • 3. Test set: evaluate model’s performance on completely unseen data

        • used after training and validation process is complete

        • parameters are fixed

        • output is error metric: accuracy, precision, recall, MSE

  2. How can we tell how well our machine works in supervised learning?

    • Regression: How close the system matches the OG data

    • Classification: how close you can predict the outcomes

  3. What is the accuracy rate?

    • ratio of correctly predicted instances

    • (TP + TN) / Total

    • best when data is balanced

  4. What is recall/sensitivity?

    • correctly predicted positive instances over the actual total positive instances

    • TP/(TP + FN)

    • how well model identifies actual positives

  5. What is precision?

    • correctly predicted positive instances/ total predicted positive instances

    • Of all the predicted positives, which of them are actually positive?

    • how accurate is the model when predicting positives

    • Ex: in medical diagnosis, high precision minimizes false alarms

    • TP/ (TP + FP)

  6. What are all the rates?

    • TP: TP/(TP + FN)

    • TN: TN/(TN + FP)

    • FP: FP/(FP + TN)

    • FN: FN/(TP + FN)

  7. What is specificity?

    • correctly predicted negative rate/ total negative instances

    • TN/ (TN + FP)

    • how well model identifies actual negative value

  8. What are some applications of generative AI?

    • Image/ Video generation

    • Music/sound generation

    • text generation and copyright

    • healthcare: personalized treatment plans

    • gaming: character and environmental creation

    • customer service and chatbots: virtual assistants

  9. What is an ROC curve?

    • plots FPR (x-axis) and TPR(y -axis), the closer curve is to 1, the better

    • the bigger the area under the curve, the better the classification

    • can properly identify between positives and negatives

  10. What is a chatbot?

    • computer program designed to simulate conversations with human users

  11. What is natural language understanding?

    • uses computer software to understand text/speech input

    • Parsing: breaks text into structured format that computers can understand

    • allows computers to respond to human text instead of relying on computer language syntax

    • makes it possible to carry out dialogue with computers with human language (since human language is complex and ambiguous)

  12. What are the key components of NLU?

    1. Intent Recognition:

      • identifying the user’s objective

      • establish meaning of text

    2. Entity Recognititon:

      • identifies things in the message and extracts details about those entities

      • Ex: Name: John (person, organization, etc… different groups of entities)

  13. What is the main goal of NLP?

    • enable computers to understand, interpret and generate human language in a way that is meaningful and relevant.

  14. What are the phases of NLP?

    • Lexical Analysis:

      • breaking text into smaller units/ tokens

      • indeitfyig words, numbers, punctuations

      • removing spaces and formatting (unnecessary)

    • Syntactic Analysis:

      • examines sentence structure

      • parts of speech

      • determines how words relate to each other

    • Semantic Analysis:

      • understand literal meaning of words and phrases

      • identify relationships btw. words

      • synonyms, antonyms

      • interpret figurative language

    • Discourse Analysis:

      • understand relationship btw. sentences

      • identify main topic and theme

      • recognize overall structure

      • track reference (like pronouns) across the text

    • Pragmatic Analysis:

      • interprets intent behind words

      • considers real-world context

      • understands implied meaning

      • shift from literal meaning to what text implies in the specific context

  15. What are some examples of NLP?

    • Googling: Q & A

    • Summarizattion: text analysis and generation

    • Machine Translation: translate from one language to another (combines fragments of translations together)

  16. What is genrative AI?

    • AI that generates new content like images, sounds, text

    • Ex: Chat, DALLE (image generation), deepfake

  17. What are the types of generative models?

    • Explicit Desnity Models

      • Explicity define PDF (compute likelihood of data points and generate samples from them)

      • Ex: PixelCNN, Autoregresive models → put together pixel by pixel

    • Implicity Density Models

      • Do not define PDF (generate samples directly and learn to approximate data distribution in a non-parametric way)

      • GANs

  18. What are GANs?

    • Generative Adversial Networks: 2 part model

      • Genrator: creates data

        • Trains by backpropagating to the generator

      • Discriminator: evaluates authenticity (generated vs. real)

        • Trains by backpropagating weights from the real and generated image to adjust

  19. What are the steps to training GANs?

    1. Collect and preprocess dataset

    2. train generator and discriminator iteratively

    3. evaluate and fine-tune model

  20. What are deepfakes?

    • synthetic media where a person’s likeliness, action ,voice is replaced with another using models like GANs

    • Ex: Entertainment: movies, dubbing, Education: re-enactments, Creative Arts

  21. What are some ethical issues concerning deepfakes?

    • privacy

    • fraud

    • damage to reputation

    • misinformation

    • manipulation

    • copyright

  22. How are deepfakes made?

    • Training: collecting image and vids

    • AI techniques: GANs

    • Tools: software like DeepFaceLab

  23. What are autoencoders?

    • DNN used for generating deepfakes

    • Encoder: maps input data to lower dimension (captures only details)

    • Decoder: creates the OG as close as possible

  24. How do shared encoders help in deepfakes?

    • using shared encoders will produce overlapping latent space

    • can exhibit same emotion, head posture

  1. What is deep learning?

    • uses neural networks to mimic the way the brain works

    • effective for complex tasks

  2. What is computer vision?

    • allows computers to enable dericing info from images, videos, visual inputs

  3. What are some things we can see in the future with AI?

    • Customized Chatbots: create their own mini chatbots catering to specific needs

    • Science: researchers can analyze mass data, discover complex relationships, and uncover patterns

    • Foreign Policy: accelerate AI development to maintain competitiveness

    • Climate Crisis: optimizing energy consumption, predicting natural disasters