AI

• Weak AI (Specialized)

• Strong AI (General)

• Super AI (self-aware)

• Artificial intelligence - simulation of intelligent behaviour.

  • Machine learning - is a subset of ai, uses computer algorithms to analyze data and make intelligent decisions based on what it has learned.
    • Deep learning - subset of machine learning that uses neural networks to model human decision-making.

• Neural networks - a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data. Are the reason why deep learning is so powerful. Backpropagation (uses a set of training data that match known inputs to desired outputs, an error function) -> Gradient descent -> Activation function -> Weights -> Bias more than 1 hidden layer -> deep neural network

• Cognitive Systems:

  • Weak AI (Specialized): Refers to AI systems designed and trained for specific tasks or domains.
  • Strong AI (General): Refers to AI systems with the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence.
  • Super AI (Self-aware): Hypothetical AI systems that possess consciousness and self-awareness, surpassing human intelligence.

• Supervised learning - the algorithm is trained on a labeled dataset.

  • Regression - predict a continuous value.
  • Classification - predict a discrete value. (Features -> properties)
  • Neural networks

• Unsupervised learning - the algorithm is trained on an unlabeled dataset. (clustering)

• Reinforcement learning - the algorithm learns by trial and error to achieve a clear goal. How to achieve the goal.

Perceptrons

The simplest form of a neural network. It takes several binary inputs, x1, x2, x3, and produces a single binary output. The perceptron calculates the weighted sum of its inputs and then passes the sum through a step function to produce the output.

Convolutional Neural Networks

Multi-layer neural networks that are trained to recognize patterns. They are used in image and video recognition, recommender systems, and natural language processing.

Recurrent Neural Networks

Perform the same task for every element of a sequence, with the output depending on the previous computations. They are used in speech recognition, language modeling, and translation.

NLP (Natural Language Processing)

• board array of linguistic to draw inferences from language
• can understand intent
• can identify emotions

it is not speech-to-text or text-to-speech

Self-driving cars

• radar data
• lidar data
• vision data
• laser data

Lesson Summary

• Natural Language Processing (NLP) is a subset of artificial intelligence that enables computers to understand the meaning of human language, including the intent and context of use.
• Speech-to-text enables machines to convert speech to text by identifying common patterns in the different pronunciations of a word, mapping new voice samples to corresponding words.
• Speech Synthesis enables machines to create natural sounding voice models, including the voice of particular individuals.
• Computer Vision enables machines to identify and differentiate objects in images the same way humans do.
• Self-driving cars is an application of AI that can utilize NLP, speech, and most importantly, computer vision.