An AI Glossary - Written by ChatGPT 3.5
(With plain English subtitles ... written by Human 1.0!)
Artificial Intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction. (An electronic approximation of the human brain typically focused on a domain like text search, self-driving, finance, medicine and so on.)
Machine Learning (ML): A subset of AI that focuses on the development of algorithms that allow computers to learn from and make predictions or decisions based on data. (Computers using machine learning learn from data and make decisions or predictions without being explicitly programmed for every task.)
Deep Learning: A subset of machine learning that uses neural networks with many layers (deep neural networks) to model and extract patterns from complex datasets. (One might argue that this is a superset of machine learning since it is more complex.)
Neural Network: A computer system modeled after the human brain's network of neurons, used to recognize patterns. (Accomplished via input layers - like human senses, hidden layers - like human thought, and output layers - human decisions/conclusions/actions.)
Supervised Learning: A type of machine learning where the model is trained on labeled data, and its goal is to learn a mapping from input data to output labels. (Think teacher and student. The teacher - an algorithm - provides data and labels for the student - or model - to process. "This is a 7" or "this is a car" or "212 degrees Farenheight is the boiling point of water at sea level." Supervised learning is used in applications suich as image processing, natural language processing, predictive modeling, and production control.)
Unsupervised Learning: A type of machine learning where the model is trained on unlabeled data and must find patterns and relationships without guidance. (In the early days of data mining aka Big Data, humans needed to identify clusters and anomalies. AI can now perform these tasks.)
Reinforcement Learning: A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward. (A simple example is Hulu using your likes and dislikes to recommend future viewing. Complex examples would be financial portfolio management based on risk profiles and investment performance or personalized healthcare recommendations based on patient data and clinical outcomes from similarly diagnosed populations.)
Natural Language Processing (NLP): The ability of computers to understand, interpret, and generate human language. (Google Translate, Grammarly sentiment and grammar checks, spam or inflamatory content detection, text generation - such as this document, and question answering such as Siri, Alexa, and Google Assistant are examples.)
Computer Vision: The field of AI and computer science that focuses on enabling computers to interpret visual information from the real world, including images and videos. (Think Tesla self-driving capabilities - perhaps after the autobraking, Autopilot, and other recalls!)
Algorithm: A set of rules or instructions designed to perform a specific task or solve a particular problem. (There are hundreds, if not thousands, of algorithms both common and specific to particular disciplines.)
Feature: A measurable property or characteristic of input data used by machine learning algorithms to make predictions or decisions. (An AI feature such as Natural Language Understanding is different from a product feature such as a user interface. Stated another way, an AI feature describes what something is vs. a product feature which describes what something does.)
Model: A mathematical representation of a real-world process or system used for analysis, prediction, or control. (In simple terms, an AI model describes a prototypical end result. More specifically, it encapsulates the learned knowledge and patterns from data and the expected outcomes.)
GPT: Generative Pre-Trained Transformer. A series of AI language models developed by OpenAI. They utilize a transformer architecture, which is highly effective for understanding and generating human-like text. These models are first pre-trained on a vast corpus of text data to learn general language patterns and then fine-tuned for specific tasks through supervised learning. GPTs are capable of a wide range of language tasks, including answering questions, writing text, and more, making them versatile tools in natural language processing. (Translation: They have been pre-trained by feeding them billions of pieces of data from the Internet and other publicly available sources. They have been taught how to interpret and transform that data to generate responses to questions like "explain transformer architecture to a non-technical person" which resulted in the following:)
Imagine you're at a busy party, trying to follow a conversation. Instead of listening to each word in order, you focus on the key words that give you the gist of the discussion. This is similar to how the transformer architecture works in handling language.
Transformers can look at an entire sentence at once, rather than one word at a time. They pay attention to important words, no matter where they are in the sentence, to understand and generate language. This makes them very good at picking up on nuances and context, much like how you tune into the most relevant parts of a conversation in a noisy room. This ability helps them understand and produce text effectively, making them useful for tasks like translating languages or answering questions.
One can even ask for the same question to be answered in olde English:
Imagine, if thee wilt, that thou art at a grand feast in a bustling hall, where many a conversation unfurls around thee. Thou dost not merely harken to thy nearest neighbour, but thine ears catch snippets of discourse from yon and hither, piecing together tales and tidings from all corners of the room.
Or French:
Imaginez que vous êtes à une réception où de nombreuses conversations ont lieu simultanément. Plutôt que d'écouter une seule personne à la fois, vous avez la capacité d'écouter tout le monde en même temps, saisissant l'essence de chaque conversation, peu importe où elle se déroule dans la pièce.
Or Cantonese, or Russian, or ... There are not a fixed number of languages because GPTs are language models, not translators. The quality of the translation will be directly affected by the amount of training material - and human training - that were available in any particular language. ChatGPT 4.0 completely froze when the same answer was requested in Esperanto, for instance!
More on Transformer Architecture
GPT stands for "Generative Pre-trained Transformer." Generative refers to its ability to generate human-like text, Pre-trained indicates it's trained on large amounts of data before fine-tuning on specific tasks like chatting, and Transformer is the architecture it's based on, particularly effective for tasks involving sequential data like text.
The Transformer architecture is a deep learning model originally introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017. It revolutionized natural language processing (NLP) tasks by achieving state-of-the-art performance on various benchmarks.
At its core, the Transformer architecture relies heavily on a mechanism called "self-attention." This mechanism allows the model to weigh the importance of different words in a sentence when processing each word. By attending to the entire input sequence simultaneously, rather than processing it sequentially like in recurrent neural networks (RNNs), the Transformer can capture long-range dependencies more effectively and in parallel.
The Transformer architecture consists of an encoder and a decoder, each composed of multiple layers of self-attention and feedforward neural networks. The encoder processes the input sequence, while the decoder generates the output sequence in sequence-to-sequence tasks like machine translation.
Key components of the Transformer architecture include:
Self-Attention Mechanism: This mechanism calculates the importance of each word in a sequence with respect to every other word, allowing the model to capture relationships and dependencies between words.
Positional Encoding: Since the Transformer does not inherently understand the order of words in a sequence, positional encodings are added to the input embeddings to provide information about the position of each word in the sequence.
Multi-Head Attention: The attention mechanism is applied multiple times in parallel, with different learned linear projections of the input, allowing the model to attend to different aspects of the input simultaneously.
Feedforward Neural Networks: After the self-attention layers, the Transformer architecture includes fully connected feedforward neural networks to process the attended representations of the input.
Layer Normalization and Residual Connections: These techniques help stabilize training and mitigate the vanishing gradient problem by normalizing the activations within each layer and adding skip connections around each sub-layer, respectively.
The Transformer architecture has been widely adopted in various NLP tasks and has served as the foundation for models like BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer), and many others, leading to significant advancements in natural language understanding and generation.
Some Trite AI Examples from ChatGPT 3.5
Several major companies across various industries are leveraging artificial intelligence (AI) for a wide range of applications. Here are some examples:
Google: Google uses AI extensively across its products and services. Google Search utilizes AI algorithms to provide relevant search results, while Google Photos employs AI for image recognition and organization. Google's DeepMind subsidiary focuses on AI research and applications in healthcare and other fields.
Amazon: Amazon utilizes AI in its e-commerce platform for product recommendations, inventory management, and logistics optimization. Amazon Alexa, the company's virtual assistant, employs natural language understanding and generation powered by AI. Additionally, Amazon Web Services (AWS) offers AI and machine learning services to developers and businesses.
Microsoft: Microsoft integrates AI into its products and services, including Office 365, Azure cloud platform, and Bing search engine. Azure AI provides various tools and services for developers to build AI-powered applications, while Microsoft Dynamics 365 leverages AI for customer relationship management.
Apple: Apple incorporates AI into its devices and services, such as Siri, the virtual assistant, which uses natural language processing and machine learning. The iPhone's camera features utilize AI for image processing and enhancement. Apple also uses AI for facial recognition in devices like the iPhone and iPad. (Since iPhone X, all iPhones have had neural network chips running the Apple Neural Engine.
Facebook: Facebook employs AI for content moderation, user recommendations, and targeted advertising. The company uses AI algorithms to analyze user behavior and preferences to personalize the content shown in users' news feeds. Facebook's Oculus virtual reality division also explores AI applications for immersive experiences.
IBM: IBM offers AI solutions through its Watson platform, which includes AI-powered tools for business analytics, healthcare, finance, and more. Watson uses natural language processing and machine learning to extract insights from unstructured data, such as text and images.
Tesla: Tesla integrates AI into its electric vehicles (EVs) for autonomous driving capabilities.