Artificial Intelligence: AI is a branch of computer science. AI systems use hardware, algorithms, and data to create “intelligence” to do things like make decisions, discover patterns, and perform some sort of action.
Machine learning: “Machine learning is a field of study with a range of approaches to developing algorithms that can be used in AI systems. AI is a more general term. In ML, an algorithm will identify rules and patterns in the data without a human specifying those rules and patterns. These algorithms build a model for decision-making as they go through data. (You will sometimes hear the term machine learning model.) Because they discover their own rules in the data they are given, ML systems can perpetuate biases. Algorithms used in machine learning require massive amounts of data to be trained to make decisions.”
Deep learning: is a subarea of machine learning and uses neural networks. Training methods that draw on and analyze large amounts of data are used to create artificial intelligence. Based on existing information and the neural network, the system can repeatedly link what it has learned with new content and thus learn again. Most deep learning models are implemented by increasing the number of layers in a neural network.
Predictive AI: refers to systems and models that use historical data to predict future outcomes or behaviors. These predictions can be about a wide range of topics, from predicting stock market trends and weather forecasts to predicting a user's next move in a digital environment or a potential equipment failure in industrial settings. Common techniques and algorithms used in predictive AI include regression models, decision trees, neural networks, and deep learning models, among others. Predictive AI is widely used in various industries due to its ability to anticipate future events, enabling businesses and organizations to make more informed decisions.
Chat-based generative pre-trained transformer (ChatGPT): “A system built with a neural network transformer type of AI model that works well in natural language processing tasks (see definitions for neural networks and Natural Language Processing below). In this case, the model: (1) can generate responses to questions (Generative); (2) was trained in advance on a large amount of the written material available on the web (Pre-trained); (3) and can process sentences differently than other types of models (Transformer).”
Acronyms:
AI: Artificial Intelligence
ANN: Artificial Neural Networks
API: Application Programming Interface
ASI: Artificial Social Intelligence
AR: Augmented Reality
DL: Deep Learning
GAI: Generative Artificial Intelligence
GPT: Generative Pre-Trained Transformer
IA: Intelligent Automation
IoT: Internet of Things
LLM: Large Language Model
ML: Machine Learning
MR: Mixed Reality
NLP: Natural Language Processing
NLU: Natural Language Understanding
RPA: Robotic Process Automation
STT: Speech To Text
TTS: Text To Speech
VA: Virtual Assistant
VR: Virtual Reality
XR: Extended Reality