Activation Function: A mathematical function used in neural networks to calculate the output of each neuron from its input data
Artificial General Intelligence (AGI), also called deep AI or strong AI, is the advanced phase of AI where it holds the cognitive abilities to carry out activities like humans. AGI can mimic human intelligence; learn, think, understand and solve problems like humans; and take decisions by combining human beings’ reasoning and flexible thinking with computational advantages. It deploys the theory of mind AI framework to understand human beings and distinguish between emotions, needs, beliefs and thought process
AI Agents: Advanced AI applications that automate and manage tasks or workflows, often through integration with other digital tools
AI Model: A computer model that mimics human intelligence by generating machine outputs from given inputs
ASI, also called as Super AI, is a highly advanced phase of AI system that exceeds human intelligence. Its human-like capabilities include beliefs, desires, cognition, emotional intelligence, subjective experiences, behavioural intelligence, and consciousness
Constitutional AI: An approach where AI behavior is guided by a set of underlying principles to ensure ethical decision-making and mitigate biases
Convolutional Neural Network (CNN): A type of neural network particularly effective for processing structured grid data like images, using layers that automatically and adaptively learn spatial hierarchies of features
Deep Neural Network (DNN): A neural network with multiple layers (input, one or more hidden layers, and an output layer); the specific layout is its architecture.
Deep Learning: An advanced branch of machine learning that uses deep neural networks to handle complex tasks. Neural Networks with more than two hidden layers are used are in Deep Learning.
Diffusion Models: Advanced neural network architectures used for generating high-quality and coherent images or videos by learning the distribution of training data and iteratively refining generated outputs
Edge AI: Combination of AI and edge computing. It brings data storage and computing, closer to the devices (such as a car or a camera) instead of remotely located data centers, leading to an increase in speed and reduction in response times. This also results in less data storage on external locations, eliminating the risks of data mishandling and misappropriation. EdgeAI is growing in popularity due to lower costs, high computing power, real-time inference and low latency. It is finding increased applications in autonomous vehicles, smart homes, smart devices, smart energy, smart factories and security cameras, etc.
Fine-tuning: A subsequent phase of model training using targeted data to refine capabilities on specific tasks or to improve performance on detailed aspects
Generative AI (GenAI): A branch of AI focused on generating new digital content from existing data
High-dimensional Data: Data represented by a large number of attributes or dimensions, often derived from unstructured sources like images
Input Variables: Factors considered by a model to influence its outputs, such as store size in sales predictions
Intelligent Automation (IA): Broader capability that aims to mimic human behavior (e.g., perceiving, reasoning) and is better for unstructured data from non‑standard sources; distinct from RPA’s rule‑based focus
Large Language Models (LLMs): A type of deep learning model specifically designed to process and generate human language
Layers: Input Layer: Receives initial data. Hidden Layers: Process data through weighted connections. Output Layer: Produces final results.
Long Short-Term Memory (LSTM): An RNN variant that includes mechanisms to remember and forget information selectively using components like the “forget gate”, aiding in handling longer sequence. This faces challenges with parallel processing
Machine Learning (ML): AI models that learn from data to improve their accuracy without being explicitly programmed for every scenario. The "intelligence" of machine learning models depends on their ability to learn from training data; training involves optimizing parameters to best fit the training data.
Mathematical Form: The mathematical equation or function defining how inputs are transformed into outputs
Meta Prompting: In this advanced technique, the AI is instructed on how to generate its own prompts for specific tasks. This approach allows for more expert-level reasoning and sophisticated responses. Example: Instructing the AI to "behave as an expert in sustainable product marketing" to generate more nuanced and impactful content.
Multi-Modal Models: AI models capable of processing and understanding multiple types of data inputs, such as text and images
Natural Language Processing (NLP): AI domain dealing with the computer–human (natural language) interactions, focused on processing and analyzing large amounts of language data.
Natural Language Understanding (NLU): Interpreting meaning from text (or speech after recognition), mapping it to a formal representation, and choosing an appropriate action.
Neural Network: A network of nodes (or artificial neurons) that process data in layers, emulating the human brain’s structure
Overfitting: Sometimes, a model becomes too good at memorizing the training data, including its noise and inconsistencies. When faced with new, slightly different prompts, it might rely on these memorized patterns rather than generating truly novel and accurate information. It is like a student who memorizes answers for a specific test but doesn't understand the underlying concepts.
Parameters: Values within a model that are optimized during training to best fit the data
Pre-training: The initial phase in training a model where it learns from a broad data set without specific targets to develop a general understanding
Prompt Chaining: This technique involves linking multiple prompts together in a sequence, with each new prompt building on the output from the previous one. This method is useful for solving multi-step tasks or generating refined outputs over time. ○ Example: In a multi-step task like writing a marketing headline, the AI would first determine the target audience, then identify the most resonant message, and finally generate a headline based on these insights.
Prompt
Engineering: The way a user phrases a question or provides instructions can
inadvertently lead an AI to hallucinate. Ambiguous prompts or those that imply
a certain answer might steer the model toward generating a plausible sounding
but incorrect response.
Quantum computing uses quantum mechanics to process information, deploying hardware and algorithms to solve complex problems surpassing the speed of supercomputers. It uses qubits instead of binary (0 or 1) to execute multidimensional quantum algorithms. Quantum computing has vast potential independently, however, its conjunction with AI yields transformative outcomes. Ongoing efforts are directed towards seamless integration of AI with quantum computing, resulting in more potent AI models along with noteworthy advancements in speed, efficiency, and accuracy of AI.
Recurrent Neural Network (RNN): A type of neural network that processes sequences by maintaining a state or memory of previous inputs. The challenge include “memory” of the context fading with long sequences and limited ability to work via parallel processing
Regression: A statistical method used to fit models to data, commonly used to find optimal parameter values
Reinforcement Learning (RL): A training strategy where models learn through trial and error, receiving rewards or penalties based on their performance. This can be used in situations where traditional training data is insufficient or ongoing adaptation is required. Example: AlphaGo's training involved rewarding winning strategies and penalizing losses. Self-driving cars use RL by receiving rewards or penalties based on maneuver success.
Reinforcement Learning from Human Feedback (RLHF): A variant of RL where human feedback directly influences the training process, guiding the model's learning
Responsible AI is an emerging area of AI governance covering ethics, morals and legal values in the development and deployment of beneficial AI. As a governance framework, responsible AI documents how a specific organisation addresses the challenges around AI in the service of good for individuals and society.
Retrieval-Augmented Generation (RAG): A technique where AI models enhance their responses by cross-referencing with up-to-date external data sources to improve accuracy
Robotic Process Automation (RPA): Use of easily programmable software (“bots”) to handle high‑volume, repeatable, rule‑based tasks previously done by humans.
Rule Based AI: AI models that operate on predefined rules set by developers
Small Language Models (SLMs): Smaller, more efficient models designed for specific tasks, requiring less computational power than larger models
Supervised Learning: A machine learning approach where the model is trained on a dataset containing inputs paired with correct outputs
Temperature: A factor in LLMs that introduces randomness into the decision-making process, affecting the selection of output tokens.
Token: The smallest unit of processing in many LLMs, varying from parts of a word to entire words.
Training Set: The dataset used to train a model, allowing it to learn from known input-output pairs.
Transformer: A neural network architecture that uses attention mechanisms to dynamically focus on different parts of the input data, suitable for large-scale and complex tasks like those needed in LLMs. Introduced in 2017, addressing both memory retention and scalability (can be parallelized). This utilizes “attention” mechanism to focus on relevant parts of input data, enhancing processing efficiency. It is dominant architecture in modern LLMs due to its suitability for handling lengthy text sequences.
Tree of Thought (ToT) Prompting: In ToT, the AI explores multiple possible reasoning paths simultaneously, evaluating different strategies before choosing the best solution. This method allows for greater flexibility and optimization in complex problem-solving. Example: The AI may explore different approaches to crafting a marketing message for an eco-friendly product, focusing on various aspects like affordability, sustainability, or innovation.
Underfitting: This happens when a model cannot learn the underlying patterns in the training data, resulting in poor performance on both training and test datasets. It is typically caused by high bias, where the model makes overly simplistic assumptions about the data. Examples include using a linear model for a non-linear relationship or a shallow decision tree for complex data. Symptoms of underfitting include consistently high errors across training and validation sets. Common causes are insufficient model complexity, inadequate features, or poor data quality.
UnderstanUnsupervised Learning: Training method using datasets without predefined labels, allowing the model to identify patterns or structures independently. Useful when labeling data is impractical, or the nature of the problem does not permit predefined outputs. Example: customer segmentation models group profiles based on detected patterns without prior output labels
Zero-Shot Learning: Ability of a model to perform tasks it has not been explicitly trained to do.