AI 101
A non-exhaustive and ever growing list of key terms and interesting concepts about AI, in no particular order.
“AI is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making and translation between languages. Modern AI is usually built using machine learning algorithms. ”
“Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.”
“A machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments.”
“Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy.”
“Artificial intelligence is a machine’s ability to perform some cognitive functions we usually associate with human minds.”
Types of AI.
In no particular order or hierarchy, with some but not all mutually exclusive, and some terms overlapping.
Weak AI: Also known as Narrow AI, this is AI designed to perform a specific task or a narrow set of tasks. It's not capable of general intelligence or understanding beyond its programmed functions.
Narrow AI: Another term for Weak AI, it excels in a single domain and can perform specific tasks efficiently. Examples include voice assistants like Siri or Google Assistant.
AGI (Artificial General Intelligence): A type of AI that possesses the ability to understand, learn, and apply intelligence across a wide range of tasks, similar to human cognitive abilities. AGI doesn't yet exist but represents the ultimate goal for some AI researchers.
Superintelligence: Hypothetical AI that surpasses human intelligence across all fields, including creativity, problem-solving, and social intelligence. It is often discussed in the context of future AI development and potential risks.
Explainable AI (XAI): AI systems designed to provide understandable and interpretable explanations for their decisions and actions. Explainable AI is crucial for trust and accountability in AI applications.
Types of models.
Machine Learning: A subset of AI that involves training algorithms to learn from and make predictions or decisions based on data. Machine learning enables systems to improve their performance over time without being explicitly programmed.
Deep Learning: A subset of machine learning that uses neural networks with many layers (hence "deep") to analyse and interpret complex data. Deep learning excels in tasks such as image recognition and natural language processing.
Neural Networks: Computational models inspired by the human brain's structure, consisting of interconnected nodes (neurons) that process and learn from data. Neural networks are fundamental to many AI applications, especially in deep learning.
Large Language Models (LLM): AI models trained on vast amounts of text data to understand and generate human-like text. Examples include GPT-3 and BERT, which are capable of tasks like translation, summarisation, and question-answering.
Generative Adversarial Networks (GANs): A class of machine learning frameworks where two neural networks, a generator and a discriminator, compete against each other to create new, synthetic instances of data that are indistinguishable from real data. GANs are widely used in image and video generation.
Predictive Models: AI models that use historical data to make predictions about future events or behaviours. These models are widely used in fields such as finance, marketing, and healthcare.
Diffusion Models: AI models used for generating high-quality images by simulating the process of diffusion, where patterns emerge from randomness. These models have applications in image generation and enhancement.
Reinforcement Learning: A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximise some notion of cumulative reward. It is commonly used in robotics, gaming, and autonomous systems.
Frontier Models: Cutting-edge AI models that represent the latest advancements in artificial intelligence research and technology. These models often incorporate innovative algorithms and architectures, pushing the boundaries of current AI capabilities. Frontier models are designed to tackle complex and previously unsolvable problems, leading to breakthroughs in areas such as natural language processing, image recognition, and autonomous systems.
Retrieval-Augmented Generation (RAG): An AI technique that enhances the model's responses by retrieving relevant information from external knowledge sources and incorporating it into the generated output. This approach combines the strengths of information retrieval and text generation to provide more accurate and contextually relevant answers.
AI Techniques and Methods.
Supervised Learning: A type of machine learning where the model is trained on labelled data, learning to predict the output from the input data.
Unsupervised Learning: A type of machine learning where the model learns patterns from unlabelled data, discovering hidden structures without explicit output labels.
Semi-Supervised Learning: A hybrid approach that combines a small amount of labelled data with a large amount of unlabelled data during training.
Transfer Learning: A technique where a pre-trained model is adapted to perform a new, but related, task. This is particularly useful when data availability for the new task is limited.
Federated Learning: A collaborative machine learning method that allows multiple devices or organisations to train a shared model without exchanging raw data, enhancing privacy and security.
Elements of AI.
Training Data: The dataset used to train the AI system, enabling it to base its actions and decisions on patterns and information derived from this data. Quality and quantity of training data are crucial for the AI's performance.
Algorithms: The set of rules and logic applied to the data, allowing the AI to process information and perform specific tasks. Algorithms are the backbone of AI, directing how it learns, interprets data, and makes decisions.
Models: The representation of the knowledge gained by the AI system after processing the training data using algorithms. Models encapsulate the learned patterns and can be used to make predictions or perform tasks.
Products: The final applications or systems that users interact with, such as AI-powered tools like Copilot or platforms like Meta Business Manager. These products integrate trained models to provide useful functionalities.
Inference: The process of applying a trained model to new data to make predictions or decisions. Inference is how AI systems provide real-time results based on their training.
Data things.
Overfitting: A situation where an AI model performs well on training data but poorly on new, unseen data because it has learned to memorise the training data instead of generalising from it. Overfitting is a common challenge in machine learning.
Poisoned Data: Data that has been intentionally manipulated or corrupted to mislead or harm an AI model. Poisoned data can lead to incorrect predictions or decisions, making it a significant security concern
Bias: Systematic errors in the AI model that arise from prejudiced or unrepresentative training data. Bias can lead to unfair or inaccurate outcomes, particularly affecting underrepresented groups.
Data Augmentation: Techniques used to increase the diversity of training data without actually collecting new data. This can include methods like cropping, rotating, or flipping images, or creating synthetic data to improve model performance.
Data Preprocessing: The process of cleaning and transforming raw data into a format suitable for training an AI model. This can include tasks like handling missing values, normalising data, and converting categorical variables into numerical ones.
Data Annotation: The practice of labelling or tagging data to make it understandable for machine learning models. This is often used in supervised learning where annotated data is required for the model to learn.
Data Drift: Changes in the data distribution over time, which can cause a previously trained model to perform poorly. Monitoring and addressing data drift is important for maintaining model accuracy.
Feature Engineering: The process of selecting, modifying, and creating new features from raw data to improve the performance of an AI model. Good feature engineering can significantly enhance model accuracy.
Training-Validation-Test Split: The division of data into three sets: training (for learning), validation (for tuning), and test (for final evaluation). This helps ensure that the model generalises well to new, unseen data.
Noise: Random or irrelevant information in the data that can obscure the underlying patterns. Noise can reduce model accuracy and needs to be managed carefully.
Class Imbalance: A situation where the classes in the training data are not represented equally. For example, in a dataset for fraud detection, the number of fraudulent transactions may be much smaller than non-fraudulent ones. Techniques like resampling or using different metrics can help address this issue.
Label Noise: Errors or inaccuracies in the labels of the training data. Label noise can negatively impact model training and lead to poorer performance.
Processing and Outputs.
Token context window: Refers to the maximum amount of text (measured in tokens) that an AI model can process and consider at once when generating a response.
Tokens: The smallest units of text, such as words or parts of words, that the model uses for its computations. More tokens = more words/bigger text to analyse.
Needle in the haystack test: The challenge of finding a small, valuable piece of information within a vast amount of data. This phrase is used metaphorically to describe tasks where the relevant information is hard to locate due to the sheer volume and complexity of the data being processed.
Hallucinations: Instances where an AI model generates outputs that are not grounded in the input data or reality. This can happen in language models when they produce text that is plausible but inaccurate or nonsensical.
