Understanding Popular AI Terms

Understanding popular AI terms can be helpful for navigating discussions about artificial intelligence. Here’s a brief overview of some key terms in alphabetical order:

Artificial Intelligence (AI) The simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, problem-solving, perception, and language understanding.

Attention Mechanism A component of neural networks, particularly transformers, that allows the model to focus on specific parts of the input data (e.g., words in a sentence) when making predictions. In translation tasks, attention mechanisms help the model align words in the source language with their counterparts in the target language.

Autonomous Machine A machine is considered as autonomous if it can perform its task or tasks without requiring human involvement.

Autoregressive Model A type of model that generates sequences (like text) by predicting the next element in the sequence based on previous elements. GPT models are autoregressive, generating text one token at a time.

Bias in AI The tendency of AI systems to reflect the biases present in the data they are trained on, leading to unfair outcomes. As an example, facial recognition systems that may be less accurate for people with darker skin tones due to biased training data.

Bias Mitigation Techniques used to reduce or eliminate biases in AI models, especially those that arise from biased training data. As an example, adjusting training data or modifying model outputs to ensure fairness across different demographic groups.

Contextual Embeddings Word or sentence representations that capture the meaning of words based on the context in which they appear, as opposed to traditional static word embeddings. For example, in the sentence "bank" might have different embeddings in "river bank" and "savings bank" based on context.

Data Mining The process of analyzing datasets in order to discover or identify new patterns that might improve the AI model.

Data Science Drawing from statistics, computer science and information science, this interdisciplinary field aims to use a variety of scientific methods, processes and systems to solve problems involving data.

Deep Learning A subset of ML that uses neural networks with many layers (hence "deep") to model complex patterns in large amounts of data.

Ethical AI The practice of designing AI systems that adhere to ethical guidelines, ensuring fairness, transparency, privacy, and accountability. For example, implementing AI models that are designed to avoid biased decisions in hiring processes.

Explainable AI (XAI) AI systems that are designed to make their decision-making processes understandable to humans. For example, a credit scoring algorithm that can explain why a loan application was approved or denied.

Fine-Tuning The process of taking a pre-trained model (like an LLM) and training it further on a specific task or dataset to improve its performance on that task. For example, fine-tuning a general language model on legal documents to create a specialized legal assistant.

General AI AI that could successfully do any cognitive task that can be done by any human being, also sometimes referred to as strong AI.

Generative AI A type of AI that can generate new content, such as text, images, or audio, based on the data it has been trained on. For example, tools like ChatGPT or DALL·E create new text or images based on prompts.

Generative Pre-trained Transformer (GPT) A type of LLM that is pre-trained on a large corpus of text and then fine-tuned for specific tasks. The "pre-trained" aspect refers to the initial training phase, where the model learns language patterns. GPT-3 and GPT-4 are versions of this model, widely used in various NLP applications.

Hallucinations In the context of AI, especially large language models (LLMs), hallucinations refer to instances where the model generates output (text / image) that appears plausible but is factually incorrect, nonsensical or completely made up.

Knowledge Distillation A process where a smaller, simpler model (student) is trained to replicate the behavior of a larger, more complex model (teacher) to achieve similar performance with less computational cost. It is used for compressing a large LLM into a smaller model that can run efficiently on mobile devices.

Large Language Model (LLM) A type of neural network model, typically based on transformer architecture, that has been trained on vast amounts of text data to understand and generate human-like language. GPT-4, the model behind ChatGPT, is an LLM that can perform tasks like text generation, summarization, and translation.

Machine Learning (ML) A subset of AI that involves the development of algorithms that allows computers to learn from and make decisions based on data.

Model In context of AI, a model is a software program / algorithm that has been trained on specific data to recognize certain patterns of input and make decisions / generate output without further human intervention. AI models apply different algorithms to relevant data inputs and generate outputs they've been programmed for.

Natural Language Processing (NLP) A field of AI that gives machines the ability to read, understand, and derive meaning from human languages.

Neural Networks A set of algorithms, modeled loosely after the human brain, designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling, or clustering of raw input.

Overfitting A modeling error that occurs when a machine learning model is too closely fitted to the training data and may not perform well on new, unseen data. A model that perfectly predicts training data but fails to generalize to other data sets.

Perplexity An evaluation metric used to measure the performance of language models, representing how well the model predicts a sample. Lower perplexity indicates better performance. A language model with a perplexity of 20 is better at predicting the next word in a sequence than one with a perplexity of 100.

Prompt Engineering The process of designing and refining the input prompts given to LLMs to elicit desired outputs. Effective prompt engineering can significantly influence the quality and relevance of the model’s responses. For example, crafting specific questions or instructions to get an LLM to generate a detailed and coherent essay.

Reinforcement Learning A type of ML where an agent learns to make decisions by performing certain actions and receiving rewards or penalties. For example, self-learning robots that improve their performance in tasks like walking or grasping objects.

Responsible AI Responsible Artificial Intelligence is a set of principles that help guide the design, development, deployment and use of AI—building trust in AI solutions that have the potential to empower organizations and their stakeholders. Responsible AI involves the consideration of a broader societal impact of AI systems and the measures required to align these technologies with stakeholder values, legal standards and ethical principles.

Self-Supervised Learning A method of training models where they generate their own labels from the input data, allowing them to learn from vast amounts of unlabeled data. As an example, an LLM might predict missing words in a sentence during training, which is a form of self-supervised learning.

Sentiment Analysis The process of identifying and categorizing opinion / emotional tone in a piece of text (emails, reviews, customer support chats, social media comments), often with the goal of determining if the writer’s attitude towards something is positive, neutral or negative.

Singularity In the context of AI, singularity is a hypothetical idea where machines are smarter than humans and artificial intelligence is more intelligent than human intelligence. Experts believe that AI can evolve itself repeatedly, leading to rapid technological advances that will be impossible for humans to control.

Strong AI This field of research, also known as General AI, is focused on developing AI that is equal to the human mind when it comes to ability.

Supervised Learning A type of ML where the model is trained on labeled data, meaning the input comes with the correct output, and the model learns to map inputs to outputs. For example, predicting house prices based on historical data of houses' features and their prices.

Tokenization The process of breaking down text into smaller units (tokens), which can be words, subwords, or characters, for easier processing by language models.

Training Data This refers to all of the data used during the process of training a machine learning algorithm, as well as the specific dataset used for training rather than testing.

Transformer A type of deep learning model architecture that is particularly well-suited for processing sequential data, such as text. Transformers are the backbone of most state-of-the-art LLMs, for example, BERT (Bidirectional Encoder Representations from Transformers) is a popular transformer model used in NLP tasks.

Turing Test Named after Alan Turing, this tests a machine’s ability to pass for a human, particularly in the fields of language and behavior. After being graded by a human, the machine passes if its output is indistinguishable from that of human participant’s.

Unsupervised Learning A type of ML where the model is trained on unlabeled data, meaning it tries to identify patterns and relationships in the data without any explicit instructions. For example, customer segmentation in marketing, where customers are grouped based on purchasing behavior.

Weak AI This is an AI model, also called Narrow AI, that has a set range of skills and focuses on one particular set of tasks. Most AI currently in use is weak AI, unable to learn or perform tasks outside of its specialist skill set.

Zero-Shot Learning A type of learning where the model can make predictions about tasks or classes that it has never been explicitly trained on, often by leveraging its broad knowledge base. For example, an LLM generating a summary of a scientific paper in a specific domain without being trained on that particular domain.

These terms will deepen your understanding of AI and its various subfields, especially in the context of language models and their applications. These terms are fundamental in understanding the capabilities and limitations of AI technologies.