Artificial Intelligence (AI) Learning Centre

AI biases result from human biases captured in training data or algorithms. They will be reflected in responses and may be overt or subtle and difficult to detect.

AI Models are used to make decisions or predictions.

Algorithms define the logic by which an AI model operates.

Artificial intelligence (AI) is technology that allows computers and machines to mimic human intelligence and problem-solving abilities. It can be used to execute tasks that would otherwise require a human. Examples include GPS or self-driving cars.

Automated data extraction uses an automated process to transform unstructured or semi-structured data into structured information.  

Automated is a system where predefined human instructions are used by machines to perform repetitive tasks. 

Automatic speech recognition (ASR) systems convert human speech into text through a highly complex process involving linguistics, mathematics, and statistics. The performance of speech recognition technology is measured by its word error rate (WER) and speed. Factors like pronunciation, accent, pitch, volume, and background noise can affect ASR systems’ performance. 

Machine learning models with inner workings so complex that it is impossible for algorithm designers, engineers, and users to know precisely how the model came to a specific result. 

Classification algorithms in machine learning aim to categorize data accurately. They identify patterns in existing data to determine how to label or define previously unseen examples. 

Computer vision is a broad discipline that employs machine learning and neural networks to enable computers and systems to extract information from digital images, videos, and other visual inputs. It allows these systems to make recommendations or take actions based on their visual analysis. 

Deep learning arranges machine learning algorithms in layers to form neural networks. Deep learning has had significant breakthroughs in recent years, and most of the AI products consumers interact with today are powered by deep learning models.

Products that combine multiple base AI models together.

The collection of processes and techniques designed to help human users understand and have confidence in the results produced by machine learning algorithms. 

Explainable AI (XAI) involves methods and processes that allow human users to comprehend and trust the outcomes of machine learning algorithms. It clarifies an AI model’s anticipated impact, possible biases, accuracy, fairness, transparency, and decision-making results. XAI is crucial for fostering trust and confidence in AI models and promoting a responsible approach to AI development, especially as AI becomes more sophisticated and less understandable. 

Forced hallucination is a term for when a user attempts to use an LLM in ways contrary to how they are designed to work, which forces the model into a position where their limitations are more stark. For instance, LLMs are not designed to be accurate or to “find” information.  Asking an LLM-powered chatbot for data or statistics that are impossible to know may result in a response that it doesn’t know but could also result in the model inventing an answer.

Foundation models are large-scale, using vast amounts of training data to facilitate use across a variety of contexts.

Generative AI, or GenAI, can produce original content based on a user’s prompt or request, such as text, images, audio or video.

Hallucinations are specific to LLMs and refer to when the model’s response to a prompt is nonsensical or inaccurate. Hallucinations can be difficult or impossible to detect if the subject is outside the prompter’s realm of expertise.

The extent to which users can grasp the reasoning behind an algorithm’s decision. It measures how accurately an AI’s output can be predicted by human users.

Large Language Models (LLMs) are a type of generative AI model trained on vast amounts of data. They can produce original content based on a user’s prompt or request such as text, images, audio, or video that uses Natural Language Processing (NLP). 

Machine Learning (ML) is a subset of artificial intelligence rooted in training machines or programs on existing data. Once training is complete, the machine or program can apply what it learned to new, unseen data to identify patterns, make predictions, or execute tasks.

Machine learning classification (MLC) uses algorithms and aims to categorize data accurately. They identify patterns in existing data to determine how to label or define previously unseen examples. 

Multimodal models analyze and produce many different types of data. Current LLM chatbots such as ChatGPT and Gemini are multimodal – they can read and analyze many types of data (text, video, audio, images), and produce other data types as part of requested outputs, such as taking a text input and producing an infographic.

Natural Language Processing (NLP) combines computational linguistics, statistical modeling, machine learning, and deep learning to enable computers to understand and generate realistic humanlike text and speech.

Neural networks are complex, layered algorithms that mimic the structure of human brains. Nodes in neural networks perform calculations on numbers passed between them on connective pathways.

Optical character recognition (OCR) is a technology using automatic data extraction and both hardware and software systems to convert images of text into a format readable by a computer. It identifies individual letters in an image, assembles them into words, and then forms those words into sentences, thus transforming physical, printed documents into text that a computer can read. 

Overfitting in machine learning happens when an algorithm models the training data too precisely, making it ineffective at predicting or interpreting new data. This undermines the model’s utility, as the ability to generalize to new data is crucial for making accurate predictions and classifications. 

Retrieval-augmented generation enhances LLM-generated responses by incorporating external sources of knowledge, enriching the model’s internal understanding. This approach allows the system to access the most current and reliable data while providing users with source references to verify the accuracy of its claims. 

Responsible AI refers to principles guiding the design, development, deployment, and use of AI to foster trust and empower organizations and collaborators. It addresses the broader societal impacts of AI systems, ensuring they align with social values, legal standards, and ethical principles. The aim is to integrate these ethical guidelines into AI applications and workflows, thereby reducing risks and negative outcomes while enhancing positive results. 

Timeboxing refers to the fact that machine learning models’ training data is only current up to a certain date. Foundation models require a massive amount of time, resources, and computing power to train, and can cost hundreds of millions or close to a billion dollars to train. Thus, continuous training is not feasible. For more information on the costs to develop foundation models, see 2024 AI Index Report (Stanford) under Top Resources. 

Training data is used by machine learning systems to learn how to recognize patterns and generate writing. The quality of training data, plus additional learning, directly impacts the quality of system outputs. 

• • For ASR, training data that contains varied voices, accents, conversation styles, background noise levels, etc., will improve the model’s performance. Models that have not been trained with diverse data will struggle to accurately capture conversations that include sounds, accents, or manners of speaking they were not trained on.
  • For LLMs, training data that includes biased, discriminatory, or violent content will be reflected in model reasoning and outputs, in ways that may or may not be clear in any given conversation.  Foundation models have added extensive fine-tuning after training to attempt to surface, correct, and eradicate harmful vectors and concepts.
  • Currently, private development companies keep details of training data secret, so consumers have no way to discover what data current frontier models were trained on.

Transformer models are advanced neural networks designed to handle data such as words or sequences of words. They work by converting input sentences into numerical values that capture their meaning. These values are processed through multiple layers, each refining the data by focusing on different aspects of the sentence. This method allows transformer models to effectively translate languages and perform other complex tasks by learning patterns and relationships within the data. 

Unimodal models can only read and produce a single type of data, such as an LLM that can only read and produce text.

Weight refers to how LLMs map the probability of different possible word or concept pairings. The more frequently words or concepts are paired together, the more weight the model will give them.

Word error rate (WER) is the percentage of words inaccurately translated by the ASR system. WER counts all words equally in an exchange. For specialized fields, such as medicine, the use of specialized WERs can help fine-tune the model to ensure high performance for capturing critical health information. Healthcare WERs include Medical Concept WER (MC-WER), Doctor-Specific Word Error Rate (D-WER), or Patient-Specific Word Error Rate (P-WER).

Word vectors are created by LLMs during training to map how words are related. They are created to capture relationships between words or concepts and extrapolate to identify analogies.