AI Resources for Learning, Teaching and Research

AI tools, such as translation services, voice recognition, and visual or auditory aids, can help students with special needs.

With the use of AI-driven technologies, interactive learning environments like virtual reality and simulations can be created, improving comprehension and engagement.

AI chatbots can provide regular inquiries with round-the-clock assistance, guaranteeing that students receive assistance when needed.

Generative AI systems sometimes produce inaccurate and fabricated answers. Assess all outputs for accuracy, appropriateness and actual usefulness before relying on or publicly distributing information.

You need policies or controls in place to detect biased outputs and deal with them in a manner consistent with company policy and any relevant legal requirements.

Enterprises must prepare for malicious actors’ use of generative AI systems for cyber and fraud attacks, such as those that use deep fakes for social engineering of personnel, and ensure mitigating controls are put in place. Confer with your cyber-insurance provider to verify the degree to which your existing policy covers AI-related breaches.

There are currently no verifiable data governance and protection assurances regarding confidential enterprise information. Users should assume that any data or queries they enter into the ChatGPT and its competitors will become public information, and we advise enterprises to put in place controls to avoid inadvertently exposing IP.

Generative AI uses significant amounts of electricity. Choose vendors that reduce power consumption and leverage high-quality renewable energy to mitigate the impact on your sustainability goals.

AI Model

An artificial intelligence (AI) model is a program that analyzes datasets to find patterns and make predictions. AI modeling is the development and implementation of the AI model. AI modeling replicates human intelligence and is most effective when it receives multiple data points. Organizational implementation of an AI model can accurately solve complex issues while keeping operational cost low.

Artificial intelligence

It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence.

Deep learning

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.

Generative Adversarial Network (GAN)

A generative adversarial network (GAN) is a machine learning (ML) model in which two neural networks compete with each other by using deep learning methods to become more accurate in their predictions. GANs typically run unsupervised and use a cooperative zero-sum game framework to learn, where one person's gain equals another person's loss.

Generative Models    

Generative models in AI are algorithms that learn to generate data similar to the training data, providing valuable insights into the underlying distribution of the data.

GPT: Generative Pre-Trained Transformer

A type of language model developed by OpenAI and is able to understand and create human-like responses based on the text-based input.

Large Language Model

A large language model (LLM) is a deep learning algorithm that can perform a variety of natural language processing (NLP) tasks. Large language models use transformer models and are trained using massive datasets — hence, large. This enables them to recognize, translate, predict, or generate text or other content.

Machine learning

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

Neural networks

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms.

Supervised learning

Supervised learning uses a training set to teach models to yield the desired output. This training dataset includes inputs and correct outputs, which allow the model to learn over time. The algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized.

Unsupervised learning

Supervised learning uses a training set to teach models to yield the desired output. This training dataset includes inputs and correct outputs, which allow the model to learn over time. The algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized.