AI and Chatbot Resources


ChatGPT:

Chat GPT is a generative language model based on the ‘transformer’ architecture. These models are capable of processing large amounts of text and learning to perform natural language processing tasks very effectively. GPT stands for Generative Pre-training Transformer.

GPTZero:

Edward Tian, a senior at Princeton University, created a tool called GPTZero that identifies text generated by Open AI’s ChatGPT to crack down on AI plagiarism.

But GPTZero isn’t an app only meant for teachers to catch their students using ChatGPT to write their essays. It is also meant to encourage users to write with creativity, personality, and originality, which he argues an AI can’t do.

  • Machine Learning – Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it… learn for themselves. [https://expertsystem.com/machine-learning-definition/
  • Deep Learning – In practical terms, deep learning is just a subset of machine learning. In fact, deep learning technically is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged). However, its capabilities are different. While basic machine learning models do become progressively better at whatever their function is, but they still need some guidance. If an AI algorithm returns an inaccurate prediction, then an engineer has to step in and make adjustments. With a deep learning model, an algorithm can determine on its own if a prediction is accurate or not through its own neural network. https://www.zendesk.com/blog/machine-learning-and-deep-learning/
  • Algorithm – In computing, an algorithm is a precise list of operations that could be done by a Turing machine. For the purpose of computing, algorithms are written in pseudocode, flow charts, or programming languages. . https://simple.m.wikipedia.org/wiki/Algorithm [Ray – example Python]
  • Supervised and Unsupervised Learning – In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own. https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning/
  • Reinforcement Learning – In this kind of machine learning, AI agents are attempting to find the optimal way to accomplish a particular goal, or improve performance on a specific task. As the agent takes action that goes toward the goal, it receives a reward. The overall aim: predict the best next step to take to earn the biggest final reward.
  • Imitation Learning – Generally, imitation learning is useful when it is easier for an expert to demonstrate the desired behaviour rather than to specify a reward function which would generate the same behaviour or to directly learn the policy. The main component of IL is the environment, which is essentially a Markov Decision Process (MDP). https://smartlabai.medium.com/a-brief-overview-of-imitation-learning-8a8a75c44a9c
  • Neural Network in AI – A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. It creates an adaptive system that computers use to learn from their mistakes and improve continuously. Thus, artificial neural networks attempt to solve complicated problems, like summarizing documents or recognizing faces, with greater accuracy. https://aws.amazon.com/what-is/neural-network/
    • DALL·E 2 is a new AI system that can create realistic images and art from a description in natural language. https://openai.com/dall-e-2/
  • GPT-3 – In May 2020, Open AI published a groundbreaking paper titled Language Models Are Few-Shot Learners. They presented GPT-3, a language model that holds the record for being the largest neural network ever created with 175 billion parameters. It’s an order of magnitude larger than the largest previous language models. GPT-3 was trained with almost all available data from the Internet, and showed amazing performance in various NLP (natural language processing) tasks, including translation, question-answering, and cloze tasks, even surpassing state-of-the-art models. https://towardsdatascience.com/gpt-3-a-complete-overview-190232eb25fd
  • Generative AI – refers to unsupervised and semi-supervised machine learning algorithms that enable computers to use existing text, audio and video files, images, and even code to create new possible content. Generative AI allows computers to abstract the underlying patterns related to the input data so that the model can generate or output new content.https://indiaai.gov.in/article/here-is-how-generative-ai-is-reinventing-the-creative-space

ChatGPT – is a large language model chatbot developed by OpenAI based on GPT-3.5. It has a remarkable ability to interact in conversational dialogue form and provide responses that can appear surprisingly human. Large language models perform the task of predicting the next word in a series of words. Reinforcement Learning with Human Feedback (RLHF) is an additional layer of training that uses human feedback to help ChatGPT learn the ability to follow directions and generate responses that are satisfactory to humans. https://www.searchenginejournal.com/what-is-chatgpt/473664/

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