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Predicting the Future of Augmented Intelligence
Published in Judith Hurwitz, Henry Morris, Candace Sidner, Daniel Kirsch, Augmented Intelligence, 2019
Judith Hurwitz, Henry Morris, Candace Sidner, Daniel Kirsch
The emergence of new algorithms will improve the accuracy of machine learning models. Currently, there are more than 40 key machine learning algorithms widely used for a variety of applications in science and business. Because organizations want to be able to integrate vision, speech, sound, and smell into their models, there will be new algorithms developed, or combinations of existing ones used, that will understand the nuances of these data types. One example, OpenAI’s new algorithm, called GPT-2, is designed for language modeling and makes use of a program’s ability to predict the next word in a given sentence. This capability increases the ability to generate sentences and stories. Give it a fake headline, and it’ll write the rest of the article, complete with fake quotations and statistics. Feed it the first line of a short story, and it’ll tell you what happens to your character next. It can even write fan fiction, given the right prompt. Although this very recently developed algorithm does not integrate vision, speech, and so on, it indicates that new techniques developed over the next 10 years might have rather surprising capabilities.
Synthetic Colleagues
Published in Ron Fulbright, Democratization of Expertise, 2020
In 2019, OpenAI announced a language model called GPT-2 able to predict the next word in a block of text. The result of unsupervised machine learning, and trained on a dataset of 8 million Internet pages, GPT-2 has a broad set of capabilities, including the ability to generate conditional synthetic text samples of unprecedented quality. In fact, GPT-2 is so good OpenAI is refusing to release the full model to the public for fear of malicious use. GPT-2 also is able to answer questions, display reading comprehension, summarize, and translate (Radford, 2019). Although GPT-2 falls short of these higher-level skills, it represents an important new way these capabilities can be self-learned possibly leading to cogs able to learn and improve rapidly to superhuman levels.
Introduction: Deep Learning in Natural Language Processing
Published in Shalom Lappin, Deep Learning and Linguistic Representation, 2020
Transformers are pre-trained on large amounts of text for extensive lexical embeddings. Many like OpenAI GPT (Radford, Narasimhan, Salimans, & Sutskever, 2018) have unidirectional architecture. GPT-2 Solaiman et al. (2019) is a large transformer-based language model that OpenAI released in 2019. It is pre-trained on billions of words of text, with 1.5 billion parameters, where these support large-scale embeddings. It is unidirectional in that the likelihood of a word in a sentence is conditioned by the likelihood of the words that precede it in the sequence. The probability of the sentence is the product of these probabilities, computed with the following equation.
State-of-the-Art in Automated Story Generation Systems Research
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2023
Rebeca Amaya Ansag, Avelino J. Gonzalez
GPT-2 (Radford et al., 2019a) is an open-sourced AI project by OpenAI that can generate extended text given an initial text input. Traditionally, such systems have utilised supervised learning techniques, where the system is trained to mimic certain behaviours, such as image classification. However, this leads to an inability for the system to generalise and expand into different tasks. Unlike other narrative generation systems, GPT-2 is not explicitly trained to create stories. Instead, it is trained via unsupervised learning on corpus of texts drawn from 8 million web pages (Radford et al., 2019b). This allows the AI to work from a large and varied dataset that contains text in different contexts and applications. The AI then creates text by predicting the next word, one at a time, through language modelling. Language modelling is the use of probabilistic and statistical techniques to predict the probability of a sequence of words (Rouse & Lutkevich, 2020). In this way, the system can generate logical text given all the previous text it has been given and/or generated.
Integrated Recognition Assistant Framework Based on Deep Learning for Autonomous Driving: Human-Like Restoring Damaged Road Sign Information
Published in International Journal of Human–Computer Interaction, 2023
Jeongeun Park, Kisu Lee, Ha Young Kim
Van den Oord et al. (2016) proposed PixelRNN and PixelCNN as models for image restoration. PixelRNN is divided into using the LSTM layer (Hochreiter and Schmidhuber, 1997) and diagonal BiLSTM (Schuster and Paliwal, 1997) and aims to generate one image, pixel by pixel. Most recently, Chen et al. (2020) introduced an image-generative pre-trained transformer (iGPT) model. The iGPT used a GPT-2 (Radford et al., 2019) structure, which is primarily used for natural language processing (NLP) tasks, and the high-level representation extracted through this exhibited high performance in unsupervised learning.
Assisting academics to identify computer generated writing
Published in European Journal of Engineering Education, 2022
El-Sayed Abd-Elaal, Sithara H.P.W. Gamage, Julie E. Mills
Identifying a work generated by AAG creates additional burdens on researchers, teachers and anti-plagiarism tools. Researchers at OpenAI (an AI research company) claimed that they have developed an advanced text generator that is too dangerous for the public to use (Whittaker 2019). They mentioned that their new natural language model, GPT-2, predicts the next word from 40 gigabytes of internet text, allowing users to generate realistic and coherent text for a given topic (Whittaker 2019).