Automated Fake News Generation Sample Clauses

Automated Fake News Generation. ‌ In February 2019, OpenAI released the original version of the GPT-2 model, an ex- tension of the original GPT-1 (Generative Pre-trained Transformer) which mainly processes and generates text [212]. The large framework of GPT-2 is actually the xi Apart from the research on human deception that I have described in this section, there is a set of studies worth mentioning to the reader who wishes to understand more of the speci c complexities of human deception. Notably, the Special Issue of the Topics in Cognitive Science Journal on Lying in Logic, Language and Cognition , edited by Xxxx xxx Xxxxxxxxx, Xxxxx Xxxxxxxx, and Xxxxxx Xxxxxxxxx, has both clari ed and partially con rmed some of my intuitions about the fact that complex cognitive processes are being executed inside the minds of humans when they deal with dishonesty [278]. The theoretical [81, 73] and empirical [147, 36, 91, 96, 20] work described in this special issue delves into the complexities and underpinnings of the logical, linguistic, cognitive, and epistemological aspects of lying and deception in social cognition. framework of GPT-1, but one which turns supervised NLP (natural language pro- cessing) tasks to unsupervised ones for training during a Fine Tuning phase [7]. Essentially, GPT-2 uses a large amount of unsupervised training data to expand the language corpus and obtain highly complete text generations. Although the user only inputs one sentence or several words, GPT-2 will return large articles. The problem with GPT-2 is that while it sometimes outputs logically coherent text, other times it outputs text that is incoherent or nonsensical. When users are presented this type of AI-generated text, they might initially find it challenging to verify its semantic authenticity [283]. However, after verifying keywords, users can easily discern such AI-generated fake news. These limits can be tracked back to the limitation of knowledge about a spe- cific domain [58, 188]. Strongly represented categories in the training data (Brexit, movies, popular personalities and so forth) are best suited for the generation of new deceptive text, as well as programs for capturing common sense knowledge such as OpenCyc [65] and Open Mind Common Sense [257]. At the other end of the spectrum are deeply scientific topics and much less known topics that hinder even GPT-2’s performance. Another limitation is these type of models’ capability to deal with changes from English to some other language for predicti...