AIvsAI：人工智能自我识别的挑战报告（英）DeltalogiX-2025

Al vs Al The Challenge of Artificial Intelligence in Recognizing ItselfINTRODUCTIONGenerative Artificial Intelligence(GenAl)is redefining the boundaries of content creation,offering possibilities previously unimaginable in digital production.This advanced form ofAl,through systems like GPT(Generative Pre-trained Transformer)and BERT(BidirectionalEncoder Representations from Transformers),possesses the ability to generate texts withsuch a high level of complexity and coherence that it raises questions about authorship:isit a work of human intellect or the product of an algorithm?This research aims to explorethis frontier by deeply analyzing generative Al and its capacity to create content indistin-guishable from human creations.We will focus on the mechanisms underlying text gene-ration,from simple neural networks to more advanced models,highlighting how parallelprocessing and access to vast datasets are fundamental to their development.Special emphasis will be placed on text recognition tools,those software capable of evalua-ting whether content is the result of artificial intelligence or human intellect.We will inve-stigate their reliability through a comparative analysis of different types of texts:Al-genera-ted works,human writings,historical documents,and texts translated with the help of Al.This investigation,conducted with an empirical approach,aims to assess the effectivenessof five major recognition tools,shedding light on the challenges and limitations characte-rizing the current ability to discern between Al-generated content and human-producedworks.The empirical approach adopted in this research involves the systematic observation andanalysis of data collected through direct experiments and tests.This method allows us tobase our conclusions on concrete and verifiable evidence,ensuring a more rigorous andreliable analysis of textual recognition tools.In practice,we subjected the tools to a series oftests using Al-generated texts,human writings,historical documents,and translated texts,collecting quantitative and qualitative data on their performance.It is important to note that,although generative artificial intelligence can autonomouslycreate content,it relies on training developed through human-produced texts.Algorithmslike GPT and BERT have been trained on enormous amounts of human-produced textualdata,meaning that the knowledge base and linguistic structure of the Al entirely derivefrom human work.In other words,even if Al-produced texts seem autonomous,behindevery fragment of generated content there is always a trace of human intellect that pro-vided the training data.This aspect highlights the complexity of the authorship issue andthe need for reliable tools to recognize the origin of content.Antonio Grasso,in his book"Toward a Post-Digital Society:Where Digital Evolution Meets People's Revolution,"offersa pertinent clarification:Leltalogi2Insights