Historian in the world of neural networks: the second wave of artificial intelligence technology application.

Over the last decade, artificial intelligence (AI) technologies have become one of the most sought-after areas of scientific and technological development. This process has also impacted historical science, where the first research in this area began in the 1980s (the so-called first wave) – both in our country and abroad. Then came the "AI winter," and at the beginning of the 2010s, the "second wave" of AI emerged. The subject of this article is the new opportunities for applying AI in history and the new problems arising in this process today, when the main focus of AI has shifted to artificial neural networks, machine learning (including deep learning), generative neural networks, large language models, etc. Based on the experience of historians applying AI, the article proposes the following seven directions for such research: recognition of handwritten and old printed texts, their transcription; attribution and dating of texts using AI; typological classification and clustering of data from statistical sources (particularly using fuzzy logic); source criticism tasks, data completion and enrichment, and reconstruction using AI; intelligent search for relevant information, utilizing generative neural networks for this purpose; using generative networks for text processing and analysis; and the use of AI in archives, museums, and other institutions that store cultural heritage. An analysis of the discussion of similar issues organized by the leading American historical journal AHR has been conducted. These are conceptual questions regarding the interaction between humans and machines ("historian in the world of artificial neural networks"), the possibilities for historians to use machine learning technologies (particularly deep learning), various AI tools in historical research, as well as the evolution of AI in the 21st century. Practical aspects were also touched upon, such as the experience of recognizing newspaper texts from past centuries using AI. In conclusion, the article addresses the problems related to the use of generative neural networks by historians.

Artificial Intelligence, artificial neural networks, machine learning, deep learning, generative neural networks, image recognition, text atribution, algorythms, data, historical source