id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
hintze-artificial-2020		hintze-artificial-2020			.docx	application/vnd.openxmlformats-officedocument.wordprocessingml.document	5083	336	56	Artificial Intelligence, with its ability to machine learn coupled to an almost humanlike understanding, sounds like the ideal tool to the humanities. Machine learning allows us to learn from these data sets in ways that exceed human capabilities, while an artificial brain will eventually allow us to objectively describe a subjective experience (through quantifying neural activations or positively and negatively associated memories). The following paragraphs will explore current Machine Learning and Artificial Intelligence technologies, explain how quantitative or qualitative they really are, and explore what the possible implications for future Digital Humanities could be. Currently, machines do not learn but must be trained, typically with human-labeled data. At the same time, memory formation (Marstaller, Hintze, and Adami 2013), information integration in the brain (Tononi 2004), and how systems evolve the ability to learn (Sheneman, Schossau, and Hintze 2019) are also being researched, as they are building blocks of general purpose intelligence.	./cache/hintze-artificial-2020.docx	./txt/hintze-artificial-2020.txt