Bulletin No. 1, 2021

THE NEW GOSPEL ACCORDING TO A.I. 17 Ultimately, for machines to be able to make aesthetic judgements the ways humans do—beyond learning what humans find appealing—is a question of whether they can feel or consciously experience the emotions aroused by an artwork. That will require, of course, consciousness, and it is unlikely the machines will come with one for as long as we, their architects, lack an understanding of how our own consciousness came to be, let alone how it might be recreated for computers. ‘AI can have knowledge about emotions the way people who never like heavy metal still know it’s exciting, but knowing an emotion is not the same as feeling it,’ Dr. Szeto explained. We all know heavy metal is exciting by virtue of its loudness, tempo and some of its other objectively definable features, but to feel the excitement is to also register that indescribable, visceral rush. This is what AI misses, and this limited grasp of emotions is why computers still need human intervention when it comes to, say, colourizing manga. ‘Our model will need a guide if there’s an uncommon colour that the human artist wants to use to express a certain emotion,’ said Professor Wong. For instance, if the artist wants the normally blue sky to be painted red for a sense of menace, they will have to intervene and give the model a palette of different shades of red. ‘There are ongoing efforts to make machines extract emotions from a drawing. If they do end up being able to learn what the emotions of a drawing are while knowing what colours normally express those emotions, they might be able to do without a guide. But it’s tough to say how successful it will be, given how hard it is to describe emotions mathematically with all their subtleties.’ ‘A THING ABOUT PUBLIC UNDERSTANDING of AI is how extreme it tends to get. People like thinking of AI as either inept or godlike,’ said Dr. Szeto. Two years ago, the Chinese tech conglomerate Huawei caused a stir on the Mathematical and statistical approaches Examples include Formalized Music , a 1963 treatise by the Greek composer- engineer Iannis Xenakis. In his book, Xenakis proposes writing music using set theory and stochastic processes. Knowledge-based approaches These approaches involve experts providing the computer with the rules for writing music. Examples include CHORAL, developed by the American engineer Kemal Ebcioğlu in 1988. Language models These models treat music as a language and apply natural language processing (NLP) techniques to composition. Examples include the 1987 Experiments in Musical Intelligence ( EMI ) by the American composer- scientist David Cope . Evolutionarymethods These methods involve the computer generating a certain number of melodies, which are then rated to decide which of them will be retained. They get their name for approximating to evolution in nature, where the fittest survive. Machine-learning approaches These approaches work on neural networks, which learn what constitutes music from large collections of samples and use those insights to write music. Examples include DeepBach, AIVA and OpenAI Jukebox, which makes new songs given the lyrics and specifications of the genre and the artist.