Chapter One, “Extracting the Essence: Toward Machine Translation of Literature,” rashly inquires whether artificial intelligence (AI) and machine translation (MT) may eventually be applied to literary translation. Such translation strives to somehow preserve the essence of a work while carrying it over to a different language and culture and giving it rebirth there. To recognize that essence, the translator must accurately capture the meaning of the original; appreciate its metaphors, connotations, register, references, and other abstract or associative factors; and choose among available target language expressions by exercising aesthetic judgments. Computers, however, presently remain incapable of such accuracy, abstraction, and judgment. We revisit these shortfalls in light of developments in MT and AI. We tease apart several separable aspects of literary translation – literal meaning, meter, rhyme, and the abovementioned associative elements – with reference to arguments about Vladimir Nabokov’s hyper-literal translation of Pushkin’s poem Eugene Onegin. Then, we discuss several avenues for improvement in MT which may help to extract these aspects of a text’s essence – first, those which may enhance textually grounded MT (i.e., MT trained on text only), leading to delivery of high-quality literal translations; and second, those related to future perceptually grounded MT (i.e., MT trained on simulated perception, e.g. of audiovisual input, as well as text), which might extract more abstract or associative elements of a text. We suggest that recognition of perceptually grounded categories will prove central to the essence extraction sought by translators. As this categorization improves, MT should increasingly support literary, and thus cultural, preservation. However, artificial aesthetic judgments will await artificial emotion.

Chapter Two, going beyond Chapter One’s lookahead toward artificial translation of literature, asks whether an AI might eventually gain the ability to actually create works of literary art. We again take as exemplar the hyper-conscious art of Vladimir Nabokov. To be sure, the suggestion that artworks combining Nabokov’s superhuman intricacy and wholly human depth could be authored by a collection of switches would horrify this transcendent author and does seem to fly in the face of everything that is most human. But while we are concerned with what machines might do, our more fundamental concern is to understand the human thoughts and feelings to which machines might aspire, and this understanding, promising to bridge the gap between C.P. Snow’s two cultures, is finally coming within reach. In our literary context, Nabokov scholarship provides many specific examples – in Ada: Or Ardor, Pale Fire, and other works – of the author’s hyper-conscious artistic techniques: glorying in memory; repetition to establish themes and motifs; allusion to wide-ranging works and facts; intricate puzzle posing; and relentlessly careful structuring at multiple levels of the text. Here, we consider several such techniques, speculating about the extent to which current or coming AI capabilities could approach them. In Section One, to clarify assumptions, I set forth my own current conceptions of computation, consciousness, feeling, language, and thinking, providing in the process a somewhat prejudiced AI primer for the computer-shy humanist. In Section Two, I apply to Nabokov’s prodigious work my understanding of these aspects of mind. Subsections focus on self-awareness, perception, memory, and puzzles.

In Chapter Three, focus is upon the recent successes of generative AI – of large language models (LLMs) for text and diffusion models for images and videos. These are yielding an explosion in AI capabilities, many of them “emergent,” meaning “largely unexplained.” I speculate about the underlying knowledge and procedures acquired through the current methods and their likely successors, with focus on linguistic skills and the implications for artificial translation and literary composition.

Mark Seligman, PhD, is Founder, President, and CEO of Spoken Translation, Inc. In 1998, he organized the first speech translation system demonstrating broad coverage with acceptable quality. Mark also publishes on speech translation and cognitive science topics.