Photogenic drawing

Long before smartphones and digital cameras were everywhere, inventors in the 1830s and 1840s were experimenting with new ways to capture pictures. One innovator, William Henry Fox Talbot (1800-1877), a true polymath, on a visit to Italy in 1833, and using a camera lucida, this simple draftsman’s aid produced a refracted image of the Italian landscape superimposed on the pages of his sketchbook. It seemed a simple task to trace the features of the village buildings, lake, and distant mountains with his pencil. But Talbot was frustrated by his poor drawing skills that day with the camera lucida leading him to recollect his experiences ten years earlier with another drafting aid, the camera obscura—a small wooden box with a lens at one end that projected the scene before it onto a piece of frosted glass at the back, where the artist could trace the outlines on thin paper. The camera obscura, too, had left Talbot with unsatisfactory results, but both experiments prompted Talbot to jot down thoughts about experiments he could conduct at home to see if Nature, through the action of light on material substances, might be brought to draw her own picture. He called his new discovery “the art of photogenic drawing.” Talbot’s early photogenic drawings, however, remained fugitive, for they were only partially stabilized with a solution of salt. A more permanent means of “fixing” the image with hyposulfite of soda was proposed by Talbot’s friend the eminent scientist Sir John Herschel; “hypo” was adopted by Talbot for most prints beginning in the early 1840s and is still used today as a fixer for black-and-white photographs. Moreover, Talbot demonstrated the commercial viability of his invention by means of a photographically illustrated book, The Pencil of Nature, published in parts beginning in 1844. But more than this; whether a personal project or a brief set by a third party, photography can help generate, develop and communicate ideas. https://www.metmuseum.org/toah/hd/tlbt/hd_tlbt.htm

The Power of Language

Natural language processing (NLP), the branch of artificial intelligence or AI is what gives computers the ability to understand text and spoken words in much the same way human beings can. That is, NLP drives computer programs that translate text from one language to another, respond to spoken commands, summarise large volumes of text rapidly, as exemplified by spam detection, Google Translate and Chatbots. NPL also has the ability to convert text to an image using text-to-image generating AI models such as ChapGPT and Stable Diffusion. The power of NLP, however, should come as no surprise - after all, language models have been around for decades. Indeed, natural language is the main means of communication, between humans, between humans and computers, and even between computers. Moreover, the human brain is good at pattern recognition or making connections between seemingly unrelated things and this ability is boosted by AI, and just by using words (text data or text prompts). AI, then, is a transformative tool, indeed, an ideation tool. An open question remains though: are complex AI models (machine learning) truly doing something new or just getting really good at statistics? Well, since machine learning uses “statistical techniques” it can easily be construed as rebranded statistics. But the way statistics is used by statisticians is different than the way it is used by the machine learning community. That is, and according to US statistician Leo Breiman, statisticians use data modelling whereas machine learning practitioners use algorithmic modelling*. Both models can be used to understand data and make predictions. But machine learning lets nature, data and trial-and-error speak about the function that drives inputs to outputs in a complex system: "Let the data do the talking". In contrast, statisticians believe they can guess about this mechanism using best practices making upfront assumptions about the process that generated the data.*  https://projecteuclid.org/journals/statistical-science/volume-16/issue-3/Statistical-Modeling--The-Two-Cultures-with-comments-and-a/10.1214/ss/1009213726.full