Between the Lines: How AI Unwrapped New Stories from the Titanic's Glowing Rivers of Data
The Titanic's dead spoke in ribbons of light. Standing in the hushed gallery of New Orleans' traveling exhibit, I found myself transfixed not by the expected rusty pocket watches or waterlogged leather shoes, but by two floor-to-ceiling Sankey diagrams pulsing with electric blue and fuchsia pathways. Each glowing band represented human lives—hopes and heartbeats from 1912, transformed into streams of data flowing across black space.
Two floor-to-ceiling Sankey diagrams pulsed with ultraviolet blues and candy-store pinks, transforming dry passenger statistics into rivers of light. If you've never encountered a Sankey diagram, picture luminous ribbons of varying thickness flowing across black space, each stream's width encoding a precise quantity, each intersection a storytelling node. At their best, these visuals dance in the liminal space between art and analytics; at their worst, they overload the retina until meaning dissolves into kaleidoscopic noise.
The exhibition designers had created something magnificent. Standing before the wall-sized graphic labeled "City of Origin, Country of Origin, and Class on Board of the Titanic," I felt the hair rise on my forearms. Each glowing band wasn't just data—it was a current of flesh-and-blood hopes, fears that once breathed and dreamed in 1912. The thickest band, a royal blue torrent streaming from Southampton to the "Crew" category, represented hundreds of stokers, stewards, and mechanics who had signed on for steady work. A whisper-thin green filament traced Belgians in third class, while a stout magenta channel carried wealthy Americans to first-class cabins.
Yet the longer I stood there, mesmerized by color and contour, the more those variegated bands fused into a single word in my mind: noise. Beautiful noise, yes—but to a data scientist, noise is never merely chaos. It's fertile soil. To researchers like me, noise signals the presence of undiscovered patterns waiting just beneath the surface.
I raised my phone and captured a dozen photographs of the displays, knowing full well the images would be flawed—uneven lighting, tourists bobbing in and out of frame, reflections on the protective glass. Still, a question nagged at me: what could modern artificial intelligence make of this visual feast? Could a multimodal model cut through the noise and extract a coherent narrative? Perhaps even stories that human researchers had overlooked?
My family scattered toward the gift shop. I settled onto a bench, opened ChatGPT's o3 model on my phone, and typed five simple words: "Interpret this infographic." I dropped the first image into the chat and pressed send.
What followed in the next thirty minutes forever changed my understanding of AI's role in historical research. This essay explores that revelation—why it worked, what it uncovered, and how it signals a seismic shift in how scholars might extract fresh knowledge from visual artifacts. Consider this both a travelogue through digital possibility and a field guide to your own expeditions: by the end, I hope you'll pack a camera on your next museum visit, confident that a conversation with AI can transform those imperfect smartphone captures into genuine discovery.
