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The “Ah, AI See!” project
The "Ah, AI See!" Project is the name of our systematic approach to helping neural networks
(AI) "see" better and thus "think" better. After all, when we say, “I see,” we mean “I (AI)
understand! Indeed, as Professor Fei-Fei Li says, in a recent article in Wired magazine by
Marguerite McNeal, “Understanding vision and building visual systems is really understanding
intelligence.” Through "feeding" AI complex visual images based primarily on the semiotic
logic of Charles Sanders Peirce, who writes, ‘‘[T]hat every thought is an external sign, proves
that man is an external sign," we of the “Ah, AI See” Project hope to help to prove that "AI is an
external sign." This, then, is the epistemological basis for helping AI “learn to see” through
“feeding” neural networks visual images “from the outside world” (outside in) rather than
programming them from the inside out. However, as McNeal writes in the above-mentioned
article,
Today, computers can spot a cat or tell us the make, model, and year of a car in a photo,
but they’re still a long way from seeing and reasoning like humans and understanding
context, not just content. (A bat on a youth baseball field and at a crime scene has two
very different meanings.) “The next step for my lab,” Li says, “is to build the cognitive
capability we need in fundamental vision tasks like understanding scenes, human
behaviors, and relationships, and reasoning and telling stories.”
To this end, our Peircean visual-semiotic-strategies put into play a veritable genome of visual
codes: icons, indices, symbols, images in context, images that embody Chomskyan syntactic
structures, images that ground the visual dynamics of predator-prey interactions based on the
solving by predators of complex visual puzzles by prey species, natural visual irony, images
with built-in narratives structures, and image sequences embodying narrative syntax: a kind of
cultural DNA of visual signs that allow neural networks to encode themselves from without—so
that someday our machines will say, “Ah, I see said the blind man as he picked up his hammer
and saw!”—and get it!
AI See Description INT3RP Website Version

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AI See Description INT3RP Website Version

  • 1. The “Ah, AI See!” project The "Ah, AI See!" Project is the name of our systematic approach to helping neural networks (AI) "see" better and thus "think" better. After all, when we say, “I see,” we mean “I (AI) understand! Indeed, as Professor Fei-Fei Li says, in a recent article in Wired magazine by Marguerite McNeal, “Understanding vision and building visual systems is really understanding intelligence.” Through "feeding" AI complex visual images based primarily on the semiotic logic of Charles Sanders Peirce, who writes, ‘‘[T]hat every thought is an external sign, proves that man is an external sign," we of the “Ah, AI See” Project hope to help to prove that "AI is an external sign." This, then, is the epistemological basis for helping AI “learn to see” through “feeding” neural networks visual images “from the outside world” (outside in) rather than programming them from the inside out. However, as McNeal writes in the above-mentioned article, Today, computers can spot a cat or tell us the make, model, and year of a car in a photo, but they’re still a long way from seeing and reasoning like humans and understanding context, not just content. (A bat on a youth baseball field and at a crime scene has two very different meanings.) “The next step for my lab,” Li says, “is to build the cognitive capability we need in fundamental vision tasks like understanding scenes, human behaviors, and relationships, and reasoning and telling stories.” To this end, our Peircean visual-semiotic-strategies put into play a veritable genome of visual codes: icons, indices, symbols, images in context, images that embody Chomskyan syntactic structures, images that ground the visual dynamics of predator-prey interactions based on the solving by predators of complex visual puzzles by prey species, natural visual irony, images with built-in narratives structures, and image sequences embodying narrative syntax: a kind of cultural DNA of visual signs that allow neural networks to encode themselves from without—so that someday our machines will say, “Ah, I see said the blind man as he picked up his hammer and saw!”—and get it!