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By writing prompts, we create our own expectation of what we will see. When the pattern-matching machine produces something roughly like it, we use our own imaginations to frame, filter, boost, sharpen and polish the output into what we want to see. We render that output as commensurate we can with our original instructions. | By writing prompts, we create our own expectation of what we will see. When the pattern-matching machine produces something roughly like it, we use our own imaginations to frame, filter, boost, sharpen and polish the output into what we want to see. We render that output as commensurate we can with our original instructions. | ||
When we say, “fetch me a tennis racquet”, and the machine comes back with something more like a lacrosse stick, we are far more impressed than we would be had a human done the same thing. We would think the human a bit dim. But with [[generative AI]] we don’t, at first, even notice we are not getting what we asked for. We might think, “oh, that will do,” or perhaps, “ok, computer: try again, but make the basket bigger, the handle shorter, and tighten up the net.” | When we say, “fetch me a tennis racquet”, and the machine comes back with something more like a lacrosse stick, we are far more impressed than we would be had a human done the same thing. We would think the human a bit dim. But with [[generative AI]] we don’t, at first, even notice we are not getting what we asked for. We might think, “oh, that will do,” or perhaps, “ok, computer: try again, but make the basket bigger, the handle shorter, and tighten up the net.” We can iterate this way until we have what we want — or we could just use a conventional photo of a tennis racquet. | ||
AI-generated image generation famously struggles with hands, eyes, and logically possible three-dimensional architecture. First impressions can be stunning, but a closer look reveals an absurdist symphony. Given how large learning models work, this should not surprise us. They are all trees, no wood. | AI-generated image generation famously struggles with hands, eyes, and logically possible three-dimensional architecture. First impressions can be stunning, but a closer look reveals an absurdist symphony. Given how large learning models work, this should not surprise us. They are all trees, no wood. | ||