How much can you edit a photo before it stops becoming true? That’s the question CNET tech reporter Stephen Shankland recently asked in the opening lines of his story, How Close is that Photo to the Truth: What to Know in the Age of AI ([ Ссылка ]) .

The article, which examines digital photography and advanced smartphone image processing in the era of AI, reaches beyond the polarizing visual minefield of generative AI by delving into aspects of this technologythat’s been quietly pre-baked into most every camera on the market these days.

The sophisticated processing under the hood of your digital camera is our jumping off point for a wide-ranging discussion with Shankland that touches on many aspects of the digital workflow, before scaling the slippery slopes of generative AI.

A few of the many points we cover include: Comparing the three primary generative AI platforms and discussing their differences, an assessment of AI manipulations and deepfakes, the ways in which a proliferation of camera phones can serve as a buttress against fakery, and the factor of a social contract in weighing the veracity of an image.

Today’s AI landscape seems to be morphing by the minute, a reality that’s reflected here with bonus content! Barely a week after our original discussion, Open AI’s new text to video application, Sora, was released to a tidal wave of interest, so we got Shankland back on mic. Stay to the end to hear our first impressions of this new technology and listen closely to discover how an AI bot got the last word in our chat.

Guest: Stephen Shankland

Top shot © Allan Weitz, [ Ссылка ]

0:00 Intro
2:22 How much can a photo be edited before it stops “becoming” true? Plus, the digital processing that goes on under the hood of your digital camera.
7:06 The sophisticated processing in your camera phone and how the resulting images compare to pictures made with a 35mm digital camera.
13:02 How much digital editing is too much and what’s the least amount of image adjustments possible before a photograph stops “being true.”
18:22 The matter of generative AI manipulations and deepfakes, the democratization of altering images, and how the proliferation of camera phones can serve as a buttress against fakery.
23:24 Comparing the three big generative AI platforms Stephen has worked with—Open AI’s Dall-E, Google’s ImageFX, and Adobe’s Firefly—and discussing how they differ, plus Allan’s impressions about working with Adobe Firefly, and how much of an AI-generated image is truly one’s own.
31:58 Prompt engineering, the bias of training data, the role of having fun when assessing the creative aspects of generative AI, and the factor of a social contract into reading the veracity of an image.
40:22 Episode Break
41:30 The potential for career opportunities in prompt engineering, new educational programs to arise from these new technologies, plus reasons why illustration is the creative area most threatened by AI.
48:27 The democratization of creative tasks due to computer technology, and the value of having a unique style or vision to creative success, plus the advantages of AI for stylistic
52:08 Ethical considerations, intellectual property rights, and copyright concerns in relation to AI generation.
57:03 In-camera authentication, content credentialing, and following the provenance of an image to be assured of its trustworthiness, plus whether this technology will ever show up in camera phones.
1:04:24 Episode bonus: Stephen’s first impressions of Open AI’s new text to video application, Sora.

Guest Bio: Stephen Shankland has covered technology, computing, and digital imaging as a principal writer and reporter for CNET since 1998. He’s also a professional photographer who’s particularly intrigued by new trends in AI. Stephen stumbled into journalism as a fledgling science reporter covering the Los Alamos National Laboratory. His first, and biggest, scoop was about radioactive kitty litter discovered at the town dump.

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- Stephen Shankland’s CNET article on AI: [ Ссылка ]

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