AI image generation and the credibility of photos

When AI-assisted photo manipulation is easy to do and hard to detect, the credibility of photos as evidence is diminished:

No one on Earth today has ever lived in a world where photographs were not the linchpin of social consensus — for as long as any of us has been here, photographs proved something happened. Consider all the ways in which the assumed veracity of a photograph has, previously, validated the truth of your experiences. The preexisting ding in the fender of your rental car. The leak in your ceiling. The arrival of a package. An actual, non-AI-generated cockroach in your takeout. When wildfires encroach upon your residential neighborhood, how do you communicate to friends and acquaintances the thickness of the smoke outside?

For the most part, the average image created by these AI tools will, in and of itself, be pretty harmless — an extra tree in a backdrop, an alligator in a pizzeria, a silly costume interposed over a cat. In aggregate, the deluge upends how we treat the concept of the photo entirely, and that in itself has tremendous repercussions. Consider, for instance, that the last decade has seen extraordinary social upheaval in the United States sparked by grainy videos of police brutality. Where the authorities obscured or concealed reality, these videos told the truth.

Perhaps we will see a backlash against the trend where every camera is also a computer that tweaks the image to ‘improve’ it. For example, there could be cameras that generate a hash from the unedited image and retains it, allowing any subsequent manipulation to be identified.

Related:

Internet-ed

As of last night, my new dwelling has that most indespensible of features that makes a modern building a home: home internet and wifi.

I had been holding off due to my lack of income, but my brother Sasha asked me to give a remote presentation to his class and I have had enough of the stress of trying to leach Starbucks and Massey College wifi for important meetings.

A broad-ranging talk with James Burke

As part of promoting a new Connections series on Curiosity Stream launching on Nov. 9, I got the chance to interview historian of science and technology, science communicator, and series host James Burke:

The more interview-intensive part begins at 3:10.

Some documents from the history of fossil fuel divestment at the University of Toronto

Back in 2015, during the Toronto350.org / UofT350.org fossil fuel divestment campaign, I set up UofTFacultyDivest.com as a copy of what the Harvard campaign had up at harvardfacultydivest.com/.

The purposes of the site were to collect the attestations we needed for the formal university divestment policy, to have a repository of campaign-related documents, and to provide information about the campaign to anyone looking for it online.

The site was built with free WordPress software and plugins which have ceased to be compatible with modern web hosting, so I will re-list the important content here for the benefit of anyone seeking to learn about the campus fossil fuel divestment movement in the future:

Of course, U of T announced in 2021 that they would divest. Since then, the Climate Justice U of T group which developed out of the Leap Manifesto group which organized the second fossil fuel divestment campaign at U of T (after Toronto350 / UofT350) has succeeded in pressuring the federated colleges of St. Michael’s, Trinity, and Victoria University to divest as well.

DeSilva and Harvey-Sànchez divestment podcast series complete

The fifth and final episode in Amanda Harvey-Sànchez and Julia DeSilva’s series on the University of Toronto fossil fuel divestment campaign, successively organized by Toronto350.org, UofT 350.org, and then the Leap Manifesto and Divestment & Beyond groups.

The episode brings back guests from each prior era, and includes some interesting reflections on what organizers from different eras felt they learned, the value of protest as an empowerment space and venue for inter-activist networking, the origins of the Leap Manifesto group in the aftermath of the 2016 rejection, as well as how they explain President Gertler’s decision to reverse himself and divest five years after he rejected the Toronto350.org campus fossil fuel divestment campaign.

Threads on previous episodes:

Can a machine with no understanding be right, even when it happens to be correct?

We are using a lot of problematic and imprecise language where it comes to AI that writes, which is worsening our deep psychological tendency to assume that anything that shows glimmers of human-like traits ought to be imagined with a complex internal life and human-like thoughts, intentions, and behaviours.

We talk about ChatGPT and other large language models (LLMs) “being right” and “making mistakes” and “hallucinating things”.

The point I would raise is — if you have a system that sometimes gives correct answers, is it ever actually correct? Or does it just happen to give correct information in some cases, even though it has no ability to tell truth from falsehood, and even though it will just be random where it happens to be correct?

If you use a random number generator to pick a number from 1–10, and then ask that program over and over “What is 2+2?” you will eventually get a “4”. Is the 4 correct?

What is you have a program that always outputs “4” no matter what you ask it. Is it “correct” when you ask “What is 2+2?” and incorrect when you ask “What is 1+2?”?

Perhaps one way to lessen our collective confusion is to stick to AI-specific language. AI doesn’t write, get things correct, or make mistakes. It is a stochastic parrot with a huge reservoir of mostly garbage information from the internet, and it mindlessly uses known statistical associations between different language fragments to predict what ought to come next when parroting out some new text at random.

If you don’t like the idea that what you get from LLMs will be a mishmash of the internet’s collective wisdom and delusion, presided over by an utterly unintelligent word statistic expert, then you ought to be cautious about letting LLMs do your thinking for you, either as a writer or a reader.

Ologies on invisibility

Alie Ward’s marvellous science communication podcast has a new episode on invisibility: Invisible Photology (INVISIBILITY CLOAKS) with Dr. Greg Gbur.

I was just about bowled over during my exercise walk on the Beltline trail, when Alie and Dr. Gbur discussed my Hyperface Halloween costume, designed to confound facial recognition systems.

Tomorrow I will read Dr. Gbur’s latest book: Invisibility: The History and Science of How Not To Be Seen.