aifaq.wtf

"How do you know about all this AI stuff?"
I just read tweets, buddy.

#challenges

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@AlanShemper on November 08, 2023

#challenges   #society   #digital literary   #tweets  

@emollick on August 01, 2023

#challenges   #shortcomings and inflated expectations   #trust   #real-world experience   #medicine   #tweets  

A common refrain about AI is that it's a useful helper for humans to get things done. Reading x-rays, MRIs and the like is a big one: practically every human being who's worked with machine learning and images has worked with medical imagery, as it's always part of the curriculum. Here we are again, but this time looking at whether radiologists will take AI judgement into account when analyzing images.

They apparently do not. Thus this wild ride of a recommendation:

Our results demonstrate that, unless the documented mistakes can be corrected, the optimal solution involves assigning cases either to humans or to AI, but rarely to a human assisted by AI.

And later...

In fact, a majority of radiologists would do better on average by simply following the AI prediction.

It's in stark contrast to the police, who embrace flawed facial recognition even when it just plain doesn't work and leads to racial disparities.

My hot take is the acceptance of tool-assisted workflows depends on accomplishing something. The police get to accomplish something extra if they issue a warrant based on a facial recognition match, and the faulty nature of the match is secondary to feeling like you're making progress in a case. On the other hand, radiologists just sit around looking at images all day, and it isn't a case of "I get to go poke around at someone's bones if I agree with the AI."

But a caveat: I found the writing in the paper to be absolutely impenetrable, so if we're being honest I have no idea what it's actually saying outside of those few choice quotes.

The Fallacy of AI Functionality

#shortcomings and inflated expectations   #bias   #link   #lol   #challenges   #papers  

This paper, introduced to me by Meredith Broussard a couple months ago, is the funniest thing I have ever read. It's a ruthless takedown of AI systems and our belief in them, demanding that we start from the basics when evaluating them as a policy choice: making sure that they work.

From the intro:

AI-enabled moderation tools regularly flag safe content, teacher assessment tools mark star instructors to be fired, hospital bed assignment algorithms prioritize healthy over sick patients, and medical insurance service distribution and pricing systems gatekeep necessary care-taking resource. Deployed AI-enabled clinical support tools misallocate prescriptions, misread medical images, and misdiagnose.

All of those have citations, of course! And while yes, the AI-powered systems themselves often don't work, it's also the human element that repeatedly fails us:

The New York MTA’s pilot of facial recognition had a reported 100% error rate, yet the program moved forward anyway

Ouch. You can read the story on that one yourself at MTA’s Initial Foray Into Facial Recognition at High Speed Is a Bust (free link).

But yes, the full paper is highly highly recommended.

May 17, 2023: @ndiakopoulos

#plagiarism   #education   #challenges   #ai detection  

Actual article is here: Instructor Accuses Texas A&M Class of Using ChatGPT, Withholds Grades

“In Grading your last three assignments I have opened my own account for Chat GTP [sic],” the teacher wrote. “I copy and paste your responses in this account and Chat GTP will tell me if the program generated the content. I put everyone's last three assignments through two separate times and if they were both claimed by Chat GTP you received a 0.”

Sigh. The big problem is while this is obviously very very silly, there are plenty of tools that claim to be AI detectors. They don't work.

May 15, 2023: @ofirpress

#fact-checking   #challenges  

May 4, 2023: @mayfer

#hallucinations   #challenges   #evaluation   #lol  

We're impressed by the toy use cases for LLMs because they're things like "write a poem about popcorn" and the result is fun and adorable. The problem is when you try to use them for Real Work: it turns out LLMs make things up all of the time! If you're relying on them for facts or accuracy you're going to be sorely disappointed.

Unfortunately, it's easy to stop at the good "wow" and don't not get deep enough to get to the bad "wow." This tweet should be legally required reading for anyone signing off on AI in their organization.