Generative AI sans horse sense – unreliable narrators by design?

Gear up!

I like the way that I can do a Google search using a photo – drag & drop it into a search box – to identify its content. I like the way I can extract text from a photo (providing the photo has sufficient resolution on that content). And Amazon uses AI to summarize the gist of product reviews (to some degree).

But in the rush to embed AI for directly answering user questions – in a manner like “Ask Mr. Wizard,” this article notes that something is amiss. With large language models. Perhaps with no way out.

Not all movies can be saved in post. Not all software can be saved by updates. GIGO.

Rolling the dice is not a good basis for trustworthiness. “Mostly correct” incurs hapless harms. A form of quantum uncertainty: horse sense or horse pucky, eh.

Who vets fact-checking mechanisms? And if it comes to using low-wage human labor to fact-check, …

• Washington Post > Tech Brief > email news > “Google’s AI search problem may never be fully solved” by Will Oremus (May 29, 2024) – Last week, Google’s new “AI Overviews” stretched factuality.

“All large language models, by the very nature of their architecture, are inherently and irredeemably unreliable narrators,” said Grady Booch, a renowned computer scientist. At a basic level, they’re designed to generate answers that sound coherent — not answers that are true. “As such, they simply cannot be ‘fixed,'” he said, because making things up is “an inescapable property of how they work.”

But that [citing and summarizing specific sources] can still go wrong in multiple ways, said Melanie Mitchell, a professor at the Santa Fe Institute who researches complex systems. One is that the system can’t always tell whether a given source provides a reliable answer to the question, perhaps because it fails to understand the context. Another is that even when it finds a good source, it may misinterpret what that source is saying.

Other AI tools … may not get the same answers wrong that Google does. But they will get others wrong that Google gets right. “The AI to do this in a much more trustworthy way just doesn’t exist yet,” Mitchell said.

1 comment

  1. Talking dog 1.0

    As a counterpoint to the bumpy ride of LLMs, Steven Levy reminds us of previous tech revolutions – in the context of recent AI announcements:

    • OpenAI’s GPT-4o homage to the movie ‘Her” – “like the screenplay was a blueprint.”

    • Google’s rollout of a new version of its most powerful AI model, Gemini Pro … and a Project Astra tease.

    In his “Time Travel” commentary, Levy notes that “actually, reality [of 1995’s Internet] exceeded my hyperbole [at a time when most people in the United States had yet to log on, let alone net-surf].”

    So, while naysayers highlight LLM’s quirky performances (noted in my post), he marvels at the “talking dog.” Something which heralds a future arc like the once mundane smartphone – for better or worse (indeed).

    … like the story where someone takes a friend to a comedy club to see a talking dog. The canine comic does a short set with perfect diction. But the friend isn’t impressed – “The jokes bombed!”

    Like a sitcom about a talking horse, eh.

    • Wired > email Newsletter > Steven Levy > Plaintext > The Plain View > “It’s time to believe the AI hype” (May 17, 2024) – There’s universal agreement in the tech world that AI is the biggest thing since the internet, and maybe bigger.

    Folks, when dogs talk, we’re talking Biblical disruption. Do you think that future models will do worse on the law exams?

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