Technology

The difference being that the Hindenburg was a perfectly functioning rigid airship that had a lot of inherent risks due to the nature of its design.

AI isn't good enough at its actual job to be in this position. The risk of AI is people pretending that it works when it doesn't. It would be like if you made a blimp and filled it with carbon dioxide and people kept buying tickets and just sitting there waiting for it to take off.

“It’s the classic technology scenario,” he said. “You’ve got a technology that’s very, very promising, but not as rigorously tested as you would like it to be, and the commercial pressure behind it is unbearable.”

Is it promising though, Michael Wooldridge? Have you recently attended any magic shows and become excited by the potential of invisibility technology?

This is a good comparison if all it took for the Hindenburg to explode was just asking it to role-play as a ship that could explode. Conscious effort had to be expended to make the thing fail, but most models start to fail spectacularly if you use it in good-faith for more than like 30 minutes.

Except for the one person on the ground, the only people harmed in the Hindenburg disaster were the ones on board. If you're not "on board" when the AI bubbles pops and burns I expect you will not be hurt as much as those blindly taking that ride.

A disaster that causes a lot of bad publicity despite the majority (62/97) of the passengers surviving, and that may have been caused by sabotage?

“Oh the inhumanity!”

At this point, it'll cause a disaster and they'll still keep going.

Wooldridge sees positives in the kind of AI depicted in the early years of Star Trek. In one 1968 episode, The Day of the Dove, Mr Spock quizzes the Enterprise’s computer only to be told in a distinctly non-human voice that it has insufficient data to answer. “That’s not what we get. We get an overconfident AI that says: yes, here’s the answer,” he said. “Maybe we need AIs to talk to us in the voice of the Star Trek computer. You would never believe it was a human being.”

Hmm. That's probably a pretty straightforward modification for existing LLMs, at least at the token level.

You can obtain token probabilities, so you can give some estimate out-of-band confidence in a response, down to the token level. Don't really need to change anything for that, just expose some data.

And you could make the AI aware of its own neural net's confidence level, feed the confidence back into the neural net for subsequent tokens, see if you can get it to take that information into account.

https://en.wikipedia.org/wiki/Recurrent_neural_network

In artificial neural networks, recurrent neural networks (RNNs) are designed for processing sequential data, such as text, speech, and time series,[1] where the order of elements is important. Unlike feedforward neural networks, which process inputs independently, RNNs utilize recurrent connections, where the output of a neuron at one time step is fed back as input to the network at the next time step. This enables RNNs to capture temporal dependencies and patterns within sequences.

And now we hear stories about how easy it is to hack systems with built in LLM's and when you think about it, they are basically trained to be as helpful and forthcoming as possible, and then we give them the keys to the system!

Tangent: that pic reminds me of the terrorizing tit in Everything You Always Wanted to Know About Sex (*But Were Afraid to Ask)

Hindenburg was a hiccup in history relative to the fallout from an AI bust.

What? Global interest? Self-driving cars? Hindenburg? Is this professor a cat? Markov chain? The provided info is so crazy that I decided to NOT read the article.