erostrate

How do I rule out the masses being attracted by electromagnetism?

How palatable an argument is determines its actual impact. It's not logical to spend effort making arguments that are so unpalatable that they will just make people ignore you.

Did the author factor in the impact of this kind of article on the external perception of the rationalist / utilitarian / EA community when weighing the utility of publishing this?

Should you push arguments that seem ridiculously unacceptable to the vast majority of people, thereby reducing the weight of more acceptable arguments you could possibly make?

The author leads infrastructure at Cresta. Cresta is a customer service automation company. His first point is about how happy he is to have picked AWS and their human-based customer service, versus Google's robot-based customer service.

I'm not saying there's anything wrong, and I'm oversimplifying a bit, but I still find this amusing.

> this is only for paid surface parking for non-residents. Residents price is not impacted (which is controversial). Underground parking is private and not affected. Free parking (outside of 8AM to 8PM Monday to Saturday) stays free for everyone.

If my end goal was to apply this to everyone at some point, I would probably start with such a restricted law, then extend it step by step later.

This is the strategy famously identified as "commoditize your complement" by Joel Spolsky 22 years ago.

Are you saying Schmidhuber should also get credit for smartphones?

He talks a lot about the singularity but the biggest singularity I can see is in his ratio of "talks about AI" divided by "actual AI contributions".

Agreed, Ian Goodfellow wasn't the first to come up with the idea of jointly training a generator and discriminator. But he was the first to make it work for image generation with modern neural networks. For that Ian Goodfellow deserved to get most of the credit, and he did.

List of "famous" ML people not to waste time on:

- Gary Marcus

- Juergen Schmidhuber

- Pedro Domingos

- Max Tegmark

- Eliezer Yudkowsky

Some context for people unfamiliar with ML research: the author, Schmidhuber, is well known for claiming that he should get credit for many ML ideas. Most ML researchers think that:

- He doesn't deserve the credit he claims, in most if not all cases.

- There's a few cases where his papers should have been cited and weren't. That's fairly common.

- People do not get much credit for formulating an abstract idea in a paper or implementing it on a toy problem. Credit belongs to whoever actually makes it work.

- Credit assignment in ML is not perfect but roughly works.

The main problem with Alexa / Google Assistant / Siri is that the tech was not ready when they launched. We didn't have models that could understand non-trivial user requests, generate non-trivial actions or keep track of context properly. Now we do.

Amazon, Apple and Google are all working on incorporating LLMs but why is it taking so long? Why are these assistants still so bad? ChatGPT has been available for a year, GPT3 API for 3 years. I suspect some of it is legacy tech and legacy researchers from the pre-LLM era.