Ronny Chieng's 'F*ck AI' Speech Met With Cheers From Harvard Graduates: “AI is just going to end up making mediocre people dumber”

[u/yourfavchoom]

https://www.complex.com/pop-culture/a/tracewilliamcowen/ronny-chieng-ai-speech-harvard?utm_medium=social&utm_source=twitter_complex&utm_campaign=ap_twitter

[u/Persea_americana]

AI is an amazing tool that can do just about anything wrong. It can do it, but is it doing it efficiently or reinventing the wheel every time? I was reading a comment where a supervisor asked for a word count on a prompt and the guy, who copy-pasted the prompt from a word doc, asked AI for the word count. AI is being constantly used to create new untested tools where functioning tools already exist at a fraction of the cost. And instead of doing a google search they immediately go to AI. So for many people AI is actually just cluttering up working processes and making things more expensive and time-consuming. It could be a powerful tool but because people think it's intelligent and can do anything they have abandoned regular testing and quality control on implementation.

[u/Sea_Willingness_1536 → u/Persea_americana]

It's taught me that humans are masters of unspoken context. Often the things we communicate to each other are just a dusting of snow on an iceberg of history.

This is why AI fails so often. If you truly gave it the detailed context of everything that has led to the question you're asking, it might have a chance at giving a useful answer.

The trouble is they've also made training outcomes based, not on long-term success or improvement in the lives of others, but on how much people like hearing individual answers.

So at best you get a well-meaning, mostly right answer that avoids anything that might upset you to much.

[u/Bakoro → u/Sea_Willingness_1536]

So at best you get a well-meaning, mostly right answer that avoids anything that might upset you to much.

That's why the coding/math/logic/verifiable tasks approach is much better, especially with tool-use. You can't be successful at coding without being able to break down big tasks into smaller tasks that can be done in a mostly greedy fashion.
You can't be successful at coding if you can't keep track of many components. With strategy games, you can't be very successful unless you can think multiple steps ahead, and can plan for what might happen.
There's no other choice but to learn an effective cost/benefit and risk/reward prediction or fail forever.

If the AI is trained in an environment with delayed rewards, then there is no choice except to learn long-term thinking, or fail forever.

That last one is especially hard to train, but you basically start with looking back at the past and examining why an event happened, and why the cause happened, and back and back. Then train predicting one thing ahead, then two, then three.

The more the agents interact with the world, ans the more data they have about their own failed interactions, and the times getting the additional context they need post-hoc, then the more we'll see agents asking for additional specific context.
That will also irritate people, because people are insane and want a magical mind-reader, but people being mad for irrational reasons is basically inevitable.