Humans are wrong all the time. Do we really want "human-like" AI? And maybe this is where everything falls apart: will a machine as smart as a human be prone to the same errors as us?
Yes. Because we need AGIs to be able to tell the difference between a child in the roadway and a truck that is far away.
From the article: The issue is not simply that deep learning has problems, it is that deep learning has consistent problems.
Image recognition DL systems, no matter how big the training set, no matter how "strong" they are, consistently make the same kinds of errors. When deciding whether something is a truck far ahead, or a child just a short ways ahead, you need something that understands that trucks don't have arms, can recognize that instantly, and decide to apply brakes sooner.
No one is arguing for the creation of morally flawed, overconfident, selfish beings. When AGI is discussed, we're arguing for the creation of machines that understand. This is critical.
I can tell DALL-E to generate a restaurant scene where the patrons all have realistic faces... and it can't do it. The reason is that it paints with statistics, not with abstractions. It doesn't understand proportion or what a person looks like.
When a DL network demonetizes someone's YouTube channel, it doesn't understand fair use at all. It can only match riffs. It can't distinguish why those riffs are there, and that perhaps it is OK for those riffs to be in that video.
This is important because we're turning more and more decision making over to AI systems, due to the sheer scale of information flowing around the internet. People's lives and livelihoods are starting to be impacted, and the main flaw is that algorithms entrusted to make decisions make statistical matches and act... without understanding.
The mistakes those algorithms make will be ever larger, and you can't dispute with a statistical model when it arrives at an incorrect assumption. When humans make such mistakes, they can take in new information and update their credence accordingly. DL networks would require large training sets to sway the statistics.
So yes, we do want human-like AI, but not the straw-man version of it.
I don't think that conclusion is warranted at all.
The bigger problem with human reasoning is that it is impossible to keep 'the sum total of human creativity' in your head, but there is no reason why an AI could not do this, minus the 'head' part.
I was just thinking about this yesterday. Humans get stuff wrong way more than right. Our savior is being able to (most of the time) see when we are wrong and how to find out why. I think that is an important part of AGI.
"Sorry, the presentation isn't ready. My AI had a psychotic break over the weekend."