[ddalex]

I feel that for there are three requirements for a NN-based AGI, inspired by biology:

a). an internal feedback loop that evaluates a possible output without actuating it, and self-modifies the parameters if the possible output is not what it's needed

b). the capability (based on a) to model own behaviours without acting on them, and to model other agents behaviours and incorporate that model into the feedback

c). the ability to switch between modelling own behaviour and other agents behaviour intentionally by the model itself - as part of the feedback loop

i.e. what I feel it's totally missing in the self-driving cars today is the capability to model OTHER traffic participants actions and intentions; an experienced and attentive human driver does this all the time, pays attention to the pedestrians on the side if they want to jump in front of the car, pays attention to where other cars are LIKELY to go, pays attention to how the bicyclist that's currently overtaken may fall, even pays attention to random soccer balls flying out of a courtyard because a kid may be chasing that. I am not seeing any driving car trying to model any agent outside its own.

[joakleaf]

Cruise actually consider both social dynamics and uncertainty (i.e. what can hide behind an obstacle, or where are pedestrians/bikes/cars likely to move to).

If you are interested in self-driving cars, I can highly recommend their presentation from November 2021:

https://youtu.be/uJWN0K26NxQ?t=1467

For me it felt more convincing than Tesla's (a few months prior);

https://www.youtube.com/watch?v=j0z4FweCy4M

[ddalex]

Oh I haven't have heard about Cruise up until now. Will follow them, thank you

[Jasper_]

That's what gets me about self-driving cars. The road is a very social space, and follows social rules. Pretty much all of the communication and norms happening on the road are social ones.

The thing that would convince me AGI is ready would be to play a convincing game of poker. Or join in on a conversation mid-way through, listen to it, and engage with it actively. Show that machines are able to pick up on social cues, understand them, and learn new ones. It's a high bar, yes, but it's in my opinion a prerequisite for a self-driving car that's able to share roadways with other cars, cyclists, and kids playing in the street.

[curiousllama]

NNS can win at poker - one recently beat a bunch of pros. Games are great challenges but bad tests.

The structure both makes them tractable and not as generalizable as we'd like. To your point, social interactions aren't nearly so structured.

https://www.nature.com/articles/d41586-019-02156-9

[endymi0n]

https://www.nature.com/articles/d41586-019-02156-9/

[rileyphone]

https://www.creativemachineslab.com/uploads/6/9/3/4/69340277...

"A robot modeled itself without prior knowledge of physics or its shape and used the self-model to perform tasks and detect self-damage."

[knodi123]

lol, "the morphology was abruptly changed" is the most coldly scientific description of an injury I've ever heard.

[arduinomancer]

Well the theory for the end-to-end image based self-driving models is that they are supposed to cover that.

The reasoning is that given enough training data the system would know the pedestrian is going to jump out or the cyclist is going to fall just based on sheer volume of training examples. It would have seen that scenario tons of times in the image data.

Whether that will actually work is the question though

[Nasrudith]

Personally I think that biology may be a flawed approach for most applications. Although the others arr worthy ends in themselves just for its role in understanding ourselves in a forensic archaeologist try to replicate sort of way, let alone any potential insights to biological brains.

Biology is glacially slow in comparison and one of the advantages from computing is being fast.

I believe that not modeling it is partially by design as a result of responsibility and blame frameworks. If you depend upon possible actions taken by others to be safe you are reckless. Extrapolating from current motions is more reliable than trying to profile everything. "They are moving towards the street at 3mph and 20 ft away, their vector will intersect with car, brake to avoid collision or accelerate enough to leave intersection zone before they can even reach us" seems a more reliable approach. It isn't like a kid will suddenly teleport into the road.

[mrkramer]

I doubt there will be AGI in our lifetime. Maybe some breakthrough happens but it won't be even close to human intelligence.

[bduerst]

I dunno. I didn't think consumer-common machine vision was achievable in our lifetimes either, yet everyone has a phone that can do it.

It's like all major tech breakthroughs - it seems impossible despite all the pieces being there, right up until someone puts them together.

[mrkramer]

Computer vision, image recognition, audio recognition, speech recognition were somewhat easy when Moore's law kicked in and when computer software industry emerged. But AGI is whole another beast. For general intelligence you need to have underlying infrastructure that runs it and guides it just like nervous system does for us people or like operating system does for computers. You can not for example glue together computer vision and speech recognition and call it intelligence when all it does is recognize what it sees and what it hears.

[chillacy]

My observation with statements like this both for and against some event occurring is that you'd have to be very specific with the definition of "AGI" and "human intelligence", otherwise everyone ends up claiming they predicted the outcome correctly (e.g. ray kurzweil's prediction evaluations seem to me like an exercise in motivated reasoning)