technology

The problem that symbolic AI systems ran into in the 70s are precisely the ones that deep neural networks address.

Not in any meaningful way. A statistical model cannot address the Frame problem. Statistical models themselves exacerbate the problems of connectionist approaches. I think AI researchers aren't being honest with the causality here. We are simply fooling ourselves and willfully misinterpreting statistical correlation as causality.

You're right there are challenges, but there's absolutely no reason to think they're insurmountable.

Let me repeat myself for clarity. We do not have a valid general theory of mind. That means we do not have a valid explanation of the process of thinking itself. That is an insurmountable problem that isn't going to be fixed by technology itself because technology cannot explain things, technology is constructed processes. We can use technology to attempt to build a theory of mind, but we're building the plane while we're flying it here.

I'd argue that using symbolic logic to come up with solutions is very much what reasoning is actually.

Because you are a human doing it, you are not a machine that has been programmed. That is the difference. There is no algorithm that gives you correct reasoning every time. In fact using pure reasoning often leads to lulzy and practically incorrect ideas.

Somehow you have to take data from the senses and make sense of it. If you're claiming this is garbage in garbage out process, then the same would apply to human reasoning as well.

It does. Ben Shapiro is a perfect example. Any debate guy is. They're really good at reasoning and not much else. Like read the Curtis Yarvin interview in the NYT. You'll see he's really good at reasoning, so good that he accidentally makes some good points and owns the NYT at times. But more often than not the reasoning ends up in a horrifying place that isn't actually novel or unique simply a rehash of previous horriyfing things in new wrappers.

The models can create internal representations of the real world through reinforcement learning in the exact same way that humans do. We build up our internal world model through our interaction with environment, and the same process is already being applied in robotics today.

This is a really Western brained idea of how our biology works, because as complex systems we work on inscrutable ranges. For example lets take some abstract "features" of the human experience and understand how they apply to robots:

• Strength. We cannot build a robot that can get stronger over time. Humans can do this, but we would never build a robot to do this. We see this as inefficient and difficult. This is a unique biological aspect of the human experience that allows us to reason about the physical world.

• Pain. We would not build a robot that experiences pain in the same way as humans. You can classify pain inputs. But why would you build a machine that can "understand" pain. Where pain interrupts its processes? This is again another unique aspect of human biology that allows us to reason about the physical world.