Neurosymbolic AI is overhyped. It's just bolting on LLMs to symbolic AI and pretending that it's a "brand new thing" (it's not, it's actually how most LLMs practically work today and have been for a long time GPT-3 itself is neurosymbolic). The advocates of approach pretend that the "reasoning" comes from symbolic AI which is known as classical AI, which still suffers from the same exact problems that it did in the 1970's when the first AI winter happened. Because we do not have an algorithm capable of representing the theory of mind, nor do we have a realistic theory of mind to begin with.
Not only that but all of the integration points between classical techniques and statistical techniques present extreme challenges because in practice the symbolic portion essentially trusts the output of the statistical portion because the symbolic portion has limited ability to validate.
Yeah you can teach ChatGPT to correctly count the r's in strawberry with a neurosymbolic approach but general models won't be able to reasonably discover even the most basic of concepts such as volume displacement by themselves.
You're essentially back at the same problem where you either lean on the symbolic aspects and limit yourself entirely to advanced ELIZA like functionality that can just use classifier or your throw yourself to the mercy of the statistical model and pray you have enough symbolic safeguards.
Either way it's not reasoning, it is at best programming -- if that. That's actually the practical reason why the neurosymbolic space is getting attention because the problem has effectively been to be able to control inputs and outputs for the purposes of not only reliability / accuracy but censorship and control. This is still a Garbage In Garbage Out process.
FYI most of the big names in the "Neurosymbolic AI as the next big thing" space hitched their wagon to Khaneman's Thinking Fast and Slow bullshit that is effectively made up bullshit like Freudianism but lamer and has essentially been squad wiped by the replication crisis.
Don't get me wrong DeepSeek and Duobau are steps in the right direction. They're less proprietary, less wasteful, and broadly more useful, but they aren't a breakthrough in anything but capitalist hoarding of technological capacity.
The reason AI is not useful in most circumstance is because of the underlying problems of the real world and you can't algorithm your way out of people problems.
works so well on poor people. why isn't
This is mainly hype. The process of creating AI has been useful for drug discovery, LLMs as people practically know them (e.g. ChatGBT) have not other than the same kind of sloppy labor corner cost cutting bullshit.
If you read a lot of the practical applications in the papers it's mostly publish or perish crap where they're gushing about how drug trials should be like going to cvs.com where you get a robot and you can ask it to explain something to you and it spits out the same thing reworded 4-5 times.
They're simply pushing consent protocols onto robots rather than nurses, which TBH should be an ethical violation.