Matt,

You haven't followed us closely. ;-)

We are doing vision (Perception from an AGI Perspective
<http://www.cis.temple.edu/~pwang/Publication/perception.pdf>),
language (Natural
Language Processing by Reasoning and Learning
<https://github.com/PhillyAGITeam/Website/blob/master/Papers/PAGI-TR-1.pdf>),
knowledge base (ConceptNet and WordNet connections), and robotics. For
application, see A
<https://cis.temple.edu/tagit/events/papers/Hammer.pdf>reasoning
based <https://cis.temple.edu/tagit/events/papers/Hammer.pdf> model for
anomaly detection in the Smart City domain
<https://cis.temple.edu/tagit/events/papers/Hammer.pdf> and Beyond ML/DL:
Learning by Reasoning for Smarter Cities and Safer Schools.
<https://www.youtube.com/watch?v=s7EtRJatVmg>

Of course there are still many problems to be resolved, though I don't
think low-level cognition is harder than high-level cognition, nor that the
latter "never was the real problem".

Regards,

Pei

On Tue, Dec 3, 2019 at 11:17 AM Matt Mahoney <[email protected]>
wrote:

>
>
> On Mon, Dec 2, 2019, 1:19 PM Eugene Surowitz via AGI <[email protected]>
> wrote:
>
>> You might take a look at Pei Wang's NARS system;
>> an 'Open NARS' system is available.
>>
>
> Pei Wang was on this list when it was first founded but left years ago.
> NARS (non axiomatic reasoning system) again has the same hardware
> limitations of other probabilistic reasoning systems. It lacks vision,
> language, speech, and robotics. It also never had a knowledge base or any
> useful applications.
>
> Again, not trying to be critical because it's a really hard problem.
> Modeling high level cognitive processing never was the real problem. It's
> the petaflops of low level processing tuned by millions of years of
> evolution.
>
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