It's not about people's belief they understand the software. And I can't speak 
to Dave's motivation for believing it's the Best Way. But I can describe ways 
that this style of design produces better software than other styles.

We've talked on list quite a bit about agency, hallmarks of living systems, 
complexity buzzwords like attractors, and far-from-equilibrium, etc. By 
treating software components as Kantian ends, rather than means, helps ensure 
*distribution* of computational effort/cost. Wrapping each component in its own 
ball of responsibility/duty/self-interest helps the designer play the game, on 
a long-term basis, of "logic, logic, where is the logic".

The traditional systems engineering approach attempts to distribute logic 
according to a modernist kind of planning the whole thing out, a waterfall 
process where you spend lots of upstream time planning, get a blueprint, parcel 
out effort to subcontractors, verify, test, deploy, maintain. This works, but 
not for long, and not for massive heterogeneous systems.

The newer approaches like "agile", "continuous delivery", "devops", "code as 
data", etc. are evolutionary steps from waterfall in the right direction. But 
the limit point is to eventually have every unit of computation carry with it, 
its own context, its own "closure". Such "personification" is an effective 
heuristic for doing (and remembering to do) that.

On 12/1/20 12:31 PM, [email protected] wrote:
>  
> If you are saying that the more AI acts like a person, the more People will 
> believe they understand it, I totally agree. Whether they believe truthfully 
> is a whole ‘nother that matter.  If ever there were a cradle for 
> manipulation, AI is it. 
>
> On 12/1/20 12:01 PM, Prof David West wrote:
>> 
>> Everything I do in software is grounded in personification / 
>> anthropomorphization of objects - small bits of software. I would contend 
>> that this is the best way to understand and design such software. So I see 
>> no reason to avoid personification of AI software and would, in fact, argue 
>> that current approaches to designing an AI will fail precisely because they 
>> do not take that perspective.

-- 
glen ep ropella 971-599-3737

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