Re: AI for Understanding Human Goals

i  think you are right Chris
how silly of me-
what about:
using the algo to infer the goal from any non xml doc, then convert to
stratml
would that be useful?
maybe thats what I was thinking
pdm

On Tue, Jan 26, 2021 at 2:23 PM Chris Fox <chris@chriscfox.com> wrote:

> It wouldn't be much of an algorithm if it couldn't infer goals from plans
> in which they are clearly demarcated in XML?
>
> I think what you'd really want to do is run the algorithm against anything
> that *wasn't* StratML and see if it could produce StratML.
>
> On Tue, 26 Jan 2021 at 02:46, Paola Di Maio <paoladimaio10@gmail.com>
> wrote:
>
>> Maybe Chris can run the algo on a stratml set
>>
>> On Tue, Jan 26, 2021 at 10:12 AM Owen Ambur <Owen.Ambur@verizon.net>
>> wrote:
>>
>>> Yes, Paola, it would be great to see what AI/ML algorithms might be able
>>> to do with the existing StratML collection, which now comprises >5K files
>>> ... but even more so if and hopefully when public agencies start publishing
>>> their *performance reports* in open, standard, machine-readable
>>> format... as U.S. federal agencies are ostensibly required by law to do.
>>>
>>> I'm always on the lookout for partners who might be willing and able to
>>> begin to demonstrate such capabilities.
>>>
>>> While the initial benefit of enabling taxpayers to see what they are
>>> getting for their money will be great, imagine how AI agents can help
>>> agencies learn from failure and thus improve their performance over time.
>>>
>>> It is painful to watch agency leaders continue failing to capitalize on
>>> that potential.
>>>
>>> Indeed, recent direction from the Trump administration's OMB director
>>> on the way out the door
>>> <https://www.linkedin.com/feed/update/urn:li:ugcPost:6701562085794492416?commentUrn=urn%3Ali%3Acomment%3A%28ugcPost%3A6701562085794492416%2C6757844681394032640%29>
>>> goes so far as to imply that agency leaders have no accountability for most
>>> of the objectives with which they are entrusted, as if those objectives are
>>> merely jokes being played on taxpayers.  Unfortunately, all that seems to
>>> matter is what suits The Politics Industry.  The question is how long
>>> voters and taxpayers will put up with such behavior.  Hopefully, not
>>> indefinitely.
>>>
>>> Owen
>>>
>>> On 1/25/2021 7:19 PM, Paola Di Maio wrote:
>>>
>>> Thank you Owen
>>> wouldn't it be great to try the algorithm on some stratml resources
>>>
>>>
>>> On Tue, Jan 26, 2021 at 12:04 AM Owen Ambur <Owen.Ambur@verizon.net>
>>> wrote:
>>>
>>>> "In the quest to capture ... social intelligence in machines,
>>>> researchers from MIT’s Computer Science and Artificial Intelligence
>>>> Laboratory (CSAIL) and the Department of Brain and Cognitive Sciences
>>>> created an algorithm capable of inferring goals and plans, even when
>>>> those plans might fail."
>>>>
>>>> "... ability to account for mistakes could be crucial for building
>>>> machines that robustly infer and act in our interests ... Otherwise, AI
>>>> systems might wrongly infer that, since we failed to achieve our
>>>> higher-order goals, those goals weren’t desired after all. We’ve seen
>>>> what happens when algorithms feed on our reflexive and unplanned usage
>>>> of social media, leading us down paths of dependency and polarization.
>>>> Ideally, the algorithms of the future will recognize our mistakes, bad
>>>> habits, and irrationalities and help us avoid, rather than reinforce,
>>>> them."
>>>>
>>>>
>>>> https://scitechdaily.com/new-mit-social-intelligence-algorithm-helps-build-machines-that-better-understand-human-goals/
>>>>
>>>> Wouldn't it be nice if AI-assisted business networking services helped
>>>> us avoid polarization and needless dependencies on The Politics
>>>> Industry
>>>> as we strive to achieve public objectives documented in an open,
>>>> standard, machine-readable format?
>>>>
>>>>
>>>> https://www.linkedin.com/pulse/politics-industry-v-we-people-magic-formula-owen-ambur/
>>>>
>>>> Owen
>>>>
>>>>
>>>>
>>>>
>
> --
> Chris Fox
> Chris C Fox Consulting Limited
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Received on Tuesday, 26 January 2021 06:29:55 UTC