Re: AI for Understanding Human Goals

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
>>>
>>>
>>>
>>>

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