Hey did my last message show up in spam again? :P

On Tue, Apr 23, 2019 at 4:45 PM Linas Vepstas <[email protected]>
wrote:

> Hi Ben,
>
> On Tue, Apr 23, 2019 at 5:09 AM Ben Goertzel <[email protected]> wrote:
>
>> ***
>> Ah, well, hmm. It appears I had misunderstood. I did not realize that
>> the input was 100% correct but unlaballed parses. In this case,
>> obtaining 100% accuracy is NOT suprising, its actually just a proof
>> that the code is reasonably bug-free.
>> ***
>>
>>  It's a proof that the algorithms embodied in this portion of the code
>> are actually up to the task.   Not just a proof that the code is
>> relatively bug-free, except in a broad sense of "bug" as "algorithm
>> that doesn't fulfill the intended goals"
>>
>
> Recently, one week of my time was sucked into a black hole.  I read all
> six papers from the latest Event Horizon Telescope announcement. Five and a
> half of these papers are devoted to describing the EHT, and proving that it
> works correctly.  The actual results are just one photo, and a few
> paragraphs explaining the photo.  And you got that in the mainstream-press.
>
> I'd like to see the same mind-set here: a lot more effort put into
> characterizing exactly what it is that is being done, and proving that it
> works as expected, where "expected==intuitive explanation of why it
> works".  So, yes, characterizing the stage that moves from unlabeled parses
> to labeled parses is really important.  If you want to sound like a
> professional scientist, then write that up in detail, i.e. prove that your
> experimental equipment works.  That's what the EHT people did, we can do it
> too.
>
>
>>
>> ***
>>  Such proofs are good to have, but its not theoretically interesting.
>> ***
>>
>> I think it's theoretically somewhat interesting, because there are a
>> lot of possible ways to do clustering and grammar rule learning, and
>> now we know a specific combination of clustering algorithm and grammar
>> rule learning algorithm that actually works (if the input dependency
>> parses are good)
>>
>
> Yes.  Despite all the spread-sheets, PDF's and github issues that Anton
> has aimed my way, I still do not understand what this "specific combination
> of clustering algorithm and grammar rule learning algorithm" actually is.
> I've got a vague impression, but not enough of one to be able to reproduce
> that work.  Which is funny, because as an insider, I wrote half the code
> that is being used as ingredients.  So I should be in a prime position to
> understand what is being done ... but I don't.  This still needs to be
> fixed.  It should be written up at EHT-level quality write-ups.
>
>
>>
>> Then the approach would be
>>
>
> I don't want to comment on this part, because I've already commented on it
> before.  If there is an accuracy problem, its got nothing to do with the
> accuracy of MST.  The accuracy of MST should NOT affect final results!  If
> the accuracy of MST is impacting the final results, then some other part of
> the pipeline is not working correctly!
>
> In a real radio-telescope, the very first transistor in the antenna
> dominates the signal-to-noise ratio, and provides about 3dB of
> amplification. 3DB is equal to one binary-bit!  10^0.3==2^1 == Two to the
> power-one of entropy decrease. All the data processing happens after that
> first transistor.
>
> MST is like that first transistor. Its gonna be shitty.  If the downstream
> stages - the disjunct processing aren't working right, then you get no
> worthwhile results.   Focus on the downstream, characterize the operation
> of the downstream. Quit obsessing on MST, its a waste of time.
>
> --linas
>
> --
> cassette tapes - analog TV - film cameras - you
>
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