(I usually put an executive summary on these things.
But this is a shaggy dog story so we can't do that today.
This is not a late April fools joke. All of the data and stats is 100% real.
I've left a tarball with the basic numbers at <kymhorsell.com/DATA/marsquake.tgz>
if anyone wants to run them through their own spreadsheets or whatever).

The SEIS instrument package of the Insight mission has been measuring
the "pulse" of Mars for the past 6m.

The public release of the dataset show more than 2000 "events" some of
which (~200) are full-on Mars quakes, with the others mostly
unclassifiable thuds and squeaks made by the planet or wider
environment in one way or another.

The data I've looked at so far runs day by day from Aug through Sept of 2021.

And the second thing any data scientists would like to find out is --
what other daily data series highly correlate with Mars quakes so they
might be in some one predicted and/or understood.

My first run has not bothered to extract the (many!) features of each
individual event. So all the bumps and squeaks are lumped together
into daily totals with quakes and misc crustal movements.

For your enjoyment, should you wish to run your own analyses, the data
looks like this:

Date Number of events
2021.585 13
2021.587 42
2021.590 58
2021.593 50
2021.596 29
2021.598 33
2021.601 34
2021.604 25
2021.607 25
2021.609 30
2021.612 20
2021.615 24
2021.617 21
2021.620 29
2021.623 37
2021.626 28
2021.628 13
2021.631 29
2021.634 29
2021.637 36
2021.639 48
2021.642 45
2021.645 53
2021.648 17
2021.650 32
2021.653 39
2021.656 17
2021.658 25
2021.661 26
2021.664 45
2021.667 20
2021.669 33
2021.672 22
2021.675 55
2021.678 57
2021.680 42
2021.683 33
2021.686 42
2021.689 60
2021.691 37
2021.694 33
2021.697 85
2021.699 10
2021.702 10
2021.705 33
2021.708 26
2021.710 79
2021.713 40
2021.716 17
2021.719 10
2021.721 40
2021.724 25
2021.727 46
2021.730 17
2021.732 57
2021.735 29
2021.738 50
2021.740 25
2021.743 42

The "date" is my software's idiosyncratic way of rendering dates.
The fraction part is the Julian day less 1, divided by 366 and rounded
to 3 places. (Unf I've found different s/w packages do their
printing-rounding in slightly different ways meaning they are slightly
incompatible with the C version of the above algorithm; be warned that
lining up the above data with other daily data that superficially
looks similar might not exactly match up each day with those other datasets).

I use some AI-boosted states algorithms that try to ensure even a very
very large collection of statistical procedures will produce robust
answers. You need this kind of thing when you're doing what used to be
called "statistical exploration" (as opposed to "hypothesis testing").
When you churning through 1000s of datasets looking for matches you
can find some that are entirely spurious simply due to probability.
Remember the old bar trick of selecting 4 "pat" hands from just 21
random cards. Seems difficult. But there are just so many different
ways to select the 4 hands probability favors the task being easy.

So the S/W does a simple timeseries (ARMA11) regression against a
"target" dataset (in this case the above mars quake series) and 10s of
1000s of other daily datasets it has on hand. It finds the closest
matches using robust statistics and a little bit of reasoning based on
the metadata for each dataset to decide which matches are
statistically robust (generally to "4 9s" -- 99.99% likely aka .01%
unlikely) and also "make sense" based on what is being compared.

With Mars quakes the S/W knows about Mars, that it is a planet, and
that things relate logically to similar things so the best things to
look at with 1000s of choices are daily timeseries of planetary
motions. It further deduces "space weather" and other dataseries
related to the Sun might be appropriate things to check.

And this is what its first pass through the 5 TB of data it has on
hand turns up:

Suspect Lag Trans R2 Beta (90% CI +/-)
(d)
electron 16 -x -y 0.15241521 0.80137 0.505885
venus-RA 20 -y 0.13320002 -0.000936876 0.000639781
mercury-RA 5 -y 0.12643742 -0.000925927 0.00065767
mercury-v 16 -x -y 0.12541261 0.193645 0.139516
SN 1 -y 0.09657677 0.00405686 0.00332155
saturn-FV 16 -x -y 0.08995776 0.133218 0.113427
mercury-elong 16 -x -y 0.08980298 0.169894 0.144792
saturn-mag 16 -x -y 0.08434787 0.258611 0.228095
mercury-FV 16 -x -y 0.08122965 0.170773 0.153747
mercury-r 20 -x -y 0.06947440 0.656052 0.648802
uranus-phase 8 -x -y 0.04872648 897.788 1061.9
mercury-diam 8 -x -y 0.04815920 0.650431 0.77408
geomag 2 -y 0.03965379 0.28968 0.385224

The list includes most of the thing we might (in general) think are
related to causing "thuds" on Mars. The S/W has allowed some leeway
for the regression results with the "suspect" dataseries allowed to
slip up to 30 days so it finds the best match (largest R2) allowing
the delay between the "suspect" changing values to the time the
"target" (i.e. the Mars quake data, at the top) changes values.

The "trans" column shows which data transformations found the best R2.
"-x" means logs were taken for the suspect. "-y" means logs were taken
for the target.

The "R2" column gives the "explanation power" of the found timeseries
model. The suspect, with the given transformations and lag is said to
"explain" the relevant proportion of the target data. So, for example,
the first model above marked "electron" (a type of space weather
monitored above Earth) shows the electron events explain about 15% of
the day-to-day changes in Mars quakes. This is just the strength of he
match. The statistical procedures ensure there is only a very very
tiny chance -- from 1 in 1000 to 1 in 10000 -- the number of times a
change in the suspect data corresponds consistently with a change in
the target data might be just due to luck, even given the huge number
of datasets we trolled through to find it.

The Beta column shows how the transformed suspect predicts the daily
value of the number of Mars events. Unfortunately, interpreting this
depends on the transformations that were applied. For the models
marked "-x -y" it means Mars quakes rise or fall as a power law of the
respective suspect series. For the "-y" (only) models you can
interpret small Beta values as a percentage change (because exp(x) ~=
1+x for small x). E.g. the SN model says for each change (+/-) in 1
unit of the suspect the Mars quake data changes about (+/-) 0.4%. The
"SN" data is the daily sunspot number take from the Royal Belgian
Observatory series.

With these considerations in hand we can look at WHAT the S/W found
and why. For "electron events", which are just the daily number of
published Alerts for that kind of space weather without examining how
strong the event is predicted to be, the model finds more alerts in 16
days time predict more Mars seismic events. The link seems to be the
best found -- explaining 15% of seismic events. Electron events are
significant steams of electrons coming from the Sun -- a kind of solar
storm. They can interfere with satellites but generally don't do much
at the surface of the Earth. It "stands to reason" (according to the
AI) that events at the Earth that are caused by the Sun might also
affect things at Mars. So it is "logical" that particular type of
solar weather might be the "cause" of some of the seismic events seen
on Mars in late 2021 by SEIS.

Solar activity is the cause of electron weather. So it also "makes
sense" that Sunspot Numbers that predict overall solar activity, also
predict Mars seismic events and are a possible "cause". The S/W finds
changes in SN after 1 day are likely to predict changes in Mars
seismic events 1 day later. This makes sense. While electron events
might take 16 days to get from the Earth's orbit to Mars, other kings
of things including raw heat resulting from increased solar activity
might take only 1 day to get there and kick off some kind of surface
creaking or even the odd rock tumble and be detected by the SEIS
instrument.

The last item on our list, above, is another kind of space weather --
geomagnetic storms expected over Earth. Again, a possible source of
disruption to power grids and satellites here. But the S/W says it's
also one of the "logical" things that might cause seismic events on
Mars and is found to "explain" about 4% of them using the relevant
robust statistical model. Unlike slow old electrons, EM events take
only 2 days between Earth orbit and Mars orbit. But that makes some
kind of sense. Not quite as fast a reaction as changes on Sunspot
Number, but faster than electrons. All the effects take a
characteristic time to build up at Mars as well as travel the distance
between Earth orbit (where they are generally measured) and Mars.

http://ds.iris.edu/files/insight/v14/events_extended_multiorigin_v14_2023-01-01.xml

I noticed the database on mars quakes has been updated to the end of 2022.

The plot now shows some ups and downs and I had a moment to spare so I
asked an AI s/w to come up with a predictive model (i.e. where it saves 1/2 the data aside; finds the model using the other 1/2, then tests to see whether the saved part of the data works as well as the training part)
to explain/predict when the tremors would happen.

We all might expect it would have mostly something to do with the distance
between Mars and Jupiter, but I didn't tell the s/w that. I gave it a
list of data from the Horizons database and just wanted to see what it
could come up with.

And it did a reasonable job and also highlighted how AI's don't think
along conventional lines. :)
The program (experimentally) figured out there were 3 data it could combine to predict
the quakes:

phimars the phase of Mars --
the angle between Earth and Sun as
seen from Mars.
S-brtjupiter Surface brightness of Jupiter as
seen from the Earth. This is the
magnitude of a sq arcsec of Jup. Arcane!
deltamars The distance between Mars and Earth.

The plot of how it's model worked compared with the twitchy curve of
the actual quakes is here:

<kym.massbus.org/MARSQUAKES/marsquaremodel.gif>

Not a bad job. If you think about it for a second you'll see what it
did was essentially measure the dist between Mars and Jup using the
Earth as an intermediate point.

--
"Nothing in life is to be feared, it is only to be understood.
Now is the time to understand more, so that we may fear less."
- Marie Curie

A vast array of our most sophisticated sensors, including space-based
platforms, have been utilized by different agencies, typically in
triplicate, to observe and accurately identify the out-of-this-world
nature, performance, and design of these anomalous machines, which are
then determined not to be of earthly origin.
-- Jonathan Grey, NASIC intel officer, Wright Patterson AFB, 06 Jun 2023

We are not afraid to entrust the American people with unpleasant facts,
foreign ideas, alien philosophies, and competitive values. For a nation that
is afraid to let its people judge the truth and falsehood in an open market
is a nation that is afraid of its people.
-- JFK

On Friday, April 1, 2022 at 3:36:57 PM UTC-7, MrPosti...@kymhorsell..com wrote:
> (I usually put an executive summary on these things.
> But this is a shaggy dog story so we can't do that today.
> This is not a late April fools joke. All of the data and stats is 100% real.
> I've left a tarball with the basic numbers at <kymhorsell.com/DATA/marsquake.tgz>
> if anyone wants to run them through their own spreadsheets or whatever).
>
>
> The SEIS instrument package of the Insight mission has been measuring
> the "pulse" of Mars for the past 6m.
>
> The public release of the dataset show more than 2000 "events" some of
> which (~200) are full-on Mars quakes, with the others mostly
> unclassifiable thuds and squeaks made by the planet or wider
> environment in one way or another.
>
> The data I've looked at so far runs day by day from Aug through Sept of 2021.
>
> And the second thing any data scientists would like to find out is --
> what other daily data series highly correlate with Mars quakes so they
> might be in some one predicted and/or understood.
>
> My first run has not bothered to extract the (many!) features of each
> individual event. So all the bumps and squeaks are lumped together
> into daily totals with quakes and misc crustal movements.
>
> For your enjoyment, should you wish to run your own analyses, the data
> looks like this:
>
> Date Number of events
> 2021.585 13
> 2021.587 42
> 2021.590 58
> 2021.593 50
> 2021.596 29
> 2021.598 33
> 2021.601 34
> 2021.604 25
> 2021.607 25
> 2021.609 30
> 2021.612 20
> 2021.615 24
> 2021.617 21
> 2021.620 29
> 2021.623 37
> 2021.626 28
> 2021.628 13
> 2021.631 29
> 2021.634 29
> 2021.637 36
> 2021.639 48
> 2021.642 45
> 2021.645 53
> 2021.648 17
> 2021.650 32
> 2021.653 39
> 2021.656 17
> 2021.658 25
> 2021.661 26
> 2021.664 45
> 2021.667 20
> 2021.669 33
> 2021.672 22
> 2021.675 55
> 2021.678 57
> 2021.680 42
> 2021.683 33
> 2021.686 42
> 2021.689 60
> 2021.691 37
> 2021.694 33
> 2021.697 85
> 2021.699 10
> 2021.702 10
> 2021.705 33
> 2021.708 26
> 2021.710 79
> 2021.713 40
> 2021.716 17
> 2021.719 10
> 2021.721 40
> 2021.724 25
> 2021.727 46
> 2021.730 17
> 2021.732 57
> 2021.735 29
> 2021.738 50
> 2021.740 25
> 2021.743 42
>
> The "date" is my software's idiosyncratic way of rendering dates.
> The fraction part is the Julian day less 1, divided by 366 and rounded
> to 3 places. (Unf I've found different s/w packages do their
> printing-rounding in slightly different ways meaning they are slightly
> incompatible with the C version of the above algorithm; be warned that
> lining up the above data with other daily data that superficially
> looks similar might not exactly match up each day with those other datasets).
>
> I use some AI-boosted states algorithms that try to ensure even a very
> very large collection of statistical procedures will produce robust
> answers. You need this kind of thing when you're doing what used to be
> called "statistical exploration" (as opposed to "hypothesis testing").
> When you churning through 1000s of datasets looking for matches you
> can find some that are entirely spurious simply due to probability.
> Remember the old bar trick of selecting 4 "pat" hands from just 21
> random cards. Seems difficult. But there are just so many different
> ways to select the 4 hands probability favors the task being easy.
>
> So the S/W does a simple timeseries (ARMA11) regression against a
> "target" dataset (in this case the above mars quake series) and 10s of
> 1000s of other daily datasets it has on hand. It finds the closest
> matches using robust statistics and a little bit of reasoning based on
> the metadata for each dataset to decide which matches are
> statistically robust (generally to "4 9s" -- 99.99% likely aka .01%
> unlikely) and also "make sense" based on what is being compared.
>
> With Mars quakes the S/W knows about Mars, that it is a planet, and
> that things relate logically to similar things so the best things to
> look at with 1000s of choices are daily timeseries of planetary
> motions. It further deduces "space weather" and other dataseries
> related to the Sun might be appropriate things to check.
>
> And this is what its first pass through the 5 TB of data it has on
> hand turns up:
>
> Suspect Lag Trans R2 Beta (90% CI +/-)
> (d)
> electron 16 -x -y 0.15241521 0.80137 0.505885
> venus-RA 20 -y 0.13320002 -0.000936876 0.000639781
> mercury-RA 5 -y 0.12643742 -0.000925927 0.00065767
> mercury-v 16 -x -y 0.12541261 0.193645 0.139516
> SN 1 -y 0.09657677 0.00405686 0.00332155
> saturn-FV 16 -x -y 0.08995776 0.133218 0.113427
> mercury-elong 16 -x -y 0.08980298 0.169894 0.144792
> saturn-mag 16 -x -y 0.08434787 0.258611 0.228095
> mercury-FV 16 -x -y 0.08122965 0.170773 0.153747
> mercury-r 20 -x -y 0.06947440 0.656052 0.648802
> uranus-phase 8 -x -y 0.04872648 897.788 1061.9
> mercury-diam 8 -x -y 0.04815920 0.650431 0.77408
> geomag 2 -y 0.03965379 0.28968 0.385224
>
>
> The list includes most of the thing we might (in general) think are
> related to causing "thuds" on Mars. The S/W has allowed some leeway
> for the regression results with the "suspect" dataseries allowed to
> slip up to 30 days so it finds the best match (largest R2) allowing
> the delay between the "suspect" changing values to the time the
> "target" (i.e. the Mars quake data, at the top) changes values.
>
> The "trans" column shows which data transformations found the best R2.
> "-x" means logs were taken for the suspect. "-y" means logs were taken
> for the target.
>
> The "R2" column gives the "explanation power" of the found timeseries
> model. The suspect, with the given transformations and lag is said to
> "explain" the relevant proportion of the target data. So, for example,
> the first model above marked "electron" (a type of space weather
> monitored above Earth) shows the electron events explain about 15% of
> the day-to-day changes in Mars quakes. This is just the strength of he
> match. The statistical procedures ensure there is only a very very
> tiny chance -- from 1 in 1000 to 1 in 10000 -- the number of times a
> change in the suspect data corresponds consistently with a change in
> the target data might be just due to luck, even given the huge number
> of datasets we trolled through to find it.
>
> The Beta column shows how the transformed suspect predicts the daily
> value of the number of Mars events. Unfortunately, interpreting this
> depends on the transformations that were applied. For the models
> marked "-x -y" it means Mars quakes rise or fall as a power law of the
> respective suspect series. For the "-y" (only) models you can
> interpret small Beta values as a percentage change (because exp(x) ~=
> 1+x for small x). E.g. the SN model says for each change (+/-) in 1
> unit of the suspect the Mars quake data changes about (+/-) 0.4%. The
> "SN" data is the daily sunspot number take from the Royal Belgian
> Observatory series.
>
> With these considerations in hand we can look at WHAT the S/W found
> and why. For "electron events", which are just the daily number of
> published Alerts for that kind of space weather without examining how
> strong the event is predicted to be, the model finds more alerts in 16
> days time predict more Mars seismic events. The link seems to be the
> best found -- explaining 15% of seismic events. Electron events are
> significant steams of electrons coming from the Sun -- a kind of solar
> storm. They can interfere with satellites but generally don't do much
> at the surface of the Earth. It "stands to reason" (according to the
> AI) that events at the Earth that are caused by the Sun might also
> affect things at Mars. So it is "logical" that particular type of
> solar weather might be the "cause" of some of the seismic events seen
> on Mars in late 2021 by SEIS.
>
> Solar activity is the cause of electron weather. So it also "makes
> sense" that Sunspot Numbers that predict overall solar activity, also
> predict Mars seismic events and are a possible "cause". The S/W finds
> changes in SN after 1 day are likely to predict changes in Mars
> seismic events 1 day later. This makes sense. While electron events
> might take 16 days to get from the Earth's orbit to Mars, other kings
> of things including raw heat resulting from increased solar activity
> might take only 1 day to get there and kick off some kind of surface
> creaking or even the odd rock tumble and be detected by the SEIS
> instrument.
>
> The last item on our list, above, is another kind of space weather --
> geomagnetic storms expected over Earth. Again, a possible source of
> disruption to power grids and satellites here. But the S/W says it's
> also one of the "logical" things that might cause seismic events on
> Mars and is found to "explain" about 4% of them using the relevant
> robust statistical model. Unlike slow old electrons, EM events take
> only 2 days between Earth orbit and Mars orbit. But that makes some
> kind of sense. Not quite as fast a reaction as changes on Sunspot
> Number, but faster than electrons. All the effects take a
> characteristic time to build up at Mars as well as travel the distance
> between Earth orbit (where they are generally measured) and Mars.
>
> The other items are planetary parameters. It's first of all
> surprising that Jupiter is not found to contribute anything to Mars
> seismic events. The S/W checked. But it didn't find anything
> significant.
>
> The position of Venus is found to explain 13% of Mars seismic events
> after a 20 day lag. Venus' Right Ascension -- its position relative
> the the celestial equator as seen from Earth with its 23 deg tilt --
> seems to predict Mars quakes. The S/W notes with a big (*) this is
> unusual because a similar metric -- Venus' ecliptic latitude -- is
> found NOT to predict Mars events. Somehow the position of Venus as
> seen from Earth is a better predictor of Mars quakes than a "neutral"
> measure of the same kind of thing as seen from the Sun. Why this
> should be is a bit of a mystery. Unless you keep reading. :)
>
> The RA of Mercury is the next-most relevant thing. It also predicts
> 13% of Mars quakes but after only a 5 day delay. Somehow it seems
> "information" is leaking from Mercury to Mars in 5 days but it takes
> the same kind of information 20 days to travel from Venus to Mars. But
> of course Mercury might be closer to Mars in late 2021 when the Mars
> data was gathered. It orbits every ~100 days so 1/2 the time it's
> bound to be fairly close to Mars while Venus and its slower orbit
> might have been stuck on the other side of the Sun at that time.
>
> Mercury makes other appearances in our list. The "elongation" -- the
> angle between the Sun and Mercury as seen from Earth -- predicts about
> 8% of Mars quakes. The Beta shows the further Mercury appears from the
> Sun predicts more Mars quakes. If it appears close to the Sun --
> i.e. tending to be between the Sun and Earth -- there are fewer Mars quakes.
>
> The distance between Mercury and the Sun ("mercury-r") predicts 7% of
> Mars quakes. The Beta shows the further the very eccentric Mercury is
> from the Sun the more Mars quakes are predicted. It's hard to credit
> such a tiny planet even at its closest to Mars can affect anything,
> but the data shows otherwise. Maybe Mercury when it gets in the way of
> solar wind going to Mars it blocks some of it?
>
> Even more puzzlingly, the positions of Saturn and Uranus also seem to
> be linked with Mars quakes. The phase as each planet as seen from
> Earth seems to relate to 9% and 5% resp of Mars quakes. The phase
> angle is determined by the relative positions of the Earth, the Sun
> and the relevant planet. How can this affect Mars quakes? What
> "information" is travelling between these planets and Mars to knock
> rocks over somewhere?
>
> Puzzles puzzles puzzles.
>
> But now we get into very weird areas. You can also ask the S/W if any
> other data explains Mars quakes better than the "logical" datasets it
> already selected and tested.
>
> And, oops, there are a LOT of them. All seemingly statistically
> strong yet having no logical basis for suspecting they might be related.
>
> The first one is the stock price of Exxon. The match it finds looks like:
>
> Suspect Lag Trans R2 Beta (90% CI +/-)
> (d)
> xom-price 18 -x -y 0.12567289 -4.90575 3.49659
>
> I.e. 18 days after XOM changes value (+/-) on the NYSE about 13% of
> the time you find Mars quakes change (-/+). When prices rise Mars
> quakes tend to sharply go down 18 days later (the model is a power law
> with the power equal about -5). (There is no particular reason Exxon
> was selected -- it's the only stock I have daily data on).
>
> Why? The data statistically matches across almost 90 days of data. It
> can't be a "fluke". Can it?
>
> Letting the S/W look even further and relax some of its logic finds
> another bunch of datasets that match. All related to COVID. It seems
> daily covid data on Earth more closely predicts Mars quakes than any
> planetary positions or space weather we found above.
>
> The best matches look like:
>
> Suspect Lag Trans R2 Beta (90% CI +/-)
> (d)
> posAL 2 -y 0.33508070 -0.0735234 0.0277253
> deaths-cameroon 9 -y 0.25051859 0.229019 0.106041
> posCO 9 -x -y 0.22372041 -2.38069 1.20989
> posME 9 -x -y 0.22265019 -10.0426 4.97705
> posCA 12 -x -y 0.22073639 -3.72928 1.87574
> posPA 16 -x 0.21461976 62.1162 31.8091
> posOK 9 -x -y 0.20922472 1.66281 0.890644
> deaths-kenya 4 -y 0.19769733 -0.0233502 0.012711
> posTN 5 -x -y 0.19662649 2.79513 1.49854
> cases-uruguay 0 -y 0.19618060 0.0100669 0.00545494
>
> Why daily covid cases or deaths or positivity rates of certain US
> states should relate to mars quakes is "unclear". Especially given
> e.g. positivity rates in late 2021 delayed by 2 days matches up with
> 34% of Mars quakes -- 2x better than any "logical" planetary
> parameter. Even the worst match of our list above is better than most
> of the "logical" causes of Mars quakes.
>
> Puzzle puzzle puzzle.
>
> This is the problem with AI's. They can find things you never imagined
> and then refuse to explain why they found them!
>
> But we can go even further and just ask to find the best 3-39s
> statistical model that matches the 90 days of Mars quakes we have.
>
> And it spits out:
>
> Suspect Lag Trans R2 Beta (90% CI +/-)
> (d)
> ufoOK 0 -x -y 0.37411028 6.19028 2.18447
> ufoID 19 -y 0.28173884 4.02872 1.72197
> ufoRectangle 20 -x -y 0.27809017 4.40445 1.9361
> ufoChevron 9 -y 0.23750037 6.58536 3.1587
> ufoEgg 4 -y 0.23364850 17.035 8.41709
> ufoAR 18 -y 0.21302682 5.07945 2.58946
> ufoCT 14 -y 0.20908356 2.85133 1.51301
> ufoMA 15 -x -y 0.19997567 1.70376 0.929735
> ufoFL 0 -y 0.19744907 -0.446339 0.243162
> ufoblack 11 -y 0.19097419 3.05637 1.6999
>
> OK. Now it's becoming clearer! The unibers is jus totes f*ked up!
>
> Somehow the solar wind is linked with 15% of Mars quakes after days of
> "travel time" between the orbits of Earth and Mars but but some Okie
> sees some weird sh*t in the sky and within a day Mars is registering
> beyond 99% confidence some bump or grind 37% of the time.
>
> --
> "Nothing in life is to be feared, it is only to be understood.
> Now is the time to understand more, so that we may fear less."
> - Marie Curie
>
> Creator of TheBlackVault.com says Pentagon holding back secrets on UFOs
> The Hill, 24 Mar 2022 20:44Z
> The creator of TheBlackVault.com, a website that releases classified
> govt documents, said on Thu the Pentagon is holding back
> secrets about UFOs and "they don't want to tell the general ...
> [A highly redacted version of the secret version of the UFO report to
> Congress has been published at TheBlackVault. The blacked-out parts
> reveal the shape and operational characteristics of UFO's are
> apparently a US state secret. The public version of the report was
> less than 10 pages and -- after supposedly 70 y of collecting
> information -- the secret version for Congress was only 17
> pages. There were, however, about 100 videos also shown to politicians
> the public will not see anytime soon. The report underlines UFO's are
> not just the one thing. But the same is also true of the report's
> "other" basket where UFO's that are not one of the 4 mundane
> explanations are put. Military insiders currently favor the
> "ultra terrestrial" explanation -- a group of people or other beings
> that originate here on Earth, rather than some other planet. The
> secret part of the report apparently alluded to the space-going
> capabilities of UFO's, so "alien" can't be ruled out. A cautious
> evaluation would assume the explanation is a mix of all possibilities].
>
> Opinion: UFOs are a National Security Concern; the USAF Needs to Come Clean
> The Debrief/Matthew Ford, 14 Mar 2022
> Retired USAF officers have reported UAP in close proximity to military
> sites for decades. It is time for congress to demand answers.
>
> UFO Expert 'Absolutely Floored' By Revelation From Obama Library
> The news comes amid an unprecedented series of disclosures about UFOs.
> HuffPost/Ed Mazza, 16 Mar 2022 409a EDT
> A leading researcher into govt secrets says he may have found
> the "jackpot" of documents on potential extraterrestrial encounters.
> John Greenewald Jr., who operates The Black Vault, a website dedicated
> to revealing declassified govt documents obtained via Freedom of
> Information Act requests, said he asked the Barack Obama Presidential
> Library for anything it has on UFOs and related phenomena. What he
> got back left him "absolutely floored":
> [The Library said Greenewald could not come and view the items
> personally. He estimated it would take 16y to make the 26271
> electronic files and 3440 printed pages available for his FOI req].
>
> Thousands of UFO files are tucked away in a presidential library
> TweakTown, 15 Mar 2022 09:14Z
> The files residing in Barack Obama's Presidential Library have been
> uncovered by a Freedom of Information Act (FOIA) request. The FOIA request
> was filed by John Greenewald Jr., of The Black Vault, ...
>
> Mysterious flying object alerts military aircraft
> LUFOS, 11 Mar 2022
> A mysterious flying object on Kauai sent military jets scrambling last month.
> Witnesses say it's still not clear exactly what they saw.
W
Click here to read the complete article

>W
>We can't even predict it on earth, you >want to predict it on Mars?
>Crazy!

Classical science cant deal with anything complicated. It prefers to think things dont exist rather than handle a formula with more than 3 symbols in it. :)

Just demonstrated is an AI-derrived model that predicts 1/3 of Mars quakes robustly. No sweat for an AI.

No wonder they want to put the brakes on the development of better s/w -- the current AI's can write essays and solve science problems humans think "impossible".

Soon they will solve problems no human can conceive of. And the white collar class will be out of a job.

The interesting phase comes when the programs have to convince anyone they know what they are doing. Already my programs come up with things I can't follow or be sure they really are on the same page as I want them to be. They see things I cant. They try to explain them to me in baby language that is not convincing. I know they know something because they always beat me in go without trying.

It's like dealing with naughty savant children with autism. We are quickly going to interesting places.

--
Physics:
A science so advanced that every few years it discovers it had been
totally unaware of at least 90% of the universe up until that point.

On Tuesday, June 20, 2023 at 4:59:05 AM UTC-7, Kym Horsell wrote:
> >W
> >We can't even predict it on earth, you >want to predict it on Mars?
> >Crazy!
> Classical science cant deal with anything complicated. It prefers to think things dont exist rather than handle a formula with more than 3 symbols in it. :)
>
> Just demonstrated is an AI-derrived model that predicts 1/3 of Mars quakes robustly. No sweat for an AI.
>
> No wonder they want to put the brakes on the development of better s/w -- the current AI's can write essays and solve science problems humans think "impossible".
>
> Soon they will solve problems no human can conceive of. And the white collar class will be out of a job.
>
> The interesting phase comes when the programs have to convince anyone they know what they are doing. Already my programs come up with things I can't follow or be sure they really are on the same page as I want them to be. They see things I cant. They try to explain them to me in baby language that is not convincing. I know they know something because they always beat me in go without trying.
>
> It's like dealing with naughty savant children with autism. We are quickly going to interesting places.
>
> --
> Physics:
> A science so advanced that every few years it discovers it had been
> totally unaware of at least 90% of the universe up until that point.

Welcome to the world of Matrix!
😪😯