AI help example

Here is something off-beat, as I closed socials and blog posting, as per my main blog.

Update: Nature article on the continuing relevance of Young's experiment.

The question

One day I remembered the famous double-slit experiment - handy intro to quantum physics in the news - I actually remembered it partially wrong, as this   v e r y   l o n g   AI conversation will reveal: it starts correcting me with French resources as I'm in France, and I asked to switch to English ones incl. Feynman's original, but check this: it ended up proposing online experiment analogs to try out myself! And as I'm new to Python (this century's standard coding, I go back last century to Java & HTML), I asked for Python tools to load so-called Jupyter Notebooks (Wikipedia). 

The answer

As mentioned, the first conversation is a very long read, AI summarizes it neatly:

The pattern does not vanish because a human looks at it; it vanishes because the setup records which-path information, which destroys the interference... The key idea is that the interference pattern disappears when the experiment records which slit the particle went through, not because a person consciously “looks” at it.

That las part was my misunderstanding, which in turn made me wonder at first:

What if AI agents were to run a mathematical simulation of interference vs. observed-path variant of the double-slit experiment. Would that change anything, coz agents aren't observing, they're just running the experiment w no knowledge?

AI answer was quite simple, really:

 No — a mathematical simulation run by AI agents would not change the physics of the double-slit experiment. It would only calculate the outcomes; the interference pattern or its loss still depends on the actual experimental setup, especially whether which-path information is physically available... If the AI simulates the unobserved case, it will compute interference. If it simulates the observed-path case, it will compute no interference. But in both cases, the AI is only predicting the result; it is not causing the experimental outcome.

The simulation 

AI then obligingly asks:

Would you like a compact Python simulation of both cases, with a plot of the two patterns?

Then it answers: 

I can show you the simulation logic, but I can’t generate the Python plot in this environment because chart/code execution is unavailable here.

First it proceeds to offer a series of step-wise  study of available simulations you can follow (no need to login, just say "Close" and it likely goes to the top, so press "CTRL F" & search for "Python"). 

Second I asked for DIY Python tools, and again AI was helpful: I found that in Google Colab, the easiest interface was Jupyter Notebook, and check this out: toward the end of the conversation, AI offered to cast Python scripts into Notebook-ready scripts! Stored in my Colab Notebooks folder, start with readme.txt... Python-ers go! Alternatively get the Colab Project & page down to the animation for these begin & end results: 

Begin (click to enlarge, picture here)

End (click to enlarge, picture here)

It means that when you start with single trace, eventually the double-slit shows a Gaussian distribution of numbers of hits along intervals explained in the first AI conversation. The which-path recording however destroys the interference, and the distribution is more random. That first conversation expands on more options. The second AI conversation helps non-Python-ers launch their own. Enjoy!

Footnote: as with my main blog, this is under Creative Commons License.

Select presentations…

“Oil Price and Innovation” for LinkedIn Pulse post "Preserving and Protecting your Geological Assets" PESGB Conference “Amazon Web Services for mega geo-data” AWS meetup “GIS Practice Recommendations” GIS & Remote Sensing Seminar “Reservoir Drainage Workflow” Early 3D GIS presentation

test

start


View larger map

finish

SW test

 What struck me in this bog's context was the commonality among People of the Book (Wikipedia) lore on the concept of inclusivity

flickr test

a day in the life - test

The intro blog post outlined this comprehensive yet fundamental data management process:

1. pull 50K lines of flat ASCII well data from the US BOEM (MMS in 2008)
2. extract to a readable spreadsheet using the source government metadata
3. transform that using free Talend Open Studio with data QC check tools
4. load it into PPDM WELL table in SQL Server Express also a free tool
5. post the data to MSFT Access or in QGIS, ArcMap and ArcGIS Online 

The result is a complete map of all the data in their geographic and economic context of Gulf of Maxico platforms, shipping lanes, leases and seismic anomalies (as in geologic types... not as in earthquakes!). please be patient - there are 50k wells to load - it may be small data but it's still a lot over the web!

..............

cliwoc test

Six months ago I posted on CLIWOC ship location data, and last month the complete historic weather superset focusing on ArcGIS Online (AGOL). Why? Because when wind force and direction data are joined to four look-up tables from each maritime agency involved, a 1/4M point dataset balloons to 1/2M points*! So  to reduce web data fetches, time-slider is used to step through the data be decade.

*: asked on my blog why that may be so, that drew an interesting answer posted as an addendum  

Mapcentia does not offer time awareness out-of-the-box yet, but just released the ability to post zipped shape files: thus a 1Gb file zipped to 30Mb and loaded pronto, far better than geoJSON preferred for interoperability that ballooned files written in plain text.  I had not subsetted the weather data into time spans, like the location data in quarter centuries. So I loaded the entire data set, and created views for each decade - Mapcentia's Martin Hogh assured me that postGIS could handle a gigabyte dataset, as indeed giscloud.com did too but sadly without the ability to orient symbols yet.

1. the entire dataset posts online in a flash... faster than on ArcGIS or QGIS desktop in fact!
2. this allowed to post the wind data in decade slices, by simply creating views to the above

So Mapcentia's cool styling also recently updated, helped post the datasets scaled and coloured by wind force (Beaufort scale) and oriented by wind direction. Stepping through the decades offers an arresting insight into wind data from 1740- 1860 (1660-1670 turned out ot be sample dataset, with no  data until 1740).

 Click to see map in new window

Note also that in addition to that, a full complement of weather attributes recorded in the ships captains logs is available, and can be loaded and styled by climate experts.  

I posted the gazetteer data as an example of further historic data available in this amazing CLIWOC data sets. Isn't it fascinating to explore the historic places, recorded with modern names for ease of use?

  Click to see map in new window