Philosophy
TheoryKamil Szczerba

In our knowl­edge-based econ­omy, per­sonal and or­gan­i­sa­tional knowl­edge man­age­ment sys­tems have be­come ex­tremely pop­u­lar.

How­ever, sooner or later, one ques­tion arises: how to or­gan­ise that knowl­edge?

• With fold­ers? Should we put 🍔 Buying a burger in 🏪 Convenience shop or 📆 Today's tasks?
• With tags? The prob­lem reap­pears once we have a lot of tags, since we need to or­gan­ise them.

1. Ex­er­cise
   A thought ex­per­i­ment to dis­cover…
2. Mu­tu­ally ex­clu­sive, col­lec­tively ex­haus­tive
   …how to think ef­fi­ciently
3. So­lu­tion
   The method ap­plied to the ex­per­i­ment
4. MECE on­tol­ogy
   The prin­ci­ples ap­plied to the or­gan­i­sa­tion of knowl­edge
5. Comment
   The plat­form built with that or­gan­i­sa­tion
6. In­tel­li­gence aug­men­ta­tion
   How it un­locks knowl­edge for AI

Let us turn to peo­ple whose role is to solve prob­lems (in ad­di­tion to val­i­dat­ing the ex­ec­u­tives’ de­ci­sions): con­sul­tants. They come from out­side, have less in­for­ma­tion than any­one in­side the com­pany, and nev­er­the­less can find an ac­tion­able plan in 20 min­utes. It is be­cause they know how to think. Let us, there­fore, ex­am­ine the con­sul­tants’ method through a hands-on ex­er­cise:

Some op­tions prob­a­bly come to your mind:

• The mo­bile plan does not sat­isfy our cus­tomers
• The com­pe­ti­tion is be­com­ing bet­ter
• Their needs are chang­ing
• Etc.

But how do you know that have ex­plored all the pos­si­bil­i­ties?

We need to di­vide the ex­plo­ration space me­thod­i­cally, such that:

• no ex­plo­ration path over­laps an­other (mu­tu­ally ex­clu­sive),
• but taken to­gether, they map the com­plete space (col­lec­tively ex­clu­sive).

Here are 5 ways to do it, from the most sim­ple to the most com­plex (but pow­er­ful), with rel­e­vant ex­am­ples:

1. Op­po­sites ()

Many cus­tomers are leav­ing our SAAS. They may still use a ser­vice (but not ours) or no ser­vice at all.

2. Seg­ments ()

Our in­sur­ance com­pany re­ports higher costs. We can ex­am­ine those costs across the 0–24 year-olds, 25-64 year-olds, and 65+ year-olds.

3. Arith­metics ()

Our in­come is de­creas­ing. Since in­come = rev­enue - ex­penses, ei­ther the rev­enue de­creased, or the ex­penses in­creased (the “or” is in­clu­sive). Or for a restau­rant: our rev­enue is de­creas­ing. Since rev­enue = num­ber of clients × av­er­age bill, we can ex­am­ine both of these val­ues.

4. Processes ()

Thefts in­creases in our de­liv­ery com­pany. They may hap­pen: in the ware­house, dur­ing the load­ing, dur­ing the de­liv­ery, dur­ing the un­load­ing, or af­ter the de­liv­ery.

5. Con­cep­tual frame­works ()

The 3 C frame­work: if some­thing has changed, it may be be­cause the Com­pany has changed, the Com­pe­ti­tion has changed, or the Cus­tomer has changed.

The deeper the seg­men­ta­tion, the more it re­quires do­main-spe­cific knowl­edge.

Here is the in­ter­ac­tive so­lu­tion by an ex-McK­in­sey con­sul­tant, who man­aged to find the so­lu­tion (✅).

Quite an un­ex­pected so­lu­tion! When we first started list­ing pos­si­bil­i­ties, we jumped to the leaves of that tree, with­out tra­vers­ing the branches. That is why we did not ex­haust all the pos­si­bil­i­ties. Whereas, by this method, we can en­sure a proper ex­plo­ration. Each path ac­crues hy­pothe­ses, for­malised by this tree, lead­ing to a pre­cise enough sit­u­a­tion to be ac­tion­able.

When we think, we are fol­low­ing a path on such a tree. It is, there­fore, a way to map knowl­edge. Here is an ex­am­ple of a MECE on­tol­ogy:

Fi­nally, we ap­ply these tags to our atomic data:

🍔

To do

Today

Convenience shop

Kamil Szczerba

Location

🗺️

Philosophy

Article

Kamil Szczerba

This ex­am­ple is de­rived from Comment. You can edit the ti­tles and shuf­fle the notes, but the but­tons will not work. Feel free to play with it!

That is how we solve the knowl­edge or­gan­i­sa­tion prob­lem:

• Notes are atomic pieces of data: a text and some­times an icon.
• Notes can be tagged by other notes when they se­man­ti­cally com­pose it.
• Tags are tran­si­tive: para­graphs tagged by 🗺️ Structure of knowledge are also tagged by Philosophy.

Let us now look at the plat­form built on this prin­ci­ple.

Let us sup­pose I of­ten want to see my tasks of the day. I would start by cre­at­ing a note (press­ing or  dou­ble-tap­ping) with the rel­e­vant tags:

📅

Task

Today

Kamil Szczerba

This cre­ates the con­text 📅 Today's tasks. When­ever I click on the ar­row or open this note’s URL, Comment dis­plays only the notes shar­ing the same tags:

🍔

To do

Convenience shop

📝

Done

🗺️ Structure of knowledge

💸

To do

Tax office

🤖

In progress

/r/LocalLlama

📅

Today's tasks

• I added the spe­cial tag . This makes it avail­able in the nav­i­ga­tion (), like the links you see un­der Kamil’s notes.
• Be­low, you can see the cur­rent con­text: 📅 Today's tasks. Only the tags which are not shared with the con­text are dis­played.

Keep­ing the con­text un­der sight is very im­por­tant: it helps know where we are in our cur­rent think­ing. Switch­ing con­texts, there­fore, is very easy. Whereas in the past, when you were in­ter­rupted, you would lose a lot of time find­ing again your men­tal con­text, here, it is stored in the path to the cur­rent note. There­fore, we reached our goal of for­mal­is­ing in the in­ter­face the MECE think­ing!

Fi­nally, the icon means: “when­ever I fin­ish edit­ing, send a no­ti­fi­ca­tion to those sub­scribed to the changed notes” (the no­ti­fi­ca­tion in­cludes the con­text: why you sub­scribed to it). In­deed, the ease of shar­ing a con­text makes Comment a col­lab­o­ra­tive tool (hence its name: com­ment­ing is the so­cial ac­tiv­ity of shar­ing MEN­Tal COn­texts). Users can sub­scribe to con­texts (such as a pro­ject), that is, a note and all hose tagged by it. And ping­ing some­one is just a user­name away, mak­ing it work for doc­u­ments as well as in­stant mes­sages. CRDTs en­able real-time col­lab­o­ra­tion, multi-de­vice syn­chro­ni­sa­tion and of­fline work.

What helps the syn­chro­ni­sa­tion is that Comment keeps the state con­sis­tent. For ex­am­ple, if I opened two con­texts side by side,  To do and  Done, and moved a task from the for­mer to the lat­ter, its tag would be up­dated. Syn­chro­ni­sa­tion is bidi­rec­tional, mak­ing cre­at­ing tools like a kan­ban board straight­for­ward.

A pro­gram­mer can also add be­hav­iour through a sim­ple API. For ex­am­ple, an ‘in­box’ note would be pop­u­lated by his emails. He may drag an email to his to-do list, and later drop a mes­sage into his col­league’s ‘in­box’ note to send it.

An eas­ier in­ter­face for hu­mans is also eas­ier for ma­chines. Cur­rently, AI han­dles large amounts of data by break­ing it down into chunks, which are then con­verted into em­bed­dings (a vec­tor of fea­tures). Thus, it only pos­si­ble to find a chunk by whether it is about a sub­ject or not. This loss of nu­ance cre­ates an in­tel­li­gence ceil­ing.

El­liot Jaques, a re­searcher on cog­ni­tive com­plex­ity, found that the high­est in­tel­lects, able to plan on the scale of decades or more, are char­ac­terised by:

1. Con­cep­tual ab­strac­tions
2. Think­ing by equiv­a­lence (not or/​and, but if-and-only-if)

Both these char­ac­ter­is­tics are made pos­si­ble by the MECE tag on­tol­ogy. Comment, there­fore, may be the key which will un­lock our knowl­edge for AI.

Comment is cur­rently in in­vite-only ac­cess. For the lat­est ex­plo­rations of hu­man-com­puter in­ter­faces, I can rec­om­mend work by MIT’s Com­puter Sci­ence and Ar­ti­fi­cial In­tel­li­gence Lab­o­ra­tory and Ink & Switch’s thought-pro­vok­ing es­says.