Exploratory engineering

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Exploratory engineering (EE) is the exclusively usage of well established knowledge in a fail-safe wasteful way to gain rough but reliable knowledge about the lower bounds of what is in principle doable. It allows one to probe the timeless fundamental limits of technology.

Benefits of using EE:
Exploratory engineering is capable of identifying desirable technological targets. Guiding posts (big and small). These targets are useful in that one can:

  • first further identify spots where enough science has been done to move on to more targeted development (not part of EE) and
  • then march forward there (also not part of EE)

The rules of EE are:

  • limitation to: established knowledge like: textbook physics - empirical knowledge
  • usage of: standard modeling techniques
  • exclusive use of: conservative engineering methodology

This forms a basis for reliable inference.
EE is remotely akin to an indirect existence proof in math.

What EE does not answer:

  • EE does not give hints how to get to the goals it identifies from the current technological capabilities.
  • EE does not give hints about economic viability along the way.
  • EE does say nothing about speed of development. It makes claims about the timeless potential of technology.
  • EE does not predict any scientific discoveries. These are fundamentally unknowable.
  • EE does not predict specific technological developments. (it predicts general targets)
  • EE does not predict winning technologies.

"Easy" predictability of a technology does not imply that it is easily accessible.

Testability of EE and (un)testability of the associated paths:

  • The theoretical work of identification of a goal via EE is of formal nature and can be rechecked and retraced by independent groups (as has been done in the case of APM (TODO: add ref) (and sometimes reached through various theory-paths)
  • The existence of at least one physical development path leading to an identified goal is not something that EE can answer. This is a separate problem that EE does not deal with.

Chances for the existence of a path toward a goal identified by EE:

  • One may judge chances higher to get to a target that is indirectly proven to exist (all results of correctly conducted EE are of this kind) than to a target that may not even exist.
  • Parts of the results of EE can point backward pretty close to the current level of technology. More backward pointing ends => more chances for a path. (related: preparatory development, theoretical overhang)
  • In case one has a good understanding of the goal one may be able to identify starting points for paths that are ready to begin targeted engineering. More forward pointing ends => more chances for a path.

Sources of confusion:

  • Exploratory engineering is pretty different from conventional engineering (despite outlining products) (in some respects).
  • Exploratory engineering is not a science (despite making theoretical predictions). In most respects it is actually more of a polar opposite of science. (Keyword: "testability", more on that further down.)

(TODO: reformulate following paragraph) Conventional engineering lies in between science and exploratory engineering. Certainty that a design will work (like required in exploratory engineering) is usually not the prime objective of engineering. In conventional engineering the designs must always be almost right away physically manufacturable and the fine details need to be worked out. To be competitive one needs to push the border of what is manufacturable. One needs to minimize cost and or maximize performance. Thus one often needs to steps into not yet well understood domains and consequently needs to occasionally create and do tests with prototypes. Tests/experiments are necessary not quite as often as in pure science/research but still regularly needed. There is an optimal number of tests per "development unit" that lie between the very many for science and the very view for exploratory engineering. In contrast to science in conventional engineering coordinated teams that are focused on specifically chosen directions are an absolute necessity to end up with compatible parts that fit together. In the book "Radical Abundance" by E.K.Drexler it is discussed what would happen if "pure-breed scientists" are given the (engineering) task of constructing a car. It turns out you'd end up with a huge plethora of parts but none would fit together. (see: molecular sciences, AP building blocks)

Comparisons

Science vs engineering (in general)

Basic purpose

  • science: provides knowledge – "What knowledge can we discover?"
  • (conventional) engineering: provides products – "What products can we make?"
  • exploratory engineering: provides knowledge – "What capabilities does physical law permit?"

Exploratory engineering is prone to be confused with (bad) science since instead of products (like engineering) EE provides knowledge (like science). Knowledge in form of predictions. Predictions that are difficult or not even yet possible to test experimentally. No less. (Reasons why this is ok in EE later). Hence the confusion with >bad< science.

Beside that a confusion of exploratory engineering with science is problematic because EE is much nearer to engineering in most other respects. For a prime example of some consequences of such a confusion see section "best knowledge" below.

Information flow

  • science: physical world => model
  • (conventional) engineering: model => physical world
  • exploratory engineering: same as conventional engineering but just showing a target not the path leading there

best models

  • science: best descriptions (=)
  • (conventional) engineering: reliable bounds (>=)
  • exploratory engineering: very reliable bounds (>>)

Confusing exploratory engineering with a science leads one to assume that it tries to make exact predictions. And exact predictions are fragile. But actually one of the core principles of exploratory engineering is to strictly follow a conservative design methodology (meaning large safety margins - example: a cable holding ten times more than the nominal load) to make predictions. And such predictions are robust.

The robustness from large safety margins is another of the reason why EE in contrast to science is allowed to make predictions that are hard to test or not yet testable without breaking down. At least not immediately.

Exploratory engineering is somewhat like conventional engineering in this regard but not quite. (See section: "larger (design) margins" for details)

best results

  • science: surprising discoveries
  • (conventional) engineering: predictable behavior
  • exploratory engineering: (highly reliable predictions)

When one confuses exploratory engineering with science one is lead to think that EE tries to predict surprising discoveries. Which's impossibility should be obvious when stated this way.

But EE does not aim at surprising discoveries near the fragile outermost border of our knowledge. It aims at highly reliable predictions that are implied by the deepest most solidified core of our knowledge. Predictions of things where we just don't yet know that we already know them. The "unknown knowns".

While strongly connected in "knowledge space" results of EE are detached (often hugely) in "technology space". Which is one of the aspects which distinguish EE from conventional engineering.

The focus on the innermost most solidified core of knowledge instead of the outermost most fragile part as the basis for work is one of the reasons why EE in contrast to science is allowed to make hard to test or not yet testable predictions without breaking down. At least not immediately. (An other reason for that is conservative design methodology. More on that elsewhere. See section: "best models")

best knowledge

  • science: exclude all alternatives
  • (conventional) engineering: know many alternatives
  • exploratory engineering: (same as conventional engineering)
  • Science is more of a breadth search for new and highly unpredictable phenomena.
  • Engineering is a depth search for highly predictable working designs.

At every step one of the best understood and most likely to work choices is taken.

Trapdoor warning: "Castle in the sky"
When one confuses what is actually an engineering problem with what is actually a scientific problem it can lead one to a premature judge that there are no paths at all (to a goal preliminary identified by EE that is here assumed to be sound) if just one step in one path turns out to be obviously broken.

With an increasing number of starting locations and an increasing focus of them (from starting fronts with a general direction towards more concrete starting points) the problem of reaching a goal set by EE increasingly turns from a scientific one to a engineering one.

In this regard one could classify EE in:

  • weakly acessible target EE ... few known starting locations towards the target predetermined by EE – still much science needed to start
  • strongly acessible target EE ... many known starting locations towards the target predetermined by EE – engineering already possible to start

Drawing a line where a problem switches from a scientific one to an engineering one can be hard. There's a somewhat contradictory regime in-between.

  • untargeted science <> targeted science <> untargeted engineering <> targeted engineering

Note: "untargeted" means "few relevant paths known" and "targeted" means "many relevant paths known"
While "targeted science" may sound reasonable its mirror partner is "untargeted engineering" which does not so much.

Even in cases where no relevant paths are visible yet and it's really a pure science problem (not the case in APM!) EE may have some merit. Arguably less though. Some (fundamentally unpredictable) scientific discoveries may (with a big question-mark) unlock a path later. Or the EE might lead in an unexpected direction and identify a goal that may actually already have visible starting points pointing to it.

APM: In regards to paths towards advanced forms of APM there are more than plenty of starting points.

Unfortunately there is a bit of a dilemma here:

  • People only put effort in looking for and finding some paths if they closely look at and understand the goals character.
  • People only put effort in closely looking at and understanding the goals character if they look for and find some paths.

organization

  • science: independent groups
  • (conventional) engineering: coordinated teams
  • exploratory engineering: independent groups or even individuals

In science work is usually conducted in small independent research groups with little common goal. (If there where a very precisely specified common goal then reaching it would not be a discovery and thus not a goal of science). In science having no highly precise goal is normal and A-Ok since everybody is looking at the same whole (nature) and the randomly collected pieces will eventually fit together in one big unifying picture. A newly discovered law. A good analogy are the blind men and the elephant.

In engineering though there are gazillions of ways a product can be built. Instead of a single big picture where everything converges towards there is a ginormous design space where everything runs apart. Active effort must be invested (standard interfaces) to reach specified goals. As an analogy one could use automobile part makers that just make many individually working parts. But without planning in advance the parts will never fit together though.

When moving from an EE target that is easy approachable (meaning: many entry points to paths are already unlocked) to a point where the target actually gets started to be approached (meaning: one starts to take the first steps into the paths) then one meets the problem of moving from science to engineering. This can be a major pain point.

In regards to APM this is a major pain point. Most of the early work (molecular sciences) was done in a scientific setting. By now (2017)molecular sciences have accumulated loads of results (more or less by accident). Some of these results can serve as starting points for the path to advanced APM! At least for some insiders it seems that by now there are more than enough results such that a shift towards a more targeted engineering like approach is overdue.

The multi pronged problem though is that in the field of APM:

  • The science side is reluctant to move to engineering. (difficult switch of operating culture from science to engineering) (active distancing to as stigmatized perceived fields).
  • The (small) engineering side is strongly focused on the exploratory engineering side. (...)

What is missing is a conventional engineering side. What is missing is a shift from molecular sciences to molecular engineering using soft nanosystems to strongly target hard/stiff nanosystems (aka more advanced APM). Incremental path.

Conventional engineering vs Exploratory engineering

basic constraints

  • conventional engineering: manufacturing
  • exploratory engineering: valid modeling
  • In engineering there always has to be a physical design that you can manufacture.
  • In EE there has to be design that can be modeled in a valid way worthy of the name "knowledge".

Since in EE there only has to be a modeled design and no physical product one might be led to not consider EE not as a form of engineering (enginnering in general) but instead consider it a form of science. Despite the fact that EE in most respects is much more near to conventional engineering than science.

level of design

  • conventional engineering: detailed specification
  • exploratory engineering: parametric model

Concept illustrations can lead one to confuse exploratory engineering with conventional engineering. This can lead one to critique that specific implementation details are missing. Common remark: "But this is not solved yet".
(Side-note: This refers to lack of details in the far off prediction. Not to lack of details of the path there. The path is a different topic.)

But it is important to notice that the things that are not worked out in concrete detail (atomistic details, concrete geometry of robotics, ...) are only the things of which it is known that they do not matter much. In other words the things of which it is believed that it should be straightforward to solve them.

In EE going into too much detail would actually be a fallacy.

  • Too much details can shrinks design margins and consequently diminishes robustness of predictions.
  • Too concrete designs are likely to diverge too much from what actually will be built.

Very important to note though is that the level of detail is a relative property. In some areas one can get quite detailed in absolute terms. Especially when using EE to work backwards to accelerate the process of closing the gap between current technology and proposed target.

In regards to advanced APM a good (if not the best) example of EE that is highly detailed in an absolute sense is the preliminary design and analysis of tool-tips for the mechanosynthesis of diamond and its polymorphs.

An example that seems to be violating the "avoid too much details" rule would be the preliminary design of crystolecules. Concrete designs of "crystolecules" could be considered too detailed since the chance of one of >exactly< these crystolecule designs to actually be built and used in massive scale is very low. (Except one builds a gargantuan library maybe. But there is no economic motivation for this. Except making it into a game maybe ... solving graphically pleasing puzzles has some recreational value after all).

The reason for why an overly detailed implementation here was not a violation of the "avoid too much details" rule is that there was the strong need to have at least some prototypical examples representing this general class of objects. Since instances of the general class of crystolecules are per definition built in large scales when advanced APM is reached.

The other way around the "poductive nanosystems" concept video is often criticized for a lack of implementation details in some areas where EE actually has very good reasons not to work them out in too great detail, because it would be fragile and useless guesswork. Of course there are "white areas" where indeed more detailed EE is possible and likely useful.

Going even farther those "white areas" are sometimes overlooked (e.g. sorting pumps). Especially if they are not depicted at all in concept visualizations (e.g. microscale Vacuum lockout).

main cost

  • conventional engineering: production, operation
  • exploratory engineering: design, analysis

design margins

  • conventional engineering: enable robust products
  • exploratory engineering: enable robust analyses

larger (design) margins

  • conventional engineering: raise costs
  • exploratory engineering: lower costs

Example

A prime example of successful exploratory engineering in history can be found in the preparations for making low earth orbit and beyond accessible. For non-involved people without sufficient internal knowledge of the then present technological capabilities it understandably seemed lunatic to want to go to the moon. Turned out they where wrong.

Advanced atomically precise technology (APM) suffers from a similar situation. Thus one of the goals of this wiki is to provide such sufficient internal information in a way that's somewhat digest-able for the average scientifically interested reader.

Spacial analogy

Although we'll not be able to directly test it anytime soon we know with (for all practical purposes) certainty that on a planet far away in a different solar system of our galaxy stones will fall just the same way as they fall here on earth. We can know that with (for all practical purposes) certainty due to our possession of newtons (well tested!) laws.

Just as pretty certain answers can be given for questions regarding sensibly chosen questions about some isolated stuff that resides so far away in space that we cannot jet reach and directly verify it, the same can be done for sensibly chosen questions about isolated stuff that lies in the not so near future.

Notes

  • It's called exploratory and not extrapolatory engineering which would make sense too - even more so perhaps.
  • The book Nanosystems is a prime example for exploratory engineering.
  • good translation to german: "erkundendes Konstruktionswesen"

[Todo: Find out and discuss how this relates to the commonly known scientific method (Wikipedia) ]

Related

External links

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