Engineers[i] think of themselves as “applied scientists,”[ii] as opposed to those who embed intelligence into matter/energy, and, as a result, are a link between the realms of science and philosophy. Therefore, this theorem is logically true:

Theorem:  Engineering merges the realms of science and philosophy. 

An alternative definition of an engineer could be: those who perform intelligent work to create entities that natural causes either cannot or cannot in the quantity, time or speed desired.

The experience of doing this work leaves an engineer with some valuable lessons.

All designs must have a mechanism,[iii] defined broadly to mean the methodology that determines the outcome of any action.  Engineers design mechanisms as they are the means of creating functionalities.  To work, mechanisms must follow the laws of physics and at the same time, overcome the limitations of materials, sources of useable energy, and other logical limitations. 

An engineer quickly realizes not all outcomes that do not violate the laws of physics are possible, for many reasons.  In addition to design, an engineer builds prototypes and tests them.  Each is an unpublished experiment and an education regarding the difficulty of making complex systems work.   There are thousands of mistakes that can be made, and any one of them can result in failure making engineers keenly aware of limitations that restrict outcomes.

Complex systems have many components and subsystems that share information and cooperate with each other.  This produces the need for standards, specifications, protocols, and languages to achieve the coherence necessary for all the pieces to work together.  One impact is that the information needed for any given functionality will be intertwined with others and therefore is dispersed.  This fact also means that the idea of evolution by singular changes in a system is, in most cases, impossible due to information being required in multiple locations.

Natural causes and time are enemies of engineers.  Metals corrode.  Plastics age and change properties.  Sealed areas are breached.  Fluids become contaminated. And the list goes on.  The second law of thermodynamics tends to degrade engineered entities by reducing the specifitivity of the embedded information.

There is no such thing as perfection.  Everything is a trade-off.  Virtually all designs must balance performance, cost, reliability, appearance, longevity, size and weight, capabilities not possible by natural causes. 

Perhaps the most valuable lesson is the realization of the difficulty of finding and inventing designs that work and finding the fine balance that is usually needed to achieve the desired result.  Many physicists marvel at the “fine tuning” required to create a universe that could support our life. Engineers routinely deal with this problem. We are surrounded by marvels of complex design and mistakenly take them for granted.[iv]

Tools – All Static Things Embedded With Information

The discussion to this point has been about engineered machines. But there is a whole different category of intelligent work creations which are not machines.  For the purposes of this paper all such entities will be called tools.  Therefore, the definition for the term tool will be: an object created by intelligent work that does not perform intelligent work.  This is a very broad definition because it includes everything created by life except for machines.  This includes things we think of as tools (hammer, saw, wrench) but also art (paintings, literature, music, sculpture), medicines, buildings and other structures, telescopes, batteries, makeup, birds nest, bee hive, etc. 

Tools are made by intelligent machines, that is, living things plus machines designed and built by engineers.  Tools have embedded information, but not embedded intelligence like machines.  A logical distinction is that there is a statistical probability that natural causes could create some tools.  In this sense, the ID argument “where does the information come from” is treating life as a tool instead of a machine; both massive understatement because life is so much more than either!

Natural Causes can create objects that can be used as tools.  An example is a rock that has a shape that makes it convenient to use as a hammer.  Or a human can shape a rock to do the same thing.  Other tools, such as an adjustable (Crescent) wrench, must be engineered, i.e., designed and fabricated using intelligent work.

[i] Author’s Definition, Engineer:

Noun 1. a person who designs and builds machines and objects for a specified purpose.

Verb  1. Design and build a machine or object for a specified purpose.

Note: This definition is included to distinguish the design aspect with actualization as it seems they are often conflated and to exclude maintenance which is typically the role of a technician.

See other definitions here.

[ii] Princeton University calls their engineering department “School of Engineering and Applied Science”, see:  https://engineering.princeton.edu/

[iii] Author’s Definition, Mechanism:

1. a methodology including components, elements, parts and the associated energy and information flows enabling a machine, process or system that has demonstrated the ability to achieve its intended result.

Note: This definition is tweaked to include only proven methodologies.  See additional definitions here.

[iv] The reality of the gross disconnect of the complexity of complex systems is expanded in this post:  https://cs21c.com/marvels-taken-for-granted/.

Mousetrap

A mouse trap is a very simple device, but it is a machine and has logic functionality like more complex machines, albeit simpler in form. 

Initial Conditions:  The constructed design of the mousetrap is the starting point as with all machines.  In addition, to be functional, the trap must be baited, then set (rotate the hammer to the “set” position, rotate the trigger rod over it, latch under the trigger clasp).

Process Initiation:  The actual intelligent process does not start until a mouse applies a force (intelligent action) to the bait clasp and releases the trigger clasp.

Sensing/Input Means:  The sensing mechanism is the bait clasp.  The energy to activate the sensor comes from the mouse.  The form of the signal is the force of the mouse pushing on the bait clasp when trying to eat the bait

Logic Processor Means:  The logic processing means is the design of the mechanism that converts the input (mouse force) into the logical output of “kill the mouse” by the signal of un-restraining the trigger rod made possible by the design of the bait clasp, trigger clasp and trigger rod mechanism.  The off-equilibrium matter that implements the logic is the spring induced strain in the trigger bar, trigger clasp and bait clasp while the trap is set.

Actuator:  The actuator is a simple spring mechanism extended to be a hammer that strikes the mouse when released.  The spring is in a non-equilibrium state even when it is not set to provide a substantial force to hold the mouse in place after being struck.  The power source for the kill is provided by the human that further “winds” the spring during the bait/set process.  If the mousetrap was left to decay by natural causes, eventually the wood base would disintegrate and release the remaining “non-set” spring potential energy.

The mousetrap is an excellent example because it shows how logical functionality is contained in even the simplest of machines and the of use off-equilibrium conditions that normally exist in working machines[i]. It illustrates the embedded intelligence in a machine that extends the possibilities of natural causes. 

Engine

An engine is a more complex machine that has many coherent elements that must work in a precise manner for the engine to work.  Figure 2 shows a simple cross section drawing of a one-cylinder diesel engine with detail to show the actions needed to identify the input, signaling, processing and actuator for each process action. Missing in the figure are cooling system, starter motor and the battery to power the ECM (Engine Control Module).   This could be a gas engine if a spark plug was included.  More cylinders could be shown to illustrate the coherence to create smooth performance by staggering the connecting rods on the crankshaft and of the cams on the camshaft.

Initial conditions:  The mechanical design is the first initial condition.  In addition, the cooling system fluid, crankcase oil and fuel must be added.  The fuel lines and the coolant circuit must have trapped air removed.  And the battery must be charged.

Intelligent Process Initiation:  The engine must be started by rotating the engine, details omitted. 

Intelligent Process Actions:  There are three actions. 

Air/Fuel Mixture

The first action of the engine’s intelligent process is to provide the right air to fuel mixture inside the piston during what is called the intake cycle[ii].  During this time, the exhaust valve is closed, the intake valve is open, and the piston is moving downward, drawing in air/fuel mixture from the intake manifold.  The air coming into the combustion chamber passes by the injector, shown under the logic column in Figure 2.  The injector[iii] in combination with the ECM[iv] are the components that provide the coordinated, logical functionality of converting the inputs of fuel pressure, air flow, throttle position voltage, crank position pulse train, and top-dead-center pulse marker into an air/fuel mixture as a function of time to deliver a properly timed and specified air/fuel mixture to the combustion chamber. 

Valve Action

The other two actions are the operation of the intake valve and the exhaust valve.  This is accomplished with a cam that rotates at ½ the rate of the crankshaft accomplished by gearing that is not shown in Figure 2, and which is part of the logical functionality that is not identified in the figure.  The cam shaft has one cam that operates the intake valve to be open during the intake cycle (process step) and closed during the compression, power and exhaust cycles (process steps) and a second cam that operates the exhaust valve to be open during the exhaust cycle and closed for the other three cycles.  The cam operation of the valves, with the help of the valve springs, rocker arm, tappets and push rods are what provides the mechanical logical functionality.

The power to operate the logical processing comes from the power generated from the engine, in two forms.  The first is the torque generated by the engine which drives the gearing that rotates the cams and it keeps the battery charged.  The second is the battery which powers the ECM and the injectors.

Actuator Actions:  The actuator of the engine consists of the pistons, connecting rods, crankshaft, bearing and the mechanical structure surrounding and supporting these moving parts that creates the combustion chamber and the oil pan areas.  The combustion that repeatedly occurs in the combustion chamber resulting from the coherence created by the design and actions of the sensing and logical pieces described earlier, creates the up and down motion of the pistons which is turned into rotational torque by the crankshaft.  This assembly converts the thermal, expanding gas energy into torque in a repeating cycle that occurs every two rotations of the crankshaft.

An engine is an example of a complex mechanical machine whose sensor/input, signaling, processing and actuator functionalities are all merged together in the mechanical design of the system. Mechanical engineers normally do not identify or think in terms of signal and processor means in machines they design as these functionalities are typically merged together in the design.  However, electronic engineers design processing equipment where these functionalities are clearly separated and identified.  However, all machines, of all types, necessarily have these functionalities even if they are merged or hidden.

Feedback Systems and ECM

An engine is, in engineering terms, an example of a feedback control loop since output in the form of rotation and torque is used as inputs to the logical processing components (see Figure 2), as compared to an open loop system like the mousetrap.  This adds another complexity: stability.  Closed loop systems are often inherently unstable, meaning that they will revert to some form of oscillation, and with many systems, cause self-destruction.  Means to stabilize the feedback control systems must be engineered and included as part of such a system.

Feedback control technology is a complex area of engineering and its impact with respect to the engine example was not be discussed as this is an area of design that is not obvious to non-engineers.

Diesel engines in earlier days used mechanical fuel injection systems operated by cams.  One benefit of the use of the ECM coupled with electrically actuated injectors is the ability to add algorithms to stabilize and enhance the performance of the engine compared to the inflexible mechanical means. The ECM is a machine, but it is treated as a component for the purposes of this discussion.

Tools – All Static Things Embedded With Information

The discussion to this point has been about engineered machines. But there is a whole different category of intelligent work creations which are not machines.  For the purposes of this paper all such entities will be called tools.  Therefore, the definition for the term tool will be: an object created by intelligent work that does not perform intelligent work.  This is a very broad definition because it includes everything created by life except for machines.  This includes things we think of as tools (hammer, saw, wrench) but also art (paintings, literature, music, sculpture), medicines, buildings and other structures, telescopes, batteries, makeup, birds nest, bee hive, etc. 

Tools are made by intelligent machines, that is, living things plus machines designed and built by engineers.  Tools have embedded information, but not embedded intelligence like machines.  A logical distinction is that there is a statistical probability that natural causes could create some tools.  In this sense, the ID argument “where does the information come from” is treating life as a tool instead of a machine; both massive understatement because life is so much more than either!

Natural Causes can create objects that can be used as tools.  An example is a rock that has a shape that makes it convenient to use as a hammer.  Or a human can shape a rock to do the same thing.  Other tools, such as an adjustable (Crescent) wrench, must be engineered, i.e., designed and fabricated using intelligent work.

[i] Author’s Definition, Engineer:

Noun 1. a person who designs and builds machines and objects for a specified purpose.

Verb  1. Design and build a machine or object for a specified purpose.

Note: This definition is included to distinguish the design aspect with actualization as it seems they are often conflated and to exclude maintenance which is typically the role of a technician.

See other definitions here.

[ii] Princeton University calls their engineering department “School of Engineering and Applied Science”, see:  https://engineering.princeton.edu/

[iii] Author’s Definition, Mechanism:

1. a methodology including components, elements, parts and the associated energy and information flows enabling a machine, process or system that has demonstrated the ability to achieve its intended result.

Note: This definition is tweaked to include only proven methodologies.  See additional definitions here.

[iv] The reality of the gross disconnect of the complexity of complex systems is expanded in this post:  https://cs21c.com/marvels-taken-for-granted/.

[i]I am not sure that it is a theoretically necessary to have off-equilibrium conditions to achieve logical functionality, but there are logical reasons. If the logical functionality and signaling means requires too much energy, some processes, such as life would not be possible in the form that it exists. The logical functionality in life appears to be controlled by low energy, non-covalent bonds which makes them susceptible to be broken by free energy. This likely is the reason most functional proteins have half-lives. If their logical functionality was implemented by covalent bonds, their half-lives would likely be hundreds of years. Also see endnote 36. The cost of the use of covalent bonds would be greater energy requirements for implementation of the logic functionality.  This is an example of top-down, bottom-up design.  Power requirements to provide the logical functionality must be a starting point consideration before bottom-up design can intelligently started. .  For instance, an engine design could not work if the power required for the valving was more than the engine could produce. Very likely, logic functionality that was implemented by covalent bonds in life is impossible.

[ii] Wikipedia has a nice animation of a four-cycle gasoline engine. A diesel engine is the same except there is no spark plug.  Here is a video that has great animation of the various engine functions by Toyoda.

[iii] The injector and ECM are both machines and could have their own process action diagrams.  Instead, for simplicity, the injector in combination with the ECM are shown in Figure 2 as the components that provide the coordinated, logical functionality.  Physically, however, the injector is located next to the intake valve in the engine.

[iv] Wikipedia has a good description of the Engine Control Unit with explanation of mechanisms using various sensors and algorithms that adjust for various operating conditions.

Intelligent Work

Intelligent work,[i] as opposed to work accomplished by natural causes, is work performed in an intelligent fashion and therefore requires machines with embedded intelligence[ii].  The use of terminology regarding intelligence with respect to science causes much misunderstanding and confusion because the relationship is different for the materialist vs. ID viewpoints.

The definition of science varies widely.  Merriam-Webster’s first definition is “the state of knowing.”  This definition applies to anything that is considered knowledge, which implies that something that is not known is not science.  There are many aspects of physics that are not understood (e.g., entanglement, dark matter, and energy), and by Webster’s definition would therefore not be science.  For the purposes of this paper, science is defined as “the systematic knowledge the natural physical world gained through observation and experiment.”  “Natural physical world” means without intelligence involved.  If this definition was universally accepted, a lot of confusion and problems would be eliminated, author’s opinion.

Intelligence is defined as the ability to gain and apply knowledge.  Intelligence is in the realm of philosophy and has a hierarchy of levels: information, logic, and abstract.  An analogous hierarchy in the realm of science regarding space/time is: distance, speed, and acceleration.  Most people think of intelligence in terms of a combination of the logic and abstract forms.  Engineers use all three levels; abstract intelligence for design, logic for processing, and information for specifications, design documentation, data etc.

Intelligence, at all levels, has no meaning in science in the sense that in the natural world, there is no measuring of states, there is only the direct interaction of matter and energy according to the laws of physics.  Measuring and processing state variable information is intelligent activity and only occurs in matter/energy that has been manipulated by intelligent work to embed intelligence.

The fact that logic processing and abstract thinking are the results of “activity” or work and not a static thing like information is the center of the arguments presented in this paper.  It will be demonstrated that intelligent work requires matter/energy with embedded logical or abstract intelligence, which results from intelligent processes executed by machines that can only be created by an abstract intelligent machine.  It will also be demonstrated that all machines are beyond the reach of natural causes.

Strictly speaking, engineering and biology are not totally science by these definitions because both fields involve embedded intelligence.  Natural causes have no analog to logic and no mechanism to perform logical functionality.

Intelligent work differs from naturally caused work because intelligent work can:

1. perform the work on demand determined by specified state variables and/or logical signal(s), e.g., the temperature is a specified value, a mouse is present, the crankshaft is at a specified angle, the switch is “ON”, or some computed algorithm is a specified result, and
2. control the use of energy, i.e., the form, time and profile, location, direction and amount are all specified[iii].

The “on-demand” requirement is a logical operation that requires an output which has at least two states based upon the states of the input(s). Natural causes cannot perform intelligent work for two reasons:

1. The only inputs available are the state variables resulting from a previous naturally caused event with no intelligence involved, and
2. The only output is the unitary most stable equilibrium of the system.[iv]

In other words, natural causes have no mechanism for logical functionality.

What is an Intelligent Process?

An intelligent process,[v] defined to exclude natural processes, is a series of actions designed to achieve the desired result.  All intelligent work is part of an intelligent process.   The “actions” are performed by machines.  Machines use intelligent processes because they must provide a specified end based on logical decisions.  Intelligent processes can be thought of as a hierarchy of actions, layered, both vertically and horizontally such that when executed, the actions achieve the desired goal. 

Characteristics of an Intelligent Process

An Intelligent Process is Action; It Requires Machinery and Energy to Function

An intelligent process is not a static thing, it involves intelligent work, which requires functioning machinery, designed to accomplish a specific goal.  When an intelligent process is running, it is continually consuming energy due to the on-going actions required to implement the embedded intelligence.[vi]  This contrasts with a natural process which is consuming energy only during the time the natural state change is taking place.

An Intelligent Process Requires Specified Initial Conditions

Each step of an intelligent process requires specified initial conditions.  The first condition is the specified arrangement of the matter/energy involved (design) in the intelligent process coupled with setting all state variables, e.g., load software, charge batteries, set switches to specified positions, fill fluids, etc.  The action of an intelligent process step will normally change this arrangement and will be the initial conditions of the next step.  Stated differently, intelligent processes are creating a specified result, requiring specified initial conditions being acted upon by intelligently guided energy.  Natural processes will lead to an unspecified result that is determined by previous natural actions reacting to the available free energy.  Both, intelligent and natural processes follow the laws of physics.

Intelligent Processes Must Be Started by Intelligent Action

Think of any intelligent process – it always starts with some event or intelligent decision to take the “first step”.  All processes require the use of controlled energy, so “start” is often the action of activating the power source.

Intelligent Process Steps Require Inputs 

Each step of an intelligent process is intelligent action.  To be intelligent, the action must be contingent. The contingencies are provided as “inputs” to the logical processing means.  The contingency might be time, a switch position, a fluid level or a logic input.  The parameter involved must be converted to a signal or mechanism that can be “understood” by the machinery.  The conversion device might be a sensor, or a mechanical device or an electrical signal.  Simple examples are the trigger catch mechanism on a mousetrap and a float valve in a toilet.  A gasoline engine has a camshaft to provide a force to open and close the cylinder intake and exhaust valves as needed for the engine to operate.  A home computer has a keyboard and mouse that provides standardized signaling.

Intelligent Processes Require the Use of Machines

Intelligent process steps require the conversion of logical decisions based on information provided by the initial conditions to command and execute specified work, i.e., a machine.

The concept of intelligent processes and machines is confusing because intelligent processes use machines and machines use intelligent processes.  An intelligent process step is the specification of the required task and a machine is the matter/energy entity that performs the intelligent work to accomplish the specified task.

Machines

Intelligent work, which can only be accomplished by a machine[vii], is work that is controlled by embedded intelligence to provide output energy in the right form, at the needed time and profile, to achieve the desired end unattainable by natural causes.

All Machines must have several functionalities:

1. Sensing means. All machines perform an action based on state variables and/or logical inputs that are used to determine work to be performed.  Therefore, the parameters that are involved in the decision-making process must be sensed and signaled to the processing means.  This can mean any combination of:
   1. Passive sensor, a device that has no embedded intelligent functionality, e.g., thermistor, photosensor, strain gage or throttle potentiometer sensor,
   2. Active sensor, a device that does have embedded intelligent functionality, e.g., a servo accelerometer, a radar sensor, an infrared temperature sensor, or a logic output from another machine,
2. Sensor-Processor Signaling means. The signals, or information, must be conveyed to the processing means in a way that the processor can “understand” the information.
3. Logical Processing means. Logical processing is performed on the input signals to determine the action required.
4. Processor-Actuator Signaling means. The “action required” message must be conveyed in a form understandable to the actuator.
5. The actuator is a device, controlled by the signal from the processor, that performs the commanded work. 

These functionalities are implemented by specified arrangements of matter/energy.  Reverse engineering of any process could be systematically organized by first identifying the actions that are used to achieve the specified result; then for each action:

1. identify the initial conditions required for the action,
2. identify the event or intelligent action that starts the new action step,
3. identify the machine that does the intelligent work to complete the action, and,
4. determine how each of the machine functionalities is implemented.

All machines, when working, are running an intelligent process, so the characteristics of an intelligent process apply:  they require specified initial conditions and they must be started by intelligent action.  The concepts described here are hard to visualize by verbal descriptions, so examples of machines of increasing complexity are used as examples to identify the various attributes; initial conditions, start action, sensor(s), signaling means, processor, actuator and power source.

Next Section:  Machine Examples

End Notes

[i] Ibid 14

[ii] Author’s Definition, Intelligence:

1. The ability to make logical choices (logical intelligence)
2. The ability to think, reason, design, and to have self-consciousness (abstract intelligence).

  See other definitions here.

[iii] Note that a specified value can be “do not care.”

[iv] A “most stable equilibrium” may not be a singular state.  For example, molecules in a solution may be jumping between two or more states to maintain a concentration equilibrium when there is more than one product. However, this is the nature of this “state”, and it does not meet the requirement of providing a singular outcome for a given set of input conditions.

[v] Author’s Definition, Process:

1. a series of actions or steps taken designed to achieve a desired end.

  See more definitions here.

Note: This definition is tweaked to eliminate natural processes because intent requires intelligent work.  However, one cannot ignore the use of the term to refer to natural processes such a weather, erosion or even the definition of the second law of thermodynamics as being the natural process of matter and energy moving toward a more stable equilibrium.  However, in this paper, “processes” will always have an adjective to clarify the distinction between an intelligent process, and natural processes.

[vi] There are many intelligent processes that go into a pause or stop mode whereby the stop conditions are the initial conditions needed for restarting.  An example is a reservoir that is filled with water by a pump raising the water from a river.  When the water level drops to a preset level it resets the timer that runs the pump motor which later turns the motor off, resetting the water level to an initial condition for the pause/stop mode. 

[vii] Author’s Definition, Machine

1. An assemblage of parts that performs intelligent work.

  See additional definitions here.

The following statement comes from a highly regarded college physics textbook:¹

“By means of the statistical definition of entropy, Eq. 25-13,² we can give meaning to the entropy of a system in a non-equilibrium state.  A nonequilibrium state has a definite entropy because it has a definite degree of disorder.  Therefore, the second law of thermodynamics can be put on a statistical basis, for the direction in which natural processes take place (toward higher entropy) is determined by the laws of probability (towards a more probable state).  From this point of view a violation of the second law, strictly speaking, is not an impossibility.  If we waited long enough, for example, we might find the water in a pond suddenly freezing over on a hot summer day.”

The textbook goes on to say that the odds for the lake to freeze over are insanely low (10¹⁰ times the age of the universe) and that the second law of thermodynamics occupies the status of being

“… one of the most useful and general laws of all physics.”

This example, proffered to illustrate the statistical definition of entropy, is wrong.  The water molecules in the lake are in a continual process of exchanging momentum, (Brownian movement) forcing the average momentum to be constant, due to the first law of thermodynamics (conservation of energy) and thermal conductivity. Stated differently, a few molecules in a microsystem of the lake can be in a “nonequilibrium state” but the lake as a macro whole is in equilibrium.

The reality is that the lake will never freeze over on a hot summer day – the probability is zero. This assertion is made based on a micro-physics principle (probability of a molecule having a specified momentum) and ignoring a macro-physics principle that applies to the frozen lake example (the first law of thermodynamics and the logical reality that the lake’s molecules are exchanging momentum). This is an example of a theoretical possibility that is, in reality, impossible due to logical constraints.

Theoretical contraints and logical contraints are two separate things. The laws of physics provide a theoritical foundation of possibilities for matter, energy, space and time. However, not all of the possible states that do not violate the laws are achieveable. Natural outcomes are determined by actions that take place that depend upon initial conditions. The initial conditions depend upon previous actions. The initial condions include boundaries of the interacting systems, configuration of the matter, and all forms of energy involved. Such a process cannot and does not achieve all possible outcomes as the simple thought experiment illustrates. It seems as if the world of theoreitical physics assumes that there is statistical probablity that all outcomes that do not violate the laws of physics are possible, even thought I have not been able to find an explicit statement to this effect.  Typically, this is a typical probability:

…and ω is the probability that the system will exist in the state is is in relative to all the possible states it could be in.”³

 It is generally acknowledged by materialists that life seems to be a violation of the second law of thermodynamics.  However, the argument goes, if there is an outside energy source that can be used to lower entropy (like the sun), coupled with the sort of misguided thinking above, there is a finite, albeit insanely small, theoretical probability that beginning life could start by natural causes.  This is also false.  The probability is zero because natural causes are incapable of performing the intelligent work required to live.  This is a logical constraint, not a scientific one.

This is an example where engineering experience is useful.  It is obvious to an engineer by experience that most engineered entities (arrangements of matter/energy) cannot be created by natural causes with any amount of free energy.⁴

  This poses the question: what is the difference between engineered entities and natural entities?  Both are constrained by the laws of physics.  Here is a thought experiment that provides insight.

Image a house with a hallway.  On the floor in the hallway is a picture, and a nail in the wall above the picture.  The doors and windows and any other openings to the house are closed.  The man of the house is sitting at the kitchen bar sipping on a cup of coffee.  His wife asks him to hang the picture she left on the hallway floor.  He says, “no, I’m enjoying my coffee, let nature do it.”  She responds, “I don’t have time for that”, so he obediently goes to the hallway, picks up the picture and hangs it.  He gets rewarded with a kiss.

The man is a theoretical physicist.  He believed that any configuration of matter/energy that does not violate the laws of physics can occur naturally – it would just be a matter of statistical probability coupled with time.

The thought experiment is to answer this question: what possible series of natural events could accomplish this task?  Here is a straw-man scenario, that will work only if the picture is not directly below the nail in the wall.  A giant asteroid with the right momentum hits the earth at the right place in the right direction that causes the earth to accelerate the house with respect to the picture such that the house (nail) moves toward the picture.⁵  If the picture is directly below the nail, the asteroid would have to hit the house to move the earth in the right direction.  Assuming this is not the case, just as the nail passes the picture, another, asteroid hits the other side of the earth in the exact right direction to cause the house to move back in just the right direction such that the picture frame catches the nail in the wall.  Done. The picture is hung by natural causes. 

Of course, all this happens in a few milliseconds. Before equilibrium is reached, the house is destroyed, the earth’s orbit is changed and eventually, all life on earth is destroyed. But the picture was hung if only but a few milliseconds.

This is a silly but instructive example.  It is common sense that the picture cannot hang itself, because no available naturally caused forces could hang the picture without damaging the house.  Thermal energy cannot do the job.  Neither could electromagnetic or electrostatic forces as the picture is not made of magnetic material nor is the picture electrically charged.  However, the physicist was able to hang the picture without violating any law of physics. 

Theorems

The logical conclusion is that with all the openings to the outside shut, there is no internal free energy source capable of hanging the picture.  Free energy results from natural events.  If the inside of the house is in a state of equilibrium, no natural event could create the necessary free energy.  Even if there was free energy available, how could it occur at the right time, in the right form and amount to do the job?  Since natural causes cannot do the job, yet a man can, this theorem must be true:

Theorem:  Natural Causes cannot achieve all outcomes allowed by the laws of physics.

Materialist ideology denies the truth of this theorem, and, in effect, claim intelligence is created by science and therefore is in the same realm.  This theorem is true not because the picture on the wall violates any law of physics; it is true because there are logical constraints on the ability of natural causes to deliver energy at the right place, in the right form at the right time to accomplish many tasks. 

Theoretical and logical constraints are two seperate things. This is typical wording for statistical probability of states:⁶

“…and ω is the probability that the system will exist in the state it is in relative to all the possible states it could be in.”

The bold is highlighted by the author to pose the question “what defines what the possible states are?”  The presumption seems to be the totality of the theoretically possible states. If the “possible states” excluded those excluded for logical reasons, then there is no problem.  But how can this number be determined? An engineer’s experience says that it impossible to pre-determine all of the logica constraints⁷

A man can perform intelligent work,⁸ can control matter/energy to achieve desired ends not possible by natural causes making this theorem true:

Theorem:  Machines can expand upon the outcomes allowed by the laws of physics.

The next step is to determine the reason that a man or a robot can do things that natural causes cannot.  The key lies in the concept of intelligent work.

Since the beginning of recorded history, man has struggled with the question of our origins.  Religion is an answer, but not to all.  As science advanced, more and more of our universe could be explained by the laws of physics that were being discovered.  Darwin’s On the Origin of Species posited an explanation how all life evolved from a common ancestor.  This did not explain beginning life, but since then, theories have been put forward to provide possible chemical paths to fill this gap.  The combination of these ideas is the doctrine of materialism: all that exists can be explained by matter, energy and physics.  The last two decades have raised many issues that question the theories behind this point of view.

Steve Meyer, in his book Signature in the Cell showed how there is embedded information in the cell that is necessary for the existence of life and the odds of Natural Causes finding or creating this information are beyond the realm of probability.

The arguments presented by Meyer are valid and true.  This paper posits a more fundamental argument shaped by a machine and process control engineering background.  Upon discovery that there are molecular machines working in the cell, it was obvious to an engineer that life is not just about chemistry, it also involves intelligent processes¹ enabled by machinery, which, other than life, is associated with engineering.  This lead to the quest to determine the difference between engineered objects and things that result from natural causes.  This quest revealed that the difference is embedded intelligence, which means that the intelligent design argument elevates from “where did the embedded information come from” to “where did the embedded (logical) intelligence come from.”  And it adds to the static qualities necessary for life such as information, complexity and coherence to actionable hurdles such as building the cell, setting the initial conditions and starting and running the life process.  The conclusions are summarized here:

Science, defined as physics (including chemistry) absent intelligence, provides an explanation for the natural world.  Observations show that life and man-made machines also follow the laws of science.  Natural causes are the result of work performed by free energy. Free energy, defined as energy available to do work in a system that is devoid of embedded intelligence, constrained by physics, cannot achieve all possibilities allowed by the laws of physics as presumed by materialists².

Life and man-made machines can extend possibilities because they use embedded, constrained energy controlled by embedded intelligence. Embedded intelligence is only possible in matter/energy³ of specified configuration, functioning in an intelligent process.

Natural causes, that is, work resulting from free energy, always moves toward a singular state determined by and limited in outcomes by the initial conditions caused by previous naturally caused events and the laws of physics.⁴ Such events result in atoms, molecules, suns, solar systems with planets, black holes and galaxies, weather,⁵ erosion, and plate tectonics,⁶ but not machines.

Life and man-made machines can only exist because of intelligent processes running within them.  Intelligent processes require logical functionality; the ability to make logical decisions plus means to do the specified work the logical decision commands.  Logical functionality is beyond the ability of natural causes where the outcome is always a singular state independent of any logical requirement.⁷

There is nothing in science that restricts intelligent manipulation of matter/energy to enable intelligent (logical) functionality.  Such manipulation embeds intelligence into such an entity, and intelligence is in the realm of philosophy, not science.

This illustrates the flow from one state to another by work resulting from natural causes compared to machines:

Natural Processes⁸ (Science) → Unguided Results limited by lack of logical functionality

Engineering (Science + Intelligence) → Process Control enabled by machines → specified results

The mantra that Intelligent Design (ID) should not be taught in public schools because it is not science⁹ is wrong because life itself is not pure science, it is science plus logic; it is matter/energy with embedded logical functionality that can create logically defined states.  Intelligent Design, based on the arguments herein, addresses life in this manner.

This paper shows that logical constraints limit the outcomes of natural causes; that there is embedded intelligence in the cell and machines, and why logical functionality is required to achieve machine functionality. In addition, this paper explains why specifitviity of information or work has no meaning in the world of science yet can be implemented by science with embedded intelligence.

This paper provides the logical explanation for the necessity of Life being engineered.  The only caveat is that the science we think we understand is correct and that science we do not understand such as dark matter and energy and entanglement, may eliminate the necessity for engineering.

This paper provides theorems that define the limitations of natural outcomes and logical explanations why engineered configurations of matter/energy can expand upon these capabilities. Clarity and coherence impose the need to precisely define some existing terminology and to create new terminology.  Such terms have brief definitions embedded in the text and are expanded in footnotes.

This paper posits that the inability of natural causes to perform logical functionality is falsifiable where all other arguments regarding beginning life are not. In addition, a method of falsifying the possibility of a chemical path to beginning life is provided.

In addition, the realization that life and machines require embedded intelligence puts a spotlight on the ultimate chicken-egg question; what causes the other: intelligence or science (matter/energy/space/time)?  If science is the cause, intelligence must be an artifact of science.  If intelligence is the cause, then the anthropic principle is explained.  This paper does not address this question further.

The term matter/energy is used herein when referring to life or running machines because they are processes that must continually expend energy to conduct the intelligent actions (embedded intelligence) required.  This is analogous to the use of the term matter used in conjunction with the embedded information required for life and machines.

Next Section:  The Limits of Natural Causes