Making Life’s Big Decisions: Part 3

In Parts 1 and 2, we demonstrated how advances in our scientific understanding of life has led to some uncomfortable conclusions for many in the evolutionary camp, leading to a growing rift that is dismantling the gene-centric view central to the modern synthesis of evolution known as neo-Darwinism.

Among other things, the ability of even the simplest known life-forms to utilize planned decision-making to problem solve, using anticipatory behaviors and prior knowledge, has caused giants in the evolution-believing community such as Dr. Dennis Noble to declare, “Neo-Darwinism is dead.”1

Life’s Bar Has Been Raised

The gene-centric model has severe limitations. One limitation is that even if random mutations could generate new genes for forms, functions, and features that never before existed (which they can’t because mutations are simply spelling mistakes in DNA that break things), it wouldn’t matter because living things need far more than genes to be alive, let alone to somehow have evolved. Indeed, the very title of Abel’s paper, “The Common Denominator of All Known Lifeforms,” spells out neo-Darwinism’s death knell, and the article encapsulates it in this quotation: “Life is all about accomplishing highly sophisticated useful tasks.”2

What Abel is describing is the same challenge Noble and the other scientists who disagree with neo-Darwinism are saying. Science now demonstrates that the bar for what constitutes life has been set to an exponentially higher level than what was imagined before.

Science now demonstrates that the bar for what constitutes life has been set to an exponentially higher level than what was imagined before.

Life is not like a collection of Lego blocks that goes from simple to complex, starting as the humblest of life-forms and becoming more sophisticated over millions of years by collecting a library of new genes (or additional Lego pieces) that coded for new features that gradually built up over time (as proposed by atheistic evolutionists such as Professor Richard Dawkins in his book Climbing Mount Improbable).

No, the idea that “simple life” came into being is not feasible because we now have a better understanding of the minimum requirements for life to operate. To demonstrate the challenge, let me present select passages from Abel’s aforementioned paper and then provide some analogies and examples to illustrate his point.

Life is blatantly controlled at every level. . . .

Unfortunately, the true nature and initial source of controls within nature continues to be swept under the investigative rug. The only reason for this is a purely metaphysical imperative. . . .

Natural selection can’t turn configurable programming switches on or off. Natural selection is passive and secondary, never active. . . .

Controls achieve steering toward formal success. Steering requires intent in making efficacious active selections, not just secondary passive selections. The pursuit of function must begin before the function exists to “favor.”3

The Genie Behind the Gene

To unpack these quotations, we need to understand that a gene is a sequence of DNA letters that codes for the eventual production of a specific protein; a gene is not the protein itself. Genes are made of chemical letters that represent what the finished product will be, so genes are not the finished product itself.

A gene is similar to a cake recipe: It has instructions and a list of ingredients needed to put that cake together. But what good is a recipe without someone who can read the recipe, gather the ingredients, process and measure them into the right quantities, fold the mixed ingredients into a batter, put it in a greased pan, and set it in the oven? Of course, that also presupposes you already have a bowl, measuring cups, a whisk, and a pan among other things. You need to have a stove, have the stove set to the right temperature, and a timer so that you take it out at the proper time. And you’ll need to cool that cake down before adding the icing.

In summary, simply having the recipe is not enough. In order to end up with a finished product, you need workers that can translate the information in the recipe and have the tools required to perform all of the coordinated functions to end up with the finished product. The recipe may give the exact instructions for building the cake itself, but the kitchen tools and execution decisions come from the chef. So in our analogy, a gene provides the instructions for the cake, but the chef, kitchen, and timing determine how it actually turns out.

Now, in living things, the equivalent of the chef, kitchen, and timed processes are often brought about in part by other proteins that perform these functions. Regardless, having an oven with nothing to bake is useless. Having a chef to whisk a cake has no purpose until there are already premeasured and prepared ingredients to blend together. And what if the recipe calls for a flour sifter, but you don’t have one? What if you don’t already know what a sifter is, and how you would use it? The bottom line is, everything had to be there all at the same time or else the cake is never made.

Think about it, what good would it be if there were a process that just kept spitting out new recipes (genes) constantly, but there was nothing to read those recipes or to gather the ingredients and process them?

Many (such as Dr. Noble and the other Third Way scientists mentioned in Part 2 who are speaking out against neo-Darwinism) may want to argue that these decision-making capabilities we see in all living things came about without intelligent agency and are impossible to directly link to a thinking mind, but the onus is on them to provide evidence for that belief.

Because the fact is, we do know what is involved in producing intelligent machines because we’ve been doing it for quite a while now. That’s why Dr. Abel mentioned being surprised that “the Oxford Languages’ definition of Cybernetics is: ‘the science of communications and automatic control systems in both machines and living things.’”4 Indeed, engineers have even written manuals both defining and describing exactly what the communication and automatic control systems required to make an intelligent machine are.

How to Create Intelligent Machines

Take for example the 1992 manual5 from the US Department of Commerce National Institute of Standards and Technology and their Robot Systems Division. Experts involved in designing the cutting-edge technology of their day outlined and explained a process called “A Real-Time Control System Methodology for Developing Intelligent Control Systems.”

For non-engineer types, it’s not exactly a super-exciting read, so I’ll just highlight a few brief but pertinent quotations here and discuss the concepts contained within. To encapsulate the process, they identify the challenges involved in developing what they define as “intelligent machines” and their control systems in the following way:

We define intelligent machines to be machines designed to perform useful physical work while employing in situ knowledge (sensory input data), and a priori knowledge, tactics and strategy. Intelligent machines are further defined as utilizing feedback from the physical environment to manifest “intelligent behavior” in real-time via computerized real-time control of the machine's electromechanical actuators and sensors. Such systems are termed closed-loop control systems and are distinguished from open-loop control systems in that open-loop systems do not have the capacity to alter their behavior in real-time based on sensory feedback from the environment.

The definition given above for intelligent machines is intended to include: automation systems such as those found in manufacturing, materials processing, mining and construction; embedded systems such as military cruise missiles, torpedoes and other semi-autonomous devices; and robotic systems ranging from factory floor robots to space vehicles and planetary exploration robots.6

Here some brilliant engineers tell us what it takes to create intelligent machines designed to perform useful tasks by utilizing decision-making programming—all of which materialists say nature can somehow mimic. Yet again, it is up to them to provide an actual mechanism to account for doing so without a mind, as (according to engineers) intelligent machines require the following.

1. Employ in situ knowledge (gather sensory input data).
2. Have a priori knowledge, tactics, and strategy.
3. Utilize feedback from the physical environment to manifest “intelligent behavior.”
4. Do so by having real-time control of the machine’s electromechanical actuators and sensors.
5. And have the capacity to alter their behavior in real-time based on sensory feedback from the environment.7

Now, employing in situ knowledge means that intelligent machines use external information that’s gathered and processed internally within its system, all collected from its current environment in real time. However, they can only make use of that data because of the “a priori knowledge” they have before it is gathered and starts analyzing, comparing, and utilizing it. A priori knowledge means information known in advance (preprogrammed rules, constraints, models, maps, etc.), and tactics involves the usage of predefined methods for handling specific situations even before you’ve experienced them.

And finally, strategy involves having higher-level plans that guide whatever overall goals the system has, meaning it possesses a long-term decision-making framework. To put it as simply as possible, intelligent machines have systems that don’t start at zero: They begin having preloaded understanding and decision-making frameworks that they then apply during their operation.

However, the most sophisticated technological devices we have ever created all pale in comparison to the engineering inside even single-celled organisms. All single-celled organisms have an additional capability no scientist has ever duplicated—the ability to replicate itself generationally and (theoretically) indefinitely.

Decision-Making Drones Demonstrate Design

Now, a perfect example of an intelligent machine utilizing if-then programming today would be a military drone. Modern war drones constantly perform a 4-step decision loop involving 1) assessing its current state, 2) evaluating options, 3) selecting appropriate actions based on 1 and 2, and then 4) reassessing this process continuously in real time.

Their cameras, thermal sensors, lidar, radar, GPS, and IMU constantly collect data that its AI and control algorithms interpret as various objects, terrain, and motion. Its data bank of a priori information includes models it can refer to (tanks, planes, people, buildings, terrain). It then adjusts its flight path, speed, orientation, and defensive or targeting behavior accordingly, then reevaluates and recalculates based on new data that it is constantly collecting, creating a perpetual loop running many times per second. And the key to all of this is the intelligent programming built into them by their designers.

The key to all of this is the intelligent programming built into them by their designers.

Again, it’s operating on what is called if-then programming. If there is a tank, then—depending on whether it is an enemy tank or friendly, which specific kind of tank it is, how far away it is, the speed and direction it’s traveling, what weapons it has, and what armament we have available—it will either execute an attack pattern optimized to destroy the enemy tank or engage in a retreat pattern to maximize self-preservation. Their attack methods are nuanced, strategic, and devious because they reflect the combat savvy of their war-veteran-informed programmers.

So this if-then programming is dependent on a priori information because all the theoretical conditions and responses are defined in advance, before the system encounters the real-world situation they’ll be navigating in. These drones aren’t sentient (even though they may appear to exhibit those traits), but they’re using sentient-like tactics and strategies because those strategic patterns are encoded into them. It’s their programmers that specify what conditions to look for and what actions to perform.

A Priori Information in Every Living Thing

We know where the information for these battle drones came from—their designers and programmers. And yet, virtually every living organism demonstrates this type of if-then programming to spectacular degrees, such as the bacteria scientists are now describing as “sentient beings,” as discussed in Part 1. So the big question is, where did this programming come from originally. Although Dr. Noble and other modern scientists are great at pointing out the problems with neo-Darwinism, they haven’t offered an observable, naturalistic mechanism as a solution. And that is what’s causing many naturalists and atheist types to become increasingly nervous, as the most parsimonious explanation available destroys their entire worldview.

Tune in for Part 4, where we’ll use simple logic to determine the most reasonable answer to that question and then let atheists reveal in their own words that there is often a much deeper reason as to why logic and evidence won’t matter to many of those committed to the story of evolution.