I receive emails. Here is from another by Perry Marshall, author of Evolution 2.0.
But it’s not just cancer research. My Evolution 2.0 journey has made it abundantly clear to me that the system underlying virtually all science research is tragically flawed.
Now, if you’re thinking, “Why do I care about science being sold out to the highest bidder?” You can just look around you right now.
It’s an invitation to his podcast:
Science for Sale
Ken McCarthy & Perry Marshall
Wednesday April 22, 2:00 PM Eastern
Besides his Wikipedia entry, what I know of Perry Marshall is from his book. An excerpt provides some insight.
This is a science book, provoked by my burning question: If blind evolutionary forces can produce eyes and hands and ears and millions of species, then why don’t engineers use Darwinian evolution to design cars or write software? Why don’t they teach Darwinism in engineering school? Evolution and natural selection, after all, were heralded as all-powerful, to the point of having godlike qualities. If nature needs no engineers, a little evolution knowledge would surely be useful to us engineers who are stuck in cubicles designing cell phones.
Marshall, Perry. Evolution 2.0: Breaking the Deadlock Between Darwin and Design . BenBella Books, Inc.. Kindle Edition.
This is from a section with the title “What You Can Expect from This Book,” so it’s not part of the meat. It does give clue to the intent.
What Marshall wants you to understand is “blind evolutionary forces” are not sufficient. Else, engineers would use them to produce novel designs. I get from this he invokes purpose in seeking to make his case, which is what he intends to prove. Reading selections from the book will reveal Marshall is seeking to sell God. He wants to demonstrate a world created by God, as only a being of some sort can provide purpose.
Engineers developing a novel design do so with a purpose in mind. They want something that flies, so they are sure not to make it too heavy. Engineers would never invoke random choices to create a new and improved design.
Except sometimes they do.
Hybrid Genetic Algorithm and Linear Programming for Bulldozer Emissions and Fuel-Consumption Management Using Continuously Variable Transmission
This paper develops a hybrid optimization approach combining genetic algorithm (GA) and integer linear programming (ILP) to solve the nonlinear optimization problem of managing the fuel consumption and emissions of a tracked bulldozer. Furthermore, the authors propose that a continuously variable transmission (CVT) can better exploit the efficient zones of the engine maps. The original transmission system of the Caterpillar D6T bulldozer consists of a five-gear transmission, whereas the gear ratios of the proposed CVT are continuous and can be assigned according to transmission design. The fuel consumption and three emission items of the engine, unburned hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx), are studied. Vehicle-terrain interactions are formulated and the excavation program is characterized by excavation depth and speed. The target of the multiobjective optimization problem is a combination of fuel rate and three emission items. Results show that, for digging depths less than the bulldozer blade maximum digging depth, the target can be improved by more than 31% using CVT incorporated with GA compared to the conventional transmission, obtained by shifting engine operating points from low efficiency zones to optimum points. Finally, integer linear programming is used in a hybrid manner with GA to solve for the optimum combination of excavation steps in tasks of specified digging depths more than the maximum digging depth of the bulldozer blade. Results show that the proposed method can improve the target value up to 18% with the same digging time, and can improve the target value up to 32% using the hybrid optimization approach without time constraint.
The paper describes the development of an improved transmission design. The development employed genetic algorithms toward achieving an optimum design.
Genetic algorithms employ stochastic variation on workable designs to generate new designs, and then they select for those that perform better. To be sure, purpose is invoked here. Nature does not make use of purpose. What engineers achieve in short order using significant computer power, for a natural organism nature requires centuries and longer to instill “improvement.” And remember, what nature ends up with may not be what we would prefer. The wild horses people domesticated thousands of years ago are the ones produced by nature. What people want, and what they have now, are horses artificially bred for our purposes.
I have no plan to view the pod cast, but readers are invited to search it out and sign up. There may be something significant relating to real abuse of science. The past few weeks have seen egregious abuse of science for political gain, as serious scientists are sometimes mocked (one receiving death threats). We can only hope real science will be taken more seriously when the current crisis is over.