DJ Strouse

My Goodreads Rating: 2 of 5 stars

A book exploring the possibilities that “non-trivial” quantum effects are exploited in biology, where “non-trivial” refers to all the interesting bits that make quantum mechanics fun – interference, entanglement, superposition, etc. In other words, of course all processes are quantum at some level, but the question is whether the process utilizes what we might call quantum computation.

With the creepiest cover photo I’ve ever encountered (a kaleidoscope view of some impish man’s grinning face) and title font that would even make Liberace cringe, this book is not trying very hard to overcome new age stereotyping. Of course, if an unsuspecting “quantum healer” were to crack open the book, they would summarily be bludgeoned to death by wavefunctions and unitary operators.

This is the strange dichotomy of quantum biology – at the intersection of cutting-edge, speculative science and crackpot theories of consciousness, it’s hard to separate science from pseudoscience. This collection of articles hits on everything from photosynthesis and the origin of life to quantum game theory and “quantum transmemetic intelligence.”

You are forewarned – 95% of the ideas in this book will probably end up in a museum down the hall from the ether and corpuscle theories of light under “those silly theories that early 21st century scientists tossed around.” And yet, on the off chance that one of the contributors is actually on to something, you may be reading the earliest indications of a new and fascinating field at the intersection of theoretical physics and biology.

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My Goodreads Rating: 4 of 5 stars

Interested in cybernetics, theoretical biology, and philosophy but still find Dan Brown novels to require mental gymnastics? Put on your philosophical training wheels and give “Tree of Knowledge” a spin!
A mixture of dated scientific ideas, profound frameworks for thinking about living organisms, and unnecessarily complicated jargon, ToK is essentially the children’s menu version of Maturana and Varela’s Autopoiesis and Cognition papers on living organisms, communication, and consciousness.

I highly recommend reading ToK before Autopoiesis and Cognition and possibly even foregoing Autopoiesis and Cognition altogether. ToK is not only more clearly written but is laden with examples, something lacking in the uncompromisingly sterile Autopoiesis and Cognition.

The rest of this review is a summary of the deep and profound wisdom I gleaned from the Chileans, so you may want to skip it if you haven’t read the book yet.

ToK’s more gentle approach (along with post-reading conversations with a Chilean economist and Italian physicist) helped clear up a question I had after Autopoiesis and Cognition: if a unity is so deeply coupled with its environment, how does one uniquely define its morphological boundaries? It may seem obvious to look at me, carve a 2D surface over my skin, and call me a closed system, but give me a week without a consistent supply of low-entropy energy and I’ll quickly succumb to the second law of thermodynamics. The key trick is this: unique boundaries there are not. “Everything said is said by an observer.” An observer selects the features by which a unity will be defined through their shared domain of interactions. Different observers (and even the same observer at different times with different goals) will have different domains of interactions and will define a unity in a different way. For example, a given university may be a set of assets and liabilities, a collection of students, a football team, a physical space, or some combination of these things, depending on who you ask.

Some more notes:

• Referring to a unity implies an act of distinction.
• Replication, copy, and reproduction can be distinguished by the amount of historicity in each process. Replication (repeated generation) is ahistorical. Copy (creation from a mold) is historical if iterated. Reproduction (the fracture of a unity to create two unities of the same class), however, is necessarily historical.
• Heredity and variation are strongly complementary features. Heredity is the preservation of structure in a historical series of unities. Variation are the differences of structure in that series. Different components of a unity may exhibit different degrees of heredity and variation.
• Unities may couple via inclusion (think organelles) or recurrent coupling with the maintenance of individual identities (individual humans).
• The environment does not instruct an organism; it only triggers internal dynamics. To phrase it differently, the space of possible reactions to an environment is defined in the internal structure of an organism; the environment does not inject behavioral commands into an organism in any way. To phrase it differently yet again, environmental stimuli modulate, they do not control. Environmental input is imply one more “voice” in the “conversation” of internal dynamics.
• Organisms must exhibit variance of the time scale of their environment (and in a complementary “direction”) in order to adapt (remain coupled).
• Adaptation in response to a single change in the environment affects the organism in a global way. A small change in structure may occur to accommodate one new feature of the environment, but through an internal domino effect, alter the way an organism interacts with other features.
• The simplest neural systems allow detection of correlations between inputs on a sensory surface.
• A nervous system expands our possible behaviors by inserting a network with a huge range of possible patterns between our sensory and motor surfaces.

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“What are you gonna do with your life?”

College freshmen (and many graduates… and many adults) all grapple with this question. If you’ve been naive enough to answer “science!” then you’re faced with the follow-up: “Well, what are you gonna study?”

Most young knowledge warriors charge straight at whichever field their best teacher from high school or college was from, certain that their initial passion will never wane. Yet talk to these firecrackers seven years later and notice how many grad students have lost that spark. What once was a tantalizing realm of puzzles and mysteries becomes a never-ending cycle of mundane tasks.

Science can certainly be a cruel succubus, but considering one important feature of a field before hopping on the bandwagon can help avoid this.

Ripeness & Saturation: The Potential of a Science

The key is to compare the current set of ideas with the potential for developing new ones given available data, tools for acquiring data, and culture. Too little new available data, too few tools for acquiring data, or cultural norms that enforce old paradigms can all hinder the potential for interesting projects. Quantum gravity might be an instance of the first two shortages whereas 16th century astronomy (influenced by the church doctrine of a geocentric universe) is a good example of the last. Let’s call these saturated fields.

On the flip side, an abundance of data, the invention of tools for acquiring new data, or changing cultural norms can all provide opportunities for great projects. Genomics is experiencing the first two surpluses whereas complex systems research in the early years of the internet (and the metaphors about networks that accompanied it) probably benefited greatly from cultural shifts. Let’s call these ripe fields.

Your mission, young sailor of the scientific seas, is to find one of these gems.

Now, how can you tell? You can’t be sure, but here are some good signs:

• Researchers still identify themselves with another more traditional field and just list this one as an “interest.”
• It attracts researchers from many different backgrounds and much of the work done is considered “interdisciplinary.”
• A new institute pops up every week or so.
• A large percentage of new papers all reference one or a few papers written in the last five years.
• The introductory textbooks were all written in the last few years and there is no consensus on which is best.

Some signs of a saturated field include:

• Systematized jargon (new ideas and discoveries get ID numbers rather than names)
• A large percentage of new papers are written to make small corrections on old ones (the field has become recursive).
• There are introductory textbooks in their twelfth edition and most professors use the same text to teach as they did to learn.

I’d wager quantum computing and neuroscience are ripe for the picking.

Which fields would you add?  And which criteria for identifying ripe and saturated fields would you use?

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