The Singularity: Future of Biology

When the University of California – San Diego collaborative supercomputer research lab first was formed in the 1990s, 1 of 20 scientists involved was in the biological sciences. As of 2005, the number was five out of 20 (UCSD 2005). Will the field of biology exist after 25 years? Predictions certainly call into question job security. The paradigm shift is already decades underway, and we are wondering when it will occur. The Bureau of Labor Statistics projects growth of biophysicists and biochemists to grow twice as fast as average, while microbiology and wildlife biology positions will grow as fast as average (US Bureau of Labor Statistics 2013). Biology technician positions will grow slightly less than average. What have we learned about natural selection?

The reality is the foundation of molecular biology is based on x-ray crystallography data, and interpreted by a physicist and avid ornithologist. Major advances in biology have largely relied on brilliant advances outside of it as well as within it, in the oligarchies of Leeuwenhoek, Turing and Ehrlich. As is shown by the hundreds of authors in recent papers like the GENOME project, collaboration is a key to accumulating relevant knowledge (Eddy 2005).

Notably absent within this review of advances is biotechnology related to agriculture or energy development. Historically, agricultural innovations have made stepwise improvements outstripping the exponential population growth (Pender 1998). Energy advances are mainly within the fields of logistics, physics and pure engineering.

An absence of futurologists within the field of biology exist. Thus, most of the advances predicted are within the realm of artificial intelligence and robotics, and biology proper. Full-scale organ development via plastic cartridges reminiscent of an inkjet printer already has been completed and transplanted, fulfilling one dire need (Oliveira and Mano 2011).

To give an idea of how far biotechnology has advanced in the past decade, consider the following title in the July 2002 Oncology Times: “Prediction: Pig Transplant to Human within Five Years.” Not only

was this incorrect, solid organ xenotransplantation is a classic case of “moving the goalposts” since the early 1990s ( Dufrane and Gianello 2012) . While cross-species transplants risk of rejection despite harsh pharmacotherapy (Cozzi et al. 2005), these printed organs are either biologically neutral materials or made from the cells of the patient. Computer Annie screams from the balcony, “Anything you can do, I can do better.”

Predictive analysis is an interesting business. How could we more accurately predict the future than understand the past or present? Pathology of disease is already rife with scarcity, particularly in neurodegenerative disorders ( Huynh and Casaccia 2013) . Revising errors in paleontology is a full-time endeavor (Browne 1995). When a group of futurologists came together in 1988, they did get a few ideas correct, despite the internet being a distant realization. For instance, GPS within the car was accurately projected. Graphic depictions of the future car, however; do look a bit like the 1993 Honda Civic (Bennett 2013).

Hints toward the future are exemplified by the innovations today. These include: Implanted brain prostheses to enhance memory consolidation, paper-based diagnostic tests for liver enzymes, pre-natal DNA sequencing, ovary-specific stem cells, vaccine-related recombination advances and three separate ways to increase the efficiency of said DNA sequencing. These specific advances are mentioned because they are likely to directly affect the average citizen soon, especially pre-natal DNA testing without amniocentesis (MIT 2013). An advance in radiology is conceivably disturbing — an fMRI visually-interpreting optic brain data and projecting the thoughts into video (Nishimoto et al. 2011).

When discussing where biology will go within the next few decades, I take an anthrocentric thesis. The problems presenting mankind are astronomical, even present exclusion of global warming. That problem in itself is worthy of a separate extensive discussion. Two goals spring to the cortex as to the drivers of a new paradigm: extending life and extending quality of life on an egalitarian basis. Often is

discussed (and funded) is the arms race between humans and super-pathogens like MRSA and swiftly evolving viruses. Yet, our cancer mortality rates are statistically the same over fifty years (Bailar and Gornik 1997). This makes sense considering its unknown etiology, paucity of singular biomarkers and stagnant (albeit efficacious) therapies over the same time period (Kramer and Klausner 1997). Heart transplants based on stem-cell scaffolds have produced a perfusable organic heart in murine models (Zimmerman et al. 2006). Cancer mortality rates recently surpassed heart disease in the United States, in contempt of reduced smoking rates (Wyatt et al. 2012).

In a specific example, nanotechnology and cost reduction of processors will help permeate the blood brain barrier. Currently, this almost impermeable faction is made up of tightly bound immune cells. Either external or internal manipulation with so-called “nanobots” would allow the introduction of large molecule therapies in brain cancer. (Silva 2010). Artificial regrowth (in vitro ) of the nervous system has proven exceptionally difficult. Perhaps in silico advances in robotics will leapfrog this obstacle. In addition, some cancer cells are 70% softer than somatic cells, knowledge that has yet to be harnessed therapeutically (Xu et al. 2012).

In order to best predict the future, be as broad as possible when making predictions, like Nostradamus. Respected individuals in the prediction field like Ray Kurzweil are adamant about the accuracy of their predictions, and strongly defend them in hindsight. Independent analysis of these claims is variable, but frequently dips below a 40% accuracy rate (Armstrong 2013). Kurzweil (2006), Daniel Dennett and PZ Meyers (2009) all posit we will combine with the sterility of binary code, yet their proposed timescale varies to the point of effusive argument. Intriguingly, Kurzweil postulates a gradual entry into the singularity of human and machine instead of a punctuated rise. If we are indeed subject to evolutionary processes, then it is my unadulterated conjecture we’ll need an escape velocity similar to the printing press or the internet instead of incremental change. Bridging the above gaps is predicated

largely on Moore’s Law (exponential advancement of technology), which isn’t any more of a law than Godwin’s Law (likelihood of Hitler being mentioned over the period of a conversation).

Who/what would you rather give you a diagnosis:
A doctor with decades of clinical experience, taking time to do a thorough clinical exam over the period of an hour with the requisite laboratory work? A surgeon with five hundred hernia repairs under his or her belt?

Or a physical exam completed by a combination of MRI and PET, with artificially intelligent photic sensors detecting gait abnormalities and the response to a visual evoked potential? A differential diagnosis scouring millions of pages of literature in seconds, a la Jeopardy’s Wilson.

The counterarguments to each situation are voluminous, but largely irrelevant today because neither is accessible to the people that need it the most. Safe robotic surgery reduces healing time and combines the dexterity of an actual surgeon with fiber optics. The real cost, however; is outside of the realm of affordability for 99% of the world’s population (Barbash and Glied 2010). Commonly cited is the disparity between communicable disease in the developing world compared to industrialized nations, but mortality due to cancer is also incipiently divergent: 80% of childhood cancers are put into remission in the United States, yet 80% of the same cancers in the third-world lead to mortality (Kaatsch 2010). To paraphrase William Gibson, the future is already here, it’s just unevenly distributed. 11 of the 12 cancer drugs approved by the Food and Drug Administration in 2012 cost over $100,000 per annum (Experts in Chronic Myeloid Leukemia 2013).

One of the goals of paradigm evolution in biology: spend time looking at basic things instead of complex complete information systems like chess or the stock market (Kurzweil 2006). AI has extraordinary obstacles to overcome in basic things a four-year old can do, like recognizing a clap as a clap. This generally follows Comte’s explanation of the evolution of paradigms, wherein math is

considered a pure science because it is well-understood, while social research is a softer science because of the plethora of questions yet to be answered. Not surprisingly, the most recent advances have been in genomics, cancer and neuroscience, three fields with an indelible frontier of unknowns.

Organism development is thought to be unidirectional, and that alone gives reason to be optimistic. 4,000 diseases have a genetic component, with a single mutation causing the pathology (Perez-Iratxeta et al. 2007). As a comparison, only 1,002 drugs are approved by the FDA, 1/4th which are extended release derivatives or combination drugs (Sanseau and Koehler 2011). No place to go but up!

The United States recently authorized 100 million to map the brain, with the EU reportedly pledging 1.2 billion EUR to match the brain’s computational power. It’s still a question of priorities: the NSA employs an estimated 10,000-20,000 in defense cryptography analysts alone, exempt from cuts in discretionary spending. Worldwide, only a handful of cryptographers are working on the human genome, an incredibly nascent field judging from the literature available (Xiao et al. 2006). US Congress effectively cut 1.6 billion (Semuels and Flores 2013) out of the NIH budget (on a bipartisan basis), much to the silence of the average person. Once again, biology depending on the faculties of those outside the field. In this case, quite far.


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