Rather than focusing on the next generation of software development, they are focused on biotech, bioinformatics and bioengineering. It is as if those at the heights of their profession recognize how little we know and how much more we have to learn.
As we race toward the complete mapping of the human genome, and then the more difficult task of deciphering how proteins fold and interact with one another, the accelerating pace of learning is not only opening doors to the better diagnosis and treatment of disease, it is also a source of inspiration for much more powerful models for programming and complex systems development.
The genetic code and the mind have been regarded as informational systems that seemed to reside on a different plane of abstraction and beyond the reach of our comprehension. There was a huge semantic and conceptual gap between our crude programming models and the beauty of biological systems.
That gap is narrowing.
Myriad projects, from the top-down mapping of the visual pathways of the human brain to the bottom-up research in stem cells and fetal development, are rapidly unveiling the information processing of cells, genes and minds in development, and over the eons of evolution.
Technology and biology: Converging paths?
After 20 more years of progress, the fields of IT and biotech will have blurred and blended into a unified discipline.
And then there's nanotech engineering at the molecular scale. Biotech is a subset of nanotech. In fact, biotech is an existence proof of the power of molecular systems to self-replicate and process information. Modified DNA has been used as a molecular tweezer, a XOR logic gate, and as a re-engineerable engine for protein synthesis. A simple biological virus has been reprogrammed to shuttle discrete metal atoms into a flat lattice.
Well, how efficient are these biological information processors? If we took your entire genetic code--the entire biological program that resulted in your cells, organs, body and mind--and burned it into a CD, it would be smaller than Microsoft Office. Just as images and text can be stored digitally, two digital bits can encode for the four DNA bases (A,T,C and G) resulting in a 750MB file that can be compressed for the preponderance of structural filler in the DNA chain.
If, as many scientists believe, most of the human genome consists of vestigial evolutionary remnants that serve no useful purpose, then we could compress it to 60MB of concentrated information. Having recently installed Office 2001, I am humbled by its relatively simple capabilities. Much of the power in bioprocessing comes from the use of feedback in the electrical, physical and chemical domains.
Let's look at some examples:
We learned in Biology 101 that there are three types of muscle cells: skeletal, smooth and cardiac, each with distinct properties. It turns out that you can transform muscle types with electrical stimulation. In an experimental procedure called dynamic cardiomyoplasty, a skeletal muscle is removed from the back and wrapped around an ailing heart. Under the relentless electrical stimulation of a pacemaker, the skeletal muscle converts to cardiac muscle, allowing it to do the unique and unrelenting work of the heart (with changes in the energizing cellular mitochondria and lactic acid fatigue).
Different phenotypes can express from the same genotype. Biological systems also react to physical feedback. Recently, one of my partners, who needed new cartilage in his knee, had the bone surfaces roughened; under physical shear, the exposed bone converts to fibrocartilage (a cartilage-like material) in the gap. Motion and physical stresses also shape fetal skeletal development.
Cartilage and muscle can be forced to convert to bone under the right chemical and physical stimulation. Our learning in this area is rapidly expanding. I recently had the honor of meeting with several researchers at NASA Ames. They are interested in the changes in gene expression in micro gravity to understand the long-term effects of space habitation. Interestingly, non-load bearing organs, such as the kidneys, went through a profound change.
Recent research indicates that numerous genes in the kidney cells react to a low-gravity environment, such that essential proteins are no longer being created (this may explain the painful kidney stones that have bedeviled several astronauts).
Biological systems also react to chemical feedback from neuron growth, to psychopharmacology, to the hormonal homeostasis that incorporates chemical feedback to keep various bodily systems in balance. In a fetus, the initial inter-neuronal connections, or "wiring" of the brain, follow chemical gradients.
The massive number of inter-neuron connections could not be simply encoded in our DNA, even if the entire DNA sequence was dedicated to this one task. There are on the order of 100 trillion synaptic connections between 60 billion neurons in your brain.
This incredibly complex system is not 'installed' like Microsoft Office from your DNA. It is grown, first through widespread connectivity sprouting from 'static storms' of positive electro-chemical feedback, and then through the pruning of many underused connections through continuous usage-based feedback. In fact, at the age of 2 to 3 years old, humans hit their peak with a quadrillion synaptic connections, and twice the energy burn of an adult brain.
Moving beyond artificial intelligence
The brain has already served as an inspirational model for artificial intelligence (AI) programmers. The neural network approach to AI involves the fully interconnected wiring of nodes, and then the iterative adjustment of the strength of these connections through numerous training exercises and the back-propagation of feedback through the system.
Moving beyond rules-based AI systems, these artificial neural networks are capable of many human-like tasks, such as speech and visual pattern recognition with a tolerance for noise and other errors. These systems shine precisely in the areas where traditional programming approaches fail.
The coding efficiency of our DNA extends beyond the leverage of numerous feedback loops to the complex interactions between genes. The regulatory genes produce proteins that respond to external or internal signals to regulate the activity of previously produced proteins or other genes. The result is a complex mesh of direct and indirect controls.
We experience this as bundling of traits in our offspring, and it implies that genetic re-engineering can be a very tricky endeavor if we have partial system-wide knowledge about the side effects of tweaking any one gene. For example, recent experiments show that genetically enhanced memory comes at the expense of enhanced sensitivity to pain.
By analogy, our genetic code is a dense network of nested hyperlinks, much like the evolving Web. Computer programmers already tap into the power and efficiency of indirect pointers and recursive loops. More recently, biological systems have inspired research in evolutionary programming, where computer programs are competitively grown in a simulated environment of natural selection and mutation. These efforts could transcend the local optimization inherent to natural evolution.
The Web is the first distributed experiment in biological growth in technological systems. Peer-to-peer software development and the rise of low-cost Web-connected embedded systems give the possibility that complex artificial systems will arise on the Internet, rather than on one programmer?s desktop. We already use biological metaphors, such as viral marketing to describe the network economy.
Innovation sprouts in the interstices between formal scientific disciplines, such as the emergence of materials science between physics and chemistry. There is enormous potential in the multidisciplinary intersection of biology, computer science and nanotech. And for some, it will be the non-disciplinary pursuit of new science with a childlike mind.
The best way to create a large and complex system is to grow it. It's not a Microsoft Office install. It's not a brain by design.
We are entering a period of a profound learning and expansion of our capabilities in both molecular engineering and information processing. By expanding these capabilities, we further expand our ability to learn. It is a period of exponential growth in the learning-doing cycle in which the power of biology, IT and nanotech compounds the advances in each formerly discrete domain.
Despite a human tendency to presume a regression to linearity, the pace of progress will continue to accelerate.