HP Innovation Journal Issue 09: Spring 2018 | Page 11
J O N ATH A N B R I LL
G lo b al Futurist a n d Re se a rch D ire c to r
fo r Te chVisio n a n d M e gatre n ds , H P
VI K TO R S H KO LN I KOV
Re se a rch Engin e e r, H P L a bs
J I M S TA S IA K
D isting uish e d Te ch n olo gist, 3 D Printing B usin e ss , H P
This is the first in an in-depth series exploring the
converging technologies and Megatrends that will
drive greater production and efficiency in our resource-
constrained world.
BioConvergence
As was discussed in “When Trends Converge: 2018 HP
Megatrends Report” in this issue, we are seeing an increas-
ing strain put on our natural and labor resources. As we
look towards 2030 and beyond, it’s apparent we need to
approach how we design and manufacture products in
significantly different ways to overcome these resource
constraints. This will require exploring new materials
that offer greater efficiencies imploring new processes that
support the design and production of these materials, and
transforming manufacturing and the supply chain.
As we look at how to create this efficient future, we need
to look no further than to Mother Nature for inspiration.
Nature is inherently extremely efficient, defect tolerant, and
is capable of correcting errors on-the-fly. Efficiency is not
just about reduction, it’s also about adaptation and resil-
ience. It’s about embracing change, understanding it and
adapting to it. It’s about doing more with less.
Take, for example, a 300-foot redwood tree, which can
move water and nutrients from deep in the ground, through
its trunk, out and up its bark and leaves via its nutrient
transport system. That same principle is being applied to
energy resourcing efforts used to harness chemical potential
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energy —which is generated as a result of the concentration
gradient in places where freshwater meets dense salt water,
as it does along coastlines all over the world.
Or think about how birds outperform other flying
animals in efficiency and duration, with lightweight and
aerodynamic wings and feathers. The hollowness of their
bones, rather than making them fragile, actually makes
them more resilient and efficient. We see a reflection of
this in the way 3D-printed materials focus on structural
efficiency versus structural volume and meet physical
requirements with minimalistic design.
Nature also finds efficiency in emergence and self-
organization. Both concepts, in different ways, illustrate
the adage that the whole is greater than the sum of its
parts. A good example of this is how the brain’s compu-
tational capability is, in fact, the result of rapid network
generation involving neurons, axons, and synapses. One
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hundred billion neurons interact with each other to
transmit information throughout our body, using only
about 20-25 watts of energy and processing an exaFLOP—
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a billion billion—calculations per second.
Nature’s concepts of emergence and self-organiza-
tion are also being applied to computing and technology.
Energy-smart grids, cloud computing, robotics, etc. all use
these concepts to some extent to interconnect individual
elements and identify patterns to create greater efficiencies.
And natural materials can further help society with
efficiency. Scientists have already begun exploring the use
of DNA for massive amounts of computer storage. A single
gram of DNA could back up 215 million gigabytes of data.
Three grams of DNA could back up the data capacity of
every laptop HP made last year.
Today, the most powerful computer on earth uses 2.4
million times more energy than the human brain. But what
if we could harness the power and efficiency of the human
brain for future computing endeavors? That’s exactly what
Koniku, a California-based startup, is trying to do. Koniku
is exploring the development of neuron-based computer
chips that combine live brain neurons with silicon chips
to detect volatile chemicals, explosives, and, possibly one
day, illness.
BioConvergence, the intersection of biological and
computing technologies, is accelerating. Biology is rapidly
moving from analog-based, slow-moving science with long
exploration and testing cycles to a fast-moving, highly
automated science that will increasingly become reliant
on computation and massive combinatorial mathematics.
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