Nathan Gardels is the editor-in-chief of Noema Magazine.
A regular reader of Noema might sometimes think we are naïve or unaware that we publish radically different points of view which are often conceptually dissonant. They might ask if the editors are paying attention. The answer, of course, is yes. More than attentive, our intent is precisely to both search for a correspondence among disparate ideas, but also explore a singular issue from multiple aspects through the juxtaposition of perspectives.
One might call this editorial approach of exposing different angles “cubist” rather than binary (pro and con) or polemical in nature. All dimensions of a phenomenon are true, but the whole is truer than the parts.
This approach has practical as well as philosophical value. Only by grasping the “objective” whole will it be possible to reconcile the diverse and conflicting aspects of the creative-destruction dynamic of change into a governing consensus around the point of equilibrium most beneficial to all.
By way of example, we have published the philosopher of technology Benjamin Bratton on the great leap of AI-enabled planetary-scale computation. The unprecedented capacity for modeling by sorting out massive amounts of data has allowed our species, for the first time, to become collectively self-aware of climate change and promises to be a key tool in coping with that existential threat.
We’ve published the Chinese entrepreneur Kai-Fu Lee on how AI will transform health care by targeting specific diseases, finding their locations in the genetic code and designing precision medical treatment. In one essay, the pioneer of genome mapping, Craig Venter, lays out another AI-enabled leap as great as planetary scale computing — synthetic biology that might correct deficiencies such as diabetes, cancer and Alzheimer’s.
“Recent leaps in the biosciences, combined with big data analysis, have led us to the cusp of a revolution in medicine,” he writes. “For the first time, humans can intervene in changing our genetic code — and the disease genes embedded in it — that took biological evolution 3.5 to 4 billion years to produce. Not only have we learned to read and write the genetic code; now we can put it in digital form and translate it back into synthesized life.”
Yann Lecun and Jacob Browning have celebrated the advances of the “deep-learning” neural networks of AI that inch ever closer to approximating human intelligence through “massive self-learning algorithms which excel at discerning and utilizing patterns in data.”
In various essays and interviews, including early on with Elon Musk, we’ve lauded the advances of battery-storage electric vehicles as a key route among others out of dependence on fossil fuels.
Shadows Cast By The Future
At the same time, Noema has plumbed the shadows cast by all this promising light emanating from the future.
While at the personal level we experience the smooth tactile function of a Google search by tapping our smartphone screen or the convenience of plugging in an EV at a recharging station, an enormous world-spanning human and energy-intensive operational infrastructure stands behind it all.
In Noema this week Adrienne Williams, Milagros Miceli and Timnit Gebru call for an “ethical AI” that doesn’t exploit the poorest of the poor around the world, paid in some cases as little as $1.46 an hour, to prepare the raw data which feeds into the learning algorithms we rely on to run our wired world.
“Data labeling jobs are often performed far from the Silicon Valley headquarters,” they report, “from Venezuela, where workers label data for the image recognition systems in self-driving vehicles, to Bulgaria, where Syrian refugees fuel facial recognition systems with selfies labeled according to race, gender, and age categories. These tasks are often outsourced to precarious workers in countries like India, Kenya, the Philippines or Mexico.” To learn of this reality is to demystify the abstraction of the great digital revolution from the other end of our devices.
While batteries themselves have a do-good green glow, the sourcing of their components has a darker cast. As Ian Morse points out in Noema, the lithium, copper, cobalt, nickel and rare earth minerals that must be mined to manufacture “clean energy” batteries is often deeply harmful to the local environment and brutally exploitative of the poor communities employed to dig them out of the ground. The Congo cobalt mines are infamous on this score.
Some critiques, like those of Vaclav Smil, are more fundamental.
“I wouldn’t put a big trust in what people in Silicon Valley say,” he proffered in one interview in Noema casting doubt on the “salvation through technology” crowd. “They may be good at manipulating ones and zeroes and writing software, but beyond that their contribution to human progress has been pretty dismal. I’m not impressed. Their understanding of the net use of resources has been very poor.”
Much of the discussion with Kai-Fu Lee centered on the massive use of fossil-fuel energy to cool the data servers located around the world where the millions of calculations required by AI take place.
Smil doesn’t miss this point when it comes to smartphones that, while replacing the mainframe computers of old, use far more energy inputs as ubiquitous devices flooding the world: “You can digitize the control process, but not the material force. That remains the same. The idea that somehow digitalization is leading to the dematerialization of the economy is ridiculous,” he cuttingly notes.
The materials scientist calls for curbing upstream over-consumption that devours so many resources for frivolous ends as the only sure path to dematerialization. He has also pointed out that batteries can only store the energy source they download from the grid. If you are in Denmark, that is mostly from renewable sources. If you are anywhere else, it is mainly still from fossil fuels.
It All Comes Back To Governance
Putting all this together, there is no mileage in either ignoring the downside of today’s largely advantageous technological advances or regretting the future by sabotaging their promise with ideological dispositions from another era.
Beyond exposing the exploitation of precarious data workers, for example, why not champion a fair sharing of the vast new wealth AI is creating through fostering an ownership stake by all, including those workers, in the digital economy? Instead of pitting technology against nature in the effort to curb global warming or root out genetically encoded disease, why not seek to align human, machine and natural intelligence along one axis of planetary and biological resilience?
In the end it all comes back to good governance amid perpetual change, the aim of which is to shepherd the new into the old by buffering the collateral damage that at first may outweigh longer-term benefits before they can take hold.
Like the homeostasis of all organisms, governance is the regulator, arbiter and navigator of human affairs. It processes emotion, not least fear or blind fervor, through reason as the means by which societies not only survive but thrive by adapting to change.
Noema’s contribution to this endeavor is to not take sides with polemical zeal between myopic cheerleaders of the times ahead and those who only dwell on the sharper edges of creative destruction, but expose the multiplicity of dimensions and connect them through the perspective of the whole.