Amazing essay from Kate Crawford —
At this moment in the 21st century, we see a new form of extractivism that is well underway: one that reaches into the furthest corners of the biosphere and the deepest layers of human cognitive and affective being. Many of the assumptions about human life made by machine learning systems are narrow, normative and laden with error. Yet they are inscribing and building those assumptions into a new world, and will increasingly play a role in how opportunities, wealth, and knowledge are distributed. The stack that is required to interact with an Amazon Echo goes well beyond the multi-layered ‘technical stack’ of data modeling, hardware, servers and networks. The full stack reaches much further into capital, labor and nature, and demands an enormous amount of each. The true costs of these systems – social, environmental, economic, and political – remain hidden and may stay that way for some time.
Hi. I’m Charlie Stross, and I tell lies for money. That is, I’m a science fiction writer: I have about thirty novels in print, translated into a dozen languages, I’ve won a few awards, and I’ve been around long enough that my wikipedia page is a mess of mangled edits. And rather than giving the usual cheerleader talk making predictions about technology and society, I’d like to explain why I—and other SF authors—are terrible guides to the future. Which wouldn’t matter, except a whole bunch of billionaires are in the headlines right now because they pay too much attention to people like me. Because we invented the Torment Nexus as a cautionary tale and they took it at face value and decided to implement it for real.
A great result for crowd-sourced science:
We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the … SARS-CoV-2 main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property–free knowledge base for future anticoronavirus drug discovery. [….] As a notable example for the impact of open science, the Shionogi clinical candidate S-217622 [which has now received emergency approval in Japan as Xocova (ensitrelvir)] was identified in part on the basis of crystallographic data openly shared by the COVID Moonshot Consortium.