1,577 days, 2,409 entries ...

Newsticker, link list, time machine: HOLO.mg/stream logs emerging trajectories in art, science, technology, and culture––every day
● 30/05/2023
“Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks, such as pandemics and nuclear war.”
– 350+ AI executives, researchers, and engineers from, for example, OpenAI, Google DeepMind, and Anthropic, in a one-sentence open letter released by the Center for AI Safety (CAIS). The brevity of the statement—a “coming-out” for some industry leaders who thus far had only expressed concerns in private—was to unite experts who might disagree on specifics, CAIS director Dan Hendrycks tells the New York Times.
Center for AI Safety
● 07/04/2023
“What just drives me up the wall is that we appear to have decided the way AI is going to work is through a competitive dynamic between Google, Microsoft, and Meta.”
– New York Times columnist Ezra Klein, airing frustrations about the AI ethics and safety communities’ inattentiveness to capitalism. Citing DeepMind’s AlphaFold as a prime example for positive AI breakthroughs (rather than manipulative chatbots tied to advertising), Klein imagines a world where governments offer prizes for AI challenges and results go into the public domain.
Hard Fork
● 04/07/2022
“The AI discovered a mechanism that redressed initial wealth imbalance, sanctioned free riders, and successfully won the majority vote.”
– DeepMind researchers, studying policy innovation through AI. In a new paper, published in Nature Human Behaviour, the UK-based team reports on their “human-in-the-loop research pipeline called Democratic AI” that ran an online investment game with a thousand human players. “By optimizing for human preferences, Democratic AI offers a proof of concept for value-aligned policy innovation,” write the researchers.
Nature
● 30/11/2020

Google AI offshoot DeepMind announces a major breakthrough in solving the “protein folding problem”—determining a protein’s 3D shape from its amino-acid sequence. Considered one of biology’s grand challenges due to myriad possible configurations, DeepMind’s AI system AlphaFold has demonstrated it can predict protein structures with high accuracy, vastly outperforming other more laborious, costly techniques. “It’s a game-changer,” says Andrei Lupas, an evolutionary biologist at the Max Planck Institute in Tübingen, Germany. “This will change medicine. It will change research. It will change bioengineering. It will change everything.”

Nature
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