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Dutch bootstrapper who builds and runs their internet startups alone — Nomads.com, Remote OK, Hoodmaps, Photo AI and Interior AI — and has written about it at levels.io since 2013. Posts as @levelsio.

• Our readingkorrents.com

  Rice, grains, seeds and potatoes used to be peasant foods, while kings ate fresh meat plus imported fruits and spices and had higher-protein diets than peasants.

  Their words

  rice, grains, seeds and potatoes all used to be peasant foods And the kings actually just ate a lot of fresh meat like beef, venison, wild boar, swan, and peacock, and imported fruits and spices like saffron, cinnamon, nutmeg, and pepper A king's diet was high in protein A peasant's diet was low in protein

  @levelsio on Xx.com

Chief Scientist at Google DeepMind and Google Research, at Google since 1999, where he co-created MapReduce, Bigtable, TensorFlow and the TPU.

• Our readingkorrents.com

  A narrow, highly accurate model can beat a general one inside its own domain, as AlphaFold did, and materials science and chip design are next.

  Their words

  I mean I think like if you look at uh my colleagues work on say alpha fold that was a very specific model for uh protein folding and it was highly successful um and was able to really handle that domain quite well so that all of a sudden you now have this amazing tool and model that can give you answers to questions about proteins and their structure um really effectively um but it's not a general model it's a very specific one and there are other I domains where that kind of approach can work really well. Uh maybe in material science or chip design or things like that that uh will enable you to leverage the capabilities of a very accurate but but niche model uh to do things that are hard today.

  Jeff Dean: The 1% Rule for Building in AIyoutube.com

Social scientist turned small-scale farmer; wrote A Small Farm Future and argues against the case for feeding the world from factories.

• Our readingkorrents.com

  Factory-made bacterial protein cannot feed the world, because meeting humanity's protein needs that way would take more electricity than the planet generates from renewables.

  energy

  Their words

  To meet the global population’s protein needs through factory-produced bacterial protein in 2024 would have used more than the total global electricity generated using renewable (hydro, photovoltaic and wind) energy

  chrissmaje.com

Scientist and writer on quantum computing, memory systems and metascience; co-wrote the standard quantum computation textbook and wrote Reinventing Discovery.

• Our readingkorrents.com

  Biology has already handed us an alien library: hundreds of millions of protein machines we do not understand and can only study one at a time.

  Their words

  we've been gifted uh just this incredible variety of machines which we don't understand really at all and we just have to go and sort of try and understand them on a you know, one-by-one basis.

  youtube.com

Data scientist at Our World in Data and author of Not the End of the World. Writes By the Numbers, working through energy and climate questions with the numbers in front of them, and revising in public when they say something unexpected.

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  People do not switch from meat to cheap plant protein as they get richer; they switch the other way.

  Their words

  Across much of the world, the opposite is happening: people are switching from these plant-based staples to meat and dairy as their incomes rise.

  Do subsidies make meat much cheaper?hannahritchie.substack.com

Co-founder and CEO of Google DeepMind, and a 2024 Nobel laureate in Chemistry for the protein-structure work behind AlphaFold.

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  We have not scratched the surface of what classical, non-quantum computers can do; they can already go far further than anyone expected.

  Their words

  I think we've proven, and the AI community in general that classical systems, Turing machines can go a lot further than we previously thought. They can do things like model the structures of proteins and play go to better than world champion level. And a lot of people would've thought maybe 10, 20 years ago that was decades away, or maybe you would need some sort of quantum machines to quantum systems to be able to do things like protein folding. And so I think we haven't really even sort of scratched the surface yet of what classical systems so-called could do.

  youtube.com

Medicinal chemist; has written In the Pipeline, the drug-discovery blog, since 2002, and the “Things I Won’t Work With” posts about chemicals that try to kill you.

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  AI will not produce a big breakthrough in biological understanding any time soon, because biology has nothing like the clean, complete, high-quality data that made protein-structure prediction work.

  Their words

  So for these reasons, I (unfortunately) do not expect any big AI-driven breakthroughs in biological understanding any time soon.

  AI and Biologyweb.archive.org