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← Designed experiments can reveal causal relationships that observational data alone cannot determine.
15 connected korrents · 13 moments from 18 Oct 2007 to 7 Oct 2026.
Everything filed under AI and science
AI and science
Everything filed under design
design
Everything filed under measuring intelligence
measuring intelligence
Everything filed under metascience
metascience Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Read this korrent: Designed experiments can reveal causal relationships that observational data alone cannot determine. Designed experiments can reveal causal relationships that observational data alone cannot determine. Last stated 12 months ago
15 Nov 2025
EN
Elizabeth Van Nostrand — holds since 2025-11-15 — tap for who they are
Same subject: Diagnose with data and treat with design: data can tell you that you have a problem and where it is, and it can never tell you what to build. — tap to centre the map on it Diagnose with data and treat with design: data can tell you that you have a problem and where it is, and it can never tell you what to build. Last stated a year ago
21 Sept 2025
JZ
Julie Zhuo — holds since 2025-09-21 — tap for who they are
Same subject: The properties of light should be proved by reason and experiments, not explained by hypotheses — tap to centre the map on it The properties of light should be proved by reason and experiments, not explained by hypotheses
IN
Isaac Newton — holds since c. 1704–1718 — tap for who they are
Same subject: Factor analysis cannot reveal causal structure, so it provides no support for the reality of g, the general factor of intelligence. — tap to centre the map on it Factor analysis cannot reveal causal structure, so it provides no support for the reality of g, the general factor of intelligence. Last stated 19 years ago
18 Oct 2007
CS
Cosma Rohilla Shalizi — holds since 2007-10-18 — tap for who they are
Same subject: Domain knowledge can be more important than data when investigating cause-and-effect using AI. — tap to centre the map on it Domain knowledge can be more important than data when investigating cause-and-effect using AI. Last stated 2 months ago
12 Sept 2026
AK
Adam Kucharski — holds since 2026-09-12 — tap for who they are
Same subject: A small, well-chosen design team gives a better system; reasoning that works for peeling potatoes fails for design. — tap to centre the map on it A small, well-chosen design team gives a better system; reasoning that works for peeling potatoes fails for design.
MC
Melvin Conway — holds since c. 1968 — tap for who they are
Same subject: The answer to weak studies of programming practice is to fund better ones, not to conclude that the question cannot be measured. — tap to centre the map on it The answer to weak studies of programming practice is to fund better ones, not to conclude that the question cannot be measured. Last stated 12 years ago
7 Nov 2014
DL
Dan Luu — holds since 2014-11-07 — tap for who they are
Same subject: A compound's AI origin story is not by itself a reason to expect it to do better in the clinic. — tap to centre the map on it A compound's AI origin story is not by itself a reason to expect it to do better in the clinic. Last stated 2 years ago
13 May 2024
DL
Derek Lowe — holds since 2024-05-13 — tap for who they are
Same subject: A computational discovery platform does not make a biotech fast, because the thousand other steps of drug development are still the bottleneck. — tap to centre the map on it A computational discovery platform does not make a biotech fast, because the thousand other steps of drug development are still the bottleneck. Last stated 2 years ago
17 Sept 2024
AT
Alex Telford — holds since 2024-09-17 — tap for who they are
Same subject: A computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known. — tap to centre the map on it A computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known. Last stated 5 years ago
15 Jul 2021
DH
Demis Hassabis — holds since 2021-07-15 — tap for who they are
Same subject: Accurate computational approaches are needed to close the protein structural coverage gap and enable large-scale structural bioinformatics. — tap to centre the map on it Accurate computational approaches are needed to close the protein structural coverage gap and enable large-scale structural bioinformatics. Last stated 5 years ago
15 Jul 2021
DH
Demis Hassabis — holds since 2021-07-15 — tap for who they are
Same subject: AI models can be more accurate than physical experiments by averaging out noise, incorporating priors from large datasets, and interpolating between data points. — tap to centre the map on it AI models can be more accurate than physical experiments by averaging out noise, incorporating priors from large datasets, and interpolating between data points. Last stated 11 months ago
23 Nov 2025
EH
Elliot Hershberg — holds since 2025-11-23 — tap for who they are
Same subject: 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. — tap to centre the map on it 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. Last stated 2 years ago
19 Sept 2024
DL
Derek Lowe — holds since 2024-09-19 — tap for who they are
Same subject: AI could compete with human mathematicians once it acquires a mathematical sense of smell: knowing which way of splitting a problem makes it easier rather than harder. — tap to centre the map on it AI could compete with human mathematicians once it acquires a mathematical sense of smell: knowing which way of splitting a problem makes it easier rather than harder. Last stated a year ago
14 Jun 2025
TT
Terence Tao — holds since 2025-06-14 — tap for who they are
Same subject: AI excels at breadth and human experts at depth, which makes them complementary and makes today's depth-focused way of doing science the wrong shape. — tap to centre the map on it AI excels at breadth and human experts at depth, which makes them complementary and makes today's depth-focused way of doing science the wrong shape. Last stated 8 months ago
20 Mar 2026
TT
Terence Tao — holds since 2026-03-20 — tap for who they are
Same subject: AI has solved in an afternoon of compute maths problems that stumped the best human minds for decades. — tap to centre the map on it AI has solved in an afternoon of compute maths problems that stumped the best human minds for decades. Last stated 4 weeks ago
7 Oct 2026
AA
Azeem Azhar — holds since 2026-10-07 — tap for who they are
At the centre
Designed experiments can reveal causal relationships that observational data alone cannot determine.
Holds Read this korrent →
Diagnose with data and treat with design: data can tell you that you have a problem and where it is, and it can never tell you what to build.
Holds
The properties of light should be proved by reason and experiments, not explained by hypotheses
Last stated c. 1704–1718
Holds
Factor analysis cannot reveal causal structure, so it provides no support for the reality of g, the general factor of intelligence.
Holds
Domain knowledge can be more important than data when investigating cause-and-effect using AI.
Holds
A small, well-chosen design team gives a better system; reasoning that works for peeling potatoes fails for design.
Last stated c. 1968
Holds
The answer to weak studies of programming practice is to fund better ones, not to conclude that the question cannot be measured.
Holds
A compound's AI origin story is not by itself a reason to expect it to do better in the clinic.
Holds
A computational discovery platform does not make a biotech fast, because the thousand other steps of drug development are still the bottleneck.
Holds
A computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known.
Holds
Accurate computational approaches are needed to close the protein structural coverage gap and enable large-scale structural bioinformatics.
Holds
AI models can be more accurate than physical experiments by averaging out noise, incorporating priors from large datasets, and interpolating between data points.
Holds
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.
Holds
AI could compete with human mathematicians once it acquires a mathematical sense of smell: knowing which way of splitting a problem makes it easier rather than harder.
Holds
AI excels at breadth and human experts at depth, which makes them complementary and makes today's depth-focused way of doing science the wrong shape.
Holds
AI has solved in an afternoon of compute maths problems that stumped the best human minds for decades.
Holds