2020: ideas & lineages
Selekoa’s 20 ideas & lineages records dated 2020, in date order, of 25 in all topics.
Selective: what the records hold, not a history of the year.
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Ideas & lineages 20 records
January 2020
· event · dated to the day
OpenAI published its institutional release of Scaling Laws for Neural Language Models. Evidence ↗· publication · dated to the day
Scaling Laws names Jared Kaplan as a co-author. Evidence ↗· publication · dated to the day
Scaling Laws cites Attention Is All You Need for the Transformer architecture. Evidence ↗· publication · dated to the day
Scaling Laws cites An Empirical Model of Large-Batch Training. Evidence ↗· publication · dated to the day
Scaling Laws reports training primarily decoder-only Transformer models. Evidence ↗· publication · dated to the day
Scaling Laws reports using the large-batch model to adjust training-step and compute estimates. Evidence ↗
June 2020
· publication · dated to the day
“Comment on "Modeling the Human Trajectory" ("Some personal reactions on this piece")” (2020-06-15) credits Holden Karnofsky as its author. Evidence ↗· publication · dated to the day
Karnofsky, “Comment on "Modeling the Human Trajectory" ("Some personal reactions on this piece")” (2020-06-15) articulates G03. Karnofsky, who writes that he asked Roodman to evaluate Hanson's work, states what he takes from the post; he "wouldn't take the model discussed in this piece literally". Evidence ↗Karnofsky, Comment on "Modeling the Human Trajectory" ("Some personal reactions on this piece") (2020) →Claim page →
· publication · dated to the day
Roodman, “Modeling the Human Trajectory” (2020-06-15) articulates G03. Roodman fits a stochastic super-exponential model to gross world product since prehistory and draws out its instability. Evidence ↗· publication · dated to the day
Karnofsky, “Comment on "Modeling the Human Trajectory" ("Some personal reactions on this piece")” (2020-06-15) articulates G09. The same comment says he independently holds that transformative AI "could plausibly be developed in the next couple of decades". Evidence ↗Karnofsky, Comment on "Modeling the Human Trajectory" ("Some personal reactions on this piece") (2020) →Claim page →
September 2020
· publication · dated to the day
Learning to summarize from human feedback cites PPO for policy optimisation. Evidence ↗· publication · dated to the day
Learning to summarize from human feedback cites Human Preferences. Evidence ↗· publication · dated to the day
Learning to summarize from human feedback reports optimising its policy with PPO. Evidence ↗
October 2020
· publication · dated to the month
“Artificial Intelligence, Algorithmic Pricing, and Collusion” (2020-10) credits Emilio Calvano as one of its 4 authors. Evidence ↗· publication · dated to the month
“Artificial Intelligence, Algorithmic Pricing, and Collusion” (2020-10) credits Giacomo Calzolari as one of its 4 authors. Evidence ↗· publication · dated to the month
“Artificial Intelligence, Algorithmic Pricing, and Collusion” (2020-10) credits Sergio Pastorello as one of its 4 authors. Evidence ↗· publication · dated to the month
“Artificial Intelligence, Algorithmic Pricing, and Collusion” (2020-10) credits Vincenzo Denicolò as one of its 4 authors. Evidence ↗· publication · dated to the month
Calvano et al., “Artificial Intelligence, Algorithmic Pricing, and Collusion” (2020-10) bears on M02. The abstract motivates the study by algorithms replacing human pricing decisions. Evidence ↗Calvano et al., Artificial Intelligence, Algorithmic Pricing, and Collusion (2020) →Claim page →
· publication · dated to the month
Calvano et al., “Artificial Intelligence, Algorithmic Pricing, and Collusion” (2020-10) bears on M09. The abstract shows a 2020 AER article whose subject is AI pricing algorithms in oligopoly. Evidence ↗Calvano et al., Artificial Intelligence, Algorithmic Pricing, and Collusion (2020) →Claim page →