Last week, in response to a request from Secrecy News for a copy of a thirty year old history of computer development at Los Alamos in the 1940s and 1950s, a reference librarian at Los Alamos National Laboratory apologetically explained that she could not release the requested document.
“We are sorry but due to a mandate from NNSA to the Laboratory and Research Library policies, we are unable to provide technical reports until further notice,” the librarian wrote. You want information from the Library? Don’t be silly!
Fortunately, a copy of the document (pdf), which was not otherwise available online, was obtained independently and it has been added to our Los Alamos document collection.
Among other curiosities, the report describes work on an early chess-playing program for the MANIAC computer in the 1950s:
“Because of the slow speed of MANIAC (about 10,000 instructions per second) we had to restrict play to a 6 by 6 board, removing the bishops and their pawns. Even then, moves averaged about 10 minutes for a two-move look-ahead strategy.”
See “Computing at LASL in the 1940s and 1950s” by Roger B. Lazarus, et al, report number LA-6943-H, May 1978.
Americans are paying too much for almost everything, because the United States has long treated its trucking industry as an artifact to be preserved rather than as an opportunity for innovation.
These ideas aim to advance the detailed policy solutions needed to foster public trust and implement fairness in the adoption of AI across diverse domains, from healthcare and government benefits to rural access, education, and worker protections.
The evidence is clear: algorithmic pay-setting is established in app-based work, and payroll/timekeeping failures show how software can produce systemic wage harm at scale
While a few states have taken steps to implement decision-making mechanisms for certain AI systems, too many leaders are simply accepting narratives about AI’s purported public benefit at face value – jumping to the “how” of AI implementation before thoroughly vetting potential systems and deciding whether they are appropriate to use at all.