News
Four Pillars Roundup: 250 Years of the US: A Beacon of Light in the Age of AI
Redefining Trust in the Digital Age: Tarun Khanna Speaks at the AIWS-DASI Conference Harvard University Loeb House — November 4, 2025 At the Boston Global Forum’s AIWS Digital Asset Standards Initiative (AIWS-DASI) Conference, Professor Tarun Khanna of Harvard...
Pompeo’s statement and The Clean Network program
On August 5, 2020, State Secretary Michael Pompeo announced the expansion of the clean network to safeguard America’s assets. The program highlights many points that were also from the Social Contract 2020 The Clean Network program is the Trump Administration’s...
Truera applies explainability AI research to grow its model evaluation platform
As AI impacts more industries and areas of society, startups are building testing tools to help companies and governments assess the state of their models and identify potential compliance issues. Google Cloud chief AI scientist Andrew Moore recently predicted that...
Thomas Patterson in the Boston Globe: “The GOP’s moral trap”
Professor Thomas Patterson, Harvard Kennedy School, Co-founder of the Boston Global Forum and AIWS.net, is co-author of the Social Contract 2020, A New Social Contract in the Age of AI. He recently posted his writing in the Opinion Section of Boston Globe, July 27,...
MIT Professor in History, Theory, and Criticism of Architecture joins the Board of the History of AI
Professor Caroline A. Jones, MIT, has joined the History of AI Board at AIWS.net. She is a Professor in History, Theory, and Criticism of the Architecture Department at MIT. She wrote a chapter of the book “Possible Minds: 25 Ways of Looking At AI” (2019), whose...
Ambassador Ichiro Fujisaki calls for leadership of the US in the world
There were recommendations and suggestions from this conference to establish alliances to protect and strengthen democracy to face threats that undermine democracy and threats from China. In July, there were statements and speeches of leaders echoing these...
Machine Learning Is Living in the Past
Current machine learning platforms largely fail to provide time-series predictions because “correlations that have held in the past may simply not continue to hold in the future,” the London-based company causalLens notes. That’s a particular problem in areas like...