PageWeekend Archives - Page 8 of 25

Harvard Chabad

Passover ‘A Cappella’

Gallery: Other Ways of Knowing Climate Change

Life-cycle cost of education community settings are informed by climate change assumptions. In addition to the flow of research money to faculty for laboratory space, graduate student support, supercomputer installations, conference travel; these assumptions inform the architectural character of a campus — i.e. the design and operation its buildings and infrastructure. These assumptions swing back and forth over these eternal institutions with cyclical assumptions about global cooling and global warming. In the late 1960’s academic researchers found evidence of global cooling. Fifty years on academic researchers assume the earth is warming. We just roll with it as we do with all the other policy “givens” we accommodate. Stewardship of the planet — keeping it clean for those who follow us — Yes. Catastrophilia — the love of catastrophone so well documented in history — not so much.

As with all emotional issues, language changes mightily. We refer you to our journey through technical standard language HERE.

According to a report by the Congressional Research Service, federal funding for climate research and related activities totaled approximately $13.8 billion in fiscal year 2020. This funding was distributed across various agencies and programs, including the National Science Foundation’s Climate and Large-Scale Dynamics program, National Oceanic and Atmospheric Administration’s climate Program Office, and the Department of Energy’s Office of Science.

Not included in this figure is the opportunity cost and loss of brand identity of not conforming to the climate change agenda.

The “Narrative”

Mass Formation Psychosis

Climate Psychosis

Climate Science: What Does it Say?

Dialectic: Climate Change

Readings / The Administrative State

A Survey on Explainable Artificial Intelligence

Decoding the US Senate Hearing on Oversight of AI: NLP Analysis in Python

Peeking Inside the Black-Box_ A Survey on Explainable Artificial Intelligence (XAI)

IEEE Explore

Amina Adadi & Mohammed Berrada

Ben Abdellah University Morocco

ABSTRACT: At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. Indeed, the black-box nature of these systems allows powerful predictions, but it cannot be directly explained. This issue has triggered a new debate on explainable AI (XAI). A research field holds substantial promise for improving trust and transparency of AI-based systems. It is recognized as the sine qua non for AI to continue making steady progress without disruption. This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI. Through the lens of the literature, we review the existing approaches regarding the topic, discuss trends surrounding its sphere, and present major research trajectories.