Pages – AI Impacts

Do neural networks learn human concepts?

This page is a stub. It does not necessarily represent much of what is known on the topic. Our understanding is that the degree to which neural networks learn concepts that are potentially understandable to

AI Vignettes Project

Posted Oct 12 2021 The AI Vignettes Project is an ongoing effort to write concrete plausible future histories of AI development and its social impacts. Details Purposes We hope to: Check that abstract views about

Argument for AI x-risk from large impacts

An argument that since AI will likely have a large impact on society, it is especially likely to be an existential risk to humanity.

Fiction relevant to AI futurism

A list of stories potentially relevant to thinking about the development of advanced AI, including both those intended as futurism and those intended as entertainment.

What do coherence arguments imply about the behavior of advanced AI?

There is probably a pressure to become more coherent, if you have preferences, and thus to become more goal-directed.

AI Impacts 2020 review

This is a list of work done at AI Impacts published in 2020.

AI Impacts key questions of interest

Updated Dec 17, 2020 This is a list of questions that AI Impacts focuses on answering. Details We are interested in understanding how the development of advanced AI will proceed and how it may affect

How energy efficient are human-engineered flight designs relative to natural ones?

Nature is responsible for the most energy efficient flight, according to an investigation of albatrosses, butterflies and nine different human-engineered flying machines.

Energy efficiency of monarch butterfly flight

The monarch butterfly can fly around 100,000-600,000 m/kJ and move mass at around 0.065-0.36 kg⋅m/J according to very rough calculations.

Energy efficiency of wandering albatross flight

The wandering albatross
can fly around 481m/kJ and moved mass at around 2.8—6.1 kg.m/J