In 2014 Stanford began a 100-year study on the societal implications of artificial intelligence. Over the next 100 years a (gradually changing) board of top AI researchers will publish periodic papers giving a bird’s eye view on the current state of AI research. The purpose of these papers is to record AI’s effect on humanity, anticipate future developments, and inform policy makers of relevant issues; to separate substance from sensationalism.
Five days ago, Stanford released the first of these papers (pdf). It’s worth reading yourself—there’s an executive summary if you’re short on time. In this post I’ll list some of the things that stood out to me as I read through it.
In this first paper, the researchers narrow their scope to North American cities. That is, they describe the current impact of AI on the inhabitants of North American cities, and envision possible developments to the year 2030. While the One Hundred Year Study has international ambitions, this is a practical starting point given the study’s newness and North America’s comparative development.
The paper covers many aspects of society—transportation, healthcare, education, public safety, employment, entertainment, home life—though they leave out defense and military. The study acknowledges this gap, asserting that military applications fall outside the scope of North American cities. It seems likely that a thorough treatment of military applications would have dominated the paper. It also seems likely to me that they had difficulty accessing information on the state of military tech, given their intention of publicizing it.
The paper dedicates much discussion to “low-resource communities”, describing the effect of AI on disadvantaged demographics. It frequently stresses the importance of ensuring that our technologies are unbiased and do not perpetuate unfair discrimination. This makes my budding involvement in algorithmic fairness feel relevant. So that’s nice.
The paper notes the recent trend of data-intensive machine learning (e.g. “deep learning”) displacing most other lines of inquiry, but suggests that it is worthwhile to recognize the limitations of this path. As someone who feels that the current trend does little to improve our understanding of intelligence (though I acknowledge the usefulness of deep learning), I felt validated when the paper gave the following advice:
We encourage young researchers not to reinvent the wheel, but rather to maintain an awareness of the significant progress in many areas of AI during the first fifty years of the field, and in related fields such as control theory, cognitive science, and psychology.
Well, that’s all for now.