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The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
The possibility that AI will automate most cognitive labor is worth taking seriously. How should we adapt to this transformation? I start from the perspective, articulated in the essay “AI as normal technology”, that the true bottlenecks lie downstream of capabilities and that AI’s impacts will unfold gradually over decades. If this is true, there are major gaps in our current evidence infrastructure, because it over-emphasizes the capability layer.
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The possibility that AI will automate most cognitive labor is worth taking seriously. How should we adapt to this transformation? I start from the perspective, articulated in the essay “AI as normal technology”, that the true bottlenecks lie downstream of capabilities and that AI’s impacts will unfold gradually over decades. If this is true, there are major gaps in our current evidence infrastructure, because it over-emphasizes the capability layer.
At no time in recent memory has the impact of disease on society been more palpable. But how do we study the nexus between society, ecology, and disease? Our team utilizes a precise and novel integration of archaeological, historical, anthropological, climatic, and ancient human and pathogen genetic datasets, using a longitudinal lens to achieve a better understanding of disease impact- specifically malaria - over time. Rich, robust, and large datasets are drawn from two regional contexts capturing critical periods in global disease transformations. Data science approaches are used to extricate critical features of the human-malaria relationship over the last 300 years, and despite the research being in the early stages of development, have already revealed key aspects of how socio-political factors influenced the impact of the disease on human lives.
