AI tipping points
Read a disturbing article about an outbreak of the Ebola virus in the DRC. The causes include a lack of clean water and sanitation facilities, i.e., clean toilets and sinks. At the same time, an AI bubble created a trillionaire and the rapid construction of water-guzzling data centers. We don’t need to solve intelligence and then solve everything else, the stated goal of Demis Hassibis of DeepMind. We need to redress poverty, inequalities and dire circumstances starting now. A long-term benefit of AI is that it’s radicalizing society and making us realize that there are different visions of our future and different ideas about what constitutes progress. AI caused our economic system to descend into an absurd caricature. First tragedy, then farce, as we were warned.
Another tipping point may be happening in academia. For the last few years, researchers have jumped on the bandwagon of AI for science, promoting hype that AI will accelerate scientific progress. This is reflected in academic literature, conferences, personal statements and webpages, and grants. From what I can tell, the bandwagon is now full. AI-scientists are ten a penny. Insights and lasting advances remain rare, and the actual expertise and value of AI scientists, in many cases, looks threadbare.
Only time will tell, but for now I am happy to ying while everyone else yangs. Paradoxically, as a consequence of the rush towards AI, actual understanding of statistics, statistical reasoning, statistical computation, and reasoning under uncertainty in general are at present rare and underdeveloped. I remain interested in AI, but through the perspective of traditional statistics and computing, and with a critical eye on who made it, how and why, and what harm it might cause.