Recent QRI highlights – November 2018

Some things going on in and around QRI:

States of Consciousness as Points of View — One thing to consider is that the value of a service like Mechanical Turk comes in part from the range of “points of view” that the participants bring. After all, ensemble models that incorporate diverse types of modeling approaches and datasets usually dominate in real-world machine learning competitions (e.g. Kaggle). Analogously, for a number of applications, getting feedback from someone who thinks differently than everyone already consulted is much more valuable than consulting hundreds of people similar to those already queried. Human minds, insofar as they are prediction machines, can be used as diverse models. A wide range of points of view expands the perspectives used to draw inferences, and in many real-world conditions this will be beneficial for the accuracy of an aggregated prediction. So what would a radical approach to multiplying such “points of view” entail? Arguably a very efficient way of doing so would involve people who inhabit extraordinarily different states of consciousness outside the “typical everyday” mode of being.

  • Mike did an interview with Adam Ford on a fairly wide range of topics. An excerpt on the need for bold, testable theories (and institutions which can generate them):

I would agree with [Thomas] Bass that we’re swimming in neuroscience data, but it’s not magically turning into knowledge. There was a recent paper called “Could a neuroscientist understand a microprocessor?” which asked if the standard suite of neuroscience methods could successfully reverse-engineer the 6502 microprocessor used in the Atari 2600 and NES. This should be easier than reverse-engineering a brain, since it’s a lot smaller and simpler, and since they were analyzing it in software they had all the data they could ever ask for, but it turned out that the methods they were using couldn’t cut it. Which really begs the question of whether these methods can make progress on reverse-engineering actual brains. As the paper puts it, neuroscience thinks it’s data-limited, but it’s actually theory-limited.

The first takeaway from this is that even in the age of “big data” we still need theories, not just data. We still need people trying to guess Nature’s structure and figuring out what data to even gather. Relatedly, I would say that in our age of “Big Science” relatively few people are willing or able to be sufficiently bold to tackle these big questions. Academic promotions & grants don’t particularly reward risk-taking.