Two Points on Penrose, and One On Motivated Reasoning

Sir Roger Penrose is, without doubt, one of the most interesting polymaths of recent history. Even where I find his ideas fantastical, they are most definitely worth reading and understanding. Sean Carroll’s Mindscape podcast interview with Penrose from early January of this year is a treat.

I’ve previously discussed the Penrose-Hameroff conjectures concerning wave function collapse and their implication of quantum operations in the micro-tubule structure of the brain. I also used the conjecture in a short story. But the core driver for Penrose’s original conjecture, namely that algorithmic processes can’t explain human consciousness, has always been a claim in search of support. Equally difficult is pushing consciousness into the sphere of quantum phenomena that tend to show random, rather than directed, behavior. Randomness doesn’t clearly relate to the “hard problem” of consciousness that is about the experience of being conscious.

But take the idea that since mathematicians can prove things that are blocked by Gödel incompleteness, our brains must be different from Turing machines or collections of them. Our brains are likely messy and not theorem proving machines per se, despite operating according to logico-causal processes. Indeed, throw in an active analog to biological evolution based on variation-and-retention of ideas and insights that might actually have a bit of pseudo-randomness associated with it, and there is no reason to doubt that we are capable of the kind of system transcendence that Penrose is looking for.

Note that this doesn’t in any way impact the other horn of Penrose-Hameroff concerning the measurement problem in quantum theory, but there is no reason to suspect that quantum collapse is necessary for consciousness. It might flow the other way, though, and Penrose has created the Penrose Institute to look experimentally for evidence about these effects.… Read the rest

Narcissism, Nonsense and Pseudo-Science

I recently began posting pictures of our home base in Sedona to Instagram (check it out in column to right). It’s been a strange trip. If you are not familiar with how Instagram works, it’s fairly simple: you post pictures and other Instagram members can “follow” you and you can follow them, meaning that you see their pictures and can tap a little heart icon to show you like their pictures. My goal, if I have one, is just that I like the Northern Arizona mountains and deserts and like thinking about the composition of photographs. I’m also interested in the gear and techniques involved in taking and processing pictures. I did, however, market my own books on the platform—briefly, and with apologies.

But Instagram, like Facebook, is a world unto itself.

Shortly after starting on the platform, I received follows from blond Russian beauties who appear to be marketing online sex services. I have received odd follows from variations on the same name who have no content on their pages and who disappear after a day or two if I don’t follow them back. Though I don’t have any definitive evidence, I suspect these might be bots. I have received follows from people who seemed to be marketing themselves as, well, people—including one who bait-and-switched with good landscape photography. They are typically attractive young people, often showing off their six-pack abs, and trying to build a following with the goal of making money off of Instagram. Maybe they plan to show off products or reference them, thus becoming “influencers” in the lingo of social media. Maybe they are trying to fund their travel experiences by reaping revenue from advertisers that co-exist with their popularity in their image feed.… Read the rest

Theoretical Reorganization

Sean Carroll of Caltech takes on the philosophy of science in his paper, Beyond Falsifiability: Normal Science in a Multiverse, as part of a larger conversation on modern theoretical physics and experimental methods. Carroll breaks down the problems of Popper’s falsification criterion and arrives at a more pedestrian Bayesian formulation for how to view science. Theories arise, theories get their priors amplified or deflated, that prior support changes due to—often for Carroll—coherence reasons with other theories and considerations and, in the best case, the posterior support improves with better experimental data.

Continuing with the previous posts’ work on expanding Bayes via AIT considerations, the non-continuous changes to a group of scientific theories that arrive with new theories or data require some better model than just adjusting priors. How exactly does coherence play a part in theory formation? If we treat each theory as a binary string that encodes a Turing machine, then the best theory, inductively, is the shortest machine that accepts the data. But we know that there is no machine that can compute that shortest machine, so there needs to be an algorithm that searches through the state space to try to locate the minimal machine. Meanwhile, the data may be varying and the machine may need to incorporate other machines that help improve the coverage of the original machine or are driven by other factors, as Carroll points out:

We use our taste, lessons from experience, and what we know about the rest of physics to help guide us in hopefully productive directions.

The search algorithm is clearly not just brute force in examining every micro variation in the consequences of changing bits in the machine. Instead, large reusable blocks of subroutines get reparameterized or reused with variation.… Read the rest