What is the most similar work that you know of?

For other non-fictional fear-mongering analysis of tech trends (outside MIRI/FHI) perhaps check out:

• Hugo de-Garis
• Bill Joy
• Martin Ford
• Martin Rees
• Unabomber manifesto

In most of these cases, the end of the world is used as an attention-attracting superstimulus.

Of course, there are many well-known cases of marketing the apocalypse involving fiction as well.

Reputational concerns apply to psychopaths too, and that's why not all of them turn violent. However it doesn't prevent all of them from turning violent.

People have predicted that corporations will be amoral, ruthless psychopaths too. This is what you get when you leave things like reputations out of your models.

Skimping on safety features can save you money. However, a reputation for privacy breaches, security problems and accidents doesn't do you much good. Why model the first effect while ignoring the second one? Oh yes: the axe that needs grinding.

I’ve never seen any good general justification for parsimony...

This is a strange statement for a Bayesian to make. Perhaps he means that there is no reason to require absolute parsimony, which is true; sometimes if you have enough data you can justify the use of complex models. But Bayesian methods certainly require relative parsimony, in the sense that the model complexity needs to be small compared to the quantity of information being modeled. Formally, let A be the entropy of the prior distribution, and B be the mutual information between the observed data and the model parameter(s). Then unless A is small compared to B (relative parsimony), Bayesian updates won't substantially shift belief away from the prior, and the posterior will be just a minor modification of the prior, so the whole process of obtaining data and performing inference will have produced no actual change in belief.

The difference between the MDL philosophy and the Bayesian philosophy is actually quite minor. There are some esoteric technical arguments about things like whether one method or the other converges in the limit of infinite data, but at the end of the day the two philosophies say almost exactly the same thing.

I often use simple models–because they are less effort to fit and, especially, to understand. But I don’t kid myself that they’re better than more complicated efforts!

Recommended reading: Boyd and Richerson's Simple Models of Complex Phenomena.

In practice, I often use simple models–because they are less effort to fit and, especially, to understand. But I don’t kid myself that they’re better than more complicated efforts!

Parsimony is a prior, not an end goal. At least, that's how it's used in Solomonoff induction.

The reason the Solomonoff prior doesn't apply to social sciences is because knowing the area of applicability gives you more information. Once you take that into account, as well as the fact that you don't have the input data or computational power to recompute the cumulative process that spit humans out so the simple low level theories are out of reach, your prior is skewed towards more complex models.

probably not. I'm not exactly sure what you mean by this question since I don't full understand hamilton's rule but in general evolutionary stuff only needs to be close enough to correct rather than actually correct.

eh, this just seems like a repeat of arguments against greedy reductionism. Parsimony is good except when it loses information, but if you're losing information you're not being parsimonious correctly.

It's a really well-made video. However, they make the claim that the doubling period for Moore's Law is shrinking, which I think is false.

It's been suggested that the Flynn effect is mostly a matter of people learning a kind of abstract reasoning that's useful in the modern world, but wasn't so useful previously.

There's also a broader point to be made about why evolution would've built humans to be able to benefit from better software in the first place, that involves the cognitive niche hypothesis. Hmmm... I may need to do a post on the cognitive niche hypothesis at some point.