At the beginning of the year, with BTC around 600 to the dollar, I made a bet with a friend (I was given 3:1 odds) that BTC/USD would be below 150 at the end of the year. I think there are some core flaws with cryptocurrency that, in order to alleviate them, effectively make their benefits moot.
As a target for theft, Bitcoins are basically a dream. Once transferred away from an owner they can be mixed with other (fungible) BTC and be no longer feasibly traceable to the a single thief. To harden the target, you can put BTC into a wallet that’s in cold storage (put it on a hard drive, take hard drive out of computer, hide under bed). This is all well and good, and there are even fancy services popping up that will do all kinds of things to ensure the security of your coins, but at this point you can ask yourself — why? Is it really that good of a store of value that it’s worth going to such an effort to preserve their possession? In fact, I see few sensible arguments for BTC being a useful store of value other than as it’s value as a transactional tool…
I think the concept of a block chain is interesting and has potential for many applications (that’s a whole other argument, here I only refer to Bitcoins and other currency usage). If the exchanges gets super liquid, you may even be able to use one for transactional purposes for lower total cost than a credit/debit card (debit is really the only applicable comparison here). I could feasibly see, with a large enough transaction flow, the profit potential from market making into fiat resulting in tighter spreads than the ~2% paid for interchange fees using plastic. How much lower though? A few basis points would be reasonable I think — similar to liquid electronic financial markets. But for market makers to be able to get it that tight, you would need pretty low underlying volatility — a high confidence in the stability of the value.
Chicken and egg? It seems like a solid bet that continued highly publicized thefts will become the norm and this will degrade confidence in the value. Without that confidence, it’s unlikely to gain the significant liquidity necessary to succeed as a transactional platform.
What purpose citations? It seems like they are extremely susceptible to being a manipulative, rather than informative, tool for any discourse other than in hard sciences where reality is close at hand. A reference on a topic or opinion that you are deeply familiar with is of little use to you. However, the majority of the time we deal with what we are not experts in. Usually there is some inferential distance that needs to be crossed, or at least some learning of the inevitable multitude of points of view surrounding an issue. Referencing particular arguments or external sources, unless done extremely carefully and in a well-balance manner, seems to be an even greater signal that the original document aims to persuade rather than inform.
For the past 8 months or so, I’ve been making a 1 liter dense smoothie about 3 times a week. On those days it makes up a little more than 50% of my caloric intake I would imagine. I tried to come up with something that would cover most of my micronutrients as well as have a low glycemic load, taste decent and be easy to consume (drink). It’s nice, I get lots of energy and never get that tired feeling that comes after most meals. I also think it tastes good, but not everyone agrees 🙁
I’ve been curious as to the actual nutritional breakdown of my typical recipe. The ingredients vary slightly but are usually something very close to the ones listed below. I suspected I wasn’t getting much protein, and that appears to be the case. I’m not worried about the relatively low carb intake as it’s pretty easy for me (and most people) to eat carbs. My dinners (and lunches on non-goop days) are vegetarian about 75% of the time with probably more carbs than necessary in a single meal.
Here’s the ingredient breakdown. There’s obviously variation between this and what I’m actually eating, but it’s pretty close. I checked the nutritional info on the packages of the stuff I have that comes in packages and it seems close enough to the numbers below.
|| 1 individual (packet)
||Almond milk Blue Diamond Unsweetened
|| 1 fruit
||Acovados, raw, California, without skin and seed
|| 1 medium
|| 1 cup
|| 1 cup
||Kale, chopped, raw
|| 1 ounce
|| 1 ounce
||Nuts, Brazilnuts, dried unblanched
|| 1 ounce
||Nuts, Cashew, raw
|| 1 ounce
||Nuts, Hazelnuts or filberts
|| 1 ounce
|| 1 ounce
|| 100 grams
|| 1 pepper
||Pepper, serrano, raw
Data is from the USDA database, and the extra metrics + pretty pictures is generated through http://nutritiondata.self.com. The site makes pretty graphs but is very painful to use. Most of the time when updating the meal composition it would fail and load stale data or miss some new data that was saved already. I would not want to try to use that to try variations on a combination of ingredients but I wasn’t able to find a better tool. I also don’t like building GUI’s, otherwise I’d make my own.
More or less what I expected. My guess is it may be worth putting a scoop of protein powder in and maybe cut down a little on the nuts to keep it from getting too viscous.
A pretty good overview of paleo nonsense ends with a good summary of what science can agree on at this point:
Having talked to all of these people and read their work, here is how I walk away from this. Oxidative stress will increasingly be the target of medical treatments and preventive diets. We’ll hear more about the role of blood sugar in Alzheimer’s and continue to focus on moderating intake of refined carbohydrates. The consensus remains that too much LDL is bad for you. We do not have reason to believe that gluten is bad for most people. It does cause reactive symptoms in some people. Peanuts can kill some people, but that does not mean they are bad for everyone. I agree with Katz that the diets consistently shown to have good long-term health outcomes—both mental and physical—include whole grains and fruits, and are not nearly as high in fat as what Perlmutter proposes.
The amount of hype over a major dietary change with so little scientific evidence is not surprising; this ain’t the first time
A very interesting potential solution to the Fermi paradox which turns the Kardashev scale on it’s head. It suggests that intelligent lifeforms will have a tendency to evolve and expand inwards rather than outwards. It seems like this balances on the idea that transforming biological lifeforms into a denser substrate (computational on silicon, initially?) and then acceleration of energy efficiency gains by increasing the “computational density” will occur quicker than expansion into space (very time consuming without energy to reach very high speeds) to find more energy.
In an Aeon article from last year, David Deutsch seems to be taking a dim view of Bayesianism as a core component of an AGI:
The doctrine assumes that minds work by assigning probabilities to their ideas and modifying those probabilities in the light of experience as a way of choosing how to act. This is especially perverse when it comes to an AGI’s values — the moral and aesthetic ideas that inform its choices and intentions — for it allows only a behaviouristic model of them, in which values that are ‘rewarded’ by ‘experience’ are ‘reinforced’ and come to dominate behaviour while those that are ‘punished’ by ‘experience’ are extinguished.
Or rather, since the Bayes AGI just does what rewards it the most, it will align its ‘values’ to correspond to that. I think the problem is with the hidden meaning in the word values. What he is referring to here is probably something along the lines of a compressed version of the AGI’s internal decision making model. In fact, you could say that this is in theory what we consider the personal or ethical values of a human being to really be. Of course in practice a human’s actions frequently depart from his advertised values — probably by design.
The core problem here is not with Bayesianism but rather that an unfortunately designed AGI could find itself with a fitness function (and corresponding set of values, ethics, etc.) which are not necessarily friendly to humans. Or perhaps he’s suggesting it’s a problem with any AGI which has a goal system. What is the alternative, in that case?
Mike hates feral hogs, and has always found it very satisfying to clear those hideous, rooting thugs off a piece of land. He has always been good at it too — that’s why people call him Mike the Hog-a-Nator.
The mammalian superorder Euarchontoglires split from it’s sister group into Glires (which includes rodents and rabbits) and Euarchonta (which includes primates and us) about 90 million years ago. So when you eat the nice juicy rabbit steak, you’re eating one of your furry ~9 millionth cousins (assuming about 10 years per generation on average). Of course if you live in an African jungle and lack food sources, you may be eating monkeys who split from humans only around 5-10 million years ago, so they are you perhaps 250,000th cousin (generations probably get longer in this genus; say 20 years). During instances of cannibalism, of course, you’re likely eating a 10th or 20th cousin (assuming the cannibalism is occurring where it has in the past).
How many generations would you have to go back before reaching a common ancestor with someone/something before you’re comfortable eating them, ethically? How far back until you’re comfortable mating with them, but also how far back until you’re uncomfortable? Tricky business, this
Don’t tell the TSA or soon we’ll have robocops on every plane. Perhaps it’s time to rethink the pile of rules and regulations and start with something evidence-based? Is profiling based on appearance and race so bad (ethically? bad how?) that it’s not worth the massive gains in efficiency we would experience getting through airports? Seems unlikely. Pre-check seems to be a step in the right direction; we should be able to give up privacy in exchange for not having to get to security 3 hours before our flights.
I would think that if there was one thing we learned about economics over the past 30 years, it is that these macro models are about as predictive as a moving average. What gives?