[0] https://en.wikipedia.org/wiki/Regulation_of_genetically_modi...
186 karma · joined May 28, 2015
[0] https://en.wikipedia.org/wiki/Regulation_of_genetically_modi...
In particular, you could write some Agda/Coq/Idris program that implements rule 110, and has some trivial user interaction to repeat this until a stable pattern is reached. This simulates a Turing machine.
When you cut through all the self-interest and xenophobia surrounding outsourcing, there still remain many quality and communications issues. But I believe these will be reduced as more people learn English and recognize the value of English as a market skill.
I like the way he is the only candidate with the guts to oppose illegal immigration, even though it should be obvious to everyone that illegal immigration is wrong and must be stopped. It's especially ridiculous given that almost all liberal candidates support much worse in Israel, while calling Trump a racist for proposing relatively mild measures in the US.
The difference between being Turing complete and implementing rule 110 is crucial, and any discussion on the subject needs to make clear that these are not the same.
You are obfuscating the role of the driver. In the Turing machine, the driver is part of the Turing machine. In CSS, it is not, and has to be done by the user (or presumably a JS script).
As another post by Grue3 points out, almost any language can implement rule 110, including languages that are not Turing complete, and are explicitly said not to be Turing complete in the CS literature/community.
For example, Coq, Agda, Idris (with totality checking) and other languages based on Martin Lof type theory, are not Turing complete. That it why they are able to prove totality of their functions. But they can all implement rule 110 with no problem.
By inconsistencies, I mean that anyone who is moderately left-wing with regard to Israel, will be called a crazy anti-semite (as Rand Paul is) unless they are to the far left on domestic politics. Similarly anyone who is on the moderate right on domestic politics is expected to be far right with regard to Israel and the Middle East.
Anyone with moderate, consistent views would be called anti-Semitic by the right, and racist by the left.
Imagine saying "Stock X is great, but the market can undervalue X longer than I can stay solvent"?
Those days are long gone...
What about commodity clusters (i.e. all cloud computing), MapReduce/Hadoop, Spark, or the deep learning renaissance? Yes, I am super biased, but I think Google and Facebook have done as much to advance technology as their predecessors.
But apart from that they have a point. Raw numbers matter, and even though R&D might have a fuzzy boundary with ordinary business, I think that R&D expenditure is a useful signal.
I liked the general ideas in the blog. People think about these things informally all the time, but it's nice to see the ideas laid out systematically.
Maybe you can wait for someone to write a blog expanding on these ideas for a more general audience, or write one yourself.
Any expert in a field other than pure math, will tell you that in order to evaluate an argument or evidence, you need experience and intuition, not just argumentation. Arguments are not a formal process that can definitively arrive at conclusions. The same applies to mathematical models. Economists often complain in private about the need to dress up their ideas as mathematical models, even when the model adds nothing to the discussion. This is especially annoying in empirical work, when no one is actually interested in the model, just the empirical results.
The government might sometimes compensate people, but that would depend on many factors such as 1) To what extent the person was unable to avoid the risk, and to what extent they willingly bet on the price of the medallion, and 2) Whether the government did all in its power to keep policies predictable, given the unpredictable nature of technological change.
The only time I ignored advice was when I went to a doctor for a cough, and because I mentioned chest pain (from coughing), they decided to get an EKG, which showed some abnormality. Apparently this abnormality is not uncommon in healthy people. They recommended more tests with a specialist, but I figured that a not uncommon result on a test, when doing that test wasn't justified by my original symptoms, didn't amount to enough evidence to go any further.
The main problem is that by its competitive nature, mining rewards are actually deadweight loss. Every dollar value of bitcoin that is given to a miner is value that is destroyed. To see this, imagine that a Chinese bitcoin mining farm is making a profit mining bitcoins, gaining $10 of bitcoins for every $8 of operating expenses. Then another farm could start up with an even lower profit margin. This would continue until the economic profit was zero, i.e. every dollar's worth of bitcoin mined cost a dollar in operating expenses, which are deadweight loss to the economy.
However, none of this implies that a constant inflation rate is not possible. As long as a dollar's worth of bitcoin generates more than $1 of value to the economy, the system should be stable, and the higher this number, the less wasteful bitcoin would be. Given the various figures for velocity[0][1] I've seen, it's not clear whether this system would be prohibitively wasteful or not.
[0] https://en.wikipedia.org/wiki/Velocity_of_money
[1] Note that velocity is the ratio of total money to nominal GDP but since one is a stock and the other is a flow, this doesn't measure how much economic activity a single dollar generates/supports.
Let's start with the empirical evidence. Velocity of money has not grown much at all, over the last 50 years[0]. Surely if the issue was mainly technological, then technological changes up till now would have resulted in a bigger change in velocity.
The alternate explanation is that the transaction value of money is driven by other kinds of economic frictions, in particular agency problems. Although it's not very popular, Holmstrom and Tirole have some theoretical work on the relationship between collateral, liquidity and agency problems. But the general idea is quite intuitive: any contract, whether debt or equity, changes the incentives of the counterparty and results in moral hazard. This places a limit on how much we can rely on credit instead of money.
That this logic is applied to hard money and not to smartphones is most parsimoniously explained by the political incentives that led to the abandonment of hard money in the first place,
Given the above, do you still think that the you know the simple obvious truth and that mainstream economics has ignored it out of willful blindness? Does your parsimonious explanation apply to me?
[0] https://en.wikipedia.org/wiki/Velocity_of_money#/media/File:...
The deflation issue is complex. It's hard to imagine a stable situation where money has a fixed supply. The simplest steady state one can imagine is constant economic growth r, which is also the real interest rate. If we further assume a constant velocity of money (so the total value of all money is a fixed percentage of GDP), then any currency where the total supply of currency was fixed, would have a deflation rate equal to the real interest rate. Therefore holding currency or zero interest bonds would be equivalent. But money has some transaction value that makes it more valuable than zero interest bonds, so this situation could not be an equilibrium.
Most economists think that in practice there is no way to have a stable currency except by forcing inflation by printing money (which in the current implementation is injected into the economy by buying bonds). I don't think it is possible to create an inflationary currency except through central banks. So I really think that bitcoin faces long term problems.
So a sufficient improvement in toolchains/package management etc. would probably enable bitwise reproducibility, but I think a lot of people in this thread, and the original article, were implying that current tools are sufficient and scientists just need to stop being lazy.
On the other hand, I do think that combination of extreme scarcity leading to government regulation is so out of line with how the world is moving, that it reflects flawed thinking. It's almost as if people want some kind of crises to escape the decadence of modern capitalism (and there are as many on the right who think this way). The only thing that could create this kind of scarcity is global warming. But maybe the reference to a farmer on the arctic circle was a reference to this?
[0] https://www.facebook.com/yann.lecun/posts/10152184295832143
Also projections of population don't suggest any more than doubling by 2080 at the maximum. And raising and killing animals humanely should become easier with improvement in robotics technology.
My original point is that the complexity of dealing with differing environments and code versions, combined with the sensitivity of floating point results on the exact source code and environment, practically kills the possibility for exact reproducibility.
That's why I'm claiming that a better goal is approximate reproducibility.
For what I am proposing, keeping track of the major versions of dependencies should be enough.
See also sibling comment.
What do you mean by "reorder matrix multiplies in a source-to-source transformation?" To be clear, the issue is that matrix multiplication (which forms the basis for a huge part of numerical computing) is only deterministic when the order of operations is known. But any kind of fast algorithm is going to have a highly complex order of operations that depends on the algorithm and parameters. You can't reorder these back to some canonical order without completely changing the algorithm (and destroying its performance gains).
Given the above, posting code and data won't be enough for binary reproducibility. Source code for every dependency would be needed too.
As I noted I do support testing and reproducibility, just not at the level of binary data.
Do we really expect everyone who does linear regression to say "I did linear regression, using the following implementation of a linear solver". Because that's what is being asked.
A much better alternative is to test that algorithms give expected answers (within some tolerance) on simulated but non-trivial data. It will still require some care to distinguish bugs from rounding errors, but it is more, er realistic, than expecting bitwise reproducibility.