With most of currency being digital, this change of reserve ratio requirements has almost no effect in the way banking and economy works. Added a Wikipedia reference which has more references to empirical studies.
“ Many economists and bankers now realize that the amount of money in circulation is limited only by the demand for loans, not by reserve requirements.”
Japanese central bankers figured out a way of creating money for purposes that are good for society. Like AI, Quantum computing or bio technology and any other hyped up stuff you see on internet.
There were many bad consequences in the past because of money created that way.
With the way SoftBank is giving away money, it looks like “smart credit guidance” is still happening with out any public policy or public awareness.
Aim of Academic institutions conducting research is for the benefit of humanity. Their principles are to hire the highly qualified people irrespective of their nationality or religion. It’s a merit based hiring.
There is no secret conspiracy here to bring down wages of researchers. Some people just do it for the likes.
How this usually works is that samples are relevant to specific experiments. For example, maybe you're testing how some particular genetic modification affects how cancer cells behave in response to some drug, or whatever. These vials of cells would be what you get halfway through such an experiment, after you've treated the cells but before you've had a chance to analyze them. Postdocs and studentships often end midway through projects like this. The raw value of the materials is low, and it might take a postdoc days to weeks to reproduce them, depending on complexity. There is also an extremely high chance that the cells are completely worthless, i.e. that the treatment was botched or inconclusive. If the student hadn't taken the vials, the project might well have been abandoned, due to lack of manpower.
With that background, the morality of "smuggling" these cells is a bit of a gray area. On one hand, they're just incomplete work that the student wants to finish elsewhere. Keeping them is just like me keeping my notebooks when I move, even if I haven't finished solving the equations therein. On the other hand, if the project is finished elsewhere, credit won't properly flow to the affiliation where the work was started. In theoretical physics, we really don't care about this (I just finished a project where 2 members changed affiliation midway through), but in biology there's the cost of facilities, and the local expertise that gets you the raw materials.
So there is an issue here, but I certainly don't think it's worth the hysteria that will unfold in this comments section. I think the real issue is that a lot of people on this site are disgusted at the idea of Chinese people learning science. But to us scientists, the spread of knowledge is a virtue. After all, the end goal of this student would have been to publish in a journal -- which people from any country can access.
> With that background, the morality of "smuggling" these cells is a bit of a gray area.
The grey area here is from lack of foundational knowledge surrounding these cells, and your assumptions aren’t useful (nor are they likely, I suspect, to hold). For example, we have no idea if these cells were originally provided to the lab under an MTA, nor any restrictions placed by the funding source.
> Keeping them is just like me keeping my notebooks when I move, even if I haven't finished solving the equations therein.
This is not a grey area: lab notebooks and lab data are generally speaking the property of the lab. The proper procedure is to request permission to create a copy of them when you move to a new institution. Check your university’s IP policy, it should be the governing rule set.
I thought this was a great comment until the petty jab at the end. Being concerned about this issue and being "disgusted at the idea of Chinese people learning science" are clearly different things. Don't let a few bad apples cloud your view of the rest of us.
If you see such comments you should bring them to our attention by flagging them or, in egregious cases, emailing us at hn@ycombinator.com about them. This is something we moderate heavily:
Skew is inevitable on certain issues given that HN demographics are (though highly international) mostly Western. But that doesn't mean that people get to post slurs or use the site for nationalistic flamewar.
1. The stolen material by these Chinese scientists were worthless and akin to a student taking home a notebook after a lecture.
2. The real issue is here is not of the rampant theft of intellectual property by rogue Chinese scientists, but that Westerners are disgusted by the mere thought of Chinese people learning science.
I didn't need to learn your name ('Zhou') to see that you are another CCP loyalist. And honestly I am quite concerned that a visiting Chinese scientist like yourself holds sympathetic views of these intellectual theft crimes by your countrymen.....
As part of the exact employment contract, a researcher's work may be patented by the institute they work for (with the researcher being remunerated for this).
Biomedical patents are not trivial software patents. Billions of dollars of investment goes into developing a single new therapy. The molecular structure of medicine can often be easily replicated once discovered, so patents are a key part of commercializing new medicine and techniques. (See the CRISPR-Cas9 patent debacle.) Taking the right to patent some work away from the institute it was developed at means theft of the licensing fees derived from the patent, and an impact on an institute's ability to keep funding R&D.
Your typical research doesn't lead to billions in licensing fees. 99% of research produces nothing of direct monetary value at all, and research that does is generally done in startups or pharma/biotech companies, where the rules are far more stringent than in university-affiliated labs. Still, of course you are right that this is all a gray area.
While the vast majority of academic labs aren’t producing finished products like drugs, it’s not uncommon for researchers to obtain patents on certain promising leads.
Most universities have a “tech transfer” office that helps with this (the university usually gets a share) and tries to find licensees.
There isn’t enough information in the linked article to say how unique or important these particular cells are or how difficult they would be to replicate. On the more valuable end could be cells provided by a company for testing (maybe transformed to express some sort of protein they’ve created), or primary cells (isolated from an organism) that are unique in some way, or hybridomas that produce monoclonal antibodies for something druggable or diagnostically relevant.
The Boston Herald article linked by another commentator suggests that in at least one element this case fits the pattern of others that have been appearing: the accused is receiving funds from the Chinese government under an ostensible scholar program. In some of the other cases, such as with the Thousand Talents program, the scholars signed contracts that agreed to disclose or assign IP only to the Chinese institutions, conceal the source of funding for studies (both to journals or funding agencies), or agree to work at the Chinese institutions in excess of the norms for visiting appointments. The Thousand Talents program and others like it are a coordinated, calculated, and deliberate effort run by the Chinese government, not the accidental missteps of some aloof academics.
Honestly, that just sounds like the usual rules for academic funding, phrased in a conspiratorial way. Would you also call NSF funding "a coordinated, calculated, and deliberate effort run by the American government"? Is science good for nothing but proxy war to you?
> conceal the source of funding for studies (both to journals or funding agencies)
doesn't sound like the usual rules for academic funding. I heavily doubt whether the Thousand Talents program really has such a rule. It's the exact opposite of what organizations funding scientific research usually want: getting their name acknowledged in as many papers in prestigious journals as possible, in order to demonstrate success.
This post was amusing to me for so many reasons. The author did this for his personal project, cheers to him for his passion to try out new technology.
This is roughly similar to what happens in technology teams all across the business units.
Manager 1: We are streamlining all our product offerings to a Kubernetes container cluster. Why you ask? We want to modernize our stack and we want to attract best talent.
Manager 1 and the team collects the rewards
...... few months later
Manager 2: We reduced the cost of operations by x% by simplifying operations. Aka rolling back to something other than Kubernates.
Manager 2 Collects the rewards
Every time, there is a detailed intelligent write up about what we are going to do and what we did and how awesome it was.
Believe it or not, internet has a way of influencing the really smart people with branding and advertising driving them to a form of resume driven development.
Kubernetes to developers is like what Axe body spray is to teenagers.
Just use Axe Deodorant and women will be all over you.
It makes more sense if Bitcoin is viewed as an insurance rather than an investment or a commodity.
It's an insurance against the whole world falling apart. Why would the whole world of finance fall apart? Same reason Roman empire fell part and lost its significance.
Long back 1000$ was a lot of money, later a million $ was a lot of money and then billion $ was a lot of money. In today's news, people are describing events in trillion $ costs. If you extrapolate this trend, money as we know will be losing its value exponentially.
Bitcoin is a money system that is defined very clearly in terms of quantity, creation process and exchange etc. This is a good minimum viable product. It definitely has value just like a commodity. It also has people working on improving it as if it were an ipo'd company.
Just like language and law, bitcoin is a product of "spontaneous order" and our knowledge of traditional finance may not have enough mental tools to value it like another financial product. Some people certainly value it more than other people. Over a long period of time take span of 100 years, it becomes easy to imagine having a bitcoin like digital currency with standardized rules for money creation not controlled by any single party.
Sorry, it's not insurance against the world falling apart because cryptocurrencies (Ccs) have no intrinsic worth. With no internet access, no internet or no miners, you can't conduct trades (unless you give wallet PK, but then it still doesn't have worth beyond what you can actually buy with it in a post-internet world).
OTOH, vital, scarce commodities and property have intrinsic worth because they can be used or are valued. Gold, silver, platinum, diesel fuel, guns, knives, equipment, huge tracks o' land
From the begging and until now, Ccs only make sense as a transactional money analog, not an investment other than an extremely speculative (volatile) one that could lose most/all of its value anytime.
If you want to hide money for a couple of days, do some money laundering (j/k), buy some drugs (j/k^2) or send money anonymously, Ccs are great. Please don't put the bulk/large amount of your savings into Cc unless you're leaving a repressive regime or want to give it all to me to avoid taxes. }:D
Nothing has intrinsic value. When you say something can be used or has value, you are speaking subjectively.
To be valuable economically, something just needs to be both scarce and sought after. Just because they are valuable to a specific set of persons does not mean they have intrinsic value.
Pricing of goods and services is emergent in nature, based on demand, scarcity and rate of consumption.
The 'bitcoin hype' is exactly what you are describing when you say:
> Ccs only make sense as a transactional money analog
Bitcoin was designed to just be a general accounting ledger that can't be forged. It is supposed to be a transactional money analog.
The 'value' that you should be investing in when talking about bitcoin is not its price in dollars, but rather that the idea:
The 'value' in bitcoin is that non-mutable accounting ledgers will be more reliable and provide a better account of economic activity over some duration than its pre-existing counterpart. The only way it will lose this is if the security of the system breaks.
You're not making an honest argument while making a sweeping generalization and a strawman about something else. Money has value if you can buy things with it. Gasoline has value if you need it. Bitcoin has no value if no one will accept it or trade it. That's reality, not hypotheticals.
I believe you are missing the point I was trying to make.
I was stating that nothing, not even bitcoin or money or gold or water has intrinsic value. Anything can have value to a person who is in need of a seller's goods. That was not my argument. Things have subjective value. Things do not have some mystic intrinsic property that makes them inherently valuable.
"Obfuscation" and "delusions of grandeur" are practically synonyms for ML and Data "Science" in this industry. I've been around for a while and I've never quite seen something as over-hyped and hyper-glamorized as these two specializations.
Calm down. Machine learning is a part of software engineering. Like multiprocessing, computer graphics or network protocols. It is here to stay. It is a part of a pallete of algorithms with which one can build software.
Your comment is absolutely correct but further points out just how far astray data science has become from any meaningful work. This issue is that a huge number of "data scientists" have limited programming ability and nearly zero engineering sense.
As a perfect example of this is the trend in most places I've seen where data scientists strive to increase the complexity of their model (so they can prove how "smart" they are). A huge part of a software engineering education (whether in the classroom or in dev shop) is learning that complexity is the enemy. No engineer would choose a 3 layer MLP over a simple linear regression for an imperceptible improvement in performance.
The additional irony of all this is that a decade+ ago a software engineer who had strong quantitative and numeric programming skills was rare and an elite find. You would have thought that the data science boom would have dramatically increased the number of these people but I find them even rarer.
What are these data scientists? Most statisticians I know would just use the linear regression unless they needed a neural network for marketing purposes. Statisticians will spend years studying linear regression and variations in graduate school. I thought it’s a CS guy who would be more fascinated with neural networks.
Well there are some fairly distinct camps forming in data science. You are correct that those coming from a statistics background would generally prefer simpler, more parsimonious models. There is a not-insignificant group that seem to be coming into the field via other channels (CS, boot camps, self-teaching, etc.) who view statistics as a field as a bit of a dinosaur and therefore the statistician mindset to be backwards. Simpler models aren't a good thing, they are a bad thing. Any amount of increased complexity is worth even a small amount of improvement in performance.
I think some of this is exacerbated by modern pillars of machine learning and data science. Competition sites like Kaggle are entirely based on maximizing test set accuracy, and so winning submissions these days are huge morasses of ensemble methods that are trained for days and weeks on GPUs, but in the end they are often only marginally better than some of the fairly basic standard approaches. And when companies like Google are building their bots for Go or Starcraft, they are using cutting edge techniques. When people see that and get inspired to get into data science, thats what they want to do, even the the majority of problems are more rooted in data quality, thoughtful understanding of the problem, and more rudimentary methods.
Its also the result of some of the rhetoric of important figures in the field. Yann LeCun has pushed back strongly in the past on criticisms of modern day machine learning's occasionally lack of concern with introspection and model understanding. Judea Pearl, a Turing award winner for his work in machine learning, devotes large portions of his pop-sci The Book of Why attacking the field of statistics on the whole, as well as engaging in multiple attacks on historical influencers in the field with such ferocity it borders on character assassination. He has even rebuffed modern critics, such as the very widely respected Andrew Gelman, by saying they are "lacking courage" by failing to accept his "revolutionary" causal inference methods over the traditional ones used in statistics.
The attitude is driven a lot by the people and institutions at the top, and as someone in the field, I unfortunately encounter this kind of thinking way too often.
Yes! It's one tool in the software engineering toolbox! It's a great tool for some problems!
Due to the hype it becomes a goal in some organizations however. "We need to do machine learning because we have big data" or some such. Doesn't matter if the problem could've been solved in 5% of the time and cost with 20 lines of code, thou shalt use machine learning.
It doesn't help that data scientists (creating and training the ML model) and software developers (creating and maintaining the software) usually come from different backgrounds, requiring a "data engineer" as an additional intermediary.
It always a problem with hype, blockchain (or merkle trees) has the same problem but worse, because the problems it solves well are rarer and more narrow.
To me, it seem to be larger than one tool. I think of it as a color in a pallete, with which one can paint software. Octarine.
To put this statement into context, I'm speaking as someone who had been writing code in C, from the era of PC XT. Perhaps NIPS 2010 was a rite of passage to ML for me. There is a screen, full of industry grade C++ and PyTorch in front of me, right now...
ML can be useful, but it is getting too much attention. Far more hype than the value it actually provides in many domains, IMHO.
Yes, I know that there are folks that deal with vast amounts of data with inscrutable relationships where you need fancy algorithms to make progress. But seriously, most problems just don't need it, and many folks would be better off with mastering basic statistics and data analysis.
Heh, given that I am starting to see more and more companies that offer ML engineers $2-6k/month (before tax), it's starting to resemble gaming industry in all its negative characteristics instead.
I cannot tell from your comment whether 2-6k/month before tax should be considered a lot or a little. I think in the major tech centers that 2-6k/month is quite low for anyone with significant experience (>5 yrs). Do you disagree?
Nobody talks about operations research today. But techniques that fell under that umbrella, like ARIMA and linear programming are still widely used, and aren’t going anywhere. (And it’s not without some irony that automated bulk time series forecasting is now sold as AI).
It's funny but at my last company, one of our systems used some linear programming to generate a model of physical processes.
The problem could have been tackled with greater accuracy using machine learning, but it would have taken a long time for the system to generate enough data points for a sound model and would have required more storage space. This was also complicated by the fact that the model had to be regenerated whenever the physical system being modeled was changed.
The linear programming solution was a lot cheaper and was "close enough" to serve as a useful approximation.
Linear and quadratic programming are amazing and totally underappreciated. Often they are the fastest way to get useful answers for problems (the solvers got really good over the past few decades).
Basically a mathematical approach to problems of logistics and scheduling developed first in WW2. Very powerful in the domains for which it was developed but less generally applicable than enthusiasts hoped, leading to the usual “hype cycle”.
If you have a problem OR could solve or just want to fool around with it PuLP is very easy to use https://pythonhosted.org/PuLP/ Of course the ease of use means that it is a commodity skill now.
Yep. Taxi routing service, and not even the best one, you'd think they're launching those taxis to Mars.
That said, SpaceX interview process is even more ridiculous. The first step is to talk on the phone with a non-engineer recruiter who has to ask you highly technical questions, but doesn't understand a word of your response, and you know it. They then sort of have to correlate what you're saying with the answers they have and decide whether you know anything or not. The most uncomfortable interview situation I've ever been in. Or at least that's how it was a few years ago, maybe they've changed it. I was so thrown off by this, I totally fucked it up and never got to the second step, in spite of nominally having all the right experience. To relate, imagine trying to explain low level assembly to a five year old, over the phone.
Not saying this is the case for spacex, but in my field (totally not space or engineering or software (but very much "tech" (physics/chemistry)) related), these types of interview are for weeding out the non-standard folks (of which there are many, including me but many of us (including me) have become good at hiding it). A person with high E would presumably (but not always because it is indeed a difficult task, casualties are regrettable but expected) "grok" the task and begin feeding the right keywords to the recruiter. Once you realize the game, it becomes fairly easy. Just read the job description and sprinkle the keywords provided therein.
Thank you so very much for saying this. When will these people realise that nobody gives a toss about their overly long and overcomplicated selection process.
> This is a symptom of "bullshit" going on around in big tech companies. "bullshit" here is an economic term defined in the book "bullshit jobs".
Bullshit is neither an economic term nor an anthropological one. David Graeber is an anthropologist, not an economist, though he has written inexplicably popular books on economic topics that betray his lack of understanding of economics.
Bullshit is actually used as a technical term in philosophy occasionally.
> One of the most salient features of our culture is that there is so much bullshit. Everyone knows this. Each of us contributes his share. But we tend to take the situation for granted. Most people are rather confident of their ability to recognize bullshit and to avoid being taken in by it. So the phenomenon has not aroused much deliberate concern, or attracted much sustained inquiry. In consequence, we have no clear understanding of what bullshit is, why there is so much of it, or what functions it serves. And we lack a conscientiously developed appreciation of what it means to us. In other words, we have no theory. I propose to begin the development of a theoretical understanding of bullshit, mainly by providing some tentative and exploratory philosophical analysis. I shall not consider the rhetorical uses and misuses of bullshit. My aim is simply to give a rough account of what bullshit is and how it differs from what it is not, or (putting it somewhat differently) to articulate, more or less sketchily, the structure of its concept.
> not an economist, though he has written inexplicably popular books on economic topics that betray his lack of understanding of economics.
"Debt" I think shows a deep understanding of the relationships economics has with history, philosophy, and society. Graeber knows he's not an economist but he's got a point to make and he's not shy about making it even though it says less than flattering things about some aspects of economics.
> Now, this may sound a little silly - if someone wrote a book called "Metal: The First 5,000 Years," and then filled that book with stories of war and bloodshed, never failing to remind us after each anecdote that metal was involved in some way, we might be left scratching our heads as to why the author was so fixated on metal instead of on war itself. And in fact, that is indeed how I felt for much of the time I was reading Graeber's book. The problem was exacerbated by the fact that Graeber continually talks around the idea of debt in other ways, mentioning debt crises (without reflecting deeply on why these happen), the periodic use and disuse of coinage (which apparently is just as bad as debt in terms of enabling the capitalism monster), and any other phenomenon related to debt, without weaving these observations into a coherent whole.
> In other words, I am now angry at myself for paraphrasing the book, and trying to put theses into Graeber's mouth, because this is such a rambling, confused, scattershot book that I am doing you a disservice by making it seem more coherent than it really is.
> The problem of extreme disorganization is dramatically worsened by the way that Graeber skips merrily back and forth from things he appears to know quite a lot about to things he obviously knows nothing about. One sentence he'll be talking about blood debts and "human economies" in African tribes (cool!), and the next he'll be telling us that Apple Computer was started by dropouts from IBM (false!). There are a number of glaring instances of this. The worst is not when Graeber delivers incorrect facts (who cares where Apple's founders had worked?), it's when he uncritically and blithely makes assertions that one could only accept if one has extremely strong leftist mood affiliation
> It seems to be “capitalism bad” but that may be too kind to the book’s coherence.
have you read the book? The book is an exploration, and an interrogation, with so much to learn from that to say that about it seems pretty philistinic.
Maybe you were just summing up the review you linked from Noah Smith. I read most of it, it's a bit meh but Noah doesn't really seem to be trying too much in it. This though: "leftist mood affiliation". That's cheap 'preaching to the choir' language.
If you have a link to a more serious review I'd genuinely like to read it.
Shit, I remember most of the "bad stuff" in Debt predating capitalism by somewhere between centuries and millennia. Seems like a weird way to write it if its Secret Purpose was to be a long-winded hit piece on capitalism.
Sometimes, lessons from the past can help to remove some of the rose-tinted glasses that people seem to associate these newer companies with. For example, it's worth reading Enron's Vision and Values statement (http://www.agsm.edu.au/bobm/teaching/BE/Cases_pdf/enron-code...) from 2000.
I don't think there have been any fundamental changes since 2000 that would incentivize make communication from large public corporate entities to be more honest or logically rigorous.
I would classify this as "Self help" advice based on personal experience. Over all its very good advice to learn to collaborate with others.
There are so many persuasion filters here "Partner at Ycombinator", "Co-Founder", "Director", "Apple", "High Achiever writer in general". With this many strong credentials, it would be very very difficult to critically read such advice and then think of alternative ideas.
"If you don’t retrain your model based on input from the crowd, you’ll never converge on truth." This is true for machines.
Other high achievers believe in the alternate version.
"Reasonable men adapt to the world around them; unreasonable men make the world adapt to them. The world is changed by unreasonable men." Edwin Louis Cole
"truth is my goal. Controversy is my gym. I'll do a hundred reps of controversy for a 6 pack of truth" - Kanye west.
"Your life goal should be not to win any particular game, rather to win the sum of all games." This metaphor of everything is a game really really works well for certain people. I personally witnessed it.
It would be a sad life if you turn everything you do in life as a game where you need to compete with others to win and never question the nature of the game itself.
"Do what you love" is an over all long term approach that sustains your energy and focus for long term.
Most ideas of high achievers are very contradictory, in general its a good idea to listen to so many of them and pick something that meets your style.
Definitely agreed. The version of introspection presented here is inherently outward then inward, which is fine, but I'm not sure that's actually that useful. If the crowd has fucked up values, adapting to their fucked up values isn't a victory, it's just conforming to fucked up values. I think YC is a great example of this, many of the companies they fund are unethical and shallow moneygrabs (I'd be happy to detail them, but it's not useful). If you adapt to that, well, good luck because you might be an unethical and shallow carpetbagger.
I've got the problem of being too introspective, which can lead to a lot of beating yourself up, but after 30 years I get myself, which is nice. I'm not sure you can do that if you're constantly chasing what a lot of people value though which is success, "influence", money, a social life, etc. Those are all great indicators of success, but if you don't slow down how can you really stop and think about yourself? Not to introduce Trump (okay, going to do it anyway), but that's a guy who has spent 70 years chasing stuff, being addicted to fast food and TV. Has that guy ever stopped and thought about what's wrong with himself? What he can do better? Strongly doubt it. Much of our society is that, caught up in some loop where they never have the ability to do anything but react.
If we're honest with ourselves, much of what the world values is stupid if not outright evil. When people don't see that I don't believe they've really thought things through. That's pretentious, but we gotta get real about how we and others work or nothing will change.
Guess what I'm saying is by all means be introspective, but really be introspective. Don't measure yourself against bad yardsticks, be honest about your flaws and strengths. Don't do it because it's some game to maximize your utility, do it because if you don't do it, you are worse off.
It does a good job of explaining how trust evolved in the world and now that we are comfortable using computers to organize our lives, it makes sense to have a global ledger to keep track of "karma" we owe each other.
“ Many economists and bankers now realize that the amount of money in circulation is limited only by the demand for loans, not by reserve requirements.”
https://en.wikipedia.org/wiki/Money_creation#Credit_theory_o...