This reads like you have not done any calculation to determine whether the entire ocean can be heated by any plausible number of datacenters or not.
3,480 karma · joined February 9, 2015
If you somehow had an experience of "both outcomes simultaneously", that would violate the quantum mechanical prediction that there is no mutual information between the two superpositions. There are two brain states, an x state and a y state, and they know nothing about each other.
To each brain state (with the limited information available to it) it would appear that something "definite" had happened, even though in the global picture, a superposition still exists.
Why does the narrative have to be about "caving" to someone instead of weighing principles against each other and taking the action which protects what's most important?
Companies have to make money to exist and survive— but optimizing for money above literally all else is a choice. Again, the word for that is "greed", and it's optional. The leaders of a company can steer towards it or away from it; the company will follow where they lead, raise or lower itself to the standards that they (fail to) set. I worked for 10 years at a successful, well-known company which emphasizes quality over easy money, and it's maddening to see people claiming that such a thing is impossible. It's really not.
This is the exact perspective that I'm talking about. It's the perspective of the GM manager in the 1980s who'd insist— despite all the evidence to the contrary— that Toyota's worker-positive, lean production strategy is impossible. Not only is he wrong, but he'd be losing money because of his lack of imagination and his unwillingness to raise the bar out of the dirt.
It's the attitude of officials in second- and third-world countries, who see corruption, fraud, and abuse— the bottom of the moral barrel— and shrug their shoulders; they justify it to themselves, to others, to the public under the mantra "it can't be helped; the bar cannot possibly be any higher. It's too much to ask". Lo and behold, those countries are economically irrelevant, while countries with higher institutional integrity exist and dominate all around them. Lowering the bar doesn't help them.
Given all the examples available, "a company with high standards" is not "theoretical" at all. Saying so is but an excuse to insist on a pitifully low bar. And it's disappointing to me to encounter the perspective that "behaving with integrity" is beyond conceivable.
You can make money by making people feel good instead of bad. You can be rotten.com, or you can be Pixar. You have a choice. An organization with integrity will look at not just how much money they're immediately making, but whether they're pushing the world towards better or worse. A hands-off attitude of "it's not my problem that people like this sh*t" is not integrity, it's a rationalization for greed.
You can make choices that result in making less than the absolute maximum amount of cash you can get your hands on, in service of building a product/experience/brand with value and goodwill of its own. There are countless examples of this in other places— just look at any company that builds its reputation based on quality. Each of these brands could make their products for cheaper and lower quality, and make more immediate profit, at the (much larger) long-term cost of destroying the brand, the customer goodwill, and the market advantage. Defending against such short-term greediness is uphill work, but it's both the enlightened and profitable thing to do.
Instead of actively amplifying memes and misinformation, they could have chosen to build features supporting community and/or expert moderation. They built an algorithm that optimizes purely for attention, but they could have made something that accounts for quality; paying attention to patterns in good and bad sources of information, and reliable/unreliable discriminating tastes in the community. The emphasis on quality and reliability of content was the pitch for Quora, for example, and they did much better at that task than Facebook. Which is not surprising, because Facebook seems to clearly not be trying to optimize for this at all. Wikipedia and StackOverflow are also two huge success stories of community/expert moderation. It works if you actually prioritize it.
They could have chosen to hire journalists, editors, and artists to produce and vet high-quality content to drive people to the platform, and step responsibly and effectively into the media void that was created when newspapers began to collapse. An analogy for this would be the way that Netflix, Amazon, HBO, and friends have created a new boom and golden age of content creation to fill the void left by the dying medium of broadcast TV. There could have been something like this for print, and Facebook was well positioned for it.
[Jaron Lanier](https://www.ted.com/talks/jaron_lanier_how_we_need_to_remake...) has lots of ideas about how to make an internet that isn't hostile toward its own users. One of his revolutionary ideas: Charge people money for services instead of siphoning their data and their attention in ways that hurt them.
There are a zillion directions they could have gone.
They made a choice to throw gasoline on the flames of these aspects of human behavior. Few people seem to realize that Facebook could have been a force for good, if they had made different choices or had more integrity when it comes to the design and vision of their platform.
The way that things happened is not they only possible way they could have happened, and resigning to the current state as "inevitable", to me, reeks of an incredible lack of imagination.
Solved by the ability to pan and zoom. Node graph tools in professional visual effects environments can grow to >20,000 nodes in a document, especially given that nodes can be nested in groups or cross-included from other documents.
> Need for automatic layout. [...] For example, generating an optimal layout of graphs and trees is NP-Complete [95].
Doesn't need to be absolutely optimal to make the problem go away; making something that helps the user enough that they don't have to think about it is completely tractable.
Many professional node graph tools don't do any automatic layout at all and let the user handle it; which while less than perfect, doesn't stop the tools from being incredibly powerful.
> Lack of formal specification. Currently, there is no formal way to describe a Visual Language.
First, a formal specification is not necessary in order to build something useful.
Second, if you really need a formal specification, then write/invent one. There is nothing about visual systems that makes them less possible to specify than any other software system.
> Tremendous difficulty in building editors and environments. [...] These editors are hard to create [...] the language designer must create a system for display [...] which usually requires low-level graphics programming.
So... do that stuff? What about that work is more difficult than the other parts of writing a compiler; or any other piece of complex software? Maybe this was more of a big deal in 1989, but basically all software today is based on a visual UI, so the complaint that the need for a UI makes visual languages intractable is... pretty ridiculous now. None of this would be harder than, say, writing a simple video game.
> Lack of evidence of their worth. There are not many Visual Languages that would be generally agreed are "successful"
"Nothing good exists yet, so nothing good is possible" doesn't hold water for me.
> Metrics might include learning time, execution speed, retention, etc.
From my experience in visual effects, non-coders can pick up a node graph tool very quickly and get very creative/inventive with nodes— but the moment they have to fall back to writing a script, they struggle or don't even try. It seems abundantly clear to me that node graphs are far easier to learn (but can be just as powerful) as writing with a machine grammar.
I also did an image processing development project in a node graph tool that took about 4 days of experimentation to get to patentable IP. If I had to do the same project in Python or C++, it would have taken weeks or months, if I was even able to solve the problem at all without the fluidity and responsiveness of the node based editor. So even for "serious developers", a good node-based editor can multiply speed by a factor of 10.
> Poor representations. Many visual representations are simply not very good.
Can't argue there— but the solution is to make one that is good. :)
> Lack of Portability of Programs. [...] Graphical languages require special software to view and edit
...Like basically all other file formats in the universe. :)
It is not 1989 anymore, and it's time to fix this stuff.
It might be clear from the context what the wrongness might be, or it might be a gateway for more elaboration/discussion.
It seems like a perfectly fine construction to me, and a good example of the efficiency mentioned in the grandparent comment.
If you have problems/complaints and you never say anything about them, then don't be surprised if they never change.
If there is a 1-on-1 meeting happening, then a communication channel is open. I suppose you can choose to cynically shut it again, but if you make that choice, then don't be surprised to find that you don't have a voice.
A person could try hard to avoid being the squeaky wheel. They could take no curiosity about themselves, or their surroundings, or their organization's goals, or how their boss sees them, or how others see them, or how their habits affect their work, and basically stay the same for years on end, doing the same job, at basically the minimum quality and execution to stay employed.
And then "at the next round of layoffs" that person will find themselves surprisingly replaceable. Probably by a cheaper, more junior person who can do the exact same work at the exact same minimum quality. No reason to keep the guy who hasn't improved for years, and basically tells you to F-off any time you ask him how you can help him.
Guess what: Being invested in your work can make you more valuable. The cynical route often doesn't pay, and frequently fulfills itself.
Where I worked, as a lead I was probably doing up to 50% individual contribution and the rest "management". But I have seen new leads in the same role do 150% individual contribution, and then let their team down on leadership tasks because they've let themselves become swamped. This is a very common error for any newcomer to a job that involves leading other people (which OP specifically says is their role).
I don't think it's an error to put a coder in a position of leading others; it's just that being a productive coder is a weak predictor of being a good lead— you have to look at other markers. Having that kind of "on-the-ground" understanding can be a great strength that can't be had any other way.
There is a line between this and manipulative dishonesty, of course. Bad faith (when it's clearly that beyond reasonable doubt) should be treated with close to zero tolerance.
- Not delegating. New leads often feel that if they're not writing code (or doing whatever it means to be an individual contributor), they're slacking off. The definition of "work" has changed. It now means "helping your team succeed". If you try to be an individual contributor, you are doing your old job instead of your new one, and you'll get overwhelmed and sink.
- Not asking questions. New leads often think that being a lead means being an expert, so they're embarrassed to ask or consult someone else about their team's domain, for fear of looking inadequate. Your team and your peers are the experts— it is your job to ensure that expertise prevails, whether it's yours or someone else's. You'll look much more dumb if you're too insecure to admit what you don't know, or fail to ask basic questions that allow you to get the job done.
- Hiding problems. When things aren't going well (and often they don't), don't hide this from the leadership above you. They are there to help you and guide you, and can save you from crashing and burning. You will look much worse if a problem comes to light months after it could have been communicated and dealt with.
- Not communicating. Share what's going on with your team. Share what's going on in the big picture. Don't keep people in the dark, either above you or below you.
Some leads (moreso if they're prone to impostor syndrome) think they have to wait for permission to speak, or to be offered something that they need— don't do this. If you have something important to say/ask, it's your responsibility as a leader to say it and get what you need.
- Forgetting the big picture. As an individual contributor, you often only have to think about near-term or small-scope problems. It is an easy mistake to make as a new lead to spend priority on those things instead of keeping in mind what's necessary for the entire team to succeed at its high level goal.
Good luck!
The gist: It's efficient (adds and mults in a few instructions), and preserves the decimal representation.
It's quite simple, really: Store a whole-number integer with a smaller one representing the number of shifts to the decimal point. This is probably a good choice for sensitive financial calculations.
A big fraction of product cost is the overhead of setting up the manufacturing pipeline, so often the product tiers will be very small or even software changes to the exact same object.
This doesn't strike me as unfair. It's helped me recently, when I bought the lowest tier iPad pro (I don't care about storage or built-in cellular, and I want the smallest one). If someone wants to pay more for those things, let them. If it came full-price with all those features bundled, I simply wouldn't have bought it at all.
But still tractable, I'd say. My core claim is that counting independently-variable assumptions will be a highly performant way to select between theories which agree with the data. Or put another way, it's the best Fermi approximation calculation for measuring "how good is your theory". How you do that for any given theory, while important to do correctly, is an implementation detail which I think is secondary to the discussion of whether doing it at all is a good idea. :) (seems like we might agree that it is?)
> We can incorporate (partial) evidence from past elections, but it's going to be very sensitive to the priors
To the extent that election forecasts are unreliable, I think that's because they are forced to involve a lot of assumptions (e.g. similarity to past elections) that turn out not to correspond well to reality. Models which make fewer such assumptions will do likely do better! (and IMO fivethirtyeight's forecasts did the best job of this out of any; most of the rest put Hillary at around 97%).
Unfortunately with elections, there is a comparatively high lower bound on the number of assumptions we must make, thanks to the complexity of their dynamics and sparsity of data/knowledge we have about each. I think this is much less the case with physics, where we are varying comparatively small physical assumptions to explain mountains of data. But in either case, I contend that the most performant models will make fewer (unmeasured) independent assumptions.
> How much evidence do we need before we can be confident the "belief levels" we're throwing around aren't that subjective anymore?
The point I'm trying to make is bigger-picture than the above level of detail: Counting independent assumptions, in the limit, matters more than the specific constants of each assumption (assuming they're not low/zero), precisely because it's so hard to come up with "accurate" numbers for each.
That is to say, the probability is not sensitive to those belief levels: We could choose widely varying distributions for the probabilities of our assumptions, including choosing probabilities very close to 1, and it will hardly ever matter to the total probability as much as the absolute number of independent assumptions we make.
Because I've defined them that way. I mean them to be independent choices you could make when designing your model that could be varied to fit the data. If two aspects of the model are not independent; i.e. they are covariant in some way, then there is some common parameter that explains them both, and that parameter is the one that should be seen as an input to the model.
> We can roll a die 100 times, 1000 times, 10K times [...] That's what we mean (if we're frequentists)
We're not frequentists.
You can't "re-roll" the 2016 election 10K times, either. There was only one, and there was only one way it could come out; we just didn't know enough to say what it would be before it happened. All the particles in all the voters were obeying the laws of physics at every moment; never was there any freedom for a different outcome. Nonetheless, even though there was/is only one "ground truth" that could ever be, we assigned probabilities to each possible outcome, given our incomplete knowledge.
This is a pretty standard application of probability. State estimators (e.g. the Kalman filter) are doing the same thing— you have some noisy readings of reality, and you use Bayesian logic on some assumed probability distributions to pick the estimate from the space of possible "ground truths" that has the highest probability of being the right one.
Concretely: I'm measuring roll rate, local acceleration, compass heading, barometric pressure, and GPS, all with significant error, and I want to know where my quadcopter is most likely to be at the current moment. There is only one true answer to that question, the quadcopter is in one place, not 10,000 places (or 10,000 flights), there is a single ground truth. But Bayes will give me a probability, given my readings, that any given estimate is the true ground truth (and some math will help me solve for the highest one).
In this case, instead of assigning probabilities to possible election outcomes or system state "ground truths", the "configuration space" is models of reality. But all we've changed is the domain of our probability distribution; the math doesn't care what kind of thing our "ground truth" represents. And it doesn't matter if reality contains only one "ground truth" or many; the fact is that we are choosing between many options (and we are ranking them by likelihood).
A sketch: Suppose we have two theories, one ("A") with assumptions (a, b); the other ("B") with assumptions (a, p, q, r, s, t); and our evidence 'e'.
Pr(A) ~= Pr(a|e) * Pr(b|e)
Pr(B) ~= Pr(a|e) * Pr(p|e) * Pr(q|e) * Pr(r|e) * Pr(s|e) * Pr(t|e)
That is, the probability of each theory being right is the probability that each of its assumptions are simultaneously true. It might be difficult to come up with a specific number for each of those component terms, but we'd do well to estimate the total probability of each theory simply by counting the terms, since in the limit that will matter more than the probability of each term (assuming we think none of them are obviously low).
Also note that when counting, we can trivially factor out the common assumptions "a" (a.k.a. the things we don't wish to doubt or vary between theories, e.g. QM in "ordinary" regimes, GR, the Newtonian approximation to GR, etc.)
Yes, there is a "ground truth" theory which is absolutely true or false, but we don't have access to it. And I don't see how it's more problematic to use probability here than on any other classical event on which we have imperfect information, like a specific dice roll, or a baseball game, or the outcome of an election— One specific thing will happen, and no other outcome was possible, but we can still use probability to model our incomplete knowledge. How is physics different?
Example stress tests of this idea:
e = the motion of the planets; A := "G = m1*m2/r^2"; B := [long list of epicycle parameters] --> pick "A".
e = the varied appearance/adaptations of animals/species; A := [reproduction, inheritance, variation, selection]; B := foreach animal x {"God zotted $x into existence like that because just_so_story($x)" --> pick "A".
e = my empty garage; A := "there's nothing in it"; B := "there's a dragon in it; the dragon is invisible; the dragon dodges your touch; the dragon has no heat signature; the dragon floats and leaves no footprints; ..." --> pick "A".
e = the behavior of the universe; A := [the standard model]; B := [the standard model; also it's a simulation; there are intelligent beings who set up the simulation; there is an external universe in which the simulation is occurring; the number of simulations happening in this universe is large; ...] --> pick "A".
The theory you paint would be composite of other assumptions (involving lighting, forces, or stars, e.g.?) which in turn are either in contradiction with observation (implying Pr=0%), or rely on further (very low probability) assumptions in order to avoid contradiction.
If that's not true, then you've just given a strange name to the empty set of assumptions, which is the null hypothesis; and the null hypothesis DOES get epistemological privilege.
To maximize the chance of choosing a model which corresponds to reality, we must involve as few unchecked assumptions as possible when deciding between theories that agree with the data; and this matters more than any particular assumption having a relatively high chance of being right, because of this exponential sensitivity.
So if we want theories that are correct, yes, we should value simplicity.
In both cases, I get that some users can't or don't like to use a machine grammar/markup, however simple. For some people markup is bad UX. Give them a WYSIWYG; that's fine.
But don't remove the markup editor if your WYSIWYG editor is anything but a perfect one-two-one replacement for markup (and I have never seen one that satisfies that).
IIRC there was a time when Confluence axed their markup, and inevitably a table or a template would get completely screwed, and there was nothing you could do but recreate it. TERRIBLE design.
It's a good 1.5" from the right arrow key to the edge.
(Of course that particular problem would be less of an issue if the keyboard had home/end/pgUp/pgDn, which I'm still sore about, years after they got rid of them).
Arrow keys are so important I might almost want them all full size, in a "+" configuration.