Symbolic mathematics finally yields to neural networks - <AUTHOR> https://www.quantamagazine.org/symbolic-mathematics-finally-yields-to-neural-networks-20200520/ <comment><USER2> About 50 years ago I was fascinated to see a program that could do symbolic integration. Now, programs like Mathematica can do much harder, more complex integrals.

Professor Patrick Winston pointed out that most of the AI programs we were going to look at in his class (chess, natural language, 3D vision in block world, etc.) ended up, like integration, as simple code plus a database of facts.

Back then, there was general optimism about the future of AI. No one anticipated how slow progress would be while the capabilities of the hardware grew a million-fold.

The work referenced in the article is interesting. It appears to be another small step in advancing AI. It's a small, but difficult, step like almost every other advance in AI, and I admire the work done by those working in this field.

The problems of solving simple differential equations and symbolic integration at the first year calculus level are not really advanced math. Humans solve these problems with a relatively small bag of tricks that transform a symbolic integral into a simpler form. A program can do the same thing with an even more detailed database of transforms that can be attempted at each point in the search tree until a simple solution is reached.

The article claims that the new program can solve difficult integrals. This is interesting because hard to solve integrals are often associated with real physical phenomena. See for example the triple integrals of W. F. van Peype which arose while he was studying magnetism in different materials. These, relatively, plain looking definite integrals stumped some of the world's most famous mathematicians. See [1] and/or [2] for their interesting history.

[1] Paul J. Nahin, _Inside Interesting Integrals_ , Springer, 2015. Section 6.5, The Watson/van Peype Triple Integrals.

[2] I.J. Zucker, 70+ Years of the Watson Integrals, [http://www.inp.nsk.su/~silagadz/Watson_Integral.pdf](http://www.inp.nsk.su/~silagadz/Watson_Integral.pdf)

<comment> amw-zero Honest question. Are you fearful of the moral implications of AI? Whenever I hear someone that is fascinated about AI and only thinks about it as an intellectual pursuit, I'm curious if they are thinking at all about the consequences of powerful AI.

From where I sit, the bad outweighs the good.

<comment><USER3> These types of worries always strike me as worrying about keyhole surgery going wrong, before we are capable of making scalpels, anasthetic, or even video cameras -- or hell, before we even know what a tendon is or what the purpose of blood is. Or worrying about the challenger 2 explosion when we can't even make gunpowder. Or worrying about the logistics of flying cars in three dimensions and traffic crashes before we are even able to build a boiler engine to drive a train.

We are _so far_ away from GAI at the moment that I don't for one second _actually worry_ about the moral implications of General Artifical Intelligence.

I don't see the point in worrying about something when we know literally _nothing_ about that thing, and barely have a path to making it. It's very likely that by the time we are capable of making GAI (Excusing the very probable idea that we will be able to simulate a brain, but not at any proper speed -- see the three body problem and the challenges with simulating _literally any other_ physical systems), there will be half a dozen problems we _do_ need to worry about, that we _cannot forsee_. There will also be half a dozen limitations that mean that our current worries are _essentially worthless_. It's the same with all new technology.

It's also interesting that people who tend to worry about GAI never worry about _current levels of AI_ , especially in a military context. They seem entirely unconcerned with being worried about literally _crappy_ and half- baked neural networks being deployed for use in drones. They seem entirely unconcerned with the lack of proper dataset balancing and sorting that ensures that _current AI models do not have racial bias_ (or, indeed _other_ types of bias).

Just last year I saw a twitter post about a startup that was re-creating _literal phrenology_ , using AI to try and profile whether people were criminals or not based on _facial shape_. The typical Less Wrong / MIRI folks never seem to be worried about that, no, they spend their time in fear of Roko's Baselisk and other currently-impossible scenarios. They literally _purged_ posts, threads, and comments that made _any mention_ of that under the utter and complete fear that maybe in the far flung future a very bad simulation (Unless, their brains are cryogenically frozen, I guess, but it's very likely that brain structure would degrade under the immense timespan anyway) of them would be tortured for their current actions, by a _good AI_ , that had apparently gone so insane that it thought that torturing low-fidelity simulations of people in the future could affect the past and cause it to be created faster.

Speculating about the future can be a positive thing, but I don't see how this is at all useful or healthy.

<comment> amw-zero You can't imagine any reason to worry? At all?

Worrying about the future of surgical technology is very different. The end goal of surgery is to save a life or improve the quality of life, and it involves restoring a single person back to working order.

The end goal of AI is to _think_. The upper bound on that is horrifying. Once something can think it can build. Once something can build it can multiply. The upper bound on AI is replacing the human species.

I'm not saying I'm nervous about this happening next year. I know how terribly inept we are at true GAI. I'm thinking purely abstractly, and in that light I think we should more serious about ground rules for AI.

<comment><USER3> Can you re-read my post more closely and actually critique it. You chose one of my points, arguably the weakest (partly because it's an analogy -- analogies are mostly for flavour, they don't make a good argument but they help you to appreciate where I am coming from) and ignored the stronger criticisms I posted after that.

</comment></comment></comment></comment></comment><comment><USER4> This is one of the most exciting things I've seen come out of AI research for my own interests. The way that they distributed the mathematical equations is called an abstract syntax tree (maybe they mentioned this, but I was excited- skimming), and is also how computer code is represented. It also happens that there is _a lot_ more computer code laying around in computer-readable format than high school and college math problems. With this, you get: metamorphic code, better optimizers, more abstract programming languages (maybe), and probably lots of other steps forward.

If anyone is working on AI+programming languages, please send me a message. I'd love to work with others on this problem.

<comment><USER5> I'm planning on applying for PhD programs this fall to work in this area. There are only a few places in the world right now that I know of working on these types of problems. They are:

* Martin Vechev, ETH Zurich

* Dawn Song, University of California Berkeley

* Eran Yahav, Technion

* Miltiadis Allamanis, Microsoft Research Cambridge

If anyone knows other advisors looking for graduate students in this area, please let me know. Due to personal circumstances I can most likely not apply to ETH Zurich or Technion (I don't speak Hebrew anyway), which leaves me with only one potential advisor in a program that I really want.

There is also the Python writing model that Open AI showed recently at the Microsoft Build conference, so maybe there is some interest growing at other places as well.

I was also recently working on a deep learning decompiler but was unable to get my transformer model to learn well enough to actually decompile x64 assembly. I have the source code for the entire Linux kernel as training data, so it's not an issue with quantity. If anyone is interested in helping out with this project, please let me know in a comment.

<comment><USER6> _I have the source code for the entire Linux kernel as training data, so it 's not an issue with quantity_

Linux kernel is only ~30M LOC. That's a really small dataset. For comparison, the reddit based dataset for GPT-2 is 100 times larger. Try using all C code posted on Github.

 _decompile x64 assembly_

You can't "decompile" assembly. Either you decompile machine code, or you disassemble assembly code. The latter is easier than the former, so if you're trying to decompile executables, then perhaps you should train two models: one to convert machine code to assembly, and the other to convert assembly to C. Assembly code produced by an optimizing compiler might differ significantly from assembly code which closely corresponds to C code.

<comment><USER7> > perhaps you should train two models: one to convert machine code to > assembly, and the other to convert assembly to C.

Is the step of going from machine code to gcc-produced assembly not trivial? Is gcc actually producing assembly code that an assembler needs to do more with than convert to the corresponding opcodes?

<comment><USER6> There are two kinds of assembly: 1. assembly that corresponds to optimized machine code, and 2. assembly that closely corresponds to the original C code. As I said, these two assembly versions might look very different depending on optimizations performed by the compiler. You can reduce the difficulty of learning the conversion from machine code to assembly at the expense of increasing the difficulty of learning the conversion from assembly to C code (and vice versa).

</comment></comment></comment></comment></comment><comment><USER8> To me, the article somewhat misses the point of what's interesting here. Using ASTs to represent equations, or even whole programs has plenty of precedents in ML/AI. I'd have liked to know how exactly they translate these trees to a representation suitable for an ANN. Fortunately, the paper seems to be easy to find and access (it's [1], I guess).

[1] [https://arxiv.org/abs/1912.01412](https://arxiv.org/abs/1912.01412)

<comment><USER9> It looks like they go from tree -> sequence via prefix notation. I'm curious why Lample decided on this seq2seq approach when it seems that there might be models which could be more naturally applied to the tree structure directly [1, 2]

[1] [https://arxiv.org/abs/1902.07282](https://arxiv.org/abs/1902.07282) (an AST translation system)

[2] [https://arxiv.org/abs/1609.02907](https://arxiv.org/abs/1609.02907) (GCNN)

<comment><USER10> For the same reason people use huge transformers for sentences in natural language (which are also tree structured): they scale really well. If you have enough data, huge transformers have huge capacity. If you notice, this paper is entirely about how to cleverly generate a massive dataset. There is no novelty in the model -- they just use a standard approach described in two paragraphs.

</comment></comment></comment><comment> globular-toast Symbolic mathematics (or computer algebra as it is more commonly known as) was one the original driving forces for AI. Differentiation turned out to be quite easy once things like ASTs and other data structures were developed to represent polynomials and other elementary functions. Integration is way more difficult and while AI research made great inroads the problem was eventually solved by algebraic methods. But the full algorithm (the Risch algorithm) is about 100 pages long and has never been fully implemented. The Axiom computer algebra system is the closest AFAIK, but the premature loss of Manuel Bronstein set it back a bit (that system is fascinating as well, it's a literate program in Lisp).

The AI approaches always had one great problem: if they can't find an integral you still have no idea whether an integral exists or not. The Risch algorithm, on the other hand, can tell you for sure if an (elementary) integral doesn't exist. Axiom is fully capable of saying "no", but can't always tell you what the integral is if it does exist.

Using an AST to represent expressions isn't novel, by the way. I implemented such a system as an undergrad computer science student (I also implemented complete integration of rational functions).

</comment><comment><USER11> Discussion about the paper a while back: [https://news.ycombinator.com/item?id=21084748](https://news.ycombinator.com/item?id=21084748). Conclusion seemed to be that the comparison against Mathematica was unfair since Mathematica's execution time was capped at 30s.

<comment><USER12> Interesting, since in my experience Mathematica's execution time is also lower-bounded by 30s.

</comment></comment><comment><USER13> Here's a somewhat more technical critique of the approach (and of the evaluation cases the authors used): [https://arxiv.org/pdf/1912.05752.pdf](https://arxiv.org/pdf/1912.05752.pdf)

<comment><USER14> > It is important to emphasize thatthe construction of LC is entirely > dependent on the pre-existingsymbolic processors developed over the last 50 > years by experts in symbolic mathematics.Moreover,as things now stand, > extending LC to fill in some of its gaps (e.g. the simplification > problemsdescribed in section 3) would make it even less of a stand-alone > system and more dependent onconventional symbolic processors. There is no > reason whatever to suppose that NN-based systemswill supercede symbolic > mathematics systems any time in the foreseeable future.

That's the gem of the review.

</comment></comment><comment><USER15> From article: "You need to be confident that it's going to work all the time, and not just on some chosen problems" by mathematician Frédéric Gibou

If I understand correctly, mathematical solutions can be verified, while neural network solutions would be very hard, if not impossible to verify in reasonable time.

<comment><USER16> It depends on the problem.

For integration, you can just derive.

For infinitely many other problems... verification is way harder.

<comment><USER17> is it usually the case that derivatives are easier to compute than integrals?

<comment><USER18> The general algorithm for calculating integrals [1] is rather complex and I guess not suitable for humans so that calculating integrals sometimes looks much more like a black art then calculating derivatives does. On the other hand one could argue that there are algorithms for doing both and so there is no real difference.

[1] [https://en.wikipedia.org/wiki/Risch_algorithm](https://en.wikipedia.org/wiki/Risch_algorithm)

</comment></comment></comment></comment><comment><USER19> > The Facebook researchers compared their method to only a few of > Mathematica's functions —"integrate" for integrals and "DSolve" for > differential equations — but Mathematica users can access hundreds of other > solving tools.

> [...] it only included equations with one variable, and only those based on > elementary functions. "It was a thin slice of possible expressions,"

> The neural net wasn't tested on messier functions often used in physics and > finance, like error functions or Bessel functions. (The Facebook group said > it could be, in future versions, with very simple modifications.)

> Other critics have noted that the Facebook group's neural net doesn't really > understand the math; it's more of an exceptional guesser.

> Still, they agree that the new approach will prove useful.

<comment><USER20> > exceptional guesser

Considering neural networks are inherently maximizing probabilities and statistical descriptions of data, this should come as no surprise. This work has not dissolved the dichotomy between rules-based and statistical methods, but rather transmuted the syntax of rules-based expressions into a representation that can be exploited by statistical machines in a way that makes "guessing" more fruitful.

There are some examples near the end of the paper showing how the authors take an initially intractable expression and are able to simplify it with their approach so that Mathematica can actually perform the integral for them. It seems much more appropriate to market this method as a preprocessor for massive expressions to a more chewable size.

<comment><USER21> > exceptional guesser

This IMO describes how mathematics itself moves forward... A matematician is an extremely well-trained 'guesser' who is also able to sink a lot of time into formal verification.

The process is essentially: a) Find an interesting conjecture that you've got a strong guess to be true. b) Check for obvious (or less obvious) counterexamples, or conflicting theorems. c) Prove the thing is true.

A large part of the art of being a working mathematician is in part (a): you need to make a really good guess. An ideal conjecture is correct AND proveable AND leads to other interesting results, or says interesting things about bigger problems.

So what happens when we apply really good versions of current AI to this area? Picking out an 'interesting' conjecture is still Strong-AI-Complete: it requires lots of domain knowledge, and an understanding of what this particular conjecture would 'unlock.' But we could perhaps come up with good 'guessers' which quickly tell us whether a given idea might work out, perhaps saving a bunch of effort. Perhaps we could even get to the point of generating a proposed proof which can be fed to an automated proof checking system, allowing for inspection and modification by the human in the loop.

</comment></comment></comment><comment><USER22> So what's the earliest known AI solver for calculus textbook problems? I bet it goes all the way to the 1950s.

This quote seems to be massively misleading:

 _But it's clear that the team has answered the decades-old question — can AI do symbolic math? — in the affirmative. "Their models are well established. The algorithms are well established. They postulate the problem in a clever way," said Wojciech Zaremba, co-founder of the AI research group OpenAI.

"They did succeed in coming up with neural networks that could solve problems that were beyond the scope of the rule-following machine system," McClelland said. "Which is very exciting."_

<comment><USER23> Grace Hopper was experimenting with symbolic differentiation of functions in her A-2 compiler in the 1950s, and she may well not have been the first. My reference for this is her paper in the proceedings of a 1957 British conference on "Mechanisation of Thought Processes" \-- if you set up an account at archive.org, they may let you page through it here: [https://archive.org/details/mechanisationoft02nati/page/n9/m...](https://archive.org/details/mechanisationoft02nati/page/n9/mode/2up)

<comment><USER24> Not AI, though (or almost every computer program would be "AI"). Differentiation can be done with simple deterministic rule following.

<comment><USER23> Earliest reference I'm aware of for integration is James Slagle's MIT Ph.D. thesis work, from 1961. [https://dspace.mit.edu/handle/1721.1/11997](https://dspace.mit.edu/handle/1721.1/11997)

</comment></comment></comment></comment><comment><USER25> It seems that the neural net just spits out an answer, rather than deriving it step-by-step like humans do. That's interesting, but would still get you an F on a real calculus test.

<comment><USER26> I think the idea with this kind of thing is that ML can make pretty-good guesses really quickly, and then a formalized process can verify them (usually much more quickly than it could derive them). This hybrid model fits lots of different kinds of problems.

</comment></comment><comment><USER27> You might find the Sledgehammer tool for Isabelle quite interesting. It has been using machine learning techniques to find proofs automatically since at least 2013. It uses previous proof to learn how to select facts to send to off-the-shelf automated provers in order to discharge a goal. See e.g. [http://isabelle.in.tum.de/~blanchet/mash2.pdf](http://isabelle.in.tum.de/~blanchet/mash2.pdf)

On issue I have with it, and that automated proof tools based on ML are going to have to solve, is that it's quite unpredictable. Even if it finds stunning proofs from time to time, it is hard to use an unpredictable tool efficiently.

</comment><comment><USER28> How about using [http://us.metamath.org/](http://us.metamath.org/) as a db for math theorems / definitions and do some heavy data mining there?

<comment><USER29> There's some work on this already, including Holophrasm and work by OpenAI on proof shortening. These efforts are linked from the Metamath wiki:

[https://github.com/metamath/set.mm/wiki/Automated- proving](https://github.com/metamath/set.mm/wiki/Automated-proving)

<comment><USER28> very cool, thanks for sharing

<comment><USER30> OpenAI has managed to derive a number of Metamath proofs, some of which are smaller than the human-created proofs, using machine learning.

Here is a gource visualization of metamath proofs overtime in the set.mm database: [https://m.youtube.com/watch?v=LVGSeDjWzUo](https://m.youtube.com/watch?v=LVGSeDjWzUo)

Note that near the end, one of the contributors is OpenAI, who is not a human contributor.

</comment></comment></comment></comment><comment><USER31> This looks really interesting! Although, I'm not sure if this was "first". I know of another approach tried by Hinton and Sutskever in their paper "Using matrices to model symbolic relationships" [0]. I don't see a date, but I remember Hinton mentioning it in a talk from a few years ago..

[0] [https://www.cs.toronto.edu/~hinton/absps/ilyamre.pdf](https://www.cs.toronto.edu/~hinton/absps/ilyamre.pdf)

</comment><comment><USER32> Just need to build a tool that predicts the next version of a program from an earlier version and suggests improvements (ideally bug fixes).

Then you have [http://www.scp-wiki.net/scp-914](http://www.scp- wiki.net/scp-914) for programs.

</comment><comment><USER33> It would be really interesting to see code simplification via neural network.

While working in enterprise I see plenty of code that after some massaging collapses into a much simpler form (for the programmer) and equivalent for the computer.

(fix typo)

</comment><comment><USER34> What would a confidence interval for this type of prediction look like?

<comment><USER35> A confidence interval is a sort of meaningless notion in this case. You can just do the derivative of the algorithm's answer, and see if you get back an expression that is equivalent to the input. If you did, then the answer is correct.

<comment><USER34> I don't believe a model (neural network or otherwise) is capable of knowing that its predictions are correct.

</comment></comment></comment><comment><USER36> Since ML outputs are usually "fuzzy", how does one check that the output is actually correct when using a technique like this in the field?</comment>


Ask HN: How common is preferred dividend in a seed round nowadays? - <AUTHOR> An investor asks for a 6% cumulative dividend. I've been reading up but there seems to be a disagreement whether this is a common term or not and whether to accept or avoid in a seed round. Any thoughts? <comment><USER2> Random internet remark:

To me, an investor seeking dividends has objectives that are misaligned with the capital structures typically beneficial for startups of the type associated with Silicon Valley. Dividends provide regular cash flows to investors at the cost of a reduction in the working capital of the business. Nothing wrong with that, it can be a good fit for businesses that maintain a steady size or expected to have linear growth. However, those are not characteristics of businesses typically considered by Silicon Valley venture capital type investors. That sort of money seeks compounding growth. Rather than extracting dividends, the goal is reinvestment of cash on hand to further fuel growth. These investors seek returns based on an increase in the value of the underlying equity (i.e. shares of stock). Amazon's historic lack of profits and ever rising share price provides a good example of this model...Amazon kept getting bigger by reinvesting revenue and putting more assets on the books and Amazon's shareholders made capital gains by holding the shares. Most investors are not really suited to Silicon Valley type startup investing. Likewise, most new businesses are not really startups in the sense that they are structured to provide returns via compounding equity growth. This doesn't mean that the investment terms are bad. It just means they may or may not be suited to your particular case. Good luck.</comment>


Britain's housing crisis is a human disaster - design-of-homes http://www.theguardian.com/society/2015/mar/14/britain-housing-crisis-10-ways-solve-rowan-moore-general-election <comment><AUTHOR> _The underlying belief is that the market will provide, if only it were properly stimulated and enticed, but it is one unsupported by evidence. At no time since the second world war has the private sector built at the rate now required, and usually it has fallen a long way short._

Worth noting for folks who think that supply is the only problem. Similarly in my home of Vancouver the construction of towers has been constant for decades and prices have only skyrocketed in response.

Certainly if you stopped building completely you'd eventually have a real problem, but the evidence is clear to me that you'll never reach the incredible supply levels that would cause prices to drop (why would builders do this anyway?).

 _There is also what Ebenezer Howard, the inventor of garden cities, called the "unearned increment", which is the uplift in value when land becomes available for housing. It was the basis on which postwar new towns were built. Currently it is being squandered – when the government, for example, relaxes planning constraints on certain properties, or makes it easier to convert offices or shops into homes they drench the lucky owners of such places in cash, without asking for much in return._

Vancouver captures this. It goes toward community centres, the arts and increasingly housing. [http://vancouver.ca/news-calendar/community-benefits- from-de...](http://vancouver.ca/news-calendar/community-benefits-from- development-improving-neighbourhoods.aspx)

<comment><USER2> _Worth noting for folks who think that supply is the only problem. Similarly in my home of Vancouver the construction of towers has been constant for decades and prices have only skyrocketed in response._

Orangecat's comment is appropriate: [https://news.ycombinator.com/item?id=9207631](https://news.ycombinator.com/item?id=9207631) but I'll note too that Vancouver, like other cities, is probably substantially _still_ underbuilding the market—which high prices reflect. Houston, on the other hand, is not doing that: [http://marginalrevolution.com/marginalrevolution/2015/03/hou...](http://marginalrevolution.com/marginalrevolution/2015/03/houston- v-california.html) :

 _Unlike most other big cities in America, Houston has no zoning code, so it is quick to respond to demand for housing and office space. Last year authorities in the Houston metropolitan area, with a population of 6.2m, issued permits to build 64,000 homes. The entire state of California, with a population of 39m, issued just 83,000._

Which is why so many people are moving to Texas. [http://www.bizjournals.com/austin/news/2014/12/23/big-tex- in...](http://www.bizjournals.com/austin/news/2014/12/23/big-tex-indeed-texas- added-more-residents-than-any.html?page=all) is one link.

<comment><USER3> Although Houston doesn't have zoning, it does have development regulations in place that mimic the density restrictions of cities with zoning - lot sizes, parking minimums, etc. In effect the entire city is one big zone.

[http://www.slate.com/blogs/moneybox/2011/11/30/the_myth_of_z...](http://www.slate.com/blogs/moneybox/2011/11/30/the_myth_of_zoning_free_houston.html)

</comment></comment></comment><comment><USER4> I've a one bedroom in a flatshare in east london, its nothing special & costs north of £1k / month. If a political party outlined how they'd fix spiralling house prices they'd get my vote.

<comment><USER2> _If a political party outlined how they 'd fix spiralling house prices they'd get my vote._

It's really not that hard: remove height limits and parking minimums, per Yglesias in _The Rent Is Too Damn High_ : [http://www.amazon.com/Rent-Too- Damn-High-Matters-ebook/dp/B0...](http://www.amazon.com/Rent-Too-Damn-High- Matters-ebook/dp/B0078XGJXO). Glaeser's _The Triumph of the City_ is also good on this subject and discusses the UK more: [http://www.amazon.com/Triumph- City-Greatest-Invention-Health...](http://www.amazon.com/Triumph-City- Greatest-Invention-Healthier/dp/0143120549). This is a simple issue of supply and demand: rising demand in the face of limited supply means higher prices. Want lower prices? You need more housing or fewer people who want it. The former is easy to accomplish with century-old technologies, like steel and elevators.

<comment><USER5> What do you mean by 'parking minimums'?

FWIW, the London Plan currently contains restrictions on the _maximum_ amount of parking provided:

[http://www.london.gov.uk/thelondonplan/docs/londonplan08_ann...](http://www.london.gov.uk/thelondonplan/docs/londonplan08_annex04.pdf)

      table A4.2 Maximum Residential Car Parking Standards

      Predominant housing type   4+ bed units   3 bed units   1–2 bed units
      Car parking provision      2–1.5 spaces   1.5 – 1 space 1 to less than 1
                                 per unit       per unit      space per unit*

      * All developments in areas of good public transport accessibility and/or
      town centres should aim for less than 1 space per unit.

<comment><USER6> Parking minimums are fairly common in the US, and cause problems especially in cities because they take up so much land. Here is one random article about them, and how inconsistent they are: [http://www.citylab.com/commute/2013/08/exposed-americas- tota...](http://www.citylab.com/commute/2013/08/exposed-americas-totally- inconsistent-minimum-parking-requirements/6598/)

Brazil has problems too - [http://cities-today.com/2014/07/finished-brazils- largest-cit...](http://cities-today.com/2014/07/finished-brazils-largest-city- eliminate-minimum-parking-requirements/)

</comment></comment></comment></comment><comment><USER7> Obviously also applicable to SF and Manhattan/Brooklyn.

It's interesting to compare this to other cities: a cursory glance at LA craigslist shows some apartments in nice areas that would easily cost twice as much in SF/Manhattan. Same with Chicago.

Is it really just population density? A cursory google search says that SF's population density is about 17,000 people per sq. mi, which is about the same as West Hollywood. Yet looking at apartments in West Hollywood, I see apartments much nicer than I would in SF for the price. Maybe I'm grossly oversimplifying though.

But then is the answer really just 'sprawl'? Are Chicago and LA only 'reasonably priced' because they're so expansive? Is that how our cities have to move lest they suffer some sort of housing implosion?

As it is, I don't understand how anyone earning minimum wage lives in SF/Manhattan - or why they'd even want to commute in to Manhattan to work for Chipotle if they don't.

<comment><USER8> The answer isn't _just_ sprawl, it's "lots of housing", which can mean more tall apartment buildings on a little land instead of individual homes on a lot of land.

Unfortunately, San Francisco proper is very very hostile to real estate development for a variety of reasons and is trapped in a steadily worsening local maximum. A large part of that is policies like rent control, which bestows a sort of quasi-ownership of an apartment that may be even more effective than real ownership at spreading around the benefits of what the article calls a "tax by the haves on the have-nots, and by the old on the young."

Building out the suburbs could also be an answer if you could trust the region's government agencies to provide decent transit options, whether public transit or highways. You can't. At least if you're commuting to work in Manhattan you have a cheap $2.50 subway ride into town from many places that are much further away than you do in San Francisco.

</comment></comment><comment><USER9> "Another reason is that inflation in housing – so taboo when it comes to other commodities – has since the 1980s been celebrated by governments and encouraged by policies on taxation and borrowing."

Right. Rising house prices are just runaway inflation. If inflation indexes include housing (which, in the US, they did until the 1980s) inflation is much higher.

<comment><USER10> UK too: [http://en.wikipedia.org/wiki/Retail_Price_Index](http://en.wikipedia.org/wiki/Retail_Price_Index)

For most purposes, the UK now uses a measure (CPI) which excludes housing costs. However, RPI is still used when calculating changes to the state pension.

</comment></comment><comment><USER11> I'm always amazed to see the same people who place all sorts of statutory restrictions on new housing blame the free market when there's not enough housing.

<comment><USER12> Exactly! How can we say the free market isn't keeping up when it's hardly free? California has an insanely slow build rate because of how much red tape sits in front of it. This slow build rate leads to less housing which gives us high prices. And then people seem to think we somehow need even more red tape to save the day.

</comment></comment><comment><USER13> British isles urbanism is a joke

Terraced houses? In a place with high housing demand? Really?

You don't need to go to NYC heights, just look at what France and Germany do

They seem to hang on their historic buildings, with cramped accommodations that cost an arm an a leg to follow current regulations.

It's similar in Ireland, really.

<comment> design-of-homes _" Terraced houses? In a place with high housing demand?"_

What's wrong with terraced housing? It is more suitable for high-density developments than detached or semi-detached housing. A mixed development of houses and flats brings in a variety of different households and people. This makes a place much more attractive to live in than a place with only one type of dwelling. Well-planned terraced housing doesn't have to lead to sprawl.

In the UK, families with growing children have an aversion to flat living. It may be irrational, but this attitude hasn't changed in decades and is unlikely to change in the future.

<comment><USER13> > What's wrong with terraced housing?

It's less dense than apartments.

> In the UK, families with growing children have an aversion to flat living.

Maybe because of the abysmal quality and ridiculous size of most accommodations.

Brought to you by people that think carpet in a bathroom is acceptable.

<comment><USER5> We have separate taps for hot and cold water, and you think the _carpet_ is the problem?

<comment><USER6> It makes a lot of sense having them separate. Tom Scott explains [https://www.youtube.com/watch?v=HfHgUu_8KgA](https://www.youtube.com/watch?v=HfHgUu_8KgA)

Here in California showers and much other plumbing are required to have backflow valves so that hot and cold don't cross contaminate.

<comment><USER14> Not from a usability point of view.

<comment><USER6> Given how important water is, the consequences of contamination etc, safety was given first priority, then simplicity. How would you have done better?

<comment><USER14> Mixed taps are all over the US and many other places in the world and have been for a while and we have no water safety issues from this. Water safety has nothing to do with it:

"Most bathroom sinks in Britain still have separate hot and cold taps today, 60 years after Mr. Churchill's conversion and decades after nearly all dual taps were scrapped in the U.S. and most vanished from continental Europe. For reasons of thrift, regulations and a stubborn attachment to tradition, the British have resisted the tide of plumbing history. Even when they renovate old homes, many choose two-tap systems, and builders often install them in new, low-end housing. Separate taps account for an estimated 40% of all bathroom-faucet sales in the U.K."

From: [http://blogs.warwick.ac.uk/mhillebrandt/entry/british_peculi...](http://blogs.warwick.ac.uk/mhillebrandt/entry/british_peculiarities_i/)

</comment></comment></comment></comment></comment></comment></comment></comment><comment> guard-of-terra In Russia there is a popular family of conspiracy theories where UK is the driving force behind most events of the last 100 years, including rise and fall of the USSR.

If so, it doesn't look like UK is able to extract much for its own citizens from said world domination.

<comment><USER15> The UK middle and upper classes are doing fine (although the castle development sector has slumped this century). The UK poor on the other hand, yes, they fail to extract enough welfare in order to keep up with the new middle class of landlords.

<comment><USER5> I'm not sure i'd say the middle class is doing fine. The older part of the middle class is largely okay. The junior division, though - anyone who doesn't already have a house and a good pension - has a pretty bleak outlook. Unless they're making Google/stockbroker money, or live in the North.

The working class certainly have it much worse, though.

</comment></comment></comment><comment><USER16> So, taking the office of national (UK) statistics - in 2013 the growth rate of the population is 0.6%, on 64 million. Giving you 384,000 extra per year - about half of the growth is (births - deaths) and the other half is net migration [1].

Meanwhile from 1990 onwards we're seeing an average of less than 200,000 [2] - In the last few years the rates are the lowest for the time period on the graph (back to the 1970s).

[1] [http://www.ons.gov.uk/ons/rel/pop-estimate/population- estima...](http://www.ons.gov.uk/ons/rel/pop-estimate/population-estimates- for-uk--england-and-wales--scotland-and-northern-ireland/2013/sty-population- changes.html)

[2] [http://www.bbc.co.uk/news/magazine-30776306](http://www.bbc.co.uk/news/magazine-30776306)

</comment><comment><USER17> I think UK needs US style property taxes to discourage this 'housing as investment' behavior. Yes, UK has council tax but from what i understand its not that high.

In most US cities (except SF, LA, NYC) the ratio of average house price to median income is very reasonable (about 2.6). One main reason is the high property taxes which average about 1.5 to 2% of the house value. The tax also increase substantially for second homes. This discourages using a second house as investment/store of value.

</comment><comment><USER15> "When asked about the poor, the Market Economy raised one finger of the Invisible Hand and whispered.. f __k the poor " youtube.com/watch?v=eBuC_0-d-9Y</comment>


Happtique certified mobile health apps store your UN/PW/Pin/PHI in plain text - <AUTHOR> https://twitter.com/haroldsmith3rd/status/407657992826261504 <comment><AUTHOR> From the press release/announcement today: "Each app that receives certification has passed both technical testing—the verification of privacy, security, and operability standards by global testing leader Intertek—and content testing, as completed by relevant, independent clinical experts." [http://www.sacbee.com/2013/12/02/5962902/happtique- announces...](http://www.sacbee.com/2013/12/02/5962902/happtique-announces- inaugural.html)

And their final standards rule: [http://www.happtique.com/app- certification/](http://www.happtique.com/app-certification/)

</comment><comment><USER2> _" 78% of smartphone users say they will not download a mobile app that they don't trust."_

Now you can't trust the people that tell you which apps you can trust.

<comment><AUTHOR> Thing is, last year several people reached out to the founders of Happtique, and their "Blue Ribbon Panel" with security concerns, and were dismissed or ignored. And now those concerns are in production.</comment></comment>


My Watch List, Into 2010 - <AUTHOR> http://blog.darkhax.com/2010/02/14/my-watch-list-into-2010

<comment><USER2> I'm with this guy about a lot of these things, except for CSS3 and HTML5. I just can't get excited about either of these standards. I'm so upset that my utopian world where HTML could be parsed by anything other than a huge C program has been killed again. CSS3 is feeling very disjointed to me also.

</comment><comment><USER3> I quite like the look of MacRuby, my favourite on the list. Wondering if it's a plausible way to kill two birds with one stone, learn Ruby and a little bit about the frameworks available to me on my Mac. Only one way to find out I guess.

</comment><comment><USER4> Thanks guys! This is the first time anything of mine has been on Hacker News, and it resulted in 1300 visits on Friday, which is pretty big for me. Glad you enjoyed it!

</comment><comment><USER5> I am not much of a Ruby guy but iPad, HTML5 and Go are definitely on my watch list.</comment>


Show HN: 1Keyboard – Type on your phone and tablet using your Mac's keyboard - <AUTHOR> http://www.eyalw.com/1keyboard

<comment><USER2> This is amazing... I hope it works. I've wanted something like this for testing on device.</comment>


'Allow no escapes': leak exposes reality of China's vast prison camp network - <AUTHOR> https://www.theguardian.com/world/2019/nov/24/china-cables-leak-no-escapes-reality-china-uighur-prison-camp <comment><USER2> [https://news.ycombinator.com/item?id=21622138](https://news.ycombinator.com/item?id=21622138) turned out to be the thread that got the discussion. I'm not sure which article is better, so I guess we'll just merge the 3 comments from here into there.</comment>