Flynn - Open Source PaaS powered by Docker - <AUTHOR> https://flynn.io?hn <comment><USER3> I wanted to contribute but I am a single freelance developer, I don't have the amount of money shown on the sponsorship options. I would be great to have a USD 50 option there.

<comment><USER4> For now, the best way you can help is to try and sell your organization on sponsoring a larger amount. We can help you if they're interested. :)

<comment><USER5> You are leaving money on the table.

<comment><USER6> I don't think the point is to "profit" as much as it is to fund the project. Notice these aren't "customers" but sponsors. Maybe the mindset of "money is being left on the table" isn't quiet the right one for this sort of project ...

</comment></comment></comment></comment><comment><USER7> This is great guys, good to see more people bringing PaaS-like workflow without the baggage. We've been working in this space with Juju ([http://juju.ubuntu.com](http://juju.ubuntu.com)) but we're using standard LXC for containers.

Using LXC allows us to support applications that run on multiple processes, and since Juju is also written in Go, It seems like it might be possible for us to work together in some interesting ways.

We already have charms for Rack, node.js, Django, etc. already working and we can replicate the example in the video, just on raw LXC, EC2, and OpenStack. And we've put a lot of time and energy into managing the relationships between services, which is the big complicated part of a PaaS So, there seems to be pretty good overlap.

Have you looked at Juju? Perhaps there are places where you can leverage the heavy lifting that the juju team has already done in creating a "bring your own" kind of PaaS.

<comment><USER8> Juju is a great project and you guys are way ahead of the game but the AGPL licensing is problematic for proprietary services built on that platform.

<comment><USER9> Do you mind explaining that? Would the fact that they use AGPL for Juju matter if you're not hacking on Juju itself? I don't see how it would be an issue, it wouldn't extend to the proprietary software you run on a Juju-setup service, would it?

I could be completely wrong, mind you.

<comment><USER10> Yea, it's exactly like MongoDB which is also AGPL.

Using the database doesn't impose any restrictions on your software.

Even hacking on juju to add API'S you for your client software doesn't impose restrictions on the client -- it just means you are subject to the AGPL licence source distribution rules on the patched version of Juju.

<comment><USER11> Mongo is a different beast, because the mongo client has a GPL exception which allows your code, that is linked to the mongo client, to use a different license. This is a friendly exception, that the mongo developers allowed. Without that exception everything using mongo even over the Internet would automatically also be AGPL licensed.

</comment></comment></comment></comment></comment><comment><USER12> We at OpDemand wish the Flynn crew the best of luck. I expect more Docker- powered PaaS's to sprout up, so I'm glad Solomon and the dotCloud crew are focused so heavily on interoperability.

OpDemand will be releasing a very similar open-source project called Deis in the next few weeks. It's an open-source PaaS powered by Docker & Chef, with a Heroku-inspired workflow.

We're busy ironing out a few final issues, putting together documentation, quick-start guides, tutorials, etc.. but we are very close to launching our public 0.1.0, which will allow you to deploy your own private PaaS providing you complete control over hosting, backends, proxies and more.

<comment><USER13> Any chance you'll be adding/have Puppet support as well? We have our cart hitched pretty firmly to it at this point. Congrats on the upcoming release!

<comment><USER12> Thanks. We're excited to share our take on private PaaS with the open source community.

Re: Puppet -- A good chunk of the "orchestration" is done by updating data bags on a Chef Server, and force-converging Chef nodes over SSH. However I know Puppet has similar constructs, so it's definitely possible to fork the Deis controller and Puppetize it. We'd encourage that sort of contribution.

</comment></comment></comment><comment><USER14> My main problem with Docker is that container IP addresses and MACs are changed after every container reboot. This means I can't have a database in one container and a web app in another because I have no way for the web app to discover the address of the database server.

Seems like this would be an issue for the host-only (private) network?

<comment><USER4> Docker is in very active development and issues like this get resolved every week. That said, it's not a problem because of the service discovery and routing that would be involved in the Flynn system.

<comment><USER14> Yes, Docker is moving quickly but one barrier to resolving this is philosophical: static IPs and private networks are un-Dockerish. Right now it's not obvious what a clean solution would look like. I'd be keen to see how Flynn's discovery and routing develops, it sounds very promising.

<comment><USER15> Static IPs and private networks are not particularly un-Dockerish. They are just not the default implemented by Docker :)

Docker 1.0 will include an API which will make it much easier to customize how you want networking to happen. That includes static IPs and private networks.

Note that the 1.0 API will also include hooks for service discovery - specifically to facilitate the development of projects like Flynn.

<comment><USER16> Are these feature plans documented somewhere? Private networks, in the spirit of EC2 security groups or VPCs, would help simplify many distributed computing scenarios where securing services with passwords isn't an option.

</comment></comment></comment></comment></comment><comment><USER17> And this is why you shouldn't open source the core part of your business (looking at you DotCloud). Sure, Docker is good for the community. But it's also a key competitive advantage for DotCloud. By giving away that advantage, they create opportunities for viable competitors.

<comment><USER15> Hi, I'm the founder of dotCloud and author of Docker. Projects like Flynn are the reason we opened Docker in the first place. They are a good thing for the docker ecosystem, and they are definitely good for dotCloud.

The dirty little secret of PaaS is that there is not, and will never be a single PaaS that fits everyone's needs. With docker we offer a foundation for many PaaSes to engage in a healthy competition while preserving interoperability.

So Shopify will always be free to mix and match Flynn's lego bricks with the bricks of other offerings - because they are all Docker-compatible. The authors of Flynn get that, which is why I believe their product will be good and I will be personally sponsoring it :)

<comment><USER17> So what is the strategic business advantage? Is it that Docker (presumably, your core piece of infrastructure) improves as a product as more PaaS's adopt it? So essentially your competition will improve your own infrastructure.

Much respect for DotCloud's success thusfar btw.

<comment><USER18> Not from dotCloud but I've noticed that there is a focus for a central index. It might be possible for them to get revenue from an app-store. There is also the usual consulting that can be derived from expertise.

</comment></comment></comment></comment><comment><USER13> Very cool! Been looking for something like this since Docker was announced. Basically Cloud Foundry is far too complex for what I/we need but this would fit the bill nicely.

One question though, what is going to be 'the cluster' (The site says it distributes work across 'the cluster')? Basically, will it be capable of interfacing with cloud API's or will it just be you manually setting up a group of instances to host the machines to run Docker on top of?

<comment><USER19> The 'cluster' is a group of nodes running the Docker + Flynn software. It can be anything from a single server to a fleet of EC2 instances to a rack of servers. It would be easy to make a tool that spawns instances running Flynn on your choice of public/private cloud.

<comment><USER20> So integrating with VPS providers such as Linode and Digital Ocean will be possible? If so, would this be easy to do for end-users?

Never got in to Cloud Foundry because it's overly complex and very limiting. Always went with Heroku, but wished I could have a Heroku-style infrastructure on the above mentioned VPS providers for the benefits of running better hardware, being more affordable, being multi-region, etc, etc.

<comment><USER21> Docker runs on linode and digital ocean ([https://github.com/dotcloud/docker/issues/469](https://github.com/dotcloud/docker/issues/469), [https://github.com/dotcloud/docker/issues/424](https://github.com/dotcloud/docker/issues/424)) by loading a custom kernel. Assuming the requirements for Flynn aren't much more than Docker, the whole stack should run there as well. Easy for end-users is probably a matter of opinion, especially until Flynn releases some code.

<comment><USER22> Here are notes on getting Docker running on

Digital Ocean: [http://kencochrane.net/blog/2013/06/running-docker-on- digita...](http://kencochrane.net/blog/2013/06/running-docker-on-digital- ocean/)

Rackspace: [http://kencochrane.net/blog/2013/05/running-docker-on- racksp...](http://kencochrane.net/blog/2013/05/running-docker-on-rackspace- cloud/)

Linode: [http://stinemat.es](http://stinemat.es) (site seems to be down at the moment)

</comment></comment></comment></comment></comment><comment><USER23> "Unlike existing open source PaaS products, Flynn focuses on providing a set of modular components that can be replaced and reused."

I wonder if there is anyone here who works on CloudFoundry or OpenShift and could comment on this, or comment on the proposed capabilities of Flynn ([https://github.com/flynn/flynn-spec](https://github.com/flynn/flynn-spec)) compared to the capabilities of their systems.

<comment><USER13> Well I can say that the closest alternative to this would be Cloud Foundry. Their Warden system is what manages the containers: [https://github.com/cloudfoundry/warden/tree/master/warden](https://github.com/cloudfoundry/warden/tree/master/warden)

They actually don't use LXC though and implement resource control with Cgroups. That said, Cloud Foundry is extremely difficult to get setup and running in my experience (even with the v2.0 recently released). It sounds like one big differentiating factor is that Flynn would have fewer moving pieces (Cloud Foundry is at least: BOSH, Warden, VCAP?, CF itself, etc.).

<comment><USER24> +1 on the fact CF installation is tough! They recently releases a suite of scripts to help installing it on AWS. Forget about installing it manually on Open Stack (talking from experience). I can only hope Flynn gets the easy install right, so people will be able to try it out.

CF is actually very plugable as well - there is a well defined internal API, which you could just add components that listens/publishes to - however, I don't think its encouraged, neither have I seen a single example of it.

<comment><USER13> Yeah, CF is a very powerful toolkit (as you mention with the API), but part of the power seems to come coupled with the complexity. I've seen some scripts/documentation to install on Openstack (this is the best so far [http://www.pistoncloud.com/2013/06/deploying-bosh-cloud- foun...](http://www.pistoncloud.com/2013/06/deploying-bosh-cloud-foundry- openstack/)), but still, it's a huge, huge pain.

</comment></comment></comment></comment><comment><USER25> What are the advantages/disadvantages compared to Dokku ([http://progrium.com/blog/2013/06/19/dokku-the-smallest- paas-...](http://progrium.com/blog/2013/06/19/dokku-the-smallest-paas- implementation-youve-ever-seen/))? It is also based on Docker, super easy to set up on your own server and: Available today.

<comment><USER4> Dokku is sort of a proto-Flynn. It has different, more reachable goals because it's intended for a single host. It solves some of the same problems, just in a non-distributed way. Breaking Dokku down into its components starts providing part of the plan for Flynn.

They may even share code in the end, but Dokku will remain focused on simple, single host deployments. Flynn is about a real, distributed system that could be used in production.

Since Flynn is really just a packaged collection of components (similar to Dokku), Dokku may just end up being a single host version of Flynn someday.

</comment></comment><comment><USER26> Heroku I'm looking at you, lower your prices.

<comment><USER27> Heroku has been able to charge a premium for ease of use. It's hard to imagine, though, that ease of use will be a competitive advantage much longer. Much of their creativity (and it's substantial) has been in showing the way and it's only a matter of time, it seems to me, before that will be imitated (in a flattering way) by open source projects.

Ultimately, it seems, heroku will need to find an additional axis (besides ease of use) to differentiate on. Maybe I'm wrong, though. Apple sure has been able to turn ease of use into a sustainable competitive advantage. In heroku's case, though, there are fewer UI decisions and its audience is super- technical, so I'd be surprised if ease of use alone is sustainable in this domain.

We'll see. I want to see them win, to be honest, because I feel like they've done so much good in a providing such an accessible and (at first at least) cheap way for a developer to get his or her stuff out there.

<comment><USER4> Yeah. And by the way, I love Heroku, if it's not obvious. I'm friends with their engineers and visit their offices occasionally. It's been one of the most inspiring platforms I've seen.

</comment></comment></comment><comment><USER28> Is there an explain-like-I'm-5 explanation of what this is and why it is useful? Because I've only a very vague idea of what it's about.

<comment><USER29> Think of a Platform-as-a-service like a web server that is already configured for multiple languages and all you have to do is install your web application and start/restart it.

The idea is that you can turn on and off web servers really quickly to do tests and also that it should scale as the amount of work that your web application needs scales.

</comment></comment><comment><USER30> Re: 1500 Minimum, definitely understand the model you're going for, but some of us ARE companies, just really small.

I'd be happy to contribute at a lower level (at least $100, possibly up to $500) as I do see the value in this.

Dokku is more than sufficient for my needs at the moment, but see Flynn as a natural extension to that.

<comment><AUTHOR> Thanks for your interest. Of course we're happy to accept small contributions from companies. I just sent you an email with details. for anyone else who's interested in contributing on behalf of an organization, please email: firstname.lastname@example.com and we'll set you up at whatever amount you feel comfortable with.

We'll start accepting personal contributions next week as well.

</comment></comment><comment><USER31> I like the fact that Flynn will be written primarily in Go. That should mean very little overhead for this PaaS.

I wonder if using Heroku buildpacks is a good idea, though. Something that can more fully take advantage of Docker layers, and in some cases APT packages for language runtimes, would be better IMO.

<comment><USER15> They are not mutually exclusive. An ideal system would honor the Dockerfile when it is present, and attempt to auto-detect the build method when it is not. Buildpacks are great defaults when auto-detecting.

That way, you get a nice out-of-box experience (push your code, something magic happens), and when you need more control over your build and dependencies, you can add a Dockerfile.

I'm hoping this is what Flynn will do :)

</comment></comment><comment><USER32> I got interested in building something like this when I saw Docker issue 410[1]. If my understanding is correct Mesos uses Zookeeper to keep a Mesos master available. Zab, Zookeeper's distributed consensus algorithm, is similar to Raft which has a popular Go implementation[2] written by Ben Johnson. Perhaps Flynn agents can use this implementation.

1\. [https://github.com/dotcloud/docker/issues/410](https://github.com/dotcloud/docker/issues/410)

2\. [https://github.com/benbjohnson/go- raft](https://github.com/benbjohnson/go-raft)

</comment><comment><USER24> I was trying to built something similar, already had a proof of concept ready built on top of components from Dokku. I can't wait to follow this project - <USER2> might even pull myself together and learn go!

<comment><USER33> So am I. What's your project?

<comment><USER24> [https://github.com/KristianOellegaard/puppet- dokku](https://github.com/KristianOellegaard/puppet-dokku)

Lots of uncomitted changs locally. With this announcement, not sure if its worth continuing down this path.

<comment><USER34> Nope, keep going. Always worth working on your side project!

</comment></comment></comment></comment><comment><USER35> I'd love to see a comparison between this and [deliver]([https://github.com/gerhard/deliver](https://github.com/gerhard/deliver))

</comment><comment><USER36> I'd love to know more about how the cluster will organized. Is there going to be some kind of master controller node, or will it be distributed and self organizing somehow?

It almost feels as tho Flynn (or maybe something build on top of Flynn) could/should be a whole minimal linux distribution (SmartOS-ish) that could just boot up into RAM, be told to read a cluster config from somewhere and then start working for the cluster accepting Docker services.

<comment><USER4> Yes, you're on to something. :)

</comment></comment><comment><USER37> Dokku on Steroids! gg guys!

</comment><comment><USER38> Just read the FAQ and saw that Flynn is "Super Dokku"

Awesome job.

<comment><USER14> Here's a link: [http://progrium.com/blog/2013/06/19/dokku-the-smallest- paas-...](http://progrium.com/blog/2013/06/19/dokku-the-smallest-paas- implementation-youve-ever-seen/)

</comment></comment><comment><USER39> So it's a git post-receive hook that parses a Procfile and starts your app?

<comment><USER33> It's probably a pre-receive hook. That's how Dokku works. It's easier to abort the deploy this way.

<comment><USER4> I don't think they were being serious. :)

</comment></comment></comment><comment><USER40> I'm curious what type of deployment APIs people want. It is the docker rest API + git push for your own code? Should we be using heroku APIs? Are we going to invent something new like OpenStack did?

<comment><USER4> I know why you're asking. :D

<comment><USER40> I feel like we are early enough into all of this that we might have a chance at standardization... vs the IaaS APIs which have all diverged at this point.

</comment></comment></comment><comment><USER41> Seems like there's a CSS issue with the sponsor modal [http://d.pr/i/Pgko](http://d.pr/i/Pgko)

<comment><AUTHOR> thanks-- fixed a bug where part of the modal was inaccessible on smaller screens

<comment><USER41> Awesome, great job! Really looking forward to this.

</comment></comment></comment><comment><USER42> Does anyone know anything like this (a self hosted PaaS) for .NET?

I'd love to use docker or something similar for .NET applications.

<comment><USER19> If your app works with Mono, there's no reason why you can't use Docker/Flynn: [https://github.com/friism/heroku-buildpack- mono](https://github.com/friism/heroku-buildpack-mono)

<comment><USER42> That's a good option. Thank you for your insight there.

I can't help but think that this won't really work for many Microsoft shops because the Microsoft CLI is very different from the Mono CLI.

The reason I ask is that there's a number of providers of private PaaS solutions that are willing to install their stuff in your organization for Microsoft stack(HP Matrix, I'm looking at you), but they are full of holes and aren't nearly as robust as what Docker/Dokku/Flynn can offer in terms of plugability and modularity.

Also, free is a pretty good price.

</comment></comment></comment><comment><USER43> This looks amazing! I wish I could contribute $20 or so, but looks like $1500 is the minimum?

<comment><AUTHOR> Thanks! We're experimenting with a new funding model for open source: publish a spec for a project that's useful to (and can save lots of money for) companies and see who will chip in. We don't think there are enough personal donors for a project like this to raise significant cash (and the sponsorship levels are affordable for most companies) so we want to try sponsorship first. Once that market is saturated, we'll experiment with individual donation options, maybe after the MVP ships

</comment></comment><comment><AUTHOR> Just added a mailing list form at the bottom of the page for anyone who's interested.

</comment><comment><USER44> Can't I contribute with my $10? I'll use it personally.

</comment><comment><AUTHOR> Join the discussion in #flynn on freenode

</comment><comment><USER45> So it's not open source yet?

<comment><USER19> It's still in the early stages of development. We're currently fundraising so that we can work on it full time. All code will be pushed to GitHub as we write it.

<comment><USER46> I don't see this in the FAQ: the magic that makes all of this happen (which you're integrating) is technology built by other developers. Why should we be supporting you to do this work rather than the other developers who broke ground and already did a majority of the heavy lifting?

<comment><USER4> Docker was a big piece of the puzzle. I worked with DotCloud to make Docker happen and we're involved in that community quite heavily. There's still a lot more work to do, otherwise we would not be bothering with a campaign like this.

<comment><USER46> Not to discount your work, but the way I see it: the kernel (e.g. cgroups, namespaces) and the corresponding lxc work dwarfs anything docker brought to the table. This project smells like a campaign to get a product funded.

<comment><USER4> Considering LXC support will soon be a module to Docker and not core, I'm not sure you understand what value Docker adds. Just as there is value added all the way up the stack.

Regarding the product funding remark, that's not the intention, though with the license anybody could build a product with this project. I'm not convinced that's a bad thing.

</comment></comment></comment></comment></comment></comment><comment><USER47> $ wtf is paas

<comment><USER37> [http://en.wikipedia.org/wiki/Platform_as_a_service](http://en.wikipedia.org/wiki/Platform_as_a_service)</comment></comment>


Is Twitter Successful? - <AUTHOR> http://bokardo.com/archives/is-twitter-successful/ <comment><USER2> They may be growing oranges, but they're not _selling_ any oranges. Say what you want, but sooner or later that's going to present a problem for the company's viability.

<comment><USER3> Exactly. This is dotcom bubble logic - we don't need an income stream, we have _investors_.

Amazon ran a loss for a long time, but at every point in time, they were actually selling product and getting money. This Porter guy may say Twitter is wildly successful because they're a business that's not _about_ such peasant concerns as making money, but of Amazon and Twitter, which is more likely to be around in 10 years?

<comment><USER4> The Amazon comparison is terrible. They were still losing money! Whether they are doing it by giving something away for free or by selling on ridiculously tight margins they are still losing money!

<comment><USER5> The difference is that Amazon had a clear way to make money: make their revenue stream bigger. That doesn't and won't work for Twitter, because Twitter doesn't have a revenue stream to make bigger.

Saying they're the same thing is like saying that the U.S. and Afghanistan are the same, because they both had elections. Well, true, but in one place the elections were not rigged, and in the other there's a good chance they were. "They still had an election!" of course, but there are significant practical differences.

</comment></comment></comment></comment><comment><USER6> What worries me with 'growth over revenue' is that they are presented as a tradeoff. The implication is that they are essentially operating a loss leader to trap users in and then they will degrade the free produt to extract revenues. As a twitter user, this makes me look to the exits.

<comment><USER7> But for a service that's 100% social (i.e., they offer little to no value without the content that their users offer), it's very very difficult to put revenue on the same level or above growth. The growth is essential to gaining the momentum required to monetize a social service like that.

I don't think you have to degrade a product when adding premium features. For example, I'd pay a few bucks to have lists or user groups in my stream. But adding that feature at a pay level doesn't degrade the service I already have. I really think Twitter is smart enough to avoid degrading the service while adding value. They've done well with that so far, at least.

</comment></comment><comment><USER8> You can't be successful until you have some sort of finality; a cash out, a merger... something that adds to the bottom line (besides investors). Twitter is building a huge base on which to make a pretty good size company that does some pretty cool things... So my answer is "So far; they are successful"

</comment><comment><USER9> they are most definitely VERY successful as a brand...

<comment><USER3> Wasn't Pets.com, too?

<comment><USER9> I'm not sure people heard of pets.com outside USA. I never did, for instance. As for Twitter, people have heard of it in EVERY single country I've been lately (including some in Europe, Asia and South America)...

But relatively (to it's time and the size of internet), yes - pets.com was probably successful at that too... :-)

<comment><USER10> I've heard of Pets.com and I'm in Europe :)

<comment><USER9> did I ever say you wouldn't...?</comment></comment></comment></comment></comment>


This guy should be explaining spirituality to logical people [video] - <AUTHOR> http://vedanta.tv/chennai0212_Day1.htm

<comment><AUTHOR> FFWD to 4:30 if chanting is not your cup of tea</comment>


Mathematical Strategies for the Most Popular Games - <AUTHOR> https://www.washingtonpost.com/news/wonk/wp/2015/05/08/how-to-win-any-popular-game-according-to-data-scientists/ <comment><USER2> Misleading title. (The original title is even worse, claiming to give "mathematically proven winning strategies".)

The article is actually about simple models for various games. What are the most popular chess moves. Thinking of Diplomacy as a graph. Etc. Not particularly deep.

<comment><USER3> Thanks. Every time I see a WP link on hn these days, I first visit the comments to see what the actual content behind the clickbait title is, because the site has become so deliberately misleading.

</comment></comment><comment><USER4> Those "house percentage" numbers in the last section don't make any sense to me. House edges per bet aren't nearly that bad[1] (and if they were nobody would ever have fun at casinos), and if the numbers are based on the average amount players wagered and lost, then there are a ton of biasing factors that make the claim "Your best bet is the $100 slot machines, where casinos take only 3.6 percent of your money." misleading at best. For example, I'm guessing that the average person pulls the lever on $100 slots far fewer times than on the $1 slots. In which case the advice should be to play a single hand of video poker.

Also, the correct advice is to play bet/come-line-craps: it's social, involves throwing dice, and has a very small house edge (and very high variance, which is the point of going to a casino!).

[1] [http://gaming.unlv.edu/casinomath.html](http://gaming.unlv.edu/casinomath.html)

<comment><USER5> A point of clarification regarding the pass/come-line in line in craps. , pass-line bet by it's self is not that great of a bet. With a house edge of 1.41% which while good is not the best in the casino. What makes pass line bet so good is that when a point is set, if you made a bet on the pass line you are allowed to make the "odds" bet which is a bet with 0% house edge. (Yes that's right 0%, but remember to make this bet we have to have already made a bet with a house edge.) If the table allows you to make a 100x(the pass line bet on the odds) you can push the overall house edge down to 0.02%. However on the cheap $10 min table this would require a min bet of ~$1015 every point. So not for the faint of heart.

</comment></comment><comment><USER6> "To win a game of Battleship, you need to do two things: maximize your probability of getting a hit at every turn, and hope your opponent doesn't do the same."

Not exactly. You need to maximize the amount of information you get about the distribution of your opponent's ships with each shot, and distribute your own ships in such a way that it minimizes the amount of information your opponent is able to gather with theirs. And of course you need to take into account that your opponent has the same goals (the probability grid for hitting ships shown in the article of course ignores that your opponent, knowing the center is a juicier target for you, could choose to place her ships around the edges.)

<comment><USER7> It's also a race to _apply_ what you know. Knowing exactly where all your enemy's ships are is won't help if you don't have enough turns to hit them before your own ships are removed.

</comment></comment><comment><USER8> it is not mathemical strategies but just bare visualisation</comment>


Vernacular Economics: How Building Codes and Taxes Shape Regional Architecture - <AUTHOR> https://99percentinvisible.org/article/vernacular-economics-building-codes-taxes-shape-regional-architecture/ <comment><USER2> The more common response to NYC's setback laws was what Lewis Mumford called the ziggurat, typified by the Paramount Building: [https://needled.files.wordpress.com/2010/10/paramount- buildi...](https://needled.files.wordpress.com/2010/10/paramount-building.jpg)

That stepped look became part of Art Deco, though it's hard to say for sure which was cause and which was effect.

</comment><comment><USER3> Paris has the loveliest architectural tax-avoidance.

</comment><comment><USER4> These bloody people, always looking for ways to avoid taxes. They should be hanged by their necks and quartered, same as the likes of the corporations hiding their profits in Ireland or tax heavens, leaving the kings and modern kings (elected presidents) without money to spend on their favorite hobbies.

<comment><USER5> Can you please not post unsubstantive rants to HN? It lowers discussion quality.

You don't need to change your views to post here, but you do need to post in the key of thought, not rage.

[https://news.ycombinator.com/newsguidelines.html](https://news.ycombinator.com/newsguidelines.html)</comment></comment>


Schlieren Optics – See small changes in the index of refraction in air - <AUTHOR> http://www.youtube.com/watch?v=mLp_rSBzteI

<comment><USER2> I've long had this pet theory that soaring birds have a fine vision that allows them to see thermals (warm ascending currents) directly, through changes in the air's refraction index. I've searched a bit, but no research seems to ever have considered the question. Just a wild theory, though.

<comment><USER3> Interesting. I don't think that would be possible - the technique shown here relies on a mirror to get interferometry between the two light paths. I can't see how interferometry would be available to soaring birds (between its two eyes, perhaps?). Although nature has come up with some very surprising and impressive inventions before, so I might be wrong.

I would guess they're just very good at spotting the conditions which lead to thermals.

EDIT: of course, you can directly see thermals via the shimmering / "mirage" effect. It's normally obscured by the background noise, but maybe soaring birds are attuned to that. That's much more plausible, but a different effect to that shown here.

<comment><USER2> You're right of course, it can't rely on collimated light like the normal Schlieren system. But the background-oriented Schlieren technique, mentioned elsewhere in this thread, is more reasonable to look at for inspiration, and it provides pretty much the same end result.

I suppose it can be thought of as a differential filter: only minute changes are interpreted to create a representation of the flow of air.

</comment></comment></comment><comment><USER4> Invented 1864! [http://en.wikipedia.org/wiki/Schlieren_photography](http://en.wikipedia.org/wiki/Schlieren_photography)

And there's a variant in color: [https://www.youtube.com/watch?v=0g4UBeaG5fs](https://www.youtube.com/watch?v=0g4UBeaG5fs)

<comment><USER5> There was a yellow version shown on Time Warp.

</comment></comment><comment><USER6> The NPR has a good explanation of how it works in their video:

[https://www.youtube.com/watch?v=px3oVGXr4mo](https://www.youtube.com/watch?v=px3oVGXr4mo)

<comment><USER7> That was a _beautiful_ video, thanks. I think I'm going to make a handsome donation to NPR right now.

</comment></comment><comment><USER8> Purely out of curiosity, has anyone used this in conjunction with a linguistics study to visualise sounds made during speech? I did a quick literature search but couldn't find anything. Not sure whether if features would be too subtle to see anything interesting...

Edit: Spoke too soon. Did find one paper[1] (though I can't read it) which uses it to compare the production of 's' and 'z' sounds. Would love to know if there are any more papers though.

[1] [http://dx.doi.org/10.1121/1.4784877](http://dx.doi.org/10.1121/1.4784877)

<comment><USER9> I helped get Doc Edgerton's Schlieren appartaus working again in a lab class in the mid-90s.

We tried to image sound waves, but the density gradient for sound is much less than that produced by changes in temperature. We attempted to make a resonant chamber and use a high-intensity ultrasonic source, and make a standing wave that we might capture photographically, and while we saw something once we could not produce it. It's likely that the ultrasonic source we were using was gradually degrading.

The research paper you link to almost surely uses the heat differences to see the jets of air coming out of the mouth, and not actual sound waves.

<comment><USER8> Thanks for the information, that's really interesting, and a shame it didn't work out! I did see that the sound of a clap had been imaged, but I imagine that's a somewhat extreme case.

> The research paper you link to almost surely uses the heat differences to > see the jets of air coming out of the mouth, and not actual sound waves.

Regarding the paper, while I can't read it myself, I imagine they chose to look at fricatives precisely because they're the result of turbulent airflow which is probably ideal for Schlieren imaging rather than a different speech sound which would be more wave-like. If the image is the result of heat differences, I wonder if it could be improved further by changing ambient temperature or temperature inside the mouth, or alternatively if the subject could be asked to inhale sulphur hexafluoride first to increase the density differential? (Edit: Or, perhaps, introduce a 'uniform' (laminar) thermal source along the direction you're interested in, so that when a sound wave propagates, the resulting density differences would be much more pronounced? I'm not sure if that makes any sense, as a layperson this is pure speculation on my part...)

I believe this is an image from the paper: [http://www.sciencephoto.com/media/89151/view](http://www.sciencephoto.com/media/89151/view)

Thanks again for the info though, really fascinating. Hopefully we haven't reached the limits of this technique yet, and it can still be taken further.

<comment><USER9> I note that in that picture there appear to be wave fronts emanating from the person's mouth. It's not obvious but there are some circular artifacts that appear to be centered on the lips / teeth. Maybe they really did capture sound ?

</comment></comment></comment></comment><comment><USER10> Scientific American used to run a really cool "Amateur Scientist" column when I was a kid, edited by C.L. Stong. In the early 70s they ran a couple articles about people who'd built their own Schlieren optics. I was fascinated by them!

The articles appear to be pay-walled now ([http://www.scientificamerican.com/article/the-amateur- scient...](http://www.scientificamerican.com/article/the-amateur- scientist-1971-05/) and [http://www.scientificamerican.com/article/the- amateur-scient...](http://www.scientificamerican.com/article/the-amateur- scientist-1974-08/)).

It looks like the book that collected many of Stong's columns from the 50s and 60s predates those 2 articles ([https://archive.org/details/TheAmateurScientist](https://archive.org/details/TheAmateurScientist)).

There's a CD-ROM available that supposedly contains the text & images for _all_ of the Scientific American "Amateur Scientist" from the '20s to the late '90s, which would presumably also have those articles: [http://www.amazon.com/exec/obidos/ISBN%3D0970347626/scienceh...](http://www.amazon.com/exec/obidos/ISBN%3D0970347626/sciencehobbyist/)

</comment><comment><USER11> And it's kindof accessible for hobbyists, here is the website of one individual who made his own setup: [http://www.ian.org/Schlieren/](http://www.ian.org/Schlieren/)

The most expensive component (except the camera) is the mirror. According to that website, it costs about $100 from an optics company.

<comment><USER12> You can also do it without any mirror, using Background Oriented Schlieren. Basically, you print a bunch of random dots on paper, and use a camera to see the subpixel shifts in the dots. ( see [http://en.wikipedia.org/wiki/Synthetic_schlieren](http://en.wikipedia.org/wiki/Synthetic_schlieren) ). By cross-correlation, you get the change in refractive index/density of air.

One of the main limitations on a Schlieren system is that you can only image objects that are smaller than your mirror. That makes the BOS systems are pretty neat, because at least in theory, you can just print out large backgrounds to image large areas.

<comment><USER13> This paper is really nice [http://www.ualberta.ca/~bsuther/papers/procsorrento0998/repr...](http://www.ualberta.ca/~bsuther/papers/procsorrento0998/reprint_style.pdf) on the synthetic method.

</comment></comment></comment><comment><USER14> And this is the sonic boom visualized by Schlieren photography: [http://youtu.be/lbomsOPSSII](http://youtu.be/lbomsOPSSII)

</comment><comment><USER15> I'm super-curious about how something similar might be happening in the Double-Slit Experiment.

</comment><comment><USER16> This is basically the same effect you see with shimmering above a hot road surface.

<comment><USER17> Or the effect you can see after about second 30 in the following pretty[1] video of a plane passing in front of a mountain range (the narrow band of light behind it works well enough as a collimator).

[https://www.youtube.com/watch?v=wOUuj1LQRyk](https://www.youtube.com/watch?v=wOUuj1LQRyk)

[1] it could use some image stabilisation though.

</comment></comment><comment><USER18> related at all to?

[http://en.m.wikipedia.org/wiki/Adaptive_optics](http://en.m.wikipedia.org/wiki/Adaptive_optics)

</comment><comment><USER19> Is a city-scale version of this possible?

</comment><comment><USER20> What are those circle shapes?

<comment><USER21> I think they are aberrations in the mirror's glass. People used to use Schlieren optics-esque techniques to test glass for defects.

</comment></comment><comment><USER22> The HN title is incorrect. This device doesn't show movement, it shows difference of the index of refraction. From the video description:

> _Demonstration of an optical technique that allows us to see small changes > in the index of refraction in air. [...] Seen here are the heated gases from > a candle flame and a hair dryer, helium gas, and sulfur hexafluoride gas._

<comment><USER23> Technically true. But the different refraction indices can be created by turbulent flow - which will nearly always occur with moving air.</comment></comment>


AAAS: Machine learning 'causing science crisis' - <AUTHOR> https://www.bbc.co.uk/news/science-environment-47267081 <comment><USER2> Is machine learning really to blame for the reproducibility crisis? I'm not in academia, but it seemed to me that the problem was entirely present without machine learning being involed.

For example, Amgen reporting that of landmark cancer papers they reviewed, 47 of the 53 could not be replicated [1]. I would have assumed that most of them didn't involve 'machine learning'

[1] [https://www.reuters.com/article/us-science-cancer/in- cancer-...](https://www.reuters.com/article/us-science-cancer/in-cancer- science-many-discoveries-dont-hold-up-idUSBRE82R12P20120328)

<comment><USER3> The problem was there before, but there are reasons why Machine Learning is amplifying bad practices.

In the past people were manually fishing for results in available datasets. Now they have algorithms to do it for them.

In medicine a popular way to use ML is to improve diagnosis. Now there's already a problem in medicine that the benefits of early diagnosis are overrated and the downsides (overtreatment etc.) usually ignored. You get more of that.

And TBH computer scientists aren't exactly at the forefront when it comes to scientific quality standards. (E.g. practically noone is doing preregistration in CS, which in other fields is considered a prime tool to counter bad scientific practices.)

<comment><USER4> It seems what ML is really doing is exposing weaknesses in our scientific processes. The appropriate response here is to fix the processes, instead of blaming the latest fad and imploring people to "try harder". If the root cause isn't fixed, the next fad after ML will cause the same thing again. What feasible systemic changes can we make so that scientists can't get away with publishing sloppy results?

It's not an interesting question for many scientists, who prefer focusing on technical solutions over political ones.

<comment><USER5> I think it is a very interesting question for a lot of scientists, but also an extremely hard one to answer. And an even harder one to implement.

Even obvious wins that almost everyone can agree on, like getting publishing out of the hands of for-profit entities that add no value is taking forever, because cultural, social and political institutions are hard to move.

</comment></comment></comment></comment><comment><USER6> Fails to touch on the perverse incentives in academia, "publish or perish" etc. Torturing a dataset to find a p value that a journal will like (or equivalent stat measure) is better for your career than not publishing a paper that will be discredited in time. You have no incentive at all to decide "my results are unconvincing at this point, I'm not going to submit them" and every reason to write them up as a useful contribution to human understanding even if you kind of know, deep down, it really isn't. Especially if you're not senior...

<comment><USER7> Some days ago there was a post here on HN about a post doc who failed to get tenure.

And every other comment was like: "What did he expect, he had much less than the usual two papers a year."

So it seems that even here on HN, the mindset of quantity over quality still persists.

<comment><USER8> The problem really is: there is no alternative (which I know of). Having a number of papers in well known journals is everything we have to gauge the quality of people that look for a life time position. It's sad but true

<comment><USER9> No, the number of papers is an okay metric, thr problem is that journals don't like to publish honest negative results

<comment><USER10> There are a few journals that will promise to publish your results based entirely on your pre-registered plan -- e.g. whether or not you find the correlation you were looking for.

In the long term, it seems like journals that publish lots of falsified papers should be punished, and journals that don't (e.g. because of a judge-upon-pre- registration policy) should crowd them out.

<comment><USER9> Pre-registration is great, and I think it even helps researchers/scientists/post-docs to submit something (it doesn't have to be perfect, after all, it's just an experiment design, and they don't have to worry about massaging the data to have promising results to report) and then stick to the topic, and then carry out the experiment as well as they can, and gather as much high quality & fidelity data as they can, and do the analysis according to the plan, and then report it. No extra pressure to think about how to frame what you "found".

Though this will inevitably lead to the problem that grant boards face, that it'll be a lot harder to differentiate between proposed experiments. And it'll be even harder to do boring stuff. (So if we assume all submitted plans are sound, they have to publish them all. Though then we'll have journals based on how strict they are with experiment design requirements, 1 sigma, 2 sigma, 5 sigma, etc.)

</comment></comment></comment></comment></comment></comment><comment><USER11> ML is not causing a reproducibility crisis, it just exposes one that is already there.

> If we had an additional dataset would we see the same scientific discovery > or principle on the same dataset?

The same holds true for traditional science based on traditional statistics. It just seems that traditional datasets are under less scrutiny of reproducibility and are taken more easily at face value.

<comment><USER12> A specific issue with machine learning is overfitting and non- interpretability.

The first means it is possible to get results that don't generalize (even if they survive cross validation). The second means it is a lot harder to detect use of correlations that cannot possibly be causal.

<comment><USER13> One of the issues with problems of reproducibility in the social sciences/psychology is that early studies usually choose WEIRD (white/educated from a rich/industrialised/democractic country) subjects, which are often uni students who are very different from the rest of the world.

One article: [https://slate.com/technology/2013/05/weird-psychology- social...](https://slate.com/technology/2013/05/weird-psychology-social- science-researchers-rely-too-much-on-western-college-students.html)

Could you not interpret this, in the widest sense, as overfitting _interpretations_ to a 'weird' dataset, with the results that do not generalize, even though the stats (in this case, likely t-test) say everything is fine? In which case overfitting isn't a ML-only problem?

<comment><USER12> Overfitting is about reading too much in a properly sampled data set from any distribution. It comes about when your model has so many parameters it can be made to fit anything. Regularization keeps it in check, but it remains an issue.

The example you cite has an issue earlier in the chain. Here, we are dealing with a biased sample from some distrubution. The results from that won't generalize to the unbiased distrubution, no matter how great your model is.

</comment></comment></comment></comment><comment><USER14> Curious (possibly naive) question: isn't there a fundamental difference between the goals behind creating models with ML vs the "old-fashioned" way? That is, in modern ML applications, you're creating a model with dozens/hundreds of potential variables, without a hypothesis of how they relate or contribute to the target (other than that they might, hence your including them in the modeling process). You're using the model for predictions more than for explainability (though there is work ongoing into improving explainability, but it seems kind of post hoc to me). And there's an expectation that you will retrain, or at least tune, the model as its predictive accuracy decays over time.

By contrast, traditionally in science you're coming in with a hypothesis ahead of time about what variables predict what target. The goal is to come up with a model that is consistent with your hypothesis (and possibly some existing theory), and which can be applied generally, and which should need no tuning. For example, the very simple model for Beer's Law-- absorbance vs concentration. That is a law that will apply in every other circumstance, but if modern ML methods had been applied, the scientist might have chosen the model with a slightly better score but which includes nonsense variables in addition to concentration.

All that to say, it seems to me the problem stems from scientists' lack of hypotheses at the outset of a project, and/or the understandable desire to get the best bang for their buck out of an experiment by measuring dozens of variables at once and hoping the magic of ML can find a hypothesis for them.

Hope that made sense.

<comment><USER15> I think you got the point. A lot of people don't seem to realize that ML might be great for finding patterns but will never yield scientific knowledge in the sense of cause-reaction sense.

Unfortunately everyone thinks he can use it for finding "new stuff" and so in my field they "predict material properties", etc. using ML fed with data where every review about the physics tells you that the algorithms they use for extracting that data are domain-specific and might yield results different on the order of magnitudes. But nobody cares; take some SW off the net, which claims to be able to extract what you want, run it, train your ML, publish your results.

<comment><USER16> What method would you use to yield scientific knowledge in the sense of cause- reaction? Many important processes really do have large numbers of causal factors that interact non-linearly. If we want to try to learn about that, some statistical method that deals with many parameters will be needed. Such models are generally referred to as "Machine Learning". Their generalisation or causal inference properties are particular to each implementation and identification strategy, but you can't just say "ML will never yield scientific knowledge".

<comment><USER15> A tool called Mathematics which can exactly describe this interactions. And if those processes have a lot of variables, a ML-model might certainly be useful, but it will never be generally applicable! This probably also contributes to "scientific knowledge" but it's not the same as scientific facts (or whatever you call universally transferable results).

<comment><USER16> You're building your mathematical model based on the knowledge you have, which is from the data you have, and there is still the same risk that your theory won't generalize to new observations.

</comment></comment></comment></comment></comment><comment><USER17> Science works because it posits models first, and then data is sought to confirm or disconfirm it. The benefit of having a model first is that it is much more likely to be general (and hence reproducible).

ML does completely opposite. Data first, and then the model is discovered using data. It's pretty easy to see why it would lead to non-reproducible models.

<comment><USER18> From this perspective, where is the line between ML and automated p-hacking?

</comment></comment><comment><USER15> An undergrad to his supervisor in our office talking about publishing a paper: I've fixed the data, now the plots look ok. I (undergrad too) am sitting there thinking - well, you are using ML as a regression blackbox to plot a line, I can do that too w/o ML if I'm fixing the data. Supervisor: ok, that's really great. Me cringing...

I'm not hammering the ML-keyword above my work (and thus am getting considerably less academic attention), but it's nice to hear from people who made it in academia that they support my theory. 50% off the people are just showoffs throwing buzzwords and positivity around while they produce a load of sh __...

<comment><USER19> Yup. As someone who is in a junior position in the field - I'm torn between, on one hand, riding the wave so to speak and taking advantage of all the buzzwords that I can put on my resume (which is fine with me because I can back them up), and on the other hand avoiding association with a certain type of person/career path that might turn out be just hot air in a couple of years.

So if I can actually write decent code, have solid understanding of software development principles, have studied math and statistics from the ground up to an advanced level, am familiar with relevant research - then do I really want to call myself a data scientist (or ML-something) just because it might improve my job/salary prospects, or do I want to stay away from it because everyone who takes a one-week course on Udemy calls themselves a data scientist without being able to back it up with actual skills?

<comment><USER15> Having been subject to a lot of buzzword-blarers over the last term I'd say that you just tell everybody that you are working the foundations of what all the other people are doing if they ask, what makes you special. If you can back it up, why not ride the wave. I would refrain from putting the sticker on everything though. I als made a turnaround and now spin everything I do which takes time (basically automating my research and data-vis) as building blocks for (gradschool) ML-based research.

</comment></comment></comment><comment><USER20> That was a terrible article. I didn't see even one concrete example of their complaint. Blaming the reproducibility crisis on machine learning methods is just a cheap dodge.

</comment><comment><USER21> My impression from the article was that the doctor stating those opinions has no idea how ML works and how to apply it properly, leading to statements like that. "ML gap" is real I guess...

<comment><USER22> She has a PhD in Statistics from Stanford. The title of her thesis was "Transposable Regularized Covariance Models with Applications to High- Dimensional Data". ([http://www.stat.rice.edu/~gallen/](http://www.stat.rice.edu/~gallen/))

I think she knows what she is talking about.

<comment><USER23> Thanks for pointing this out. The professor cited most certainly knows the best of ML. I'm guessing the author of this article simply attended the AAAS session where Allen gave a talk on recent work on addressing inferential challenges with modern ML and wrote this piece that doesn't do her work justice. See [https://aaas.confex.com/aaas/2019/meetingapp.cgi/Session/215...](https://aaas.confex.com/aaas/2019/meetingapp.cgi/Session/2159) & list of recent papers [https://arxiv.org/search/?query=genevera+allen&searchtype=al...](https://arxiv.org/search/?query=genevera+allen&searchtype=all&source=header)

Nearly all statisticians realize the need for more inferential thinking in modern ML. E.g. [http://magazine.amstat.org/blog/2016/03/01/jordan16/](http://magazine.amstat.org/blog/2016/03/01/jordan16/) We still don't do that well in high dimension, low sample size regimes that make up the majority of life science research.

</comment></comment></comment><comment><USER24> Good read. It's also refreshing to see a mainstream article that talks about ML without once mentioning 'AI'.

<comment><USER6> ML is statistics with a different name using a computer, that should always be mentioned in articles for the general public.

On the other hand AI is fantasy BS hype boosting off the fact that ML sounds similar to AI to people who aren't aware Machine Learning _is_ stats.

Maybe AI one day but today it is utterly ridiculous. No really. Every single article should mention both of those things at least in passing. Downvote away all you AI hype Surfers but you know it's true.

<comment><USER12> I'd say currently ML is heuristics done by a computer. The rigour of statistics isn't quite present in ML yet. At least, not in the basic courses yet.

<comment><USER6> You can equally do the thing named statistics without rigour. In fact that describes most of it. Sadly. See replication crisis, p-hacking, garden of forking data etc. etc. etc. I'd say the overwhelming majority of university stats courses have no rigour at all and that may not be a bad thing in and of itself?

</comment></comment></comment></comment><comment><USER25> How does this article manage not to mention a single actual example of ML- related misconceptions?? I'm sure they exist, but there is literally nothing here except some assertions and a plug for a vaguely remedial research line.

<comment><USER23> Here is the missing context from press release:

``` "In precision medicine, it's important to find groups of patients that have genomically similar profiles so you can develop drug therapies that are targeted to the specific genome for their disease," Allen said. "People have applied machine learning to genomic data from clinical cohorts to find groups, or clusters, of patients with similar genomic profiles.

"But there are cases where discoveries aren't reproducible; the clusters discovered in one study are completely different than the clusters found in another," she said. "Why? Because most machine-learning techniques today always say, 'I found a group.' Sometimes, it would be far more useful if they said, 'I think some of these are really grouped together, but I'm uncertain about these others.'"

Allen will discuss uncertainty and reproducibility of ML techniques for data- driven discoveries at a 10 a.m. press briefing today, and she will discuss case studies and research aimed at addressing uncertainty and reproducibility in the 3:30 p.m. general session, "Machine Learning and Statistics: Applications in Genomics and Computer Vision." Both sessions are at the Marriott Wardman Park Hotel. ``` [https://eurekalert.org/pub_releases/2019-02/ru- cwt021119.php](https://eurekalert.org/pub_releases/2019-02/ru-cwt021119.php)

& the context of the AAAS session [https://aaas.confex.com/aaas/2019/meetingapp.cgi/Session/215...](https://aaas.confex.com/aaas/2019/meetingapp.cgi/Session/21598)

</comment></comment><comment><USER26> This is a misleading title. The researcher they quote is

> ... developing the next generation of machine learning and statistical > techniques that can ... also report how uncertain their results are and > their likely reproducibility.

So she's actually using machine learning to access systematic uncertainties, i.e. to get better, more reproducible research. Of course, like all forms of automation, people tend to sensationalize progress as a crisis because it makes it too easy to shoot yourself in the foot.

But doing things "the old fashioned way" isn't any better. Early particle physics experiments would get armies of undergrads classify photographs of collisions in bubble chambers. These results took thousands of researcher- hours to compile, which might seem all fine and dandy, until you realize that there may have been a systematic bias in your classification. Now what do you do?

Thanks to machine learning, there are a lot of things we can do: we can try to remove the bias and retrain the algorithm, or we can train with extreme examples of bias and use that to quote a systematic uncertainty. We can try a multitude of approaches to estimate uncertainties rerun our entire analysis in a few hours. Good luck doing that with an army of undergrads.

</comment><comment><USER27> Case in point : LHC Higgs results - how many detection's vs how many events? How were the detection's determined... The answer is with a large booster [1]

I postulate that out of 12 billion random events it would be remarkable if a booster didn't extract 100 or so items that looked similar to a Higgs detection.

Well, let's give it 20 years and a new generation of PI's who aren't invested in this and have grad students who are keen to find something different in the data.

But ohh.. all the data has been thrown aways... oh! [2]

[1] [https://indico.cern.ch/event/705941/contributions/2897000/at...](https://indico.cern.ch/event/705941/contributions/2897000/attachments/1605280/2546655/mlhepAthens- Feb22-2018.pdf)

[2] [https://www.forbes.com/sites/startswithabang/2018/09/13/has-...](https://www.forbes.com/sites/startswithabang/2018/09/13/has- the-large-hadron-collider-accidentally-thrown-away-the-evidence-for-new- physics/#d1c86469270a)

<comment><USER26> 90% of the work in LHC physics is estimating the amplitude of backgrounds that look almost exactly like your signal process. Coming up with the "large booster" for classification is only a small part of it. So yes, machine learning is used, but no, we don't use it blindly like you imply.

As for throwing all the data away, the article you link to actually does a good job of explaining how this is done: we look at every collision with thousands of sensors before deciding whether to keep it. At this stage there is absolutely no machine learning anyway (just physics knowledge), so be careful blaming machine learning for any missed discoveries.

</comment></comment><comment><USER28> Overfitting is a well-known problem in the ML community. There are methods to avoid this: cross validation, train-test splits, etc. There are also models that give you an estimate of the standard deviation of a prediction. What is the point? We don't need new algorithms, we just have to apply existing methods properly.

</comment><comment><USER29> Title makes it sound as if the AAAS made this statement, its a single researcher who is making this claim.

</comment><comment><USER30> > Machine learning 'causing science crisis'

ML or more generally mathematics do not cause anything. People who misuse mathematics are to blame here. Some fields are simply using tools they don't understand and this predates ML advances by decades. Thinking of stats use in psychology and medicine for instance.

This trend of presenting ML are some kind of magic powder is ridiculous. I blame hyped presentations by influential ML scientists for this.

</comment><comment><USER31> I wonder: don't machine learning frameworks' results come with a level of confidence?

Ps: I have no experience with anything regarding ML.

<comment><USER17> Because it's difficult. Bayesian methods don't scale well to the number of parameters that most modern ML demands. Doing inference for large models is intractable.

However, even if you're able to do that, the problem isn't solved. You have to answer level of confidence for what? The uncertainty / confidence that you get assumes your model is right. No model can tell you whether it is a true reflection of reality. I had written more about this on my twitter: [https://twitter.com/paraschopra/status/1075033048767520768](https://twitter.com/paraschopra/status/1075033048767520768)

</comment></comment><comment><USER32> I can see there being issues with reproducibility, i.e. getting the exact same results, but has there ever been a time when science was more replicable? Data/techniques/findings/papers are under more scrutiny than ever. No positive results will be taken as sacrosanct in CS anymore. This is a complete 180 from 10+ years ago.

<comment><USER33> In my view, rather than talking about time periods, it might be preferable to consider different fields of science wrt replicability. In fact it doesn't even make sense to put all of "science" in one basket. The medical and behavioral sciences get the most attention these days, but are not comparable to physics, chemistry, geology, astronomy, etc. My field (physics) doubtlessly has its own problems, yet has produced theories of astounding generality and accuracy in spite of potential flaws in the individual studies that led to the success of those theories.

Replication might not turn out to be the big problem. The lack of progress towards a unifying theory might be a more important long term issue.

</comment></comment><comment><USER34> Hopefully machine learning helps with confidence and making predictions out of experiments as opposed to the limited capability of "understanding" from the way things are done now (as if an experiment with slightly higher p values are ignored or with smaller values might have hidden biases, etc).

</comment><comment><USER35> I've often imagined how different Newtonian physics would be if we had gone the ML route from the beginning.

</comment><comment><USER36> The other day someone lamented that you can't get published as an honest ML researcher, because other scientists are rendering whole professions obsolete all the time...

<comment><USER37> > you can't get published as an honest ML researcher

If you research ML, you can publish in ML journals, there are several. If your research is about applying ML to domain problems, are you then an ML researcher or a domain researcher?

<comment><USER36> The point was that it is difficult to get noticed with down-to-earth work when the whole field seems to be aiming for the stars.

</comment></comment></comment><comment><USER38> It's not like teaching to the test works better for humans.

</comment><comment><USER39> As other comments observe, the replication crisis predates the use of ML, so the causes are clearly deeper.

I think there's actually a very simple explanation for this which lots and lots of people hate, so they're sort of in denial about it. Academia is entirely government funded and has little or no accountability to the outside world. Academic incentives are a closed loop in which the same sorts of people who are producing papers are also reviewing them, publishing them, allocating funding, assessing each other's merits etc. It's a giant exercise in marking your own homework.

Just looked at in purely economic terms, academia is a massive planned economy. The central planners (grant bodies) decide that what matters is volume and novelty of results, so that's what they get, even though the resulting stream of papers is useless to the people actually trying to apply science in the real world ... biotech firms here but the same problem crops up in many fields. It's exactly what we'd expect to see given historical precedent and the way the system works.

There's another huge elephant in the room here beyond the replication crisis ("to what extent are the outputs wrong") which is the question of to what extent are the outputs even relevant to begin with? Whenever I sift through academic output I'm constantly amazed at the vast quantity of obviously useless research directions and papers that appear to be written for their cleverness rather than utility. The papers don't have to be wrong to be useless, they can just solve non-problems or make absurd tradeoffs that would never fly in any kind of applied science.

I read a lot of CS papers and I've noticed over time that the best and most impactful papers are almost always the ones coming out of corporate research teams. I think this is because corporate funded research has some kind of ultimate accountability and connection to reality that comes from senior executives asking hard questions about applicability. For instance in the realm of PL research academia pumps out new programming languages all the time, but they rarely get any traction and the ideas they explore are frequently ignored by the industrial developers of mainstream languages because they're completely impractical. This problem is usually handwaved away by asserting that the ideas aren't bad ideas, they're just incredibly futuristic and 30 years from now we'll definitely be using them - but this kind of reasoning is unfalsifiable on any kind of sensible timescale so it's the same as saying, "I shouldn't be held accountable within the span of my own career for how I spend tax and student money".

As time goes by I am getting more and more sympathetic to the idea of just drastically cutting academic funding and balancing the books by drastically reducing corporation tax. The amount of total research would fall significantly because corporations wouldn't invest all the newly available money in research, or even most of it, but it's unclear to me that this would be a bad thing - if 75% of research studies coming out of academic biotech are wrong then it stands to reason that if standards were improved significantly, funding could be reduced by (say) 50% and still get a similar quantity of accurate papers out the other end. It's possible the science crisis is really just reflecting massive oversupply of scientists, massive undersupply of accountability and in general research should be a much smaller social effort than it presently is.

<comment><USER40> Somehow, it seems like during the Cold War (50s, 60s), there were fewer scientists and the quality of the output was higher. Not sure if that is the case, or survivorship bias. But if it is the case, what was different about the system back then?

To play the devil's advocate: scientists in industry/corporations do not come out of nowhere - they come from academia. Will the academics not move to countries where academic research is better funded? The students will follow. Corporations will set up their research labs in those countries near the universities to poach the best talent. Suddenly, your country is at a disadvantage.

</comment></comment><comment><USER41> A dishonest scientist can mine a dataset for statistically significant hypotheses and for a long time no institutional protection against it was in place:

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

[https://www.xkcd.com/882/](https://www.xkcd.com/882/)

Machine learning makes it easier to test great many hypothesis, but even going fully "by hand" it is very easy to deviate from what the statistical framework of hypothesis testing would demand. There are now some discussions about counter-measures, e.g. about preregistration of studies:

[http://www.sciencemag.org/news/2018/09/more-and-more- scienti...](http://www.sciencemag.org/news/2018/09/more-and-more-scientists- are-preregistering-their-studies-should-you)

You can see this as another chapter in the long debate about the correct way to test scientific hypotheses:

[https://en.wikipedia.org/wiki/Statistical_hypothesis_testing...](https://en.wikipedia.org/wiki/Statistical_hypothesis_testing#Criticism)

<comment><USER34> As your number of samples increase the chance that a hidden variable that explains the phenomenon but correlates with the thing you're testing also increases.

All experiments have a limit it seems

</comment></comment><comment><USER42> The issue talked about here is distinct from the larger "reproducibility crisis"; the latter is a result of shoddily designed (or simply fraudulent) _experimental_ work, whereas the issue here is the aggregate effects of the huge amount of _computational_ work that is being done- even when that work is being done correctly and honestly.

Testing a hypothesis against a pre-existing dataset is a valid thing to do, and it is also almost trivially simple (and completely free) for someone with a reasonable computational background. There are researchers who spend a decent portion of their careers performing these analyses. This is all well and good- we want people to spend time analyzing the highly complex data that modern science produces- but we run into problems with statistics.

Suppose an analyst can test a hundred hypotheses per month (this is probably a low estimate.) Each analysis (simplifying slightly!) ends with a significance test, returning a p-value indicating the likelihood that the hypothesis is false. If p < 0.01, the researcher writes up the analysis and sends it off to a journal for publication, since the odds that this result was spurious are _literally_ hundred-to-one. But you see the problem; even if we assume that this researcher tests _no valid hypotheses at all_ over the course of a year, we would expect them to send out one paper per month- and each of these papers would be entirely valid, with no methodological flaws for reviewers to complain about.

In reality, of course, researchers sometimes test true hypotheses, and the rate of true to false computational-analysis papers would depend on the ratio of "true hypotheses that analysis successfully catches" to "false hypothesis that squeak by under the p-value threshold" (i.e., the True Positive rate vs the False Positive rate.) It's hard to guess that this ratio would be, but if AAAS is calling things a "crisis," it's clearly lower than we would like.

But there's a further problem, since the obvious solution- lower the p-value threshold for publication- would lower _both_ the False Positive rate and the True Positive rate. The p-value that gets assigned to the results of an analysis of a _true_ hypothesis are limited by the statistical power (essentially, size and quality) of the dataset being looked at; lower the p-value threshold too much, and analysts simply won't be able to make a sufficiently convincing case for any given true hypothesis. It's not a given that there is a p-value threshold for which the True Positive/False Positive ratio is much better than it is now.

"More data!" is the other commonly proposed solution, since we can safely lower the p-value threshold if we have the data to back up true hypotheses. But even if we can up the experimental throughput so much that we can produce True Positives at p < 0.0001, that simply means that computational researchers can explore more complicated hypotheses, until they're testing thousands or millions of hypotheses per month- and then we have the same problem. In a race between "bench work" and "human creativity plus computer science," I know which I'd bet on.</comment>


Mimesis, Violence, and Facebook: Peter Thiel's French Connection - <AUTHOR> https://thesocietypages.org/cyborgology/2016/08/13/mimesis-violence-and-facebook-peter-thiels-french-connection-full-essay/ <comment><USER2> Girard will be looked back on as a Father of the Church:

[https://www.youtube.com/watch?v=LSzF2OG2ejI](https://www.youtube.com/watch?v=LSzF2OG2ejI)

[https://www.youtube.com/watch?v=BNkSBy5wWDk](https://www.youtube.com/watch?v=BNkSBy5wWDk)

</comment><comment><USER3> There's been some criticism leveled against Girard's work wrt it's scientific credibility[0]. Has there been any studies on mimetic desire/conflict?

0\. [https://en.wikipedia.org/wiki/René_Girard#Use_of_evidence](https://en.wikipedia.org/wiki/René_Girard#Use_of_evidence)

</comment><comment><USER4> "Man is the creature who does not know what to desire, and who turns to others in order to make up his mind. We desire what others desire because we imitate their desires."

But how do these others desire in the first place? Have the first desires begun with imitating animals?

<comment><USER5> The most basic desires are rooted in being a social animal. Food, water, sleep, comfort, shelter, bonding, play.</comment></comment>


Using Superpowers in Virtual Reality to Encourage Prosocial Behavior (2013) [pdf] - <AUTHOR> http://vhil.stanford.edu/pubs/2013/rosenberg-plos-virtual-superheroes.pdf

<comment><USER2> Very cool.

After the stigma of the antisocial impact of game have followed them for decades, I'm glad to see some potential prosocial* impact of games.

*: I hadn't even heard or read that word before now. Excellent concept.

<comment><AUTHOR> For the record, "prosocial" is not a real word in the English language [1], but the phrase "prosocial behavior" [2] is a defined term (albeit somewhat contrived) in behavioral psychology, sociology and related fields.

You'd probably want to compare "Prosocial Behavior" to general "Social Behavior" [3], as well as "Antisocial Behavior" [4] and "Asocial Behavior" [5]. To keep things simple, I consider "Prosocial Behavior" to be a combination of empathy, consideration, and kindness being shown towards others.

[1] [http://dictionary.reference.com/misspelling?term=prosocial](http://dictionary.reference.com/misspelling?term=prosocial)

[2] [https://en.wikipedia.org/wiki/Prosocial_behavior](https://en.wikipedia.org/wiki/Prosocial_behavior)

[3] [https://en.wikipedia.org/wiki/Social_behavior](https://en.wikipedia.org/wiki/Social_behavior)

[4] [https://en.wikipedia.org/wiki/Anti- social_behavior](https://en.wikipedia.org/wiki/Anti-social_behavior)

[5] [https://en.wikipedia.org/wiki/Asociality](https://en.wikipedia.org/wiki/Asociality)

</comment></comment><comment><USER3> This is very cool. There's a lot of talk in VR about the importance and power of creating empathy for characters, so this really gives the sense that VR can be a great tool for improving how people relate to and treat each other.

</comment><comment><USER4> >Recent studies have shown that playing prosocial video games leads to greater subsequent prosocial behavior in the real world.

What about the opposite? Does anti-social activity in games lead to anti- social activity in the real world?

</comment><comment><USER5> I love the extended abstract at the top that includes the principle findings and conclusion. More academic papers should use this format.</comment>


Kingdom Death: Monster 1.5 Board Game Now 4th Most Funded Kickstarter - <AUTHOR> https://www.kickstarter.com/projects/poots/kingdom-death-monster-15/ <comment><AUTHOR> For anyone not familiar, it's a complex role playing board game. Sort of "light" version of Dungeons and Dragons mixed with Monster Hunter, without the Dungeon Master requirement, but rather a self-running campaign. Beware, expensive, but the craftsmanship of the miniatures, card stock, is exceptional. Has only 5 hours to go.</comment>