Max Levchin's favorite new startups - <AUTHOR> http://www.businessinsider.com/max-levchins-favorite-startups-are-all-japanese-2009-2 <comment><USER2> My new favorite startups are the ones with a "Pricing" link and without "Share", "Collaborate", "Easy", "Friends".

<comment><USER3> It's considered "HIP" to say that at the moment. That doesn't make it right though. In the same way it's hip to hate Java right now. That doesn't mean that Java isn't good at solving a set of problems in a good way - it clearly is.

There's millions to be made from advertising revenue. If you can get people to pay for something, great, but not every product is the type where paying would make any sense at all. By charging consumers directly, you massively reduce your audience to a small subset of users.

It's quite funny to see people imply that advertising revenue isn't "real" revenue, or is in some way less valuable than getting customers to pay directly.

You know what the most powerful marketing word in the world is? "FREE". The clever thing is using that to your advantage and still making money. Tons of industries do that though, loss leaders work, 'free cellphone (with contract) works' etc etc.

<comment><USER4> > It's quite funny to see people imply that advertising revenue isn't "real" > revenue, or is in some way less valuable than getting customers to pay > directly.

As long as numbers like these hold true: "At the $1 RPM (CPM/CPA/CPC) level achieved by most general sites, you need 4 billion page views/month ... to [reach] $50M in revenue per year" ([http://www.thealarmclock.com/mt/archives/2007/03/a_vcs_view_...](http://www.thealarmclock.com/mt/archives/2007/03/a_vcs_view_of_o.html)), charging money for a product that creates value is going to be a more viable way of starting a successful company online

<comment><USER5> The value of traffic rises very sharply when it becomes more targeted though. The value of traffic on <http://bountii.com> is worth orders of magnitude more than on a random Facebook page, because it's all people who are just about to buy something.

<comment><USER4> But targeted content and user generated content are two different things. Digg's Gaming category is more targeted than a random Facebook page, but that still doesn't mean a company wants to attach their brand to any random topic a user might come up with (something like, say, "[advertised game on this page] sucks").

Getting big and targeted is a lot more expensive (and highly competitive, see gadgets, etc.) than getting big and general, since it usually means non-user generated content.

</comment></comment></comment></comment></comment><comment><USER6> Finally, someone is saying it. The ideas in silicon valley that are being produced now are just so boring. I can't remember the last time I said : wow, or even just thought - well, that's different.

Sure, it's great to make a better database examiner, but if you're young and you have nothing to lose, why not try something really fresh. Something brand new? That webcam figure is fresh, for example.

<comment><USER5> Occam's razor suggests that the reason the current ideas seem boring is that you don't understand yet which ones have big potential. Microprocessors seem a great idea in retrospect, because we've seen now what they turned into, but initially hardly anyone cared about them, even within Intel.

<comment><USER6> It may not be possible to understand what they can become, but one can look at the industry and know roughly what will happen. Let's say some guy comes up with some really great hubcaps.He can grow to become the best hubcap maker in the world. But he's still just making hubcaps, and most people don't care about them.

But the people who do stuff in media, for example music or tv, always have potential to change the industry. Same with anything communication related. These are core industries that are fundamental to human beings - so startups in those areas always have huge potential. Like muxtape for example, or justin.tv, those startups can change the world. They are not changing the world yet, but I can see that with the right things in place, they can change the world. It's the same with microprocessors, anyone who was in the field knew that if size went down, price went down and so on, microprocessors would make a huge difference. They did not predict the apps that would be enabled because of them.

Why did they not? Microprocessors created a platform, and a platform opens a brand new field for new people to create ideas within. When a startup works on something that creates a new type of platform, then breakout ideas can happen on the platform. Hubcaps are not a platform that others can build on. They're the end of a line.

<comment><USER7> Hubcaps as platforms sounds like the most interesting idea I've heard in a long time (I understand this was not the point you were trying to make).

\-- _Hubcap based games_ : Who can log the least number of hours? (if it spins,we can measure distance traveled)

\-- _Hubcap based communications_ : My cars recognize and exchange information (shopping lists, to-do lists, language courses)

\-- _Hubcap based entertainment_ : Quickly transfer songs/files/etc. -- hooks into your audio system.

\-- _Hubcap based computing_ : Use those idle cycles while your sitting at the light to help with gene folding.

\-- _Hubcap based energy_ : Spinners? Naw, they just charge my car's battery (self-powered units).

Braindumps are fun!

</comment></comment></comment></comment><comment><USER8> So... shun the superficial Web 2.0 social networks in favor of the new, innovative technology with more substance: virtual sex toys.

It's absolutely inspiring.

</comment><comment><USER9> So, he's unimpressed with silicon valley conventions like "friends lists" ... but one of his favorite startups is a "mobile social network"?

I'm unimpressed with Max's shallow evaluation.

</comment><comment><USER10> My favourite startup (if you could still call it that) is Telnic (British). I think the .tel TLD will be revolutionary, but has so far received surprisingly little attention from tech/entrepreneurial communities like this one and TechCrunch - perhaps because it's not in the Valley? I guess this might change when it launches next week, or on March 24th when general availability begins..

<comment><USER11> What's so revolutionary about it?

<comment><USER10> Well, there is _plenty_ of friction in the world of telecoms, and its tangent with media - .tel solves many of these problems.

Check out the DEMOfall 08 presentation for a brief 5 minute overview, or their in-house "Tell The World" interview for a lengthier discussion:

<http://www.telnic.org/media-resources.html>

The best blog writeup I found is here:

[http://www.disruptivetelephony.com/2008/12/is-the-new-tel- do...](http://www.disruptivetelephony.com/2008/12/is-the-new-tel-domain-more- than-just-a-pretty-face-on-top-of-dns.html)

If you want to get into a discussion then let me know and I'll pitch some thoughts at you, but wanted to introduce you to the subject first, in case you hadn't already looked into it.

</comment></comment></comment><comment><USER12> That doll is awesome. Watch the video.

<comment><USER13> It's awesome technology, but I'm disturbed by the seeming purpose of it. There's a whole section of the video devoted to molesting, and taking clothes off of, the teenage girl figure until she's crying in a corner...and then giving her a teddy bear to make her happy again.

The Japanese sexual culture is deeply disturbing is what I'm trying to say here.

<comment><USER14> Won't somebody think of the virtual children!

<comment><USER13> I just don't understand what kind of person gets their jollies by molesting a virtual child until they are crying in a corner. I'm not suggesting there ought to be a law against molesting virtual children.

<comment><USER11> From what I understand of Japanese culture, there are few sexual taboos. People are much more open and honest about sex than they are here.

In America, anything beyond missionary position is considered weird. Anal sex is a little edgy. BDSM and roleplaying are considered weird and freaky by pretty much everybody. Which is odd, because sex really should be one of the most individual, unique things for people. Each person has different standards for what they find hot. Why shouldn't that play out during sex?

Those fantasies go far and wide. I'm sure every single person reading this has had bizarre sexual thoughts and ideas. Perhaps not for long, perhaps you've denied the thoughts to yourself as wrong, but the thoughts have crossed your mind. So the concept of a robot like this is, why not let people indulge in these fantasies? Some things you cannot achieve unless you do it virtually. That means either you suppress those fantasies, which is unhealthy, or you find some other outlet - virtual, which is fine, or real, which is certainly not.

My opinion is that having a sexual fantasy about beating and molesting a child is perfectly natural. That's not my _personal_ perverted fantasy, but if it's somebody else's I'm not going to call that strange. So if they've found a way to have that fantasy that doesn't involve harming a child, then props to them! If America had such an open nature to sex, we'd have fewer cultural problems than we do (and we have a lot).

<comment><USER13> _In America, anything beyond missionary position is considered weird. Anal sex is a little edgy. BDSM and roleplaying are considered weird and freaky by pretty much everybody._

Really? I guess living in or near large cities all these years has warped my perspective of what "in America" means. None of these things seems particularly weird or taboo amongst my social set.

 _If America had such an open nature to sex, we'd have fewer cultural problems than we do (and we have a lot)._

And Japan doesn't have problems? I think you're giving the culture a huge pass. If you wanted to hold up the Netherlands as an example of a very healthy and very open sexual culture (including a societal understanding that teenagers have an interest in sex, and will have sex), then I'd be on board with you. But, we're talking about Japan here...The land of "Christmas cake", "kawaii", etc. Infantilization of women is the norm. That's not healthy or merely open...that's just broken in a different direction than America's protestant zeal.

I wasn't trying to suggest that America has a healthy approach to sex. We, on the whole, don't, particularly with regard to teenagers and sex (some of our child protection laws are bizarre and have horrific consequences, like labeling a 17 year old that has sex with a consenting 15 year old as a sexual predator and a child abuser). But I said nothing about America in my comments. This wasn't a "Go USA! Japan sucks!" conversation, and I don't think there's really anything interesting about such a conversation.

Again, I'm not suggesting it should be illegal...just that I'm rather stunned that the market for a "molest a child" game is large enough such that that is how they market this product. It is, to me, a disturbing indicator of the way a reasonably large segment of Japanese men feel about women (large enough to be the market they've chosen to target with this really clever new toy).

<comment><USER11> _Again, I'm not suggesting it should be illegal...just that I'm rather stunned that the market for a "molest a child" game is large enough such that that is how they market this product._

Phrased like that, it seems more bizarre than it did when I first thought about it.

 _Really? I guess living in or near large cities all these years has warped my perspective of what "in America" means. None of these things seems particularly weird or taboo amongst my social set._

Possibly this is just youth speaking. Those things are absolutely viewed as freak attitudes in college.

</comment></comment></comment></comment></comment></comment></comment><comment><USER15> wow! counted as the second time i saw a photo of max without his slide shirt on.

<comment><USER16> That actually is kind of funny:

<http://images.google.com/images?q=max%20levchin>

<comment><USER15> max is a marketing GENIUS. searching for someone's name and it inevitably turns up x*results of advertisements for his company ;)

i say this is a predictor of success. if you search someone's full name, count how many times you see their shirt with their company logo. until i have enough money as max to throw around, i'll pose with a 8.5x11 sheet of paper with my logo written or printed on it.

<comment><USER16> If he is such a marketing genius why am I still so unsure about what Slide actually does? :)</comment></comment></comment></comment>


Scientists create interactive map of human genetic history - <AUTHOR> http://www.latimes.com/science/sciencenow/la-sci-sn-scientists-create-interactive-map-of-human-genetic-history-20140213,0,3193388.story#axzz2tJWzt4fI

<comment><AUTHOR> Abstract (with link to full article) of underlying article published in the journal _Science:_

[https://www.sciencemag.org/content/343/6172/747.abstract](https://www.sciencemag.org/content/343/6172/747.abstract)

 _New York Times_ reporting on this same story:

[http://www.nytimes.com/2014/02/14/science/tracing- ancestry-t...](http://www.nytimes.com/2014/02/14/science/tracing-ancestry- team-produces-genetic-atlas-of-human-mixing-events.html)

The interactive online map produced by the researchers (which has timed out during the last day as I have tried to access it on my office computer):

[http://admixturemap.paintmychromosomes.com/](http://admixturemap.paintmychromosomes.com/)</comment>


How does VC behavior change when interest rates are cut? - <AUTHOR>

I was an intern at a fixed wireless broadband start-up when the dot-com bubble burst and I witnessed how all of the excitement for the start-up vanished when investors were afraid to give out money. It came down to bad timing for this start-up -- if they had only started a year earlier.<p>How will the speculation that interest rates will be cut further affect the investment activities of  angels, private equity, and VCs? This may affect when a lot entrepreneurs may launch their companies, right? <comment><USER2> Interest rate cuts generally _encourage_ business investment for two reasons.

1\. Interest rate cuts make equity investment more attractive. Equity investment is riskier than making loans (buying bonds) or cash (money markets, CDs). Investors do it anyway because equity pays a risk premium: equity returns tend to be higher than the return on cash or bonds.

Example: Today, a hypothetical venture fund might offer a 10% expected return, and a hypothetical money market might offer 6%. So, you could make 4% more by investing in a VC fund. That's nice, but not necessarily worth the risk.

But if interest rates are cut and everything else stays equal, the money market APR might drop to 4%. Now you can make 6% more by investing in the venture fund, which makes it more attractive to investors.

2\. Reduced interest rates encourage businesses to spend money. Lower rates make it cheaper for them to borrow money, which means they can justify spending money on projects which would be unattractive when interest rates are high. If your revenue comes from other businesses, such as through B2B sales or through advertising, this may make them more likely to spend money with your company.

Obviously, lower interest rates aren't going to bring customers to your door -- you'll still need to fill your own sales pipeline. But it may make it more likely that your customer will pull the trigger and close the deal.

</comment><comment><USER3> Any economist would say "Marginally."

A more useful answer: not enough to be worth caring about. Unless you're a specialist in interest rates, there is no point at which the money you'll make from an hour of pondering the Fed will exceed the amount you'd make from an hour calling customers or something. And keep in mind that experts don't do very well at predictions -- their main function is to help people hedge. In other words, even the people who care the most about such problems spend all of their time helping everyone else care less.

</comment><comment><USER4> Ryan is right. Cutting interest rates is great for startups. But, that is not what people are scared. The problem is the economy might tank as a result of all the money being lost in the equity, and debt markets right now. If that happens we are in for some pain.

</comment><comment><USER5> Does it even matter? Build something people want, and customers will come to you. When customers come to you, VCs won't be far behind.</comment>


Scispacy: SpaCy models for biomedical text processing - <AUTHOR> https://allenai.github.io/scispacy/ <comment><AUTHOR> Here's the associated paper, available from Arxiv: [https://arxiv.org/abs/1902.07669v2](https://arxiv.org/abs/1902.07669v2)</comment>


Monero Declares War on ASIC Manufacturers - <AUTHOR> https://www.ccn.com/monero-declares-war-asic-manufacturers/ <comment><USER2> You can actually see one of the pull requests about this already. [1]

My favorite part about it is response to criticism that the changes may break the underlying cryptography they are using-

> We do not have cryptographers familiar with this kind of thing, sadly. [2]

1\. [https://github.com/monero- project/monero/pull/3253](https://github.com/monero-project/monero/pull/3253)

2\. [https://github.com/monero- project/monero/pull/3253#issuecomm...](https://github.com/monero- project/monero/pull/3253#issuecomment-365115603)

<comment><USER3> It not only creates a bias (which decreases entropy), but its an operation that would easily be implemented faster on an FPGA than on a CPU. I mean, its basically a singular LUT, there's nothing more efficiently done on an FPGA than LUTs.

Basically, I think it actually weakens the FPGA-resistance of Cryptonight.

Fortunately, the main idea of the changes would discourage ASICs being made for Monero. But it doesn't seem to discourage FPGAs, which are just one "resynthesize" away from fixing any code changes. And as long as the community becomes accustomed to regular changes, then any issues in the encryption can be fixed later.

<comment><USER4> FPGAs cost 10x ASICs so this argument is unimportant. The reason for using ASICs is lower cost / lower compute resources.

<comment><USER5> I am confused by this—-aren't ASICs the costliest option until some (high) number are fabricated and put into use?

If FPGAs can implement these changes efficiently—more efficiently than cpu miners—-and be re-synthesized to implement further changes, doesn't that advantage them?

<comment><USER6> Parent was presumably leaving unsaid "... at scale", aka enough to move a chunk of the hash power.

<comment><USER4> Thanks yes I totally meant scale. Main cost of new ASIC chips is design and fabrication. Only serve a very narrow specific purpose and all that cost is sunk. But after that, unit production chip cost is negligible.

<comment><USER7> do you happen to know b/e costs given difficulty/profitability ratio using current market electricity prices?

</comment></comment></comment></comment></comment></comment></comment><comment><USER8> As a Bitcoin miner since 2010, the hostility toward ASIC-based mining rig manufacturers has rarely made sense to me. ASICs _strengthen_ your network. The requirement to have specialized hardware to mine profitably, hence pushing miners to the highest joule/hash metric physically reachable will make a PoW- based cryptocurrency much more secure with respect to majority attacks (so- called 51% attacks.) Cryptocurrency devs should welcome and embrace ASIC miners.

By far, the majority of GPU farms mine Ethereum. This mean any GPU-mineable coin other than Ethereum is trivially vulnerable to majority attacks. The only thing preventing these attacks from happening is the financial incentives _against_ performing such a purely destructive attack.

ASIC-mineable coins are safe from such attacks from GPU miners.

People point out that the high barrier of entry into ASIC manufacturing can lead to monopolies, such as Bitmain which is a quasi- (not quite) monopoly with its 70-80% market share in Bitcoin with the S9. But first and foremost, manufacturing monopolies don't matter to the _function_ of Bitcoin. A manufacturing monopoly isn't a hashrate monopoly: Bitmain cannot attack Bitcoin. They themselves only directly own <10% of the hashrate.¹

¹ Not to be confused with Bitmain's mining pools which represent more than a third of the hashrate. People usually misunderstand the function and nature of mining pools. End-users choose to mine at whatever pool they want. If Bitmain's pools started sending malicious mining jobs to stratum clients, the community would react and the end-users would promptly abandon them. So in practice Bitmain cannot do whatever they want for however long they want with their pools' hashrate. They enjoy the privilege of representing this hashrate, and this privilege could evaporate overnight.

<comment><USER9> Because not everyone can afford ASIC's, which puts the mining power _solely_ in the hands of those who can afford the hardware.

This once again reduces the playing field to a small, powerful group of players and essentially no one else can participate. Doesn't this defeat the purpose of a decentralised currency based on not having "trusted nodes"?

<comment><USER10> Realistically the cost of entry is more like $2,000 which is not a small, powerful group of players.

<comment><USER9> Yeah I've totes got $2k spare to use on just getting an ASIC, which likely won't have any resale value, and is only valuable for a very finite time

Comparatively, I do have a nice graphics card (which I can use for gaming and stuff), and I can rent from cloud providers at price points I can afford if I wanted more access to cards.

</comment></comment></comment></comment><comment><USER11> I think this is short-sighted.

Just like mining pool was not an expected event, humans will always figure out a hack to make more money where there's money. Especially when there is no legal implication for doing so.

These monero developers think they're doing good for their network, but all this is doing is weaken their network based on the limited knowledge they have about what's possible currently. I would even go further to say this is arrogance.

There are many ways to compromise a network, and the best way to protect against this is to strengthening it, instead of making foolish decisions based on some conspiracy theory that nobody has ever actually seen played out.

For example if someone figures out a way to bring together the coinhive JS mining library and a clever web exploit scheme to create a global ultimate monero mining worm, it could compromise the entire network and they will have no choice but to hard fork. The only way to protect against this sort of attack is to strengthen the network. And only then you deserve to worry about centralization.

I know this "miner centralization" is a controversial topic, but what I know for sure is that people are making decisions to permanently change protocols that are already working, in order to "fix" some hypothetical situation that may or may not happen.

If humans were so good at prediction, we would not have great depressions. The best way to deal with these issues is to solve them when they actually happen. I think this is the best for all cryptocurrencies because IF some sort of centralization actually does end up happening, people can fork and take a huge chunk of network with them always.

The people who think we can't recover from these hypothetical centralization are making some assumptions that are more likely to be false than true.

</comment><comment><USER12> Relevant quote: "Moving forward, developers will seek to protect the network's ASIC resistance by slightly modifying its PoW algorithm at every scheduled hard fork, which generally occurs twice annually. These changes will not be noticeable to ordinary XMR users, but they will alter the network's hashing algorithm enough that Cryptonight ASIC miners would become obsolete following every fork."

We'll see how this works out. Hard to tell without any technical details about what these changes to the algorithn will be. I'm not quite sure though about amateurs mucking with the details of hash functions, it smells like going against the rule of "don't roll your own crypto".

Also, and in the full understanding that this ship has sailed, it's stupid to call a planned update everyone goes along with a "hard fork".

<comment><USER13> > the rule of "don't roll your own crypto".

There is no such rule in general. In many situations it might be better to use available solutions, but in others it might be better to do something different. It depends on your attack model and what you are trying to achieve.

<comment><USER14> > _There is no such rule in general._

Actually, the general rule is, "Do not roll your own crypto."

> _It depends on your attack model and what you are trying to achieve._

It depends on so much more than those two items, which is why people are told, "don't roll your own crypto."

There is almost a 1to1 relationship between looking at homemade crypto and finding severe vulnerabilities such as oracles, input reuse, incorrect encryption modes for the purpose of use, use of broken crypto, and a million other gotchas that most people don't know they need to know.

<comment><USER13> Not all "own crypto" is "homemade".

<comment><USER14> You're playing with semantics. You didn't address any of the points about crypto, choosing instead to superficially focus on a single word.

However, "own crypto" is indeed "homemade." Crypto is not independent from the purpose of use.

</comment></comment></comment></comment></comment><comment><USER15> OK, so now if Bitmain develops an ASIC they just won't tell anyone about it. Apart from the increase in difficulty (which is increasing all the time _anyway_ ), how would you know?

The ASIC companies are already de-facto mining operations. Bitmain premines on the customer's hardware and only ship once they've taken their profit and spiked the difficulty up. BFL did the same thing back in the day.

<comment> ryan-c > OK, so now if Bitmain develops an ASIC they just won't tell anyone about it.

"Moving forward, developers will seek to protect the network's ASIC resistance by slightly modifying its PoW algorithm at every scheduled hard fork, which generally occurs twice annually. These changes will not be noticeable to ordinary XMR users, but they will alter the network's hashing algorithm enough that Cryptonight ASIC miners would become obsolete following every fork."

<comment><USER15> A cat-and-mouse game where you have no idea if you're successful or not? Doesn't sound winnable to me. And remember that those changes will have to be choreographed well in advance so that other miners can implement the changes too - at which point Bitmain will know the changes as well. And you're going to do a major enough shakeup of the algorithm to break ASICs every 6 months, and it's not going to piss off the miners?

This is a pretty tall order IMO.

</comment></comment></comment><comment><USER16> I'm surprised I haven't heard about any big FPGA mining operations. It would work nicely with this change plus it allows to mine coins with different algos depending on what is most profitable at the moment (along with running some wallet recovery / vanity address generator services).

<comment><USER10> Back in the Bitcoin days FPGA mining had little or no advantage over GPUs.

<comment><USER8> Actually FPGAs had 10× better energy efficiency. This was huge. Eg. the popular Spartan6 LX150 used in many miners was rated 50 joule per gigahash, compared to 500 J/GH for an HD 5970. I used to run both GPU and FPGA mining operations. That said, they were comparable to GPUs in terms of $/GH.

</comment></comment></comment><comment><USER17> _Moving forward, developers will seek to protect the network's ASIC resistance by slightly modifying its PoW algorithm at every scheduled hard fork, which generally occurs twice annually._

I presume they've considered this, but this seems like a dangerous approach. How will they decide which change occurs, how far advance will it be decided, and how will they keep this decision secret? Think of the potential advantage to a party who uses foreknowledge of the exact change to have ASIC's ready at the official announcement. If done on a small scale, it's possible that no one would ever know. If done on a large scale, it would be obvious after the fact that a leak had occurred, but the repercussions would likely destroy the currency.

<comment><USER18> Everything is open and transparent, so a "leak" is absolutely guaranteed. It appears the plan is to merely make some sort of change every 6 months such that an ASIC manufacturer hardly has time to develop a change, tape out, and actually mine for longer than a month or two. This should make it prohibitively expensive.

</comment></comment><comment><USER19> Couldn't you defeat ASICs pretty definitively (at least for a long time) by making the algorithm dependent upon a gigantic completely random substitution table? It would at the very least require ASICs to have giant amounts of ROM or RAM (with a memory controller etc) which would multiply ASIC cost.

AFIAK for today's ASICs a table larger than a few megabytes would be a strong deterrent. Go really big (e.g. 64mb) and you'd have some margin.

This would also be completely deadly to GPUs, especially if combined with branches and complex algorithms.

Edit: even worse: perturb the table using entropy from the block chain, making it impossible to burn into ROM and requiring tons of on-board RAM or a memory controller.

<comment><USER20> This is exactly what "memory-hard" hashing algorithms like scrypt, Argon2, and ballon hashing already do.

You can tune their parameters to use GBs of memory if you wish.

</comment></comment><comment><USER21> I don't get it.

A crypto-currency which will hard-fork twice yearly and all miners accept that hard-fork? If you trust changes not backed by computations but from central authority regularly, why don't you use the traditional bank backed by a nation-currency?

<comment><USER22> The little known secret of ALL cryptos, is that they are all fundamentally backed by a SOCIAL consensus, and not just a technical definition.

If everyone in the world just decides that a new fork of bitcoin IS bitcoin, then it really does become bitcoin.

This exact thing happened with Ethereum. A bunch of people in the community just decided to hardfork, and then it happened. And now the hard forked ethereum is the real ethereum.

Computing power doesn't stop your "real" version of the old currency from becoming worthless, and the "new" "forked" version of the same currency from becoming worth more money.

<comment><USER23> I agree this is super important and often missed.

People think the blockchain prevents bad things from happening, but it's the exact opposite:

The blockchain allows EVERYTHING.

It permits all possible universes with all possible transactions. I can go back to the first block and pretend Satoshi never mined more than the first one, and I have all the bitcoins.

That reality is just as valid as any other. This is the central idea of blockchain tech: you assume all realities will exist. Then you code ways for people to do business _despite_ inevitable lack of consensus.

Coincidentally this is exactly what young hippies miss about anarchism when they are trying to run consensus processes. They think anarchism is about obtaining consensus. But it's really the opposite: anarchism is about what happens when you DONT have consensus. If you have consensus it doesn't matter what political system you're in: monarchy, communism, libertarianism, it's all the same when there's consensus.

This all became clear to me after reading The Dispossessed (rip Ursula K Leguin). An anarchist syndicate is differentiated (from e.g. a co-op) by how people react when someone DISSENTS.

If you dissent from the co-op you have essentially no rights. Unless you have a voting quorum the others can use the police to stop you operating against policy.

In a syndicate, a dissenting member has the same rights as _the entire rest of the syndicate_. They have equal claim to the facilities, the resources, even the staff. They stand peer to peer with the syndicate itself.

Bitcoin is the same. A fork has all the same capabilities as the long chain.

</comment></comment></comment><comment><USER24> I am a bit confused by the reaction and commentary. ASICs are kind of the natural next step, so it is not surprising at all for any serious miner to go there. Hence, this appears as a too reactive really short term move and I wonder about the reasoning behind this.

The number of units to break even is not that high -- here is one random example, a google search away, that seems right to me -- from what I can recall -- [http://www.deepchip.com/downloads/fpga-vs- asic.pdf](http://www.deepchip.com/downloads/fpga-vs-asic.pdf). A reasonable volume or am I missing something?

Consider FPGAs as the efficient R&D step (due to its re-useability) towards an ASIC. ASIC will be generally faster and considerably more cost effective option per unit; plus more compact (so more units per space -- although there are other factors to account here). Usually you might end up with something less power hungry also, although I really doubt here one will see such a benefit.

Hence Monero is going against this step as a way to mitigate centralization and the introduction of more market entry barriers. I think this is a bit late. For instance, there is a great incentive for miners to join pools -- see a bitcoin analysis [http://www.jbonneau.com/doc/SBBR16-FC- mining_pool_reward_inc...](http://www.jbonneau.com/doc/SBBR16-FC- mining_pool_reward_incentive_compatibility.pdf)

</comment><comment><USER25> Ironic that the plan to keep mining decentralized requires centralization itself. Same thing happened with SIA earlier when another company beat SIA's founders to the market with an ASIC.

</comment><comment><USER26> How trustworthy are these people?

I have not heard anything bad about the Monero people, but given the number of bad actors in the cryptocurrency space they should probably describe what steps they are taking to ensure that nothing like what I'm about to describe can occur.

> Moving forward, developers will seek to protect the network's ASIC > resistance by slightly modifying its PoW algorithm at every scheduled hard > fork, which generally occurs twice annually. These changes will not be > noticeable to ordinary XMR users, but they will alter the network's hashing > algorithm enough that Cryptonight ASIC miners would become obsolete > following every fork

Suppose an insider is conspiring with one of the ASIC makers. The insider gets the modifications to the ASIC maker before they go public. The ASIC maker gets a head start on making new ASICs.

During the time between the modifications going public and other ASIC makers getting chips out, the conspiring maker does NOT sell their chips. They use them themselves, splitting the results of their mining with the insider.

<comment><USER18> There are no "insiders" with Monero. Just like many FOSS projects, everything is developed in the open and entirely transparently. The first time anyone has visibility on PoW changes is when someone comes up with specifics and opens a PR, and as can be seen on the current PR there's little chance that the first pass is the one that will go in.

</comment></comment><comment><USER27> What are these scheduled hard forks? Is it a property built into Monero's algorithm or is it a process centrally planned by the governance.

<comment><USER28> The developers have a release cycle that includes periodic hard forks. AFAIK there is nothing algorithmic to entice or force a fork like the Ethereum difficulty bomb does.

<comment><USER29> The scheduled hard fork is announced weeks in advance, and high-profile clients like exchanges are "strongly encouraged" to upgrade. If you run your own XMR full node on an old version past the fork date, you get a bright red warning message telling you to get the new version.

<comment><USER30> That sounds extremely centralized.

<comment><USER18> Anyone can be a Monero contributor and can work on the hard fork.

</comment></comment></comment></comment></comment><comment><USER31> Is it possible to synthesize cryptographic hashing function circuits randomly?

<comment><USER14> A cryptographic hash is a one-way function that produces repeatable not random output.

<comment><USER31> This is not what I meant :) my mistake, I will rephrase my question:

Is it theoretically possible for a computer program to synthesize new cryptographic hash functions which are significantly different one from the other (i.e. not just changing constants, or number of iterations)?

<comment><USER14> One would need to define what is meant by "significantly different" as well as the exact purpose of the hash functions. i.e. I suggest not to start with the implementation but with what is trying to be achieved. Putting the cart before the horse, so to speak, is a recipe for creating bad crypto.

</comment></comment></comment></comment><comment><USER32> It seems to me the only reliable way to favour general purpose computers over specifically manufactured hardware would be to change the algorithm periodically.

</comment><comment><USER33> I wonder how nicehash will handle the upcoming hard forks as there are other cryptocurrencies running on the original cryptonight algorithm.

</comment><comment><USER34> A variant are memory-hard PoW algorithms like Equihash. To calculate a hash you need a lot of memory which makes ASICs infeasible.

<comment><USER3> I disagree.

I've outlined my main points in a post on Reddit: [https://www.reddit.com/r/Monero/comments/7x82yp/technical_cr...](https://www.reddit.com/r/Monero/comments/7x82yp/technical_cryptonight_discussion_what_about/)

In short: a memory-hard PoW algorithm (such as Cryptonight) can be gamed by using exotic low-latency memory like RLDRAM3, QDR-IV, or Hybrid Memory Cube.

Its not as many orders-of-magnitude better than BTC, but it'd probably be on the order of 1x to 10x more power-efficient in the case of RLDRAM3. Although due to the costs of developing an ASIC and buying expensive RAM like RLDRAM3, it would likely cost a bit more than commodity hardware.

You'd make it up eventually in energy savings however.

The recent Hybrid Memory Cube RAM looks incredibly fast and power-efficient however. I wouldn't be surprised if HMC + FPGAs or ASICs completely destroys any "ASIC-resistance". Fortunately for the cryptocoin community, HMC is incredibly exotic, expensive, and rare at the moment.

<comment><USER10> It's interesting that you didn't consider internal SRAM; 2 MB of SRAM is less than 10 mm2 in a 10 nm process so an entire CryptoNight core might be ~12 mm2. This would be roughly twice the area efficiency of x86.

<comment><USER3> QDR-IV is SRAM.

I think buying QDR-IV and externally interfacing would be easier. But I haven't thought too much about this aspect of the problem.

In effect, QDR-IV already exists and is already mass produced. So if you wanted SRAM, there it is. Already to order from numerous suppliers. I don't see much reason why you'd want to design it into an ASIC when its a ready off- the-shelf part.

Overall, I think the optimal design would use RLDRAM3 anyway. RLDRAM3 is cheaper and uses less Watts than QDR-IV. The tRC latency issue on RLDRAM3 doesn't seem to affect the expected workload of a memory-heavy Cryptocoin, so QDR-IV (aka SRAM) wouldn't have much of an advantage over RLDRAM3 anyway.

</comment></comment></comment></comment><comment><USER35> This is awful. I would like to be able to buy a graphics card for it's intended use (gaming) at a reasonable price.

</comment><comment><USER36> So not a war on TSMC etc like I thought this article was about. But a war on Bitcoin ASIC designers.

</comment><comment><USER37> Trying to defend the mining from ASICs makes somewhat sense keeping in mind recent news. Monero could really gain momentum (more, than they have by now) by becoming the alternative to online advertising by being the coin to be mined in Browsers around the world.

Salon.com already tried it, and if it works out for them, it could become a pretty great alternative to ads. It would also change the dynamics of online content, as page-reloads would mean a dip in hashing power.

<comment><USER38> [http://coinhive.com/](http://coinhive.com/) also offers this but users do not seem to like it. They offer two miners, one which only operates with users consent and another one without explicit consent. Either one seem to have been banned by my ad blocker, although the one which requires consent has a not which clearly states that it should not be in any filter because they user wanted to run the script and it might break the website.

They even have a pretty cool usecase for it too: to have x amount of hashes as a captcha replacement, with a nice UI and such (you can find it by click on login at their page & disabling your ad blocker). Unfortunately, it does not work really well for many of my use cases. When I'm on my phone it is fairly slow. When I'm on an old phone (iPhone 5 and 5s, for instance), it is so slow that many users would just close the page because they believe it is broken but it probably is a decent defense against brute force attacks because it still requires quite a bit of work to try a login.

<comment><USER39> The biggest problem I see with Coinhive is that they've systematically failed to prevent malicious uses of their service. The Coinhive script is frequently injected into compromised web sites, or into _all_ web sites by locally installed malware, and Coinhive's payout scheme -- fully automated payouts every two hours -- makes it very easy for a malicious user to "get away" with their profits.

(Coinhive claim to disable accounts associated with abuse, but even if they do that promptly, the lack of any meaningful holding period means that abuse can still be quite profitable.)</comment></comment></comment>


Deep learning algorithm diagnoses skin cancer as well as seasoned dermatologists - <AUTHOR> http://news.stanford.edu/2017/01/25/artificial-intelligence-used-identify-skin-cancer/ <comment><USER4> As MDs, I think it is very clear that all of us who understand even the slightest about computers and tech see that machine learning is the way to go. Medicine is ideally suited to ML, and in time, it will absolutely shine in that domain.

Now for people eagerly awaiting the MDs downfall, I think you are precipitating things a bit. We all tend to believe in what we do, and I concur in saying that expert systems will replace doctor judgement in well-defined, selected applications in the decade to come. But thinking that the whole profession will be impacted as hard as factory workers, with lower wages and supervision-only roles, is not realistic. What will be lacking is the automation of data collection, because you seem to underestimate by far the technical, legal, and ethical difficulties in getting the appropriate feedback to make ML appliances efficient. I firmly believe in reinforcement learning, and as long as the feedback system will be insufficient, doctors will prevail, highly-paid jerks or not.

I myself am an anesthesiologist, a profession most people think of as a perfect use case for those techs (as I do), and wonder why we haven't been replaced already. The reality is that the job is currently far beyond what an isolated system could do. We already have trouble in making cars follow the right lane in non-standard settings. I hope people realize that in each and every medical field, the number and complexity of factors to control is far greater than driving in the right lane.

People who drive the medical system have no sense of technology. They cannot even envision the requirements for machines to become efficient in medicine. That is why we are seeing quite a lot of efficient isolated systems pop up, but we won't be seeing fully integrated, doctor-replacement systems for a long time. This will require a new generation of clinical practitioners, who will understand how to make the field truly available to machine efficiency.

<comment><USER5> My issue with doctors:

Recently, my dad was sick with a pretty bad cough. Like, so bad that he couldn't speak without coughing. He fainted twice from minute long coughing fits, one of those times hitting his head his head on the stove on the way down, leaving a deep cut and blood everywhere.

He went to at least three different doctors. He got a scan of his chest. Everything looked clear, and all of the doctors were stumped. Things were pretty bad.

I mentioned this to a UCSF resident friend, and her immediate response was "Oh, is he on <some blood pressure medication I forget the name of>?" I was like, uh, let me see. Called my mom, she checked, and, lo and behold, he was on it. So his doctors took him off it and within a week he was better.

This coughing wasn't some obscure side effect of the medication she knew through sheer brilliance: it's a side effect that's been widely known since the 1970's. Hell, it was on the drug's Wikipedia page.

So there's a couple morals you could take from this. One would be, wow, doctors are smart to be able to diagnose an issue based on a single symptom and some reasonable assumptions about a patient's background! The other is that the median doctor is pretty worthless; spending tens of thousands of dollars gives you no guarantee you'll see someone competent; and that a medical system that relies on you grabbing drinks with a UCSF resident to get good results is fundamentally broken.

Machine learning and expert systems don't have to be as awesome as the best doctors to be valuable. They don't need to be better than competent doctors, even. They just need to provide a bare level of competence to provide a huge amount of value.

<comment><USER6> I just want to add something really important onto this.

ALWAYS READ EVERYTHING YOU CAN ABOUT DRUGS YOU ARE PRESCRIBED!

Sorry for all caps, but it is super important. Not that your dad is in the wrong, lots of people have justified (to a degree) trust in their doctors. However doctors are people and just by that alone aren't perfect.

A few years ago my doctor prescribed me an antibiotic for an ongoing illness I had. I read the entire pamphlet for it and did some reading online about it. All before taking it. Turns out it can cause seizures if it interacts with polypropylene glycol, one of the main ingredients in e-cig juice, which I use daily. I had told my doctor I use an e-cig.

Really I cannot stress how important it is to be knowledgeable about the drugs you are taking.

<comment><USER7> >ALWAYS READ EVERYTHING YOU CAN ABOUT DRUGS YOU ARE PRESCRIBED!

I'm not trying to undermine your point entirely, but there is a flip side.

I can't tell you how many times I have seen a patient start a medication, then come back to the office within 48 hours because they coincidentally have every side effect that is listed in the pharmacy's information sheet or that they looked up online. The vast majority of these side effects are benign, present with next to no pertinent physical exam findings, and can't be definitively tied to the new med (like upset stomach, fatigue, headache, etc.).

 _Then_ they will start listing that medication as one of their "allergies", and if the nurse/doctor documenting doesn't dutifully probe what type of "allergic reaction" they had, they may end up not being prescribed that med in the future when it really is the drug of choice. A little nausea is a small price to pay if it kills a potentially life-threatening infection.

Also, I'm skeptical about the seizure risk. The thing about side effects is that they are supposed to be stratified according to risk. Doctors are typically aware of these risks, but patients aren't. So if your drug is listed as causing "headache, nausea, and seizures", there may have only been one patient out of millions who had a seizure while 50% experienced headache, yet the handout probably won't tell you that.

But even if it is a notable risk, I would be surprised if the propylene glycol you inhale from an e-cig could accumulate to a high enough level in the bloodstream to cause drug interactions, although I admit adequate research on the subject is lacking.

My advice would be trust your doctor first. If you don't trust your doctor, start seeing a doctor that you do trust. Then if you have a significant adverse reaction to a medication, _talk to your doctor about it_. Quite often they know something that you are not going to find by spending a few minutes on the internet.

As a side note, a good history includes asking about many habits. A lot of healthcare providers are guilty of simply asking "Do you smoke, drink, or use drugs?", but ideally the smoking aspect should be phrased as "Do you use any tobacco or nicotine products?". Patients usually won't read your mind and volunteer that kind of information. They will tend to give yes/no answers, so direct and specific questions are important.

<comment><USER8> _> yet the handout probably won't tell you that._

That seems like a major problem. Is there any reason that more detailed information can't be included? Mathematical literacy may be a problem, but that doesn't mean that there aren't millions upon millions of mathematically and scientifically literate consumers who could use this information effectively.

<comment><USER7> You would probably have to ask a pharmacist, but from the clinical side I can tell you most _prepared_ health information that we can give to patients has to be very bare bones and comprehensible to essentially everyone at or above an 8th grade education level- I assume because it is considered too resource intensive by the publishers to produce multiple versions of the same information, and people with higher education typically have the initiative/means to ask their doctor the right questions or research the information themselves.

I'm not trying to justify any of this, but that's how it is.

Not sure if it will be helpful in the future, but I can tell you that descriptors used with side effects follow a standard convention:

        very common: > 10%
        common: 1%-10%
        uncommon: 0.1% - 1%
        rare: 0.01% - 0.1%
        very rare: < 0.01%

But you will probably never know the exact origin of these figures (like how many patients were studied, what populations were included, how tightly the study was controlled, whether adverse effects were self-reported, etc.) without doing some intense searching. And even if you did, I doubt it would have a significant impact on your healthcare. I don't want to go on a tangent about the nuances of pharmacology in clinical medicine, so I'll just circle back to my point that you should trust your doctor, or else find a new one that you do trust.

</comment></comment></comment></comment></comment></comment><comment><USER9> This is the second major study applying deep learning to medicine, after Google Brain's paper in JAMA in December, and there are several more in the pipeline.

If you've developed expertise in deep learning and want to apply your skills to healthcare in a startup... please email me: email@example.com. My co- founder and I are ex-Google machine learning engineers, and we've published work at a NIPS workshop showing you can detect abnormal heart rhythms, high blood pressure, and even diabetes from wearable data alone. We're working on medical journal publications now based on an N=10,000 study with UCSF Cardiology.

Your skills can really make a difference in people's lives. The time is now.

<comment><USER10> Before neural networks got deep, there was a lot of very impressive work applying neural networks to medicine. Example:

[http://suzukilab.uchicago.edu/research.htm](http://suzukilab.uchicago.edu/research.htm)

IIRC they were outperforming the average radiologist on some tasks 10 years ago.

<comment><USER11> Why haven't they replaced the average radiologist yet then?

<comment><USER12> Healthcare plans do not reimburse machines yet.

<comment><USER13> There may be incentive soon enough, via ACOs and bundled payment programs. When dollars saved go to the bottom line, folks start trying to save dollars.

<comment><USER14> If we could have a preliminary diagnosis quickly from a machine and only then and if necessary talk to a specialist doctor, the savings in costs could be outstanding. Talk about Affordable Care. Maybe an act of Congress could move this along.

<comment><USER15> It all goes well until the machines terminate Ms. Butler's pregnancy.

</comment></comment></comment></comment></comment></comment></comment><comment><USER16> Honestly, I can't wait for deep learning and computational methods to dethrone doctors and upend the medical profession. In the next five years, expect a computer to be able to predict most diseases a lot better than doctors can -- and with none of the attitude, high cost, or inconvenience.

Mind you I'm not talking about researchers, who will always have a job. I'm talking about practitioners. I've had a medical condition from birth and I've had to deal with my share of doctors. Outside of the insurance system, they are easily the most unpleasant part of the whole ordeal to deal with. There are some gems, but most you will encounter are pompous, arrogant, and "commanding" \-- when they enter a room, they are flanked by "residents", "assistants" and generally give off this air of superiority which is really just because of their route experience. The whole thing comes off more as a performance than anything else. Worse, they often get mad when you question them or ask them to explain themselves, or how they arrived at a conclusion.

Good luck finding work when an algorithm can do your job better than you. It's only a matter of time.

<comment><USER17> I feel the exact opposite - in my treatment for prostate cancer the human interaction with doctors were a hugely positive experience for me. Interpretation of biopsies and inspection of cancer images were part of that process and I'm sure machine vision algorithms could help in this area. However, even if the classification of the cancer cells improves, the role of the doctor guiding the patient through the right treatment process still remains something I would not want to turn over to an algorithm.

I have also encountered doctors I did not like but fortunately for me I had a choice where to go. Maybe machine learning should focus on weeding out unpopular practitioners instead.

<comment><USER18> I think medicine will over time morph from a field generally perceived as an intellectual one, to a largely humanistic one, like nursing. Most people, especially the HN crowd, vastly underestimate the importance of the human touch. A doctor who possesses this can make a huge difference, but sadly the are outnumbered by those who do not.

<comment><USER19> Medicine isn't really that intellectual. It's mostly based on vast amounts of acquired knowledge via rote memorization and repetitive experiences. It's not like physics or math that requires actual creativity and intellectual vigor. (I'm a 3rd yr med student)

<comment><USER20> Especially as we become evidence based vs. eminence based.

</comment></comment></comment></comment></comment><comment><USER21> Systems that outperform doctors in some specific area of diagnostics aren't new. One of the earliest examples of such systems is Mycin [1], which also was developed at Stanford, but around forty-something years ago. Never went to production because of practical issues that have nothing to do with its accuracy. It's interesting that all of those "practical issues" are no longer relevant, and yet we don't see a widespread use of similar software.

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

<comment><USER22> I hope someday soon we'll develop systems that allow us to "ask" a ML algorithm what factors led to a decision (diagnosis in this case).

It would be interesting to compare that with the current state of the art in the field, and see if ML can contribute new scientific/medical theory as well.

<comment><USER23> I'm not so sure we could ask the algorithm itself, in any literal sense. An algorithm trained to introspect might actually be _wrong_ about its own "memories" or "motives"—just like a human might! (Though, likely, without the penchant toward political rationalizing.)

This is most simply because, whatever the algorithm is trained to do, it's certainly trained better to _do that thing_ than to introspect. Introspection is a separate skill!

But there's also a more insidious element: introspection (in humans, at least) tends to result in the creation of a lot of "personal concepts" that don't map to well-known common concepts. An introspection on _one mind_ must necessarily result in a taxonomy that contains terms for the tiny, unique features that only that mind has—which makes it very, very hard to communicate one's personal introspections to others. (You might call this a kind of _overfitting_ : the introspection capability becomes optimized for that one mind, but ceases to translate well to features in other minds—like human minds.)

I'd place a much stronger bet on our ability to train one AI to "stare at the brain" of other AIs as they make decisions [tons of them, as its training data], with the expected output being a general theory on common AI features responsible for the given calculation step. A computer psychologist, of sorts. :)

Of course, you could _include_ such a pre-trained model as a "module" alongside the AI itself, and call the combined system "one AI" if you like.

<comment><USER22> > Introspection is a separate skill!

Indeed it is. It's something that a second algorithm (perhaps ML, perhaps not) would do.

And this is beginning to remind me of Society of Mind.

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

</comment></comment></comment></comment><comment><USER24> This reminds me of a talk that I saw about wavelet based algorithms in the 1990s for detecting tumors in mammograms.

The algorithms found most of the tumors that humans had missed, with similar false positive rates. BUT humans refused to work with the software!

The problem was that the software was very, very good at catching tumors in the easy to read areas of the breast, and had lots of false positives in more complicated areas. Humans spent most of their effort on the more complicated areas. Every tumor that the software found that the human didn't simply felt like the human hadn't paid attention - it was obvious once you looked at it. The mistakes felt like stupid typos do to a programmer. But the software constantly screwed up where you needed skill. The result is that humans learned quickly to not trust the software.

<comment><USER25> This is very true and directly related to my research. (I work at a company developing software to interpret EEG data.) There's a huge difference between an algorithm with a low error rate that makes mistakes seemingly at random vs. an algorithm with a somewhat higher error rate whose mistakes are at least comprehensible. A doctor is much more likely to trust the latter than the former. Almost as important as developing a detector with a low false positive rate is developing a detector that can figure out when the problem is too hard so it knows not to even try. (And it seems that this problem is just has hard.)

One of the things we do is perform a Turing test of sorts where we test if the performance of our detector is statistically indistinguishable from a human. (In fact, we actually have a contest running right now where we give you 10 EEG records, some marked by humans, some marked by our software, and if you can figure out which were marked by which we'll donate $1000 to the American Epilepsy Society.)

</comment></comment><comment><USER18> Unfortunately the paper is in Nature, paywalled, instead of Arxiv, and data/code/model/weights inaccessible. While publishing in Science/Nature/NEJM/JAMA is definitely the right approach for deep learning to gain validity in the medical community, faster progress could be gained by having a more open platform, with constant and real-time validation with more data, more medical centers and clinics. The reason progress in DL has been so breathtaking is in no small part due to the culture of openness and sharing.

<comment><USER26> sci-hub.cc is your friend.

<comment><USER27> That's true - but the principle still matters and scihub may not be around forever.

</comment></comment></comment><comment><USER28> This is interesting and impressive work, however, I noticed that they compared the algorithm's performance to dermatologists _looking at a photo_ of a skin lesion. This seems like a straw man comparison because any dermatologist would presumably be looking directly at a patient and would benefit from a 3D view, touch, pain reception etc. I realize that this was the only feasible way to conduct this study, but still suggests that an algorithm looking at a photo cannot match the performance of a dermatologist looking directly at a patient.

<comment><USER29> Respectfully disagree. Telemedicine is going to be an important aspect of medicine, Dermatology in particular.

Rural and underdeveloped areas are going to be the largest market IMO. Everyone can access a smartphone but not everyone has the luxury of seeing a Doctor in person, and if they do the time/travel costs can be significant.

Disclosure, I work for an EHR startup with a Telemedicine product.

</comment></comment><comment><USER30> Eric Topol puts this up there as the most impressive AI/medicine publication to date. [https://twitter.com/EricTopol/status/824318469873111040](https://twitter.com/EricTopol/status/824318469873111040)

The paper ends with "deep learning is agnostic to the type of image data used and could be adapted to other specialties, including ophthalmology, otolaryngology, radiology and pathology."

</comment><comment><USER31> As someone with two melanomas under my belt (and more than a 1000 moles) what I really want is the ability to do a mass scan of my body also further down at the cellular level not just looking at the moles on the surface.

I am lucky enough to have Sloan Memorial as my hospital and no other than Dr. Marghoob one of the leading experts and I actually have a scan of my body made with 50 or so High Definition Cameras (I am litterally a 3d model in blue speedos and with a white net on my head).

They have a new system where they can look at the cell level without doing a biopsy and actually found my melanoma before they did the biopsy (i.e. they knew it was melanoma before they did biopsy) but it's really a cumbersome process and I had 6 experts studying and working to position that laser properly.

So the real challenge today is how do we get the data into the system.

</comment><comment><USER32> This is why we need a platform for these models asap. I would totally download this app today and use it regardless of what the FDA thinks.

<comment><USER33> Are you sure about that? Playing devil's advocate here, we have plenty of examples of scientists jumping the gun without being peer reviewed or going through a rigorous follow up testing progress, _especially_ when it comes to medicine. The alzheimer's 60FPS blinking light example is pretty good - <USER2> scientists got it working on mice, but we don't know any potential side effects it could have on humans. Maybe none and that'd be great! Maybe it causes schizophrenia, who knows? We have no way of knowing yet! Just very _very_ educated guesses.

I say when it comes to medicine, err on the side of caution. Obviously a diagnosis app isn't too dangerous - worse case scenario the app gives you a positive diagnosis, so you go into the doctor's, they take a sample, and find the growth to not be cancerous. No harm, no foul. But other ideas could be more dangerous.

<comment><USER34> Worst case scenario would be giving a false negative, wouldn't it? And that's the danger, not a false positive (though false positives may lead to higher health costs due to increased doctor visits).

<comment><USER35> Removing moles isn't risk free. It's minor surgery but it's still surgery, and any surgery carries risk of infection. With increasing prevalence of antibiotic resistance this is a serious concern.

</comment></comment></comment></comment><comment><USER36> This is not hard to imagine at all. I know that there must be some absolutely excellent doctors out there, but I don't trust the bottom 80% of doctors much at all, and honestly would rather have an algorithm most of the time, especially starting off. The lack of robust consumer level 'medical doctor apps' is one of the biggest mysteries to me.

</comment><comment><USER37> There's an app used by over a million doctors called "Figure 1" that allows them to share medical images for crowdsourced diagnosis and treatment of rare cases.

I wonder when we will get to a point where machine learning can help there?

[1][https://figure1.com/medical-cases](https://figure1.com/medical-cases)

</comment><comment><USER38> I read the headline and wondered how ml could train the difference between a new dermatologist and a seasoned one. Cancer I get, looks totally different than non-cancerous skin :)

That said, pulling this is one of the best ML applications to date. Recognizing cats or scenery doesn't seem nearly as useful

</comment><comment><USER39> Great results! Deep learning has been gaining track in other medicine areas as well.

One such task is lung cancer nodule detection from CT scans. A paper I recently co-authored applied many different architectures to this detection and achieved very good results. ([https://arxiv.org/pdf/1612.08012.pdf](https://arxiv.org/pdf/1612.08012.pdf))

The best combination of systems detected cancer nodules which were not even found by four experienced thoracic radiologists.

<comment><USER40> Do you participate in the current Data Science Bowl regarding CT lung cancer detection [0]? The prize pool of $1,000,000 seems quite attractive, especially if you recently developed new state-of-the-art CT lung cancer detection ML models. The only somewhat strange aspect of this competition (at least to me) is to not include locality annotations. They only provide labels of cancer/no cancer per patient...

[0] [https://www.kaggle.com/c/data-science- bowl-2017](https://www.kaggle.com/c/data-science-bowl-2017)

</comment></comment><comment><USER41> Dermatologist here. Most skin cancer diagnosis is relatively straightforward and if the lesion is suspicious will require a biopsy to establish the subtype of the cancer and plan further treatment. There is no reason why this initial visual diagnosis cannot be performed at the same level as a dermatologist by a machine or indeed by a non-doctor trained intensively for a relative short period to interpret photographs.

The difficulty is two-fold. Firstly liability - a dermatologist aims not to miss a single case of melanoma in the tens of thousands of patients seen over their career, if this algorithm is used widely in millions of patients then either the sensitivity will have to be higher and more biopsies performed or there will have to be an acceptable rate of missed diagnosis for melanoma.

Secondly, in edge cases such as moles that are slightly atypical. In these scenarios there is no way that I would be comfortable making an assessment from a photograph. Now of course, a machine could also gather further information via methods such as in vivo confocal microscopy but in this case the cost savings are likely to be negligible.

</comment><comment><USER42> Can someone clarify for me how the training and testing sets were constructed? One problem is that cancerous and benign skin are unbalanced in a representative population. How was this imbalance handled in testing? How was the testing set constructed? And so on.

<comment><USER43> For each of the 3 tests the training sets were classified with a biopsy, images were randomly seleced then blurry images were filtered out by a separate dermatologist. The ratios Benign:Malignant were 70:65, 97:33, and 40:71 respectively.

These close-to-even ratios make for a more powerful test of classification. I would assume that these test samples have biopsy data means that some dermatologist thought that they might be malignant (unnecessary medical operations are unethical). This might lead to some bias towards samples that are difficult for humans to diagnose.

Separating these into binary classifications of specific tumor types makes it easier to classify than out of every possible tumor type (as a dermatologist does).

Still the claims this paper makes are very promising. A lot of the training data was classified by dermatologists, not biopsy. Using more biopsy data could lead to even better classification, as well as improvements to the model.

</comment></comment><comment><USER10> One major, major advantage that medical imaging has for deep learning is the similarity of each data point, especially the 'background data.' For instance, human brains typically look very similar across individuals (up to scanning parameter differences), except in the abnormalities - which are often precisely what you want to highlight.

As an example, I recently trained a neural neural network to perform a useful task for our lab using 3 (!) hand-labeled brains.

<comment><USER44> It's insane that you were able to get reasonable results with such a tiny dataset.

I am learning machine learning right now and I find working with datasets with fewer than 100 examples to be quite difficult.

It seems counter intuitive when you first think about it but having way more data actually makes the task of fitting the model much easier as there is granularity that can be used to get feedback on adjustments to the structure of the model.

<comment><USER10> It was an image segmentation task, and the features were similar across data sets. The other thing that made it work well was heavy use of data augmentation that captured ways in which different data points could reasonably differ.

There was a really cool medical imaging paper recently that literally just labeled several 2D _slices_ in a 3D dataset consisting of 3 images and performed a reasonable segmentation:

[https://arxiv.org/abs/1606.06650](https://arxiv.org/abs/1606.06650)

</comment></comment></comment><comment><USER45> Diagnosis based on image recognition is something machines are already very good at, even without recent deep learning techniques (although I am sure they will help).

For instance in college I worked with a radiologist to write an image- recognition program to identify osteoporosis from 3D MRI data. We used some super-basic image segmentation algorithms to identify the bounds of the bone layer that we cared about. From there a model was able to determine mechanical properties of the bone and therefore make an assessment with much more granularity than the human eye.

This was a first-year grad student class and I was coming at this totally naive with some Matlab scripts, and we managed to get usable results in weeks.

Here's a sample of that professor's research: [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2926228/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2926228/)

While I am not in the camp of "machines will replace doctors", I think radiology and other similar fields are in for a sea-change in technique and a large reduction in the use of human judgement.

</comment><comment><USER46> Coming from a family of people in the medical professions, they've all seen reports of how _everything_ is going change in their fields because some new computer program can do X...

To which my father usually mutters something like: "Why fuck are they wasting their time with that? Can't they fix the fucking medical billing system instead?"

Most of the medical professionals I know echo similar sentiments.

<comment><USER47> Meh, seems majority of older folks say the same BS.

</comment></comment><comment><USER48> Telemedicine has a lot of regulatory hurdles to get to market, but initiatives like this are extremely exciting, since they can likely be taken to market in a way that explicitly clarifies that it's not a diagnostic, it's simply a low barrier way to actually get that mole you've got looked at. If you don't have health insurance, you could actually get an idea of how critical it is to get in to see a doctor. That said, the obvious concern would be the extreme cost of a false negative (although the evidence suggests that the algorithm is no more likely to provide one than a doctor, the concern over single accidents caused by self-driving cars, even when the overall rates are far lower makes it pretty clear that the bar for success to the public for non-humans is substantially higher than it is for humans)

<comment><USER49> > That said, the obvious concern would be the extreme cost of a false negative

Probably not. People won't go to doctor unless they sense something wrong with their body. So it is actually filling the void here.

On the other hand, false positives will cause a bigger problem, because swarm of people will get triggered by the fear of cancer, and hospital might not handle the sudden surge of traffic for treatment.

<comment><USER48> I think I agree with you from a policy perspective. However, the cost of a false negative has major PR implications (just wait for the first "I tried this algorithm and it misdiagnosed what turned out to be cancer") story. While I totally agree that those stories would be entirely unfair when looking at rates, that's not how it would play out in media response (and unfortunately, regulatory response).

</comment></comment></comment><comment><USER50> In my opinion the way to stage these technologies is not to blitz toward a fully cyborg doctor replacement, but to bolster the capabilities of the doctor with new technology - similar to how calculators did not replace mathematicians (despite historical headlines suggesting this would happen).

Giving a doctor the ability to get a "second opinion" fast and cheaply to a patient is a large boon to medicine, and shouldn't be underestimated. It allows the doctor to deal with all the nuance limited automated tools can not, and gives the MD the ability to check themselves against the computer. If the MD finds themselves disagreeing on something like a skin condition, the feedback can both improve the doctor's service and provide bug information for the code and databases used to train the AI.

</comment><comment><USER51> I wouldn't be surprised at tasks that involve image recognition - <USER3> include dermatology (visual inspection) and pathology. In fact, I wouldn't be surprised if CNN's were better at pathology as every time I looked at microscope slides, there is so much "visual clutter" in a typical tissue specimen that I'm sure I was missing a ton of information on the slide.

</comment><comment><USER52> This is going to be part of a greater trend of automation starting to affect fields considered to be white collar and paths to prosperity. I think the same is going to happen with financial analysts, entry-level lawyers etc. It'll be interesting to see the political response, especially given how charged the atmosphere has become around "preserving" jobs.

</comment><comment><USER53> A significant finding, to be sure. But like the paper itself says:

 _Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs._

What they achieved was algorithm to _classify skin lesions_ \- not perform a "diagnosis" of the overarching pathology, i.e. skin cancer.

</comment><comment><USER54> Skin conditions are one of the few modalities where ML make deep sense as a diagnostic.

I think pharmacovigilance is the other area based on my interaction with the industry of folks at pharma and healthcare provider companies who work in ML.

Disclaimer: i run mlweekly.com and help at semantic.md

</comment><comment><USER55> What about 3D aspects? The word "bump" is used in most descriptions I've seen online, although I don't know if that is something the doctors consider or just something that's enough to suggest a visit to the doctor.

</comment><comment><USER56> These new methods appear to best suited to be used in the pet world sooner as the ethical and legal issues will be a bit less stringent than in a human context. May be that is where things will start to change.

</comment><comment><USER57> My (old) dermatologist could spot skin cancer from across the room. I asked him how he could do that, he said he's seen a million of them. It's the same idea as "deep learning".

</comment><comment><USER58> I truly believe with smart algorithms and big data we can change the way we live. Smart medicine, proper diagnoses and early detection of disease we can improve our lives

</comment><comment><USER59> On a related note, does anyone know how IBM's Watson health is doing? They've been developing it for years but I haven't heard much about their results.

</comment><comment><USER60> Even though diagnosis is only one piece of the puzzle, what I would hope is that this becomes part of the answer to the high cost of healthcare.

</comment><comment><USER61> so, basically while i'm taking shower the HAL ... err ... Google Home cameras in the shower would check for moles development, blood O2 from the color of skin, vascular health from the reaction to the water temperature, pulse from visible pulsations, mental and other conditions from the eyes movements, etc...

</comment><comment><USER62> Only as well? Not faster and cheaper?

</comment><comment><USER63> Basic income can't come soon enough.

<comment><USER64> Basic income is a pipe dream. We need a new economy.

<comment><USER63> You need an economy resilient to rapid economy changes.

</comment></comment></comment><comment><USER65> Quick, someone tell me why doctors won't be obsolete in 20 years!

Geoffrey Hinton believe that we should stop training radiologists _now:_

[https://twitter.com/withfries2/status/791720748624797697?lan...](https://twitter.com/withfries2/status/791720748624797697?lang=en)

<comment><USER66> That is the kind of prediction that people will look back on and say "I can't believe the hubris." MYCIN had better diagnostic performance than infectious disease experts by 1979 ([https://jamanetwork.com/journals/jama/article- abstract/36660...](https://jamanetwork.com/journals/jama/article- abstract/366606)) and in the 1980s the question was "How soon will expert systems replace human doctors?" Putting a number like "5 years!" is asking for disappointment.</comment></comment>


Ask YC: Should we launch our startup at a conference? - <AUTHOR>

Is it worth doing a splashy launch at a conference? We're going into private alpha this week and thought we could launch it to a wider audience. Was thinking about the NextWeb conference in Amsterdam, but paying money to present seems lame.<p>What do people think about 'event' launches... Should we just launch quietly and let the product do the talking? <comment><USER2> Speaking from my own experiences with my own little startup. I have thought about doing something like this, but it relates to even when I am talking about it at networking events.

I would say you should do it if you can afford it and if your site has instant gratification/wow factor. I think peoples attention spans are limited in conference type settings, with many things going on at once it hard to take it all in, if you can wow them quickly I'd say it be worth it. My site is more community-based (user-generated blah blah), so the instant gratification factor is sort of low.

just my thoughts :)

<comment><AUTHOR> I think we have enough instant gratification elements to capture attention spans, but its the money aspect that bothers me. It goes against all our bootstrapping principles, but at the same time, I understand that distribution is key. Torn...</comment></comment>


Amazon Engineer Lives on a Boat and Works from Hawaii - <AUTHOR> http://www.businessinsider.com/amazon-engineer-lives-on-a-boat-and-works-from-hawaii-2013-2 <comment><USER2> > _He 's responsible for keeping a giant portion of the Internet up and > running. Sites like Netflix, Pinterest, Reddit and Airbnb all rely on > Amazon's web services._

He... uh... doesn't seem to be doing that great a job. AWS has some of the longest and highest-profile outages of any services that I've ever used. Maybe he should... not be on a boat in Hawaii??</comment>


Ask HN: How to best host and publish art and writings online, and protect them? - <AUTHOR> I want to help my father publish a short story, a play, and some chapters of his novel (if not the whole thing).
My aims:
1) My initial idea is to build a simple blog and host the writings as html pages. But I want to make sure in advance, should he decide to publish this same work in another medium in the future, is a certain Creative Commons license or copyright required to protect the work somehow? In the world of paper manuscripts, a handy and cheap way to copyright your work was to post the work in a self-addressed envelope - the postal service sort of notarizes it with their postmark along with the date- and not open it.
2) Curious about self-publishing. Is this what people are using Patreon for nowadays? I'm a bit too hesitant to put this in Amazon's self-publishing domain, as it stands. Does anyone have experience with this?
3) I also want to host photos of his paintings online, but I want to protect them somehow. I have not put them on Instagram or anything like that, because in my view, then Facebook owns the content. Another thought is Photoshopping in watermarks on the photos, but is that still useful somehow? I just don't want to do something wrong, and find out 10 years later some studio is selling prints of these without crediting the artist, and this is not my domain, which is why I'm seeking your help. <comment><USER2> > _is a certain Creative Commons license or copyright required to protect the > work somehow?_

Copyright is automatic. You don't need a Creative Commons license. I couldn't find a good link, but at least take a look at [https://en.wikipedia.org/wiki/Copyright_notice](https://en.wikipedia.org/wiki/Copyright_notice)</comment>


Ask HN: How much time did you invest in your startup until ramen profitable? - <AUTHOR>

Time.at(ramen_profitable) - Time.at(first_code_line) = ? <comment><USER3> About a week. Back in January 2004 I bought a domain, a design template and set up a copy of the free osCommerce shopping cart. I created packages of advertising services, put them in the store, then started posting on webmaster forums with advertising sections, and participating in the forums with the site in my signature. Total time from idea to working website was two days.

I got 5 orders the first day, a couple the next, a few more the next day, then I started advertising on search engines. Sales exploded. Over $100,000 by the end of the year... which is a lot of $10 sales!

I was simply buying up different types of ad space at wholesale rates from various networks/services, then packaging it in smaller chunks for individual site owners in the $10-40 range. The site paid for itself in its first week. A few years later and net profit, rather than revenue, was past six figures as well.

I'm not so much in that business anymore, but it was my first entrepreneurial endeavor beyond slapping ads on articles I'd written, so I was proud of that success. I later replicated the story several times with products that were income-you-could-live-on-full-time from the day the websites went live.

I really need to do it again, too. I've since sold off those businesses to pay for two college degrees, then for a new car and downpayment on a home... while my current freemium+premium services bring in enough to cover the mortgage, I need to get back into product building to get some money in the bank again.

<comment><USER4> Almost exactly the same experience here.

First month = $495 in expenses. $20 for my first month webhosting. $99 for Dreamweaver (I was still hand-coding HTML). $375 for that damn Verisign SSL certificate.

First month I made $300, so was a little in the red. Second month I made $700 and was profitable ever since.

First 10 musicians were just people I knew. After that were friends-of-friends since the first 10 were so happy with my service. Then some people started mentioning it in their newsletters, and more strangers kept signing up.

That whole first year, the site did almost nothing. I didn't even know PHP+MySQL at the time, so it was just a hand-coded HTML site that let you put a CD in a shopping cart, and enter your payment info. Whatever you entered was emailed to me, and I'd copy it by hand into my merchant account to charge their card, then copy it by hand into a label printer to mail their order, then copy it by hand into Eudora to email them a thank-you email.

Though this was 1998, yes I think the same approach applies today.

<comment><USER5> So CD Baby initially sent users' CC information via email...

<comment><USER3> Keep in mind none of the 'secure vault' type services of today existed in 1998, and PCIDSS didn't exist until 2004.

<comment><USER5> Fair enough. I was only 11 in 1998 so I don't have any idea what the fraud/security situation was like (and should have thought about that before posting criticism). I'm sure CD Baby made the transition to more secure methods as soon as it was more apparent that sending CC information via email was a bad idea.

I was just surprised to see that, since it's such a well known Bad Thing now.

</comment></comment></comment></comment></comment><comment><USER6> Depends on what level of ramenability you're setting your bar at. Here are some of my milestones for S3stat:

6 months: Regularly covering server expenses (which were ~$50-100/month at that point).

18 months: Would have paid for me to live nicely on the beach in Thailand.

30 months: Would cover my rent (and nothing else) at a nice apartment in a major city.

40 months: Passed the monthly take-home pay from my first job out of college.

That's for a SaaS subscription product that I built with the explicit goal of having a low-level income stream that I didn't have to put much time into. Early on, there were periods where I worked 80 hours or more per month to get the infrastructure ticking away to my satisfaction. These days, it requires maybe six hours of my attention each month.

So I guess the answer to your question is: longer than you think. But once you're there, it's pretty nice.

<comment><USER7> So if this is your secondary stream, what do you do for a primary income stream?

<comment><USER6> Consulting. In the short term, it blows the doors off of any entrepreneurial thing you could do.

The only problem with consulting, though, is that you need to keep doing it to keep getting paid. As such, the key seems to be to consult in short bursts and use the runway to bootstrap product stuff.

Seems to be working thus far.

</comment></comment></comment><comment><USER8> All bootstrapped:

1 year for ramen profitable for 3 founders;

3 years for full-time job for 3 founders;

4 1/2 years for hiring 1st and 2nd employee;

5 years for hiring 3rd employee;

5 1/2 years for hiring 4th, 5th, and 6th employee (present day).

Re: <http://carbonmade.com>

<comment><USER9> Just wanted to say that's a nice website. very simplistic. i assume you will let us change the url for a price.

<comment><USER8> Thanks.

Yup. With the paid plan you can use a custom domain.

</comment></comment></comment><comment><USER10> Short answer: 5 months with my new startup (to reach about $1500/mo revenue).

Longer answer: I've completely immersed myself in startups for around 3 years and spent a full 1.5 years on a startup which never reached ramen profitability nor got funding. I don't think I'd have been able to reach ramen profitability in 5 months without the learning I did in the previous one.

<comment><USER11> I'll be the first to ask... do you have a blog post with the lessons learned that enabled your current success? (congratulations, by the way!)

<comment><USER10> Thanks Mark!

I wrote this post about how I went from the idea to the first paying customer in 7 weeks: [http://blog.bufferapp.com/idea-to-paying-customers- in-7-week...](http://blog.bufferapp.com/idea-to-paying-customers-in-7-weeks- how-we-did-it)

I also write weekly on my personal blog and almost every post has some form of reflection comparing the previous startup with what I'm doing for my current startup: <http://joel.is>

<comment><USER11> Great read, thanks. Incidentally, the link at the end 404s: <http://blog.bufferapp.com/tagged/building_buffer> (looks like it should be <http://blog.bufferapp.com/category/building-buffer>)

<comment><USER10> Aha, thanks for that Mark, it's because we moved from Tumblr to Wordpress. Much appreciated.

</comment></comment></comment></comment></comment><comment><USER12> I suppose I theoretically could have bought all my ramen with bingo profits after about 2.5 ~ 3 years, although a) I kept working at the job for another year, b) actually doing that would have resulted in an "interesting" discussion with Immigration, and c) you probably shouldn't use this as a comparable since I was very, very part-time on things.

</comment><comment><USER13> 1.5 yrs part time for my work on bliss, of which a little over one year was with a commercially available product. Once RP, I went full time.

This was aided by both shrinking my outgoings as well as growing revenue. I cut out as many monthly outgoings as possible, went PAYG with my mobile phone, stopped spending daft amounts of money on wine... You've got to work out what's more important - cable television or your startup?

</comment><comment><USER14> Probably around 8 months - 1 year for us (<https://wonderproxy.com>). My co- founder and I were both employed full time elsewhere so we used revenue to expand constantly, becoming profitable wasn't on our short to-do list.

</comment><comment><USER15> This greatly depends on your experience level, how much work you put in every day, the type of business you are in, you're personal situation, and quite a few other factors.

If you're at the top of your game (you know what you are doing - <USER2> experience of 5-10+ years), I would say if the income does not come in after 3-6 months, you're wasting time and effort.

Otherwise it could take years to get things right before you find out that the market for your idea is not there.

</comment><comment><USER16> My startups first iOS game, Quotiac, was making ~$7/day within two days of making it on to the App Store. It took me about three months of full time development (programming + asset creation) to get there. My monthly expenses at the time were nil, so every penny earned was considered profit.

<http://itunes.com/apps/quotiac>

<comment><USER17> It's only profit if your time spent on it was worthless.

<comment><USER16> Time spent building anything is worthless until that thing is profitable.

</comment></comment></comment><comment><USER18> First startup, within 30 days (or less) we had revenue and profit to sustain cost. Currently working on finding next idea so nothing yet.

</comment><comment><USER19> Breakdown for freshtemp (firmware/hardware/software)

10 months full time (80 hrs/week) - first customer ($200/mth)

This time includes learning YUI to build the frontend (2 months)

Hiring consultant to build the backend (3 months)

Testing and building hardware (7 months)

This was done with less than 40K of cash, financed through myself and family.

</comment><comment><USER20> Related question: how much faster can you do it if you quit your dayjob and do the startup full time? Is it worth the added risk?

<comment><USER21> I recently (3 weeks ago) quit my day job to pursue my own startup product full time. Believe me, 3 weeks with a full-time focus was about 3 to 6 months part time with a day job. It's definitely worth it! But of course, only do it if you could afford it.

</comment></comment><comment><USER22> 2.5 years and counting, for better or worse!

</comment><comment><USER23> Interesting question, thanks for posting. I'm selling Chinese tea (a physical product sold online), so it's different from these SaaS guys, but it's about 3 months from launch and covered 1/3rd costs, but about 6 months since first line of code.

<comment><USER24> Check out this site.

<http://www.inpursuitoftea.com/>

</comment></comment><comment><USER25> <http://trackjumper.com> took almost a year to break even. Had I been working on it full time, it would have happened much sooner.

</comment><comment><USER26> 5 months for my iPhone app. I broke ground in May 2009, released in Sept, and the money became ramenable around Nov, after I fixed the glaring bugs.

</comment><comment><USER27> 18 months for Scalien.

</comment><comment><USER28> nearly a year of writing iOS apps

</comment><comment><USER29> x</comment>


Google Has Even Bigger Plans for Mobile Phones - <AUTHOR> http://online.wsj.com/article_print/SB119517445580795065.html

<comment><AUTHOR> Is Google Launching a Mobile Network? <http://mashable.com/2007/11/16/google- mobile-network/></comment>