ten_fingers

Part II

Then there's the NASA Web site

http://climate.nasa.gov/evidence/

Good. Since this is their "evidence", we can f'get about solid evidence of CO2 from humans causing significant global warming. Warm up the barbie!

Let's start with their points:

(1) IPCC.

They quote the IPCC. Tilt. They just blew their credibility, showed that they are just pushing rip-off, flim-flam, fraud, scam, political nonsense, and went to the back of the class with the D- students.

(2) The graph.

They show a graph of CO2 in the atmosphere over several hundred thousand years. Likely that data was from the ice core samples from the Russian Vostok site in Antarctica, although since then there is another sample from a US site that drilled deeper and went back further in time.

So, yes, over hundreds of thousands of years, CO2 levels have fluctuated, both up and down. We should have expected something else? And just now CO2 levels are relatively high.

But we were talking about temperature, just temperature, in degrees F, C, or K, and not CO2 levels. And when we measure temperature, just temperature, did I mention, temperature, we're okay.

Dragging CO2 levels into this discussion without a solid connection with a significant increase in temperature blows their credibility.

(3) CO2

They wrote:

> The heat-trapping nature of carbon dioxide and other gases was demonstrated in the mid-19th century.2 Their ability to affect the transfer of infrared energy through the atmosphere is the scientific basis of many JPL-designed instruments, such as AIRS. Increased levels of greenhouse gases must cause the Earth to warm in response.

True but trivial and says no more than if light a match then warm the planet.

Again, they blow their credibility.

(4) Ice Core Data.

They write:

> Ice cores drawn from Greenland, Antarctica, and tropical mountain glaciers show that the Earth’s climate responds to changes in solar output, in the Earth’s orbit, and in greenhouse gas levels. They also show that in the past, large changes in climate have happened very quickly, geologically-speaking: in tens of years, not in millions or even thousands.3

This is basically a lie, a deliberate, contemptible lie. Their lie is their statement that the ice core data shows that

"climate responds to ... greenhouse gas levels".

The ice core data shows that CO2 levels increased about 800 years AFTER the increases in temperature. So, the guess is that (A) from some cause there was an increase in temperature, (B) the higher temperature resulted in more biological activity, and (C) by 800 years later the increased biological activity resulted in higher CO2 levels.

With this lie, we should quit reading, but I will go on.

(5) Sea Levels

They write:

> Global sea level rose about 17 centimeters (6.7 inches) in the last century. The rate in the last decade, however, is nearly double that of the last century.4

Again, we were talking about temperature, just temperature, in degrees F, C or K. Did I mention we were talking about temperature? And in particular we were talking about significantly higher temperatures from higher CO2 concentrations from human activity -- for which we have zilch for good science. So now they are talking about sea levels. Where'd they get the importance of sea levels?

Maybe they want to use sea levels as a thermometer. BS. We have excellent thermometers and have had for well over 100 years. For measuring temperature over the past 2000 years, we have the results of a serious effort that I referenced above. For recent temperature, with high irony, from NASA we have satellites with just excellent measurements of global temperature. So, stuff it with the sea levels.

Of course, with the sea levels they are really trying to build a rolling snowball of emotional reaction from anecdotal evidence. That's contemptible.

Just what is it about temperature that NASA has so much trouble understanding?

That's enough.

NASA just wet it's pants and showed that it's a tool of some dirty politics.

The whole thing is not about science, CO2, global warming, or the planet. Instead mostly the subject is about a rip-off, flim-flam, fraud, scam that will let some people rip off some of what they want from some other people.

No sale: Not going to shut down our use of coal and pump more payoff, kickback, political machine money into more Solyndras and do more damage to our economy than was ever done by Tojo, Hitler, and Stalin combined. No thanks.

I'm torqued because I see an effort to sabotage the US.

My original question was, why is the media, e.g., the BBC, so eager to go along with this highly destructive scam?

My best explanation: The old KGB.

Part I

Okay, I'll bite. I'll type quickly, so there will be some rough edges. But the basics are really simple and will be clear enough.

Here I bring good news: Warm up the barbie, feel no guilt, and enjoy the picnic.

I can bite because there is, within what can be said with any seriousness, a very easy and, for what can be said, relatively good answer.

No, the answer is as science really bad, but so far likely and apparently there's nothing better. So, it's what we are stuck with. Sorry 'bout that.

First off, the NASA site is total, cooked-up, Saint Laureate Al Guru, please send more money, the sky is falling, crack-pot, rip-off, flim-flam, fraud, scam, dirty politics BS, and I will so explain.

But first the simple answer. There are just two parts.

The first part is from the PDF

Committee on Surface Temperature Reconstructions for the Last 2,000 Years, National Research Council, 'Surface Temperature Reconstructions for the Last 2,000 Years', ISBN 0-309-66264-8, 196 pages, National Academies Press, 2006,

long and still available at

http://www.nap.edu/catalog/11676.html

There we go to the graph on page 2. It's a crude graph, but it's likely and apparently about the best data available.

So, here's what we see: As of year 2006, the date of the study, as far as we can tell, the temperature of the Earth in year 2006 was exactly the same as in year 1000, and the increase in temperature over the last 100 years is exactly the same as the increase from year 900 to year 1000. As far as we can tell. The list of authors includes some of the most respected scientists on the planet, e.g., in time series, Brillinger at Berkeley.

And we don't believe that CO2 from humans or anything else was responsible for the temperature from year 900 to year 1000.

And, since year 1000, we fell into The Little Ice Age, for whatever reasons, solar activity, the orbit of the earth, volcanoes, whatever, and since then have been pulling out of The Little Ice Age. As we have pulled out of The Little Ice Age, the planet has warmed. Semi-, pseudo-, quasi-amazing.

We don't want to go back to The Little Ice Age.

So, as far as we can tell, the increase in CO2 from the industrial revolution has had exactly zip, zilch, zero effect on the temperature of the earth.

Here's a short Q&A:

Is CO2 a greenhouse gas? Sure, that's Physics 101. But CO2 is not a very effective greenhouse gas since it absorbs only in three quite narrow frequency bands in the infrared, one band for each of bending, twisting, and stretching of the molecule.

Then the 'greenhouse' effect is sunlight hitting the surface, warming the surface, the surface radiating in the infrared as a Planck black (or 'gray') body, CO2 absorbing the radiation and warming. But the amount of such radiation is limited by what comes from the sun and in the context essentially fixed so that once absorb that amount, more CO2 won't absorb more. Moreover, even if CO2 doesn't absorb some of that radiation now, water vapor, methane, etc. may. So, the warming effect of more CO2 is not so clear.

Has the concentration of CO2 in the atmosphere increased due to human activity? Sure.

Is CO2 the main greenhouse gas? Likely not. No doubt the main greenhouse gas is water vapor, but due to clouds, atmospheric circulations, thunderstorms, little things like the oceans, etc., the effect of water vapor is complicated beyond belief. Methane may also compete.

But that CO2 is a greenhouse gas is crucial? Not necessarily. A simple diffusion model can as good science show a significant warming effect from CO2, but that model is laughably simple compared with the real planet.

Does CO2 warm the planet? Likely yes, and so does lighting a match. The issue is, is the warming significant? So far the answer is, in the present context, not on Venus, apparently not.

But climate science has shown ...? For CO2 from humans causing significant warming of the planet, there's no 'science' in 'climate science'. In particular, the 'radiative forcing' of the IPCC and Ramaswamy is just catchy terminology for some simplistic model building and essentially just nonsense for serious science for the main issue of CO2 from humans causing significant warming.

Maybe there would, could, and should be a real climate science able to address this issue of 'warming', but for now the issue is far too difficult for solid science. Sorry 'bout that.

But just because the real issue is too difficult for solid science does not mean that we should shoot ourselves in the gut from crack pot 'science'.

The second part is from

http://online.wsj.com/article/SB1000142405297020364600457721...

In simple terms, the recent predictions of warming just didn't happen, didn't come even close to happening, were way, way off, were a really bad joke.

Lesson: We can't take the predictions from the 'climate science' community of significant and harmful global warming seriously.

Where does that leave us? The planet is doing fine, and there's no good reason to worry for now. As I said, warm up the barbie.

How about the part

> Global energy demand is expected to double by 2050, while the share coming from fossil fuels – currently 85% – needs to drop dramatically if we are to reduce carbon emissions and limit global warming.

That is a highly controversial statement. But it is just dropped in there. Just dropped in.

Question: Where does that statement get its legs? That is, what forces, influences, or whatever are causing it to be just dropped in, just sprinkled on like salt on food?

To me dropping in that statement makes the BBC and the author look really silly; I can't believe that dropping in that statement helps their credibility or reputation. So, why are they just dropping in that statement?

Here's one reason: Just keep pushing that story. Everyday, every way, just push it. So, keep the story going. Get others in the media, e.g., the NYT, to do the same.

Then, for the payoff, about once a week, use some anecdotal data as an excuse to write a full story about 'global warming'. That story is really easy to write -- just pull up the framework story, fill in some blanks, and, presto, get a new story with new ad revenue. Maybe that's the answer.

The fusion reaction itself is easy to cause just in a simple desktop experiment. So, the fuel is deuterium, that is, heavy hydrogen, that is, hydrogen with an extra neutron in the nucleus. So, use deuterium instead of hydrogen to make water and get heavy water. Then have a target with some deuterium and an accelerator and shoot some deuterium at the target. Presto: A fusion reaction as described in the article.

Easy? Yes. Practical? No! The reaction rate is tiny. Likely nearly all of the deuterium shot at the target causes no reaction and, thus, has the energy used to accelerate the deuterium wasted.

New? No. This has been understood for decades.

Generate a chain reaction? Nope.

Destroy the planet? Not a chance!

Your

> seemingly overflowing with the opinion that ten_fingers is the most important person in the world and the rest of us should drop everything to absorb his every utterance

is not supported with facts and is based on nothing solid, just a wild opinion, full of anger, wildly wrong, insulting, and out of place. Say something like to someone in person in a bar and risk getting a bottle broken over your head.

All I did was comment on the article's claims of some good writing lesson. I don't think the lesson was good and used examples to illustrate why. What I wrote was fine and deserved no insults.

That's a personal attack and offensive and inappropriate.

My point was about information as the desired content. For that point I gave some examples, today before coming to HN, after HN, and some recent reading, and those were good examples of information to make my point. The point is the content, and the examples were fully appropriate.

I made a point, clearly, strongly, and thoroughly. Apparently my doing so made you angry. Apparently you resent seeing points made.

You are angry about something, but I did nothing wrong. Your anger is something you have done wrong.

You are attacking me and not my thoughts.

Thanks for your interest.

I'm taking an approach that is fairly general, e.g., for that 2/3rds, and where solving the problem for video clips is just another application.

Roughly, first-cut, my work looks like a new Internet 'search engine', but that has to be only a rough description. That is, while 'search engine' may be the best two word description among widely understood two word descriptions, I don't think that much like a usual search engine can solve the problem. I believe that a solution needs to be a combination of search, discovery, recommendation, curation, and subscription!!!! How 'bout that!

Then there's another point, well connected with your "niche": Trying to provide what is 'most popular' is not promising! I.e., something in a niche is almost by definition not very popular. Or, what is in a niche is in 'the long tail'!

My guess is that we are moving to much more 'specialization' in content so that the fraction of the total content in the long tail and the niches is becoming a much more significant fraction of the total content. So, my 'search engine' is to help people mine this new fraction, the long tail.

How to do that? Well with the problem described, a key concept becomes obvious: Some case of strong 'personalization'!! That is, somehow have to get the user more involved so that the user can better indicate what they want and so that the search engine can, in some useful sense, 'learn' about what the user wants.

I've got the crucial, core, unique 'secret sauce' programmed and now am finishing up the routine parts of, really, just routine, simple Web site construction. And except for the crucial core stuff already done, it's a quite simple Web site. But the routine Web site work has taken me far too long -- those 3000 Web pages of Microsoft documentation of just routine parts of .NET really slowed me down.

Today I'm trying to finish up some work on just a simple session state store: I didn't like what Microsoft offered in ASP.NET, found a bug I wanted to get far away from in part of what they had, so did my own handling of session ID and session state with my own, simple session state store. But my session state store is via TCP/IP in my server farm, and I also need TCP/IP inside my server farm to connect my Web pages to my secret sauce servers. For using TCP/IP, have to build a 'message' service on top of the TCP/IP 'stream' service, so I did that and have used the session state store work to test it.

Then write the rest of the Web pages, load some initial data, and go live.

Another explanation: The problem with a YouTube series is connecting the people who would like the series with the series.

My view is that this problem holds for about 2/3rds of the content now on the Internet and is getting much worse rapidly.

My work is to do something about that.

No, I don't think that that's correct now with media now: Consider Web sites that want to report news on some of the common subjects, e.g., politics, the economy, international relations, business, science, technology, computing, energy. For each of these subjects, there are Web sites with interested readers.

Then, good news for aspiring writers: For such a subject and Web site, go for it! Just write some solid, new, meaningful, hopefully useful and insightful, information about the subject. Then attach an appropriate headline, subheading, and first paragraph, and, presto, be confident that you will have done 'good writing' and will reach about as much of your audience as is reasonable. No tricks. No magic. No secrets. No special techniques. Just accumulate, organize, and document the information and, then, publish it on an appropriate Web site with appropriate titles, etc. Then, if I am interested in your subject, you will get my full attention; no more secrets of writing will be required.

Here is a big, huge point about writing now, media now, and the points here: It has finally become fairly clear that the best writing on Web sites now is not from 'writers' or journalists at all! Such 'writers' can polish their craft, work on their technique, etc. all they want and will still lose. What such writers are losing out to are subject matter experts with no particular skills in writing at all. So, for a piece about, say, US-China trade, f'get about a business journalist-writer and, instead, get an expert in US-China trade. Want to know about a new supercar? F'get about the auto 'journalists' and, instead, go to a video at Jay Leno's Web site and watch the chief engineer of the car describe the details.

This lesson has been so well appreciated that many Web 'news' sites are trying to get as much of their content as possible from subject matter experts who are not 'writers', with good lessons or bad, at all.

E.g., want to know about venture capital? Okay, read the business journalists? Usually not! Instead read Hacker News, especially the comments, Fred Wilson's blog AVC.com, etc. Net, the good content is not where writers have learned lessons in writing but where the writers are subject matter experts and know what the heck they are writing about, i.e., have the information.

What I wrote is fine and not an example of the author's points. You just didn't want to read what I wrote. Okay. My content is not what you want. Okay.

There is another point I omitted (to save space!) that likely is a better explanation of what is really going on: The point goes back to McLuhan's "The medium is the message". So, in particular, the issue is not so much what the author said or what I said but just "the medium".

So, what was "the medium" and its role: The old medium was narrow, if you will, short on 'bandwidth'. So, there were a few huge audiences, and a main goal of ad copy was to reach as large a fraction as possible of one of those huge audiences. Well, that goal was difficult. In rough terms, the technique was to appeal to 'the least common denominator'. And a need was to get the message in a very short ad.

Now with the Internet, the medium has changed. It is no longer the case that the audience does not want to read. Instead the audience does want to read but only relatively narrow content.

The writing lessons he praises are not so much good for writing but at one time were good for some of McLuhan's media.

I was offended by his claim that he had found good lessons for "writing". I've done quite a lot of quite serious writing and much more very serious reading, and for that work his lessons are badly wrong. Here is a current sore spot with me: I'm writing software on Windows and, thus, am using .NET. So far I've collected over 3000 Web pages of documentation on the parts of .NET I am using. Nearly 3000 of those pages are from Microsoft's MSDN Web site of documentation. I've been programming for decades but am new to .NET.

Now for the sore spot about writing: The writing of the .NET documentation has been by a huge margin the worst part of my software project. For me, reading Knuth's TACP was fast and easy, but reading Microsoft's .NET documentation has been a total pain and very inefficient. The main reason for the difficulty is much the same as for the author -- a determination to concentrate on 'writing' styles that refuse to concentrate just on information.

You appear to have missed the point of my post: I don't care even as much as a weak little hollow hoot about "concise, appealing, and engaging" and already am looking hard for what to read. I will want to read it if and only if there is solid, useful information there. The author didn't mention solid, useful information.

"Concise" usually means omitted crucial details.

Again, the author is trying to get read by providing a form of entertainment based on emotions, and what I want is information. In the culture of the author, information is regarded as really offensive, and that is the reason many potential readers regard such writing as "shit".

To borrow from the humanities culture, I'm willing to read, I'm wanting to read, I'm waiting to read when I can find content to read with useful information.

So, when I don't read his copy, he concludes that I don't want to read. No. I DO want to read. But what I want to read is useful information. He thinks the issue of my not reading is that I don't want to read, and I think it is because he wants to provide only emotional content and not useful information.

The main claim is:

"No one wants to read your shit".

Let's check that: I came to Hacker News to read. I saw the post and read it. I read the Hacker News comments.

Today before Hacker News I read CNet. Later I will read Google News and a few more.

I may stop at TED to see if they have some new content.

For the Romney-Paul speeches in Virginia yesterday, I worked to find, download, save, and index copies, and I read the copies.

So, net, I very much do want to "read". I work hard to find things I want to read.

The flat claim that I don't want to read is just nonsense.

But the statement was about advertising. Okay. Do I ever want to read ads? Actually, yes, very much yes.

When I buy products, I very much want to read about the products. E.g., a friend urged me to buy the book 'The Amateur', so this morning I went to Amazon and read a few dozen of the highest ranked comments about that book. I very much wanted to read.

Two weeks ago the power supply on my main desktop computer started making noise each morning when I turned the computer on. So I spent about a full day reading about power supplies and, maybe, how to replace the fans in the power supply I have. Finally I ordered a spare power supply in case I need it.

I did read. I read a lot of 'ad' content. I read about everything I could find relevant to my power supply issue. And I regarded what I read as important enough to keep copies of most of it.

So, yes, I do want to read ad copy also.

But, right: The usual ads I don't want to read. Why? Well, with some irony, the author did say a little about why -- the ads are "shit".

Okay, let's take such an ad, say, for a new car. I've seen and read hours beyond belief of ads for new cars, am really eager to learn about new cars, but to me nearly every ad I've read about new cars is just the author's "shit". That is, the ad writers, apparently following what the author said, just will not, Not, NOT tell me what I want to know about the new cars. They just have their feet locked solidly in concrete, will NOT tell me, and, thus, will write "shit" that, yes, I don't want to read.

So, why the "shit"? Apparently because the ad writers, and the author, just do NOT understand what a customer might want to know.

What do I want to know?

"All new"? That's nearly always a lie. Good. No way would I buy a car that really was "all new". Instead, I want every essential mechanical part and every important system to have been on the road in hundreds of thousands of instances for at least five years. So, say "all new" and right away I hope and believe that you are lying. So, now that I know you are lying or are being a fool, I am reluctant to take any of the rest seriously.

Headlights: The cars have headlights apparently from 'designers' to yield an emotional experience of 'flowing' into the sheet metal. BS. I deeply, profoundly, bitterly hate and despise all such constructions and intentions. Why? Because such headlights have some "shit" engineering issues. E.g., it's not clear how to aim them. E.g., there is a plastic cover the is easily scratched and starts to go yellow or gray or whatever due to UV or whatever.

What I want in headlights are traditional, old, very reliable, very functional, industry standard, widely available, competitively priced, very well engineered and tested sealed beam units enclosed in glass. NO PLASTIC. Period.

Next, here's what I really do care about in cars: Utility, functionality, performance, reliability, ruggedness, and ease of maintenance.

E.g., for ruggedness, I want traditional heavy steel bumpers that protrude several inches from the rest of the car. I want no glass or plastic near the bumpers. The bumpers need to be able to take heavy bumps without damage. And I should be able easily to unbolt a bumper and replace it.

So in an ad, I'd want to know how rugged, strong, durable, and easily replaceable the bumpers were. I've yet to see an ad that gives any information at all on bumpers. Bummer.

For the dashboard, I want to know how to remove it, repair or replace various components, e.g., bulbs, the speedometer, the tachometer, the connection from the radio to the antenna, all EASILY. Ads don't provide me with any such information. Bummer. This matter of the dashboard is just a routine part of ease of maintenance.

Far and away my greatest concern about the car is corrosion -- e.g., around the front and rear windows, the body panels, from condensation inside the car, of the exhaust and the radiator, of the brake parts, of the fuel lines and brake lines, of the gas tank, of the floor of the trunk, of the wheel rims where the tire bead joins and needs to make an air tight seal, etc. I want to know what has been done about corrosion.

Okay, let's set cars aside and consider, say, desktop computers. I need to build another one. So, I want details on the motherboard, power supply, processor, processor fan, case, case fans, how hard disks are mounted in the case, hard disks, DVD, etc.

But, of course, the ad copy for these products is from feet locked in concrete writing me "shit" and just refusing to give me the information I need. E.g., it's standard now to paint the case all black; this is some Darth Vader thing? With a black case the photographs become just big, black blobs with no visible detail. With such a case, I will always need strong lights just to see what is inside the case. I deeply, profoundly, bitterly hate and despise all the dysfunctional, destructive 'stylistic' nonsense that has given us black cases. Upchuck.

Here's the fundamental problem with ad writers: They are convinced, down to the center of the cells at the center of their bone marrow, that all there is in life, writing, ad copy, and cognition is some humanities zenith of vicarious, escapist, fantasy, emotional experience entertainment. 100% emotion 100% of the time. Solid, rational, meaningful, useful information never. And that's why it's "shit" and why I don't want to read it.

My wife, brother, and I all got Ph.D. degrees. I was an MBA program prof.

Here's my reaction to the article and advice to people who want a technical Ph.D. degree:

First, it helps to be 'smart' to get a Ph.D. So, in part, think of the Ph.D. program as a path through a maze, and there don't just commit to the first path you see. Instead, look ahead over the whole maze and possible paths, investigate what the heck you are getting into, and then pick some candidate paths, with 'options' along the way to respond to 'exogenous' events.

Second, what the article said was real, but some of the 'path' decisions illustrated in that article were poor. E.g., don't get into a situation where you are depending on an advisor: So, you don't want to have to depend on an advisor for a problem, for funding, or for a 'research environment'.

When I was in graduate school, there as no shortage of tuition scholarships; the school had many more such scholarships than qualified students. My wife, brother, and I never paid even 10 cents for graduate school tuition. So, paying $0.00 tuition should be easy enough.

I didn't get any 'stipend', but then I didn't do any teaching or 'grunt' work either.

Third, realize that the main generic requirement for a Ph.D. is to produce "an original contribution to knowledge worthy of publication". If you are in a department or field that wants you to have a dozen papers published in high end peer-reviewed journals before awarding a Ph.D., then you picked the wrong department or field.

So pick a field and department that is reasonable about the research required.

Then maybe publish one paper just to help keep monsters off your back. I saw a problem in a course; there was no solution in the course; I saw no solution in the library, thought about the problem for a week and saw a rough path to a solution, took a 'reading course' to look for a solution, within minutes after the reading course was approved submitted my rough solution, worked for two more weeks and found a much nicer solution, got a nice new theorem comparable with a classic one, used the theorem to solve my problem, noticed that my solution also solved a related problem stated but not solved in a famous paper, and, thus, got a 'Teflon' back no one could attack. Later I published the paper with no difficulty.

Then, get yourself ready for doing the research, pick a problem, do the work, write it up, have the department observe that the work meets the requirements, and graduate. If there is doubt about the quality of your work, then PUBLISH your work.

Notice that computer science likes 'good' algorithms where a 'good' algorithm is one with worst case running time only a polynomial in the size of the problem. Well, usually J. Edmonds is credited with that definition of a 'good' algorithm. So, here's a J. Edmonds story (may be true): He was a math grad student at U MD but left and went to the Bureau of Standards (NBS). There he published several papers on graphs, trees, flowers, etc. A committee at his old department drove to the NBS, suggested that he stack up some of his papers, put a staple in the UL corner, and let the department call that his Ph.D. dissertation and award him a Ph.D.

Pick your own problem; do your own work; don't use an 'advisor' for anything important.

Fourth, to get yourself ready for research, take some good courses, study some good books, read some good papers, and attend some good research seminars. There read on the lines but also sometimes between the lines. E.g., at a research seminar, try to guess how the speaker selected their problem and notice the prerequisites they used in getting a solution; then notice how easy or difficult, how routine or novel, was what they did. Then consider borrowing what seemed to work. E.g., once in grad school I went to a seminar given by S. Eilenberg. Later a comment from a junior faculty member was "He sure doesn't waste time working on small problems.". Okay, try to learn from something like that.

Fifth, to pick a problem, try to start with a problem from OUTSIDE your department and hopefully outside academics. The usual criteria for peer-reviewed publication is work that is "new, correct, and significant". If you are well prepared, then "correct" will be easy for you. "New" may be also. For "significant", let that be partly or largely from the real problem and not just from some hopeless trilogy of trying to amaze your readers, knock them off their feet with your brilliance, and wow them with your chances for a Nobel prize.

Sixth, quite broadly the best work in any field is to 'mathematize' the field. Okay, basically have your Ph.D. in applied math although you may call it mathematical sciences, systems analysis, engineering/economic systems, statistics, mathematical finance, computer science, electronic engineering, mathematical genetics, etc.

So, for that path, start with a good math background -- at least a good undergraduate major in pure math, likely some selected grad courses in pure and applied math, and some more math on your own.

For the 'original' aspect, actually good work through the math path I outlined will have you doing enough challenging exercises so that your ability to do 'original' work will have a very good start. You might refine that start by drawing from others, but I never did.

For the 'competitive' aspect, nearly everyone in your field will all their careers be struggling terribly with math and be really short on it, but you will effortlessly totally blow them out of the water. You can use theorems they can't understand, and you can state and prove new theorems they can't understand and where they can't check the proofs -- both easily. Did I mention you can blow them out of the water, effortlessly? Then your research will 'mathematize' the real problem you picked and be considered good work.

Here's a big secret: The world of pure to moderately pure math is awash in super nice tools that have not been at all well exploited in applied fields. So, learn some such tools, pick your practical problem, make some extensions in the tools needed by your practical problem (those extensions may be all the challenging 'original' work you do), follow through, write any needed software, learn TeX, type in your paper, submit it, stand for an oral exam, graduate, and do something else.

If you want an academic career, okay, but that would extend what is here, be chapter two, and be beyond the scope of this post!

For your 'confidence', get that from the path I outlined here and do NOT depend on your department to give you your confidence. Don't expect to get self-esteem, praise, acceptance, approval, a sense of belonging, emotional security, or financial security from your department. Your main security is that you know the math and can blow out of the water essentially all the profs in your department.

Note: In some of the best US computer science departments, the mathematical backgrounds and abilities of over 80% of the faculty are down in the laughable area. Blowing them out of the water is trivial.

Notice that often departments don't want to teach or help students but want to 'filter' them. Or they want to insist that the students be like some impossible dream in their own mind. Or they are sadistic and want to crush students. Or they want to make sure that their students can't do better work than they do. Or they want their students to follow the dead end path they took and that stopped them. Looking at the faculty, you can be looking a ward in a funny farm. Don't let them hurt you; don't let them get their rotten hands on your program; instead, work largely independently.

If you can work through the math path I outlined, then you are plenty good enough to do your research no matter what the faculty might say if you gave them a chance. Try to stay out of their sights; don't let them shoot at you.

In my case, I had a good background in pure and applied math from school, work, and independent study, selected my dissertation problem before I entered graduate school, took some very good courses my first year (dropped some bad ones), did my research independently in my first summer, struggled with departmental nonsense, wrote my software, typed in my work, submitted, stood for my oral exam, and GRADUATED.

The specific problem is a special case of the 'assignment' problem. First cut, it looks like 0-1 integer linear programming and, thus, maybe in NP-complete.

Looked again, the problem is least cost network flows. That is a linear programming problem. In that problem, if the arc capacities are integers, and in this practical problem they are, then the simplex algorithm can easily start with a basic, feasible, integer solution, and the simplex iterations will automatically maintain an integer solution and, then, find an optimal integer solution which provides the optimal assignments.

When we apply the simplex algorithm to least cost network flows, we get to take advantage of the fact that the problem is not fully general linear programming, and the advantage we can take is huge. E.g., the arcs of a basic, feasible solution form just a spanning tree in the network; the 'entering variable' in the simplex algorithm just adds an arc to the spanning tree and, thus, yields a circuit; we run flow around the circuit in the direction that makes money until the flow on some arc is zero and that arc determines the 'leaving variable'. Using the 'strongly feasible' basis ideas of W. Cunningham, long at Waterloo, we can avoid cycling.

But, the simplex algorithm for least cost network flows, as far as I know, is not guaranteed, worst case polynomial.

However, read:

Dimitri P. Bertsekas, 'Linear Network Optimization: Algorithms and Codes', ISBN 0-262-02334-2, MIT Press, Cambridge, MA, 1991.

and in there, or elsewhere in Bertsekas's work, is an algorithm for least cost network flows and that is worst case polynomial.

Done.

And I covered nothing classified!

And for this thread, we have a "how to" with high precision from some applied math.

Sorry, Ms. Singel, your sexism card has expired.

> Engineers design materials, structures and systems while considering the limitations imposed by practicality, safety and cost.

That's where I begin to say that computing comes up short as 'engineering':

The problem is that too often in computing the "design" work, just the design work alone, before construction, cannot really tell what the "practicality, safety and cost" will be. So, too often in computing we have to build the system and then test it to know what its "practicality, safety and cost" are. That was often the situation in the construction of medieval cathedrals: Some examples stood; some fell down; some on the borderline got weaker over time. Apparently eventually the strong cathedrals were built mostly drawing from experience, that is, closely copying what had already worked before. That technique is trial and error and copying and not really the 'design' of engineering.

If bridge engineering were the same as software construction, then just to know how strong the bridge was we'd just have to build the bridge and then test it. For rocket engineering, to know if it could reach orbit we would have to build and launch it. Indeed, instead, in rocket engineering we can determine the rocket trajectory that will put the maximum payload into the desired orbit.

I explained further in

http://news.ycombinator.com/item?id=4356303

in this thread.

> Mathematics provides a framework for dealing precisely with notions of 'what is.' Computation provides a framework for dealing precisely with notions of 'how to.'"

But some parts of math deal "with notions of 'how to'" and there commonly do so more "precisely" than computer science. There are such parts in optimization, control theory, statistics, and numerical analysis.

E.g., numerical analysis says how to (1) solve systems of linear equations numerically exactly using just single precision arithmetic, (2) solve systems of linear equations with accuracy guaranteed by error bounds from condition number, (3) solve systems of large, sparse linear equations where the matrix is large, positive definite, and diagonally dominate, (4) how to solve ordinary differential equation initial value problems, e.g., for space flight trajectories, and (5) how to solve partial differential equations of heat flow, fluid flow, and electromagnetic fields, and (6) how to do discrete Fourier transforms of positive integer n points in time proportional to n log(n) instead of n^2; statistics says how (1) to get minimum sufficient statistics in the Gaussian case, (2) how to design a most powerful statistical hypothesis test, (3) how to pick a survey sample size that will give desired accuracy, (4) how to get minimum variance, unbiased estimators in multivariate statistics, and (5) at a Web site with users arriving at a rate of 10 a second, how to find the probability of a minute with arrival rate of over 20 a second; control theory can show (1) how to execute best possible decision making over time under uncertainty, (2) how a rocket can reach orbit for minimum fuel, (3) how an airplane can climb to a given altitude in least time; and optimization can show (1) how to find least cost flows on large networks while avoiding cycling in the network simplex algorithm, (2) how to assign interceptors to targets in the best possible way in guaranteed execution time, and (3) how to assign signals to satellite channels to minimize the worst case of signal interference.

For computing being 'engineering', that is an old sore point with me. I believe that computing should be engineering but so far really misses some key points.

Here, close to the article, is an example of some of what is missing:

In engineering for, say, a bridge, the design engineer can get quite comprehensive data on the 'engineering properties' of the materials, components, and processes he is intending to use. E.g., he knows the stiffness of steel beams, the tensile strength of steel cables. and how much weight a reinforced concrete column can carry.

Now in computing, for an algorithm, what do I know about the resources it requires? E.g., I just wrote a Web site session state store using TCP/IP for communications, my own code to convert the TCP/IP streams to the 'messages' I needed, and Microsoft's .NET collection class SortedDictionary to store the pairs of session keys and session values. So, how do I know fast my code will be and how much storage it will need? That is, could I get data on TCP/IP and SortedDictionary so that I could tell before writing and running the code?

That is, do I have solid engineering information about the components I used that will let me know, before writing and running the code, the final performance of my software?

NO!!!

Instead, about all I can do is run the code and measure the execution time. For the storage needed, I don't have a good solution even given the running code.

So, by analogy, if bridge building was like software writing, then a bridge engineer would not know how strong his bridge was until he built it and tested it with loads.

So, net, the bridge designer can 'engineer' his bridge just on paper before any construction has started, but I can't do the same for my session state store.

Again, a big difference is that the bridge engineer has detailed engineering information on his components and I do not.

The article started with:

"Many venture capitalists are up in the arms because their returns are down, their funds are drying up, and there appear to be a declining number of entrepreneurs pursuing big ideas."

I will try to give explain how to reverse "the decline in big ideas":

First, I should state some context:

(A) The goal of a VC investment is to make money within a few years for the VCs and their limited partners which are usually pension funds, university endowments, and a few others. Here I stay with this goal and set aside other goals such as 'improve society', 'save the planet, environment, rain forest, farmland top soil, coral reefs, whales, genetic diversity of flora and fauna, and poor people'. I am willing to leave these goals to Bill and Melinda Gates.

(B) As VC Mark Suster has reported on his blog, in the past 10 years VC returns have been poor, that is, commonly negative and on average low compared with other 'asset classes' or with history in finance. Maybe as few as 20 VC firms in the US now have a good 10 year track record for their limited partners. Returns have been so bad that

"Many venture capitalists are up in the arms"

is easy to believe.

(C) US venture capital concentrates in just two broad areas (a) 'information technology' and (b) biomedical technology. Here I concentrate on (a), 'information technology'.

With the VCs concentrating on 'information technology', I agree that they should do that. That is, broadly, computer hardware, infrastructure software, the Internet, and, now, mobile devices are enormously powerful and not yet nearly fully exploited, and, net, the big opportunity to make money.

With this context stated, I move on with two points.

First, What the VCs Are Doing

I believe that what VCs are currently doing is close to just silly and fully explains their poor financial performance. So, here is what they are doing:

Yes, a current joke is that VCs (and their entrepreneurs) are concentrating on mobile, local, social, friending, liking, linking, sharing of text, still images, and video clips.

More generally, and not just a joke, it has become clear that VCs expect that there is a 'business idea', to parody, 'Facebook but only for single cat lovers' or a 'matchmaking site for employers and job hunters'. Such a 'business idea' is to be expressed in just a few words, and everything important about the idea is supposed to be just obvious.

It is such ideas that have led to the contemptuous remark "Ideas are easy and plentiful. Execution is everything.". Well, with only such simplistic ideas, of course, execution is all that's left and has to be "everything". Bummer. I will argue that with a 'good idea', at least with good planning before execution, execution should be routine. The need for routine execution and the ability to do it are common on Main Street all across the US.

Then for the 'barrier to entry', in the words of Fred Wilson, that is supposed to be from a "network of engaged users".

All the rest of the work is supposed to be routine user interface design, software, publicity, and, in the case of major success, server farm engineering.

Then VCs evaluate such a project based essentially on just 'traction' that they want to be significant and growing rapidly. I.e., they want another Facebook.

But here what the VCs are doing is just silly: The VCs are asking far too much from the 'business idea' and its short description and from just 'traction' and are ignoring any more solid evidence of a good project and good investment. Moreover, in the sense of the HBS article of this thread, what the VCs are doing gives no real promise of identifying with any significant accuracy the coveted "big ideas."

In blunt terms, the VCs are lacking any more solid means of project evaluation.

Maybe with such processes some entrepreneur can find another Facebook, but I believe that the good opportunities are elsewhere.

Second, Much Better

Here is my suggestion for something much better that I believe is so far neglected by VCs (and their entrepreneurs). I suggest the following broad steps:

(1) Market. Pick something big. Identify a market where can get a lot of money from each of a few customers or a little money from each of hundreds of millions of customers. Something of high interest to, say, 80% of all the Internet users in the world should do nicely!

(2) Problem. In this market, find a problem where a much better, or the first good, solution clearly promises to achieve the goals of money in (1) -- a lot from each of a few or a little from each of a lot. Here "clearly" has to be quite solid and not just some intuitive guess and not just early traction.

So, e.g., what's wrong with another mobile, social, liking, linking, friending, sharing Web site is that it is just not clear that such a site will get tens of millions of users. It just isn't clear. Instead, it's a long shot, a shot in the dark.

Note: If in the market selected in (1) can't find a suitable problem, then return to (1) and pick another market. However, if the market is a significant fraction of all the Internet users in the world, then there MUST be a suitable problem somewhere in there, so keep looking!

(3) Research. Do some research to enable, in the sense of 'secret sauce', the desired solution as in (2).

There are some business advantages here: First, the research can enable a much better solution, that is, one the customers like much more. Second, the research can create a technological barrier to entry.

Note: If can't find suitable 'secret sauce', then return to (2) and pick another problem.

(4) Software. Implement the solution in software on a Web server, and deliver the solution via Web pages over the Internet.

Note: If the solution needs more than just such software and a Web server, then return to (3) and find another solution.

(5) Execution. Go live, get publicity, users, and revenue.

Note: Here the crucial work, and the work to be carefully planned, executed, and evaluated, is supposed to be in steps (1)-(4), and step (5), execution, is supposed to be routine. This situation is in wild contrast with the usual VC view that "Execution is everything".

Well yes: When the start is just a simple 'business idea' with nothing more to recommend it, then, yes, "execution" is all that's left and "everything". But such execution for "big ideas" is just vast goals with half-vast planning, a long walk on a short pier, ready, fire, aim, and sloppy work that explains the poor financial returns.

Where the VCs get off track is failing to take seriously (1)-(4).

Here's where the entrepreneurs get off the track: They don't know how to execute step (3) Research.

What can we say about such research? Well, after the research, the rest is just routine software and routine use of computing, software infrastructure, the Internet, and server farm engineering. So, basically the software just takes in some data, manipulates it, and puts out results, and the research is for the manipulations.

Well, the manipulations are necessarily mathematically something, powerful or not, understood or not. There are various ways to do the research:

(A) We can hope that the data manipulations to be done could in principle be done manually; so, the software is to implement manual operations that are well understood.

(B) We could use intuitive or heuristic methods.

(C) We could use some well known, somewhat general techniques in artificial intelligence, semantic analysis, natural language processing, data mining, machine learning, etc.

(D) Or we could proceed mathematically. Here we start with some assumptions that appear to hold well in the real problem. The assumptions become hypotheses in theorems. The conclusions of the theorems direct the data manipulations. The software performs the manipulations. The 'research' is for these connections and hopefully for some new theorems (that, of course, need solid proofs).

Via (A)-(C), it's tough to know that have powerful manipulations and to know that they are powerful early in the planning.

Where entrepreneurs get off the track is following (A)-(C) and neglecting (D).

Another place the VCs get off track is ignoring (D).

I'll make one more point: Since VCs are lacking good means of evaluating projects, they have been using poor techniques and information instead. Net, the VCs have been in a small 'echo chamber' quite distant from powerful techniques and valuable information.

E.g., apparently the VCs get their understanding of what good projects should look like mostly just from recent successful companies and what they find in their e-mail in-box. So, they are trying to learn how to select "big ideas" heavily from copying old examples and looking at small ideas. By analogy, they are trying to learn how to evaluate new directions in medicine by reading labels on bottles of snake oil. Hopeless.

In strong contrast, major parts of our economy have long been quite good at good plans for being new, better, and/or first and, given good plans, executing with low risk.

My suggestion to reverse "the decline in big ideas": Entrepreneurs, develop solid plans for projects that have much higher potential. VCs, learn how to evaluate such plans accurately.

The article started with:

"Many venture capitalists are up in the arms because their returns are down, their funds are drying up, and there appear to be a declining number of entrepreneurs pursuing big ideas."

It's easy to believe all of this except the last part:

"Many venture capitalists are up in the arms because there appear to be a declining number of entrepreneurs pursuing big ideas."

That part I don't believe.

Why don't I believe? My view of venture capitalists (VCs) is that they don't look at ideas, or big ideas, or the anything at all detailed about a proposal from an entrepreneur. So, if VCs don't bother even to look at ideas, then it's tough to believe that they are

"up in the arms"

about what they don't see!

Of course, the author gave no list of his "many" VCs! I, however, have a long list of VCs who, receiving a proposal for a "big idea", e.g., along the lines of a company worth $200 billion, just quit reading and, maybe, start laughing. My list is long enough that the author's "Many" becomes improbable!

Instead, I've long since concluded that VCs look at a few simple criteria and then, for their first serious effort, in one word, look at 'traction'. Given some significant traction, all the rest is nearly irrelevant.

In one step more detail, the VCs have no serious methodology for evaluating anything about a proposal except just traction. I gave a longer explanation in:

http://news.ycombinator.com/item?id=4289566

I tried: My wife and I both got Ph.D. degrees intending to have two careers, do well financially, and, with money in the bank, have a family.

In four words, it did not work.

A longer description is, eventually the evidence became overwhelming: Mother Nature and Darwin were there long before we were and very much did not want us doing that, and they were very strong minded about this: She struggled and struggled; the struggles caused stress, eventually the stress caused depression; the depression made the struggles and stress worse and caused severe depression; and that was fatal. No joke. Her Ph.D. had been a big investment that got 'written off'.

Or couples that could have done what we tried just were not among our ancestors. All this is in spite of what is commonly said would, could, and should be.

Thus, I suggest: In simple terms, Mother Nature and Darwin have arranged that without certainty but with high probability in practice and significantly on average a 'professional woman' is a weak, sick, or dead limb on the tree. Sorry 'bout that. Wish I'd known that earlier.

How could this "weak, sick, dead" stuff be? Here's a guess: In our 'culture' from the past few thousand or ten thousand years, women nearly never had opportunities to pursue a 'profession' and became wives and mommies whether they really, consciously or otherwise, wanted to or not. So, our 'nature', 'nurture'. 'social and psychological capital', and 'culture', or whatever, from the past kept the tree growing while, still, a significant fraction of the women didn't want to be just wives and mommies.

So, what's different now? Now the US society and economy have changed giving many women an opportunity to pursue a career or profession, and, with significantly high probability, these women are removing their genes from the gene pool.

E.g., in Finland, women are encouraged to pursue careers, and on average the number of children born to a woman in Finland is about 1.5. So, let's see: For some simple arithmetic, with one generation of 25 years, after 150 years the population will fall by

     100 * (1 - ( 1.5 / 2 ) ** 6 ) = 82.2%

So in the last 150 years or so Finland beat back the Swedes, Nazis, and Soviets but in the next 150 years are on track to lose out to careers for women!

This situation is common across Europe: The gene pool is being severely pruned. In simple terms, Europe is rapidly going extinct.

My guess is that the European gene pool is now in the period of most rapid change in at least the last 10,000 years.

Darwin stands to win this one: What will be left will be women who really, REALLY want to be mommies in good families.

Darwin has more to say: The situation for men is not easy, either: For the tree to do well, men have to be good providers, good enough that their wives can concentrate on doing well in motherhood. Some men are successful, and some are not -- Darwin again!

Without some big and chancy changes in work and families, thoughts about would, could, and should be pale to insignificance as Darwin wins again.

Or, it's not nice to try to fool Mother Nature!

Maybe in Finland and most of Europe, by the time the population falls by, say, 75% from now the new gene pool, 'culture', etc. that emphasizes motherhood along with the more favorable ratio of land to people will cause the population to stabilize and, then, start to grow again. Maybe.

Let's see: The author of the essay is in political science. Here notice 'science'!

Now, I have to wonder: (1) In political polling, could he give a solid definition of a simple random sample? Could he suggest how to select one? Could he explain just why we often want a simple random sample? (2) Could he explain why in political polling we average to estimate a population proportion? That is, the average is a statistical estimator. Just why is it the estimator we want? (3) If he is to estimate variance, does he know which of the two common algebraic expressions is biased and which is unbiased? Can he define a biased estimator? (4) A common topic in political science is principal components analysis, and key there is orthogonality in vector spaces. Can he suggestion how to give a solid description of that topic without algebra? (5) Political science likes to construct quantitative measures, and there two important issues are 'reliability' and 'validity'. Can he give solid discussions of these two issues without algebra? (6) Political polling reports results with an 'margin of error'. Could he define that? The usual calculation is based on the central limit theorem; could he describe that? (7) One of the more important research tools in political science is statistical hypothesis testing, especially nonparametrics. There the core concepts are Type I error, Type II error, power of a test, and a most powerful test. How can he describe this material without algebra?

Uh, a first course in algorithms commonly described heap sort and points out that its execution time, on sorting n items, is O( n log(n) ) in both worst case and average case and, thus, is best possible in that it meets the Gleason bound. Without algebra, how is a student to understand the significance of that material?

The world is moving on to more in automation. The main approach here is computing where we describe what we want and how to get it in terms that are essentially mathematical -- e.g., stiffness of arbitrary space frames, finite element analysis in solid mechanics, Reynolds number in fluid flow, sines, cosines, and exponentials in A/C circuit analysis with passive components, force, torque, energy, power, and momentum in mechanical engineering, etc. All these subjects commonly assume first and second year algebra, plane and solid geometry, trigonometry, analytic geometry, calculus, and ordinary differential equations.

For political science, we should include linear algebra and multivariate statistics.

That author wants less algebra but more statistics? That's like saying he wants to lose weight but eat more!

Yes, to too great an extent, public K-12 is more about babysitting, i.e., keeping the kids off the streets, than about education. Moreover, really, math is too difficult for essentially all of the US K-12 educational community.

Here is a solution in Microsoft's Visual Basic .NET making a little use of some of .NET:

     ' ADD.VB --
     '
     ' Created at 21:34:59 on Wednesday, August 1st, 2012.

     '    Note:  There are some risks of fixed overflow
     '    depending on (1) the values of the components
     '    of arr() and (2) how the logic works for ending
     '    a For-Next loop in the case n is the largest
     '    value of Int32.

       Sub add( _
         arr() As Int32, _
         val As Int32, _
         n As Int32 )

       Dim arr_upper_bound As Int32

       Dim i As Int32

       Dim n_changes As Int32

     '    http://msdn.microsoft.com/en-us/library/system.array.getupperbound%28v=vs.100%29.aspx#Y104

       arr_upper_bound = arr.GetUpperBound( 0 )

     '    http://msdn.microsoft.com/en-us/library/cy37t14y(VS.80).aspx

       Select n

         Case n = 0

           For i = 0 To arr_upper_bound

             If arr( i ) = val Then arr( i ) = arr( i ) + 1

           Next i

         Case n > 0

           n_changes = 0

           For i = 0 To arr_upper_bound

             If arr( i ) = val Then

               arr( i ) = arr( i ) + 1

               n_changes = n_changes + 1

               If n_changes = n Then Exit Select

             End If

           Next i

         Case n < 0

           n_changes = 0

           For i = arr_upper_bound To 0 Step -1

             If arr( i ) = val Then

               arr( i ) = arr( i ) + 1

               n_changes = n_changes + 1

               If n_changes = -n Then Exit Select

             End If

           Next i

       End Select

       Return

       End Sub

I was trying to be simple, elementary, and easy to read! But my comment was not at all "pedantic"!

Even with all the context you suggested, the problem statement is just not very clear, and, in computing, computer science, and software, that lack of clarity is not a small point.

Put more bluntly, there is no excuse, and in serious software, especially for embedded systems, there is no role, for abbreviations that are on or over the line into gibberish. You are correct that such gibberish is common in software; my point is that we should not do that and should object.

Yes, in my suggested problem statement, I used notation common in mathematics. Still, the math community still says that such writing is English. GOOD.

Here is a big point: Computer science, computing, and software have no good substitute for such use of how mathematics is written. There is a partial substitute in practice if assume a particular programming language.

The error of n instead of -n or the absolute value of n is small but is an example of a huge point: A subroutine needs a description. A subroutine is precisely something and should have a precise description. The problem here is so simple we can get by with being imprecise as you suggest, but computing is not limited to such simple routines, and the complicated ones also need precise descriptions. Net, we need to write precise descriptions. Knuth did this in TACP (yes, I abbreviate!); it really is possible.

For assuming that 'add1' means an argument 1 passed to a function add is asking a lot even just in the context of software.

In passing a constant to a function there will be the issue, that should be addressed, of passing 'by value' or 'by reference'. In Fortran a constant is passed by reference with all the problems, side effects, and surprises there unto appertaining! We can't yet assume that Fortran is buried!

If my work does well and I start to hire, I will give a lot of weight to people who are good at reading and writing technical material and will prefer that they know how to read and write abstract math. For learning Visual Basic .NET, a book of code samples and a one week class should be able to do a lot. Net, I'm questioning if I should require new hires for programming to know any 'computing' at all.

Right at first I would have two remarks. The first remark has to do with how to describe such a problem. The second remark points to what appears to be a serious error in the problem description.

My first remark would be that the problem statement is not in any natural language.

E.g., what is the role of the many instances of the character '#'? Those instances are not in a natural language.

E.g., what is the token

'add1'?

That token is not in my Webster's dictionary, and I have been given no way to parse it.

E.g.,

"increments items in an array matching specified value"

is not a sentence.

E.g., "param:" is not English.

Continuing, it is not clear what meant by an 'array'. So, we do not know the value of the smallest subscript or the largest or how to determine these.

Next the terms

"from left-to-right (forward)"

and

"right-to-left (backward)"

are undefined and require a reader to guess. A careful guess would want to know if the computer was big endian or little endian.

Finally, one might object to "most optimal" since, really, something is 'optimal' or it is not so that 'most' is superfluous.

Here is another way to start to write the problem statement:

Suppose you are given positive integer n, and suppose for i = 1, 2, ..., n, you are given an integer x(i).

Etc.

Lesson: In computing, clear communications are important. Those communications should be in a natural language, e.g., English.

My second remark would be to consider the part of the problem statement:

n < 0 means increment up to n occurrences of val from right-to-left (backward)

So, with this statement, if n < 0, then there is nothing to do since we cannot do something a negative number of times.

So it appears that what was intended was:

n < 0 means increment up to -n occurrences of val from right-to-left (backward)

In the US, long the main sources of employment that use math are efforts in US national security.

So, at one of the top dozen or so US research universities, about 60% of the annual budget is from research grants from the US Federal government. The main sources are NSF and NIH. The main fields are the STEM fields for US national security and biomedical for medicine.

So, an academic publish or perish job should try to be a job that gets grants from NSF, etc. In particular, such a job is not just for 'ivory tower', do what you want, 'generalized abstract nonsense' but is for what the NSF, etc. want to fund.

Associated with such funded academic groups can be research positions that just do research and don't teach.

Okay, here is a general view of such work: Generally the most respected work in a field 'mathematizes' the field. So, fields that got so 'mathematized' include physics, chemistry, electrical engineering, electronic engineering, mechanical engineering, chemical engineering, aeronautical engineering, computer science, operations research, and finance.

Of course for finance, see the career of James Simons.

Now being 'mathematized' is much of genetics aimed at cancer research. There see the work of mathematician Eric Lander and, in particular, his lecture

http://www.princeton.edu/WebMedia/flash/lectures/20100419_pu...

April 19, 2010, Eric Lander: "Secrets of the Human Genome".

Notice that it's possible to get a Ph.D. in 'applied math' without setting foot in a pure math department, especially if you already have a good Master's in math. So, get your Ph.D. in some part of 'engineering'. E.g., some departments of electronic engineering have profs interested in whatever in principle electronics might be used for and, thus, e.g., pursue stochastic integration and nonlinear filtering and far out directions in 'information sciences'. See the work of D. Bertsekas at MIT -- he's a terrific applied mathematician, but I have no idea what department he's in. At Princeton there is a chemical engineering prof who, really, does nonlinear optimization for petrochemical plants -- he is popular in Houston. At Stanford notice the career of D. Luenberger -- again, a terrific applied mathematician but maybe not in a pure math department. Similarly notice E. Cinlar at Princeton, a terrific mathematician but not in a math department. Also notice the career of H. Kushner at Brown's Division of Applied Math -- he worked on a huge range of topics, sometimes even nurse scheduling. Your Ph.D. might be in 'systems analysis' or 'engineering/economic systems' or some such.

Notice the broad field of stochastic optimal control, e.g., from E. Dynkin, D. Bertsekas, and R. Rockafellar. Then notice that Intel has stated that they already know how to build processors with 1000 cores and have the cores work together effectively and notice the extreme ability of stochastic optimal control to make use of millions of threads of conceptually simple parallelism.

Another field, I once did some work in, is multivariate, distribution free statistics, especially where we can assume a lot of data. There's more to do in that direction.

Then notice that with a Ph.D. you could get a job as a prof in a B school. They might need people to teach mathematical finance, operations research, statistics, 'supply chain optimization', 'constraint logic programming', 'production scheduling', etc.

Also consider mathematizing sociology, and for that notice the work of James Coleman and Pete Rossi, both past presidents of the American Sociological Association, and also Leo Goodman.

Don't miss the work on medical research by Persi Diaconis at Stanford, e.g.,

http://videolectures.net/nips09_diaconis_bamc/

along with mathematician David Freedman, e.g., as in

David Freedman, 'Brownian Motion and Diffusion', ISBN 0-387-90805-6, Springer-Verlag, New York, 1983.

No doubt the economists continue to struggle with math; consider moving into economics where your math background could be a big advantage. Considering that obviously the economists don't have even a weak little hollow hint of a tiny clue about either how to avoid a depression or how to get out of one, there are some good reasons to do good work in economics if that is possible.

If you have some ideas about 'applied math', e.g., maybe something reasonably respectable mathematically in 'data mining', 'machine learning', 'artificial intelligence', or, really, nearly anything in engineering or beyond, some math departments will be interested. Why? Because they want students and some of the glory of hot fields and applicable work. If you can also get an NSF grant, so much the better.

Likely you can do applied math in a computer science department. E.g., somewhere in California is a prof who specializes in speculative applications of convex sets and functions. You may have noticed that some computer science departments are eager to be more mathematical but are struggling with math, sometimes, even at famous departments, struggling poorly with what is commonly taught in a junior level course in abstract algebra.

Outside of academics, considering the main source of money, maybe it should be no surprise that work in US national security has long been a good place to find a job in applied math. So, consider the various military labs around DC and, then, the companies who work with them. So consider the whole universe of US national security work. E.g., computer security is a hot topic, and monitoring is basically statistical hypothesis tests with false positives and false negatives. There's plenty of good, new applied math to be done there.

Next, understand that generally there is a problem asking that someone else create the job you really want. Indeed, if you get very far from academics, you will have a tough time finding anyone with money enough to hire and also even able to write a good job description for an applied mathematician. And they would be terrified to have someone reporting to them doing work they didn't understand.

So, really, for the job you want, you need to create it for yourself. For this, the first consideration is, where will the money come from? In particular, who will your customers be? So, find something to do where customers are willing to pay for something better, use some math to provide something better, and deliver the results via computing and the Internet.

The VC team with the best technical qualifications and about the best background in research relevant to 'information technology' is likely YC. Alas, maybe now they invest only via their two annual 'incubator classes'.

About the best I can suggest is to try some people who:

(1) have shown some strong interest in being thoughtful (and not wacko) and least independent and hopefully innovative and

(2) have been successful and, thus, have some confidence to go 'outside the lines' or the herd or whatever.

If they are VCs with money from limited partners, then likely they need already to have been quite successful to keep their limited partners from complaining about going 'outside the lines'.

If they are angel investors, then they need a good supply of their own money and a lot of experience and success in investing.

I would avoid any investor with a lot of definite opinions: A good investor needs to appreciate and accept that they can't know more than all the entrepreneurs and for a particular, good project can't know more about that project than that entrepreneur so just MUST shut the f^^k up, turn off the electronics, listen up, and think. It's the entrepreneur who is supposed to be cutting the new trail at the frontier and the investor who is supposed to be following along behind.

In particular I would set aside any investor with a long list of Dos and Don'ts on how to give a technical presentation. E.g., here we have a VC who majored in history, got an MBA, and joined a startup in marketing and then became a VC. And there we have an entrepreneur who got a technical Bachelor's, Master's, and Ph.D., taught undergraduate courses as a graduate student, gave a successful oral defense of his research, presented research at respected conferences, sat in classes for years listening to some of the brightest and most accomplished people ever to live, taught in classes for years, etc.

So, suddenly this VC tells this entrepreneur that they don't know how to present technical material and needs this new, chopped up, watered down, made up, deliberately brain-dead, novel format. Bummer.

As bad as that is, think ahead to the VC being on the Board and that entrepreneur presenting to the Board a new project, with some research content, for improving the 'secret sauce', the ad targeting, the data collection, the system security, performance, or reliability, etc. Before 90 seconds into the presentation, the VC is connected Zynga on his iPhone. Bummer.

So:

(1) VC Brad Feld may be willing and able to be innovative. He likes to say that he invests only in 'themes' or some such, but he may conclude that your project fits one of his themes and may bend a theme otherwise.

(2) Similarly for VC Fred Wilson.

(3) A-H has tried to be at least somewhat flexible in what they do. They've raised a lot of money and apparently recently reported gains from their first fund to their limited partners. So with happy limited partners and lots of 'dry powder', A-H may take a relatively serious look at your project.

(4) Try VC Fred Destin at Atlas -- hit him over the head with something to see if he is just asleep or really dead. If he wakes up, then there is a chance that he will at least start actually thinking. He's been successful in business but been a VC for only a short time and, thus, has a chance of being smart without falling in with the VC herd behavior.

(5) Try Andy Fillat at Leapfrog. He can pay attention and be both insightful and forthcoming.

(6) History suggests that angel Andy Bechtolsheim can quickly see a diamond in the rough and write a check.

(7) Of course the same for angel Ron Conway (who has a son at A-H).

(8) One of the brightest, best qualified, wealthy people in the country is James Simons. If you can get to him, do so. Maybe claim that you need some help with S. Chern's notes on differentiable manifolds or want to teach differential geometry to 10th graders!

Otherwise I would look for investors with rock solid technical backgrounds and accomplishments with solid experience in business with usually a Ph.D., at least in computer science, better still in theoretical parts of electronic engineering, still better in applied math. Might look for profs at Stanford, CMU, or MIT.

Part III

I move on to two more issues:

(1) The Hay Stack.

Apparently a venture firm can have a polished Web site put up for them, get some publicity by writing columns in the tech media, and then, presto, get in their e-mail a huge stream of contacts from entrepreneurs.

Maybe they will fairly quickly get 2000 such contacts a year.

So, such a VC gets a huge stack of hay in which they are looking for golden needles.

Here's my guess at where the VCs are making a huge mistake: They are trying to learn from the hay. They start to conclude that the hay is 'representative' of 'what is going on in information technology', read a few hundred such contacts, and conclude that they are developing "deep domain knowledge". They start to believe that the best possible investments are from the better looking stalks of hay.

A huge problem here is that they just have no other very good criteria. In strong contrast, the guys who did the physics for the Navy's version of GPS had physics going for them; they were correct, knew they were correct, knew just why they were correct, and could convince Navy technical problem sponsors that beyond any doubt they were correct, feasible, practical, realistic, etc. No important role for guessing or intuition. Not a lot of doubt. Not much risk. And whatever else was in whatever stack of hay was just irrelevant. And revolutionize military strategy.

As an explanation there is:

"It is still an unending source of surprise for me how a few scribbles on a blackboard or on a piece of paper can change the course of human affairs."

Stanislaw Ulam

VCs and their limited partners might want to start to listen up. Especially from Ulam: On its first try, his second contribution yielded the energy of 15 million tons of TNT. On its first try, his first contribution yielded about 15 thousand tons. Before that he had a nice result in probability the French probabilist Le Cam called 'tightness' with a nice presentation in Billingsley's 'Convergence of Probability Measures'.

(2) The Research

I can believe that the VCs don't receive many promising proposals based on research. One reason is that not many talented, well trained researchers cross over to become entrepreneurs. A second reason is, researchers who contact VCs get such a poor reception that researchers have been discouraged.

So, VCs don't see much promising research in their incoming e-mail.

We might also anticipate that a VC has described to their limited partners what projects they will fund, and research on paper is NOT included!

There have long been plenty of US DoD technical problem sponsors who were 'innovative' with astoundingly high batting averages. Somehow VCs seem to have ignored such work. Net, looking for an 'innovative' angel or VC looks like looking for hen's teeth.

Final remark: What to do about the situation? For me, as an entrepreneur, just be successful, thus, solve the problem for myself, and let others draw what they will from my example.

More generally, I don't know what VCs would say to what limited partners to make a significant difference, and then I don't know how they would get good projects based on research.

So for now for me, the flip side of the problem is an opportunity.

Part II

So, what's going on here?

I start with five general points:

(1) Status of Venture Capital.

In a word, now and for the past 10 years or so, there is just one appropriate word for venture capital -- it sucks. To be more clear, the returns to the limited partners from the VC 'asset class' have been not just poor but commonly negative. It may be that there are fewer than 20 VC firms in the US that have a good track record for their limited partners.

From a graph posted some months ago by VC Mark Suster, over the past 10 years the number of venture partners has fallen from a few thousand to a few hundred, or some such. The VC layoff rate compares with the UAW Detroit layoff rate.

(2) What's Available in Practice.

It's easy to guess that, being on the VC side of the table, it's not easy to find really promising projects. That is, VCs that say that they need and want more and better proposals likely have a good point. For me, I'd rather be on my side of the table so that if I want a better project all I have to do is think of one; on the VC side of the table I'd have to wait for such a project from entrepreneurs -- bummer.

(3) What's Available in Principle

Still, whatever the daily reality is for VCs as they read their e-mail, in principle the entrepreneurs and their VCs should be printing money in huge piles.

Why? There is one, just one, key to printing money: Create economic value. For that key, there is one, just one, main approach: Increase economic productivity, that is, turn out more, much more, value per person-hour worked. For more productivity there is essentially just one good approach -- automate the work. For automation, there is now exactly one, unique in all of history, grand champion technique -- computing, especially aided by the Internet and readily available infrastructure software. So, find some much needed activity and automate it.

(4) What's Available with Some Effort.

Whatever work X the entrepreneurs and their VCs are doing, there's more, much more, that is clearly available, with a fantastic track record, far better than the VC track record, that clearly totally blows the doors off X.

We get the examples of how to do such powerful technology from the all-time, unique world-class, grand champion of everything technical, the US DoD who, thankfully for US national security, has been wildly successful for about 70 years now.

War story: Around 1940, the American Chemical Society, seeing war coming, sent a representative to the US War Department offering to be a liaison with the US community of chemistry. Soon he got back a letter of thanks but saying that the War Department already has a chemist. About five years later, there had been a, uh, change of attitude: Eisenhower said "Never again will US science be permitted to operate independent of the US military.". Vannevar Bush, James Conant, etc. arranged that US research universities would get "an offer they couldn't refuse": Take DoD money for research or cease to be a competitive research university. Result: Since then the top few dozen US research universities have gotten about 60% of their annual budgets from research grants and have dominated academic research in the world, and the US DoD is right there, sure they are not falling behind in fundamental research and getting a good stream of well selected and trained researchers.

Congress and the US DoD take research and technology very, very seriously.

So, comparing with entrepreneurs and their VCs, I will mention two differences:

(A) In the system set up by the DoD, given a problem, it is common to attack it by doing some original research, e.g., that can pass peer review. Often the work -- in physics, electronic engineering, mechanical engineering, chemistry -- has been highly mathematical.

This research, where to do it, how to do it, the standards, and the culture, are rooted in the best US research universities but also extend to many laboratories and companies working on US national security.

(B) Given some research, the results can quickly receive quite competent and serious evaluation, often from presentations just on paper. With that evaluation passed, the project can move quickly through prototype software, breadboard hardware, demonstration systems, fully militarized design and prototype development, full testing, and large scale production and deployment.

Given a good evaluation of the research, the rest has an excellent track record of success.

E.g,, at one time I was programming in the group that did the orbit determination for the Navy's version of GPS, and there I learned the story of how that system had been proposed to the Navy: Not much more than some physicists scratching on the back of an envelope. And the final system worked as planned, and a test receiver on the roof routinely 'navigated' its position to less than one foot. Yes, the later Air Force GPS is more accurate. But the Navy's system was just crucial for the US SSBN fleet and its missile targeting for years.

So, in strong contrast, for the VCs, for (A), they just will NOT take research seriously. They just will NOT do it.

Indeed, we might look at how the US research universities and the US DoD evaluate research and the qualifications they use: A researcher who publishes some good research can be invited to be a reviewer of a journal or of proposals for funded research. A good reviewer can be invited to be a journal editor. A good editor can be invited to be a journal editor in chief. Etc. Then that system just will not permit itself to be seen as unable to evaluate original research, no matter how advanced, no matter what the prerequisites, no matter how involved. Period. Write a paper with title "P = NP" and submit it to any of at least a dozen journals, and within a year will get back a letter with "The first serious error is at line n on page m" or "Your check for $1 million from Clay Mathematics is in the mail.".

Sure, the path to reviewing papers is usually a long and challenging Ph.D. followed by a few years of successful research. Editors in chief typically have plenty of gray hairs. This system for evaluating research is highly competent but challenging.

Then, for the VCs, in strong contrast, apparently one could count with shoes on all the VCs who could even direct a competent evaluation of some research; this is not a surprise since one could count with shoes on all the VCs with a relevant Ph.D. and a solid research record.

Next, for (B), funding based just on research just on paper, the VCs won't do it. At most all a proposal will get back are laughs. Send a VC a paper with some research, and the VC may break a leg slapping his thigh in wild laughter.

The US DoD has been funding projects based on research just on paper to knock balls out of the park and into orbit with high reliability from early in WWII to the present, and VCs won't even come to the ballpark.

So, net, we have to suspect that much more ROI would be available to the VCs "with some effort", that is, by taking research seriously.

(5) Fundamental Issue.

If we ask that each entrepreneur found "another Google" and be worth $200 billion, we start to have doubts. E.g., the whole US economic pie is not that big, and we wonder if computing, etc. is powerful enough to grow that pie that big soon.

So, without a big change in pie size, the number of $200 billion successes is small, only a few each decade.

I believe that by taking research seriously we can multiply this few by several quickly. So, a fundamental issue is that we are still limited to not very many $200 billion successes per decade.

But apparently for my claim of "multiply this few by several quickly", there is hardly a VC or limited partner in the country who would believe any such thing and fewer still, say, zero, who would bet more than 10 cents on it. They would be afraid they were just being swindled, and they don't want to be swindled out of even a 10 cent cup of coffee.

Again, VCs and their limited partners seem to be suck solidly in evaluating projects just by traditional financial accounting but using some surrogate measures. In particular, they look at 'traction' but will NOT look at research.

Part I

Congratulations: Don't feel like the Lone Ranger! In your feelings, suspicions, observations, analyses, and conclusions, you've got plenty of company!

It took me too long to accept that what you describe was the actual reality. Or, you ask for

"innovative VC/Angel funds"

as if we should of course assume that some exist!

For my role and position in this issue, and the 'side of the table I'm on', I'm an entrepreneur writing software for a startup. For this startup I'm the founder, 100% owner, CEO, CFO, Chief Scientist, CIO, biz dev lead, development manager, developer lead, QA tester, software architect, 'full stack' developer, server farm bridge manager, NOC manager, hardware manager, network manager, systems administrator, and janitor. How else?

To help my response to your question I will say a little more: What kind of software? A Web site. Who are my candidate users? Maybe 70% of everyone in the world with Internet access -- that is everyone on the Internet except total dead heads and small children. Porn? No, not at all; actually, nothing objectionable at all. Need? My view is that it's out there, big time, and now served at best poorly. My role? If I'm correct, then my work will serve the need by far the best in the world, with a 'technological' barrier to entry that should last plenty long enough to get barriers to entry from brand, network effects, virality, lock in, users knowing how to use my site, and some cases of continuing contact. Key? Sure, some crucial, core 'secret sauce' that is, really, some applied math with some original theorems and proofs with some advanced prerequisites.

So, I'm addressing what seems clearly to be a huge need and for my 'seriousness' and technological 'credibility' am standing on a strong, i.e., mathematical, foundation.

Status? I have all the crucial, core software ready for at least significant production, likely for much more, and now am doing just routine, and relatively simple, Web site development.

Obstacles? I selected Windows instead of Linux and, thus, had to get good with Visual Basic .NET, ADO.NET, ASP.NET, significant parts of the rest of .NET, and some obscure points of SQL Server administration, and this learning has been only routine but, still, has taken far, Far too long. Otherwise, no obstacles.

Financial status? For VCs and angels, at present my project and a dime wouldn't cover a ten cent cup of coffee.

Next steps? Finish the software, get some initial data, plug together a first server, say, an 8 core AMD processor on a $125 motherboard with 16 GB of ECC main memory and several 2 TB hard disks. Use the Microsoft BizSpark program to get needed copies of Windows Server and SQL Server. Get a static IP address, a domain name, and 15 Mbps upload bandwidth to the Internet. Get some ads from ad networks. Go live and get publicity, users, and ad revenue.

So, assume can get paid $1 per 1000 ads displayed, a Web page with 3 ads can be sent for 200,000 bits, and half fill the 15 Mbps upload bandwidth 24 x 7. Then revenue should be

1 * 3 * 15 * 10^6 * 3600 * 24 * 30 / ( 2 * 200,000 * 1000 ) = 291,600

dollars a month. Ah, grade school arithmetic!

As far as I can tell, equity investors will be interested in my work about the time -- and NOT before -- I have monthly revenue of $291,600 growing rapidly. Then they might want to invest a few million dollars for about 30% of my company. They would get 1-2 Board seats with power to set my compensation, replace me, and sell my company, and I would own 0.00% of my company and get back maybe 55% of my company after a 4 years vesting period if the Board didn't fire me first.

But with monthly revenue of $291,600, why, just why, would I need any equity investment at all? And more seriously, why, just why, would I accept such investment terms?

Basically the soonest equity investors would write a check is some months after I would no longer be interested in their check.

That's my company.

To generalize a little, this grade school arithmetic

1 * 3 * 15 * 10^6 * 3600 * 24 * 30 / ( 2 * 200,000 * 1000 ) = 291,600

is widely understood! So, from the first day I started on this project all the way through 'traction' significant and growing rapidly, I do and pay for everything.

Then, with $291,600 a month in revenue growing rapidly, and thus, qualify for equity funding, suddenly I need an equity investment?

Let's see: From the assumptions above we're talking a peak of

15 * 10^6 / ( 200,000 ) = 75

Web pages a second. An 8 core server for about $1000 might be able to do that, but even if not a week or so of an 8 core server being busy would generate enough revenue for some nice shelf units full of such servers that could deliver 75 pages a second. That is, the computers and bandwidth are cheap; given traction enough to keep a server busy, should have revenue enough to grow server capacity quickly; similarly for bandwidth; net, given the traction equity investors want, don't really need equity funding for computers and bandwidth.

The equity funders insist on all the development already being done, so can't get equity funding for that. What's left for the equity funding?

A lot of people can see this situation: Likely business is in line to see a lot of startups that don't take equity funding: If they get traction, they don't need equity funding. If they don't get traction, they can't get equity funding. Equity funding is beginning to look useless.

Now for more general remarks for this thread: As far as I can tell, for my work or essentially anything in 'information technology', the idea of

"innovative VC/Angel funds"

is nearly irrelevant. That is, as far as I can tell, one could count on one hand all the 'information technology' equity investors in the US who would be at all "innovative". That is, they just will not, Not, NOT look at anything technical. They just will NOT do it. Instead, their investment criteria boils down to one word: Traction. Being 'innovative' has nothing, nichts, nil, nada, zip, zilch, zero to do with their investing.

In more detail, for such equity investors 'traction' is numerical evidence that can be a surrogate for what a private equity investor would get from an audited financial statement. That is, the early stage information technology equity investors are thinking like accountants except are willing to use surrogate measures instead of just strict accounting measures. Then anything 'innovative' and a dime won't cover a 10 cent cup of coffee.

There is some evidence that biomedical venture capital can be more 'innovative', but I concentrate on 'information technology' and set aside biomedical, 'clean energy', materials science, etc.

Moreover I concentrate on just US early stage information technology equity investment.