15 ms·

Rise of the Scientific Programmer

walshemj 12y ago
I started on the science/engineering side at BHRA (on campus at CIT) only problem with technical/scientific vs commercial is the pay is so poor.
- aliostad 12y ago
  Yes. And this trend is reversing. Have a look here: http://www.itjobswatch.co.uk/jobs/uk/machine%20learning.do http://www.itjobswatch.co.uk/jobs/uk/machine%20learning.do Salaries have gone up by 16%
  - walshemj 12y ago
    I know I am tempted to go back to the more technical side - though when a recruiter asked about working on the MET police's Registry I wasn't that keen :-)
  - zo1 12y ago
    The page you linked to only say 9% increase? 55k -> 60k
- toufka 12y ago
  And academically the prestige is poor. One is not granted 'research time' to develop software, but to 'get things done' (see u/Danso's comment). As such there's no one to take the first step in actually making software that would benefit anyone. And in the generous circumstance that one can be allotted time to write the software, the result is a pat on the head - 'good job' - for reducing everyone's workflow from weeks to minutes. Sometimes you get an acknowledgement. And no one will ever support/read your software when you leave - it will be used ritualistically until the lab's last computer's OS no longer supports it. On the topic I see two other significant problems: 1) In basic research there is often a need for 'Every Option' style software - you're doing something that's never been done before and you need to be able to tweak it exactly how you need (but also be able to 'just hit run' for a first pass when coming from your native field). And those types of software are inherently a mess to design and build (ie. photoshop, CAD, 3D, programming languages). 2) Some of this software can only be written by those who directly do the research - or someone who very closely collaborates with them. Scientific software contains scientific assumptions in it that are very hard to evaluate if you're not part of the field. Deciding to go right-way-round, or rounding up, or leaving off the last element in an array, or any other of those programming tricks can really mess up scientific work. Or conversely, using the entire array, using a non-weighted, 'avg' or treating the red channel mathematically the same as the blue channel is a very different way of designing software than other industries - and is not common and rarely given much thought.
  - walshemj 12y ago
    true I was talking more about doing tech/scientific programing as a discipline - I started as an associate professional what's some times called a professional apprentice though at the time if you had called me or my pears apprentices we would have told you where to go in no uncertain terms I recall I was considered a bit flash because I used Mixed Case in my Hollerith statements for input prompts.
  - tjradcliffe 12y ago
    There's a modest living to be made at the interface between "scientific programming" and "commercial implementation of programs scientists write". This is an under-appreciated niche because it takes a lot of work to get into it: you need to be a good developer and a good scientist with diverse experience. My way in was through experimental and computational physics, but there are certainly other avenues these days. Modern statistics is the biggest piece of the picture that every interesting area has in common. If you're interested in scientific programming you need to understand Bayes as well as algorithms etc. I have friends in psychology, biology, etc and we can communicate surprisingly well because we all speak the same statistical language. But more importantly you need to understand how scientists think. They are amazingly hard to pin down to the kind of specs developers need. For example, a guy on my team once said after talking to one of the scientists we were working with for a couple of days, "I now have a much better grasp of the problem, but I still don't know what the default value of this parameter should be." I spent ten minutes talking to the scientist and came back and told the developer "5", because I could tell from the way the scientist was talking that he had no clue if the number should be 3 or 10, but seemed to be favouring the lower values. I didn't need to understand the problem domain in detail to make that judgement, but to have a reasonable grasp of the psychology of working scientists. So far as I know, there's no way to get that without working as a scientist yourself.
amelius 12y ago
The problem with scientific software is that the market is so small. It is far more profitable to just write mainstream software.
- Retra 12y ago
  It's more profitable to do scientific research slowly and tediously than it is to automate it?
  - texthompson 12y ago
    The people capable of the automation have financial incentives to move to industries with broader appeal. There are a lot of people doing great scientific computing work in academia (e.g. Sean Eddy, Titus Brown), but they are notable exceptions.
  - detaro 12y ago
    This of course is stereotyping, but for me it feels like there is truth to it: It has more prestige to have a bunch of Phd's doing busywork in the lab than to pay for software which could replace them. Expensive equipment at least looks cool and impressive if the bosses go round with guests and want to show off where the money went, software can at the very best make pretty pictures on a screen somewhere. Academic research is not necessarily oriented on profit on that level: It cares about getting grant money, but not always about using that money as efficiently as possible.
    - nitrogen 12y ago
      software can at the very best make pretty pictures on a screen somewhere. The solution we have for that in the commercial software world is really big screens.
      - douche 12y ago
        And report graphs. Lots of report graphs. Oh, look, a real-time dashboard. Do the metrics its tracking mean anything to anybody that is reading it? Who cares? This seems to be one of the main ways salesmen and execs communicate over enterprise software deals.
danso 12y ago
The other night I was searching for Python science books and stumbled across this one titled "Python for Biologists: A complete programming course for beginners"...the homepage for the book is here: http://pythonforbiologists.com/index.php/introduction-to-python-for-biologists/ http://pythonforbiologists.com/index.php/introduction-to-pyt... Admittedly, it only has two reviews on Amazon, but they're both five stars, and they both seem to come from biologists who are apparently thrilled at being able to leverage code for their work...the funny thing is, the book itself is not "advanced" as far as what most professional programmers would consider "advanced"...the Beginners' book ends with "Files, programs, and user input" and the Advanced book ends with Comprehensions and Exceptions... I think we as programmers vastly understimate how useful even basic programming would be to virtually anyone today. I work at Stanford and it continually astounds me when I run into non-programmers who are otherwise doing data-intensive research, who fail to see how their incredibly repetitive task could be digitized and implemented as a for-loop. It's not that they are dumb, it's that they've never been exposed to the opportunity. And conversely, it's not because I'm smart, but I literally can't remember what it was like not to break things down into computable patterns. And I've been the better for it (especially because I'm generally able to recognize when things aren't easy patterns) Sometime ago, I believe it was Stephen Hawking who speculated that the realm of human knowledge was becoming so vast that genetic engineering of intelligence might be required to continue our progression...that may be so, but I wonder if we could achieve the same growth in capacity of intellect by teaching more computational thinking (and implementation), as we do with general literacy and math. As Garry Kasparov said, "We might not be able to change our hardware, but we can definitely upgrade our software." http://www.nybooks.com/articles/archives/2010/feb/11/the-chess-master-and-the-computer/ http://www.nybooks.com/articles/archives/2010/feb/11/the-che...
- mariodiana 12y ago
  > I think we as programmers vastly understimate how useful even basic programming would be to virtually anyone today. I used to work in second-level support. Out of a department of maybe 12, I think 2 of us knew how to program. I hate to even begin to describe some of the things I saw people trying to do by hand, and we were a software development company. I remember pleading with this one guy, "Please, before you ever try to do something like that again, come find me and ask if there is a way to automate the job." There are so many people that would stand to benefit if only they had so much exposure to programming that they could tell when it would be crazy to not grab the guy next to them and ask if a quick script might not turn a 2-day job into 1 hour's worth of work -- coding, testing, debugging, and execution all included. Sadly, even people (nominally) in technology jobs are ignorant of this.
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Fede_V 12y ago
As other people have mentioned - the problem is that academia rewards producing papers, not stable software libraries. For example - scikit-learn is an amazing project, lead mostly by a small group at INRIA with Gael at the helm - and in terms of academic prestige, scikit-learn is probably 'worth less' on your CV than a couple of Nature papers. This is of course ridiculous - scikit learn is used by a huge amount of people, it takes an insane amount of work to run the project, yet the incentives are what they are.
- saraid216 12y ago
  It's just a standard consequence of underfunding. No one gets paid to develop scikit-learn full time. An academic gets paid by getting grants. When you look at a lot of the best libraries out there, how were the top contributors employed at the time? Generally speaking, you'll find that most of them were employed by companies who were willing to let them burn money to develop the library. Universities don't have that luxury anymore.
  - Fede_V 12y ago
    There actually are some full time developers for scikit-learn - and they are amazingly talented. There's lots of difficulties in securing that funding though. Getting funding for permanent staff is incredibly tough - usually you have 3 year contracts for 'postdocs' that pay around ~40% of what a similarly skilled software developer could earn. The only stable positions in academia are professorship - and even those are going away in favour of positions where people are expected to pay increasing portions of their salary from their grants. How can you credibly offer someone 40-50k dollars a year, with no job security, tied to some grants that might or might not be renewed and expect them to turn down offers from companies looking for data scientists?
    - zo1 12y ago
      "How can you credibly offer someone 40-50k dollars a year, with no job security, tied to some grants that might or might not be renewed and expect them to turn down offers from companies looking for data scientists?" The same way that restaurant owners ask their servers to work for sub-minimum wage and expect them to bring that up to minimum wage via "tips"(Law mandates they bump that up to minimum wage of 7+ if tips don't cover it, but that's separate). Not entirely the same, but it's an alternate form of payment for services rendered. If you ask me, I think it's perfectly reasonable as long as there are no laws meddling and causing an increase of such "odd" remuneration schemes.
roflmyeggo 12y ago
Computational and biological sciences will likely meet on a financial equivalent to commercial software applications at the intersection of epigenetics and pharmaceuticals in the new few decades. When scientists begin to discover feasible methods to cure or manage previously incurable diseases (a more recent example of this has been attempts to cure Cystic Fibrosis), or more specifically reversing some of the diseases that our older baby boomer populations are suffering from via epigenetic methods, you can bet your bottoms that there will be a huge influx of capital in the sector and a subsequent increase in demand for computational biologists. Of course we could end up in a sort of quasi-understanding parallel to that of quantum mechanics and end up in a epigenetic limbo, but the general feeling is that of high hopes.
sgt101 12y ago
A post about data science and scientific programming featuring a set of graphs with no y-axis scale and labels. At my gaff this kind of presentation of data leads to "scrap the whole analysis and start again".
- aliostad 12y ago
  dude, have you ever used Google Trends? It is a cut and paste from the source. The y axis is the popularity as the title says. It is so obvious even Google has omitted it.
  - bbcbasic 12y ago
    should have labelled it "Magic Google Unitless Dimension"
    - sgt101 12y ago
      You can put all of the terms on the same graph; that would allow people to compare these on a like for like basis as opposed to "look at the shapes, here is an argument".
    - deleted 12y ago
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mollerhoj 12y ago
I believe the author is right - This is the reason why I'm spending my final ECTS points on statistics and machine learning.
iamwil 12y ago
What is deep learning currently applied to besides object recognition in images?
- basaah 12y ago
  https://en.wikipedia.org/wiki/Natural_language_processing https://en.wikipedia.org/wiki/Natural_language_processing
- tangentspace 12y ago
  https://www.quantamagazine.org/20141218-machine-intelligence-cracks-genetic-controls/ https://www.quantamagazine.org/20141218-machine-intelligence...
gpcz 12y ago
I'm having trouble understanding the definitions of these roles. I see the chart, but the terms are all vague to me. What does a data scientist do that a mathematician or scientist doesn't do, and what does a scientific programmer do that a data scientist doesn't do? My impression was that "data scientist" was a colloquialism for "statistician that knows how to program." Is a scientific programmer just a programmer that knows some statistics? Why is the direction important? The author says he/she feels that a programmer that knows statistics can make "more robust software" than the other way around, but what exactly does that mean? Do they mean "doesn't crash as much", or do they mean "gives the right answer more often?"
- tarikjn 12y ago
  for one thing statistics don't really encompass A.I., computer vision etc.
  - Toenex 12y ago
    Sorry but this is wrong. Statistics and probability theory underpin most of AI and machine vision.
    - tarikjn 12y ago
      One thing can underpin another without encompassing it. Mathematics don't encompass physics and finance.
- tjl 12y ago
  Basically all of my Master's and PhD work involved scientific programming, but it was definitely not data scientist work. You're making a big assumption that science is statistics. There's scientific simulations (and deriving the models) and scientific visualization as well. Those are only two out of many possibilities.
- collyw 12y ago
  Robust software generally refers to it not crashing. "Correct software" would be how I described software that "gives the right answer more often".
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juretriglav 12y ago
Shameless plug: In January I'll be focusing on a project which deals specifically with scientific software: http://sciencetoolbox.org/ http://sciencetoolbox.org/ This current version is a product of a hackathon, but this month will be improving it and adding functionality which brings the scientific software developer and her efforts into focus. Scientific software is gaining importance, but the recognition its developers get is trailing behind - I want to raise the level of associated recognition/prestige (among other related things). Some other projects rely on data collected here, e.g. a recommendation engine for said software that enhances GitHub: http://juretriglav.si/discovery-of-scientific-software/ http://juretriglav.si/discovery-of-scientific-software/ Shameless plug continues: if you'd like to keep track of what I'm doing I suggest you either follow the project on GitHub (https://github.com/ScienceToolbox/sciencetoolbox https://github.com/ScienceToolbox/sciencetoolbox) or Twitter (https://twitter.com/sciencetoolbox https://twitter.com/sciencetoolbox).
- toufka 12y ago
  Units. I'd love so much for a standard fileformat/interpreter/concept that contained SI units as a requirement for most datatypes. Much to be learned from my TI-89.
  - dalke 12y ago
    I don't know what you mean by "as a requirement for most datatypes". I work with molecules, where distances are typically measured in Angstroms and masses in amu. I don't want to have factors of 1E−10 and 1.660538922E−27 hanging around my code.
    - detaro 12y ago
      I assume that he means that the type system knows that these are in useful units, and tracks them, so you can have it check that your calculation actually results in a value of the unit you expected.
      - wtallis 12y ago
stared 12y ago
Plug: I made a list of software that is useful for scientists: https://gist.github.com/stared/9130888 https://gist.github.com/stared/9130888.
elliotec 12y ago
I have noticed a general trend upwards in the interest of scientific programming for a few months now, and the community (most specifically Hacker News) has driven my interest in that area as well. The idea of functional programming and thinking in mathematically sound ways really appeals to me, but my lack of math and comp sci background is holding me back from going full-speed learning and getting better at it. I feel many of us are lost swimming in a sea of opinions and juggling frameworks du jour, development methods, and business strategies, that it keeps us from focusing on improving our skills in areas that matter. This frustrates me and I've been looking for ways to get out of it. There is also this fear of another bubble mixed with trying to keep up with the trends and hipness of the industry, to remain gainfully employed. I realize I am sort of just reiterating the authors point, so I guess what I'm saying is I agree.
- kirse 12y ago
  HN tends to gives this perception of rapid change because you're always hearing about hot new tech and how great it is from the various tech evangelists. It's a side effect of their faith/vision in their own tech that you feel like you're missing the train. However, I've been following HN for 7.5+ years now, and change is hardly as fast as you think. There really is nothing new under the sun, and you see the same core principles (and human needs) being expressed as the underlying technology changes. The core principles of functional programming have been around since the 30s as lambda calculus, and since the 50s with their initial expression as LISP. It's better to learn FP to add a new way of thinking about problems to your toolbox, rather than treating it as a panacea of programming. If you're feeling lost in the sea, define your lighthouse. HN will most certainly have you paralyzed by the many waves of every new choice if you haven't defined a clear vision for how you want to harness your finite energy.
- randlet 12y ago
  "I feel many of us are lost swimming in a sea of opinions and juggling frameworks du jour, development methods, and business strategies, that it keeps us from focusing on improving our skills in areas that matter. " I sympathize with this a lot. It's extremely hard to prioritize learning new things and improving existing skills over just trying to plow through all the things on your plate. However, I keep trying to remind myself that the best way to get more stuff accomplished is to keep "sharpening the saw"[1]. Making time now for improving your skills will pay dividends in the future. [1] http://c2.com/cgi/wiki?SharpenTheSaw http://c2.com/cgi/wiki?SharpenTheSaw
tarikjn 12y ago
I think many in the comments are misunderstanding what the author mean to say in his post. He is not talking about working in academia or making scientific software. He talks about improving one's skills in basic science and such fields in computer science as A.I. which have historically been entrenched more in academia than industry.
- sqrt17 12y ago
  The author of the post started by getting into programming/CS by wanting to earn more/better money, by picking up an Access book and working it out from there, and now that he's more established, he looks down at the young hungry people who picked up an Access book in the hopes of more/better money, running through all the tropes that people who got where they are through knowledge will use. Yes, it's useful to understand things better, and to know math. And as always since the first CS degree started, CS people gripe that people should Know More Math. Sure it helps. Other, less prestigious, things also help but you don't hear people griping about it. TDD allows the idiots in. Yes, that's effectively why you want TDD, you want to get more mileage and solving more complex or more bug-sensitive problems using the same people. Building software is not about being smart (although that helps on occasions), it's about getting stuff done. Yes, machine learning and AI are the new kids on the block, and like Web programming, they will see a bloom of increased customer demand, and like Web programming, we'll get a progression from bespoke boutique software to frameworks that make people's lives easier to frameworks that allow any person with the intelligence of a pet rock to do simple stuff productively. Why is that? Because building frameworks is the only way that the smartest people can earn money faster than programming the (N+1)th variation on that theme everyone follows -- frameworks are what make people more productive, or allow you to use a workforce that's more accessible. As a Wizard With a Pointy Head (aka academic), I'd say that the need for Wizards With Pointy Heads in production work is often overestimated and/or idealized. There is a large number of PhD graduates, and the market happily gobbles them up (indeed, realizing that you can hire PhDs and have them do productive work is one of the things that made Google successful as a company back in the early 2000s).
- dj-wonk 12y ago
  I like the spirit of the article, but in my view, much of the author's writing falls prey to "either-or" thinking. See https://en.wikipedia.org/wiki/False_dilemma https://en.wikipedia.org/wiki/False_dilemma. > We can argue about how to version APIs and how a service is such RESTful and such not RESTful. We can mull over pettiest of things such as semicolon or the gender of a pronoun and let insanely clever people leave our community. We can exchange the worst of words over "females in the industry" while we more or less are saying the same thing, Too much drama. > But soon this will be no good. Not good enough. We got to grow up and go back to school, relearn all about Maths, statistics, and generally scientific reasoning. The programming community is large and diverse. We can do all of these things, including the rigorous (what the author seems to call "scientific" [1]) ones and the UI/marketing ones too. I think this diversity is a strength, as long as managers and entrepreneurs find and retain the skill-sets necessary for their domains. [1] I prefer to use "science" to mean falsifiability, preferably with strong experimental designs. Much of what the author talks about is mathematical rigor (from computer science), which is also important, but not experimental science. See also: https://en.wikipedia.org/wiki/Philosophy_of_science#Defining_science https://en.wikipedia.org/wiki/Philosophy_of_science#Defining...
crb002 12y ago
Pair programming keeps it human, and transfers knowledge very well. TDD is about reproducibility of results, which is very in line with the scientific method. Benchmark tests will show you when your solutions are getting out of hand on performance. The sunk cost fallacy is a big problem. Moving to a new platform like HTML5/iOS/Android gives a short reprieve, but soon those proprietary code bases will age. The other big problem is that usually a smaller portion jobs goes towards management in flatter organizations. Managers want lots of layers for job security. Eric Meijer is right that small teams which are given narrow mission objectives instead of detailed requirements, and measure their problem domain instead of guessing, will be effective. I'm curious if a Fat-Tree model of management will take hold, http://en.wikipedia.org/wiki/Fat_tree http://en.wikipedia.org/wiki/Fat_tree You get a flatness that improves communication latency, lots of bandwidth, and managers are happy because there are a lot of jobs at the top.
placebo 12y ago
Mostly agree with the article. Being myself fascinated with machine learning and in the process of refreshing mathematic knowledge I haven't used since university (too many years ago) in order to dive deeper into it, I can definitely relate. However, I think the main point is not that software developers should all hone their academic math skills (that would be probably be pointless for many if not most software developers), but rather that it would be best if software developers would strive to follow the scientific mindset when developing software - In my experience, occam's razor is just as important in software development (design, architecture, algorithms, testing, you name it) as it is in physics, chemistry or other sciences and it is this aspect (which I feel is the most basic and most important) that gets lost sometimes in the noise of software development trends and fashions.
samuell 12y ago
A look at the trends for "R programming", compared to "Python programming", is quite interesting too: http://www.google.com/trends/explore#q=R%20programming%2C%20python%20programming&cmpt=q http://www.google.com/trends/explore#q=R%20programming%2C%20... (Their curves are more or less parallel since 2011)
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michaelochurch 12y ago
First of all, I hate this "Agile" nonsense. I've seen it kill companies. It's truly awful, because it gives legitimacy to the anti-intellectualism that has infected this industry. It's that anti-intellectualism that, if you let it, will cause a rot of your mathematical and technical skills. Before you know it, you've spent five years reacting to Scrum tickets and haven't written any serious code, and your math has gone to the birds as well. It's insidious but dangerous, this culture of business-driven engineering mediocrity. I hope that it'll be the fakes and the brogrammers who get flushed out in the next crash. Who knows, though? Obviously I can't predict the future better than anyone else. To me, Python doesn't feel like a "scientific" language. Python's a great exploratory tool, and it's got some great libraries for feeling out a concept or exploring a type of model (e.g. off-the-shelf machine learning tools). That said, science values reproducibility and precision, which brings us around to functional programming and static typing... and suddenly we're at Haskell. (Of course, for a wide variety of purposes, Python is just fine, and may be a better choice because of its library ecosystem.) I do think that, as we use more machine learning, we're going to have a high demand for people who can apply rigor to the sorts of engineering that are currently done very quickly (resulting in "magic" algorithms that seem to work but that no one understands). I also agree that "deep learning" and machine learning in general are carrying some substance, even if 90% of what is being called "data science" is watered-down bullshit. I still don't feel like I know what a "scientific programmer" is, or should be. And I'd love to see the death of business-driven engineering and "Agile" and all the mediocrity of user stories and backlog grooming meetings, but there's nothing yet that has convinced me that it's imminent just yet. Sadly, I think it may be around for a while.
- vskarine 12y ago
  Curious, what are the software development processes that you do not consider nonsense?
  - yuhong 12y ago
    I think it is the fake "Agile" processes that michaelochurch is referring to, not the real one.
  - 12y ago
z3phyr 12y ago
So Machine Learning in general is an almost solved problem?
- patkai 12y ago
  I think the author claims more like "it works", not that "it is solved".
vonnik 12y ago
Deeplearning4j and ND4J contributor here: We've created a distributed framework for scientific computing on the JVM, ND4J, which is the linalg library behind Deeplearning4j, which includes ConvNets and other deep-learning algorithms. Contrary to the author, we believe Scala is the future of scientific computing. While Python is a wonderful language, the optimization and maintenance of scalable programs often happens in C++, which not a lot of people enjoy.
- aliostad 12y ago
  Thanks guys for your hard work.
briantakita 12y ago
What if we define intelligence, not from an anthropomorphic view, but from a systemic view; as all systems have intelligence. What is "Artificial Intelligence"? The opposite of "Natural Intelligence"?
- basaah 12y ago
  Saying that all systems have intelligence == anthropomorphizing all systems IF AND ONLY IF you consider intelligence to be a uniquely human trait. A more useful distinction for the realm of intelligence could be 'designed intelligence vs. grown/evolved intelligence' instead of 'artificial intelligence vs. natural intelligence'; however, stuff like reinforcement machine learning is then neither, or perhaps a hybrid form of intelligence. In the end the pragmatic value of the concept intelligence is low, both for systems as for humans.
patkai 12y ago
Great perspective on the future, we would need many more similar discussions. I love the main message of the post, i.e. "we need to grow up". I have the impression that while we are all optimistic that the future is owned by software developers we don't realise that not for all. There will certainly be more segmentation in our profession and there will be great demand for high-end developers. This requires a lot of learning, and I personally feel it's a tough challenge. The post also made me realise how much we still think in terms of disciplines. E.g. we think a developer should learn more mathematics. If we were thinking in terms of problem solving, or "modelling reality" (at least in part with software) we couldn't separate these so easily. E.g. if you are writing a software for vehicle condition monitoring you use a combination of engineering, physics, mathematics, computer science - the less you try to - or need to - separate them the better you do. I can't quite put it simple, but in my mind I can see the future "developer", how got a BSc in Physics, went on to work as a software developer for a couple of years and then continued to learn every day maths, physics, biochemistry, worked in various projects where she could use all these. She is neither a physicist, nor a software developer or mathematician.
- lightcatcher 12y ago
  > you use a combination of engineering, physics, mathematics, computer science - the less you try to - or need to - separate them the better you do. I disagree that mentally separating math, physics, and computer science is a good thing. I think a good scientific programmer should understand the science on its own, and then figure out how to model/approximate the science to do something useful on a computer. For instance, if you want to implement Reed-Solomon codes efficiently, you'll realize that understanding the algebra required to understand the code is more or less an orthogonal skill to designing an efficient encoders and decoders. As a personal anecdote, I had much better luck learning about waves and quantum mechanics after I knew about differential equations, orthogonal decompositions, and a fair amount of linear algebra than I did before I knew this math, even though the physics classes included all of the necessary math. I attribute this better understanding to have a cleaner mental map, because I knew which statements were true because of some sort of physical fact, and which statements were just mathematical results. In line with the generally smiled upon principle of decomposing problems, I think its particularly critical that the scientific programmer can decompose these interdisciplinary problems. A scientific programmer should understand the science (at least mostly), understand the hardware, and figure out how to efficiently and cleanly map the scientific problem to something that to be solved by a program.
thyrsus 12y ago
Here's my attempt at a TL;DR: * The '90s "Access in 24 hours" programmer has been replaced by the latest anecdote-based technique/toolset preacher; e.g., TDD. * Becase deep learning is better than humans at finding useful patterns in data (whether concerning biochemistry or web site interaction) it is the best technique. * Aesthetic (e.g., language) and social justice (e.g., feminisim) issues distract from utilitarian effectiveness. * Utility is only furthered by math and science (where for "science" read "patterns inferred from data"), and we should aspire to be "scientific programmers" who apply only math and science.
- thyrsus 12y ago
  My biases may be guessed at in my summarization, but let me make them more explicit. I think it odd that some of the techniques he rails against are inspired by mathematics: TDD tries to preserve invariants; RESTful design tries to impose the invariance of idempotency. If the question is whether those techniques make those who use computers more productive, then a scientific answer would involve a stunningly expensive human subjects experiment involving large numbers of people and complex problems. The likely result would be this: https://xkcd.com/1445/ https://xkcd.com/1445/ I'm a sysadmin doing my best to automate (e.g., puppet); I rarely have the luxury of collecting data sufficient to the immediate problem, so I rely on math and (unreliable) heuristics. I write perl/shell/puppet/ruby/anything in small fizz-buzz complexity chunks; an "artisan" if you will. I support CAE environments with low-latency and poor parallelism opportunities, and until that changes (e.g., becomes cloud compatible) I don't see my tactics changing significantly.
- emmanueloga_ 12y ago
  Your summary is indeed very biased. Maybe someone could use the first bullet point. Better to look at the last paragraph: We got to grow up and go back to school, relearn all about Maths, statistics, and generally scientific reasoning. We need to man up and re-learn that being a good coder has nothing to do with the number of stickers you have at the back of your Mac. It is all scientific - we come from a long line of scientists, we have got to live up to our heritage.

I started on the science/engineering side at BHRA (on campus at CIT) only problem with technical/scientific vs commercial is the pay is so poor.

aliostad 12y ago
Yes. And this trend is reversing. Have a look here: http://www.itjobswatch.co.uk/jobs/uk/machine%20learning.do http://www.itjobswatch.co.uk/jobs/uk/machine%20learning.do Salaries have gone up by 16%
- walshemj 12y ago
  I know I am tempted to go back to the more technical side - though when a recruiter asked about working on the MET police's Registry I wasn't that keen :-)
- zo1 12y ago
  The page you linked to only say 9% increase? 55k -> 60k
toufka 12y ago
And academically the prestige is poor. One is not granted 'research time' to develop software, but to 'get things done' (see u/Danso's comment). As such there's no one to take the first step in actually making software that would benefit anyone. And in the generous circumstance that one can be allotted time to write the software, the result is a pat on the head - 'good job' - for reducing everyone's workflow from weeks to minutes. Sometimes you get an acknowledgement. And no one will ever support/read your software when you leave - it will be used ritualistically until the lab's last computer's OS no longer supports it. On the topic I see two other significant problems: 1) In basic research there is often a need for 'Every Option' style software - you're doing something that's never been done before and you need to be able to tweak it exactly how you need (but also be able to 'just hit run' for a first pass when coming from your native field). And those types of software are inherently a mess to design and build (ie. photoshop, CAD, 3D, programming languages). 2) Some of this software can only be written by those who directly do the research - or someone who very closely collaborates with them. Scientific software contains scientific assumptions in it that are very hard to evaluate if you're not part of the field. Deciding to go right-way-round, or rounding up, or leaving off the last element in an array, or any other of those programming tricks can really mess up scientific work. Or conversely, using the entire array, using a non-weighted, 'avg' or treating the red channel mathematically the same as the blue channel is a very different way of designing software than other industries - and is not common and rarely given much thought.
- walshemj 12y ago
  true I was talking more about doing tech/scientific programing as a discipline - I started as an associate professional what's some times called a professional apprentice though at the time if you had called me or my pears apprentices we would have told you where to go in no uncertain terms I recall I was considered a bit flash because I used Mixed Case in my Hollerith statements for input prompts.
- tjradcliffe 12y ago
  There's a modest living to be made at the interface between "scientific programming" and "commercial implementation of programs scientists write". This is an under-appreciated niche because it takes a lot of work to get into it: you need to be a good developer and a good scientist with diverse experience. My way in was through experimental and computational physics, but there are certainly other avenues these days. Modern statistics is the biggest piece of the picture that every interesting area has in common. If you're interested in scientific programming you need to understand Bayes as well as algorithms etc. I have friends in psychology, biology, etc and we can communicate surprisingly well because we all speak the same statistical language. But more importantly you need to understand how scientists think. They are amazingly hard to pin down to the kind of specs developers need. For example, a guy on my team once said after talking to one of the scientists we were working with for a couple of days, "I now have a much better grasp of the problem, but I still don't know what the default value of this parameter should be." I spent ten minutes talking to the scientist and came back and told the developer "5", because I could tell from the way the scientist was talking that he had no clue if the number should be 3 or 10, but seemed to be favouring the lower values. I didn't need to understand the problem domain in detail to make that judgement, but to have a reasonable grasp of the psychology of working scientists. So far as I know, there's no way to get that without working as a scientist yourself.

amelius 12y ago

The problem with scientific software is that the market is so small. It is far more profitable to just write mainstream software.

Retra 12y ago
It's more profitable to do scientific research slowly and tediously than it is to automate it?
- texthompson 12y ago
  The people capable of the automation have financial incentives to move to industries with broader appeal. There are a lot of people doing great scientific computing work in academia (e.g. Sean Eddy, Titus Brown), but they are notable exceptions.
- detaro 12y ago
  This of course is stereotyping, but for me it feels like there is truth to it: It has more prestige to have a bunch of Phd's doing busywork in the lab than to pay for software which could replace them. Expensive equipment at least looks cool and impressive if the bosses go round with guests and want to show off where the money went, software can at the very best make pretty pictures on a screen somewhere. Academic research is not necessarily oriented on profit on that level: It cares about getting grant money, but not always about using that money as efficiently as possible.
  - nitrogen 12y ago
    software can at the very best make pretty pictures on a screen somewhere. The solution we have for that in the commercial software world is really big screens.
    - douche 12y ago
      And report graphs. Lots of report graphs. Oh, look, a real-time dashboard. Do the metrics its tracking mean anything to anybody that is reading it? Who cares? This seems to be one of the main ways salesmen and execs communicate over enterprise software deals.

danso 12y ago

The other night I was searching for Python science books and stumbled across this one titled "Python for Biologists: A complete programming course for beginners"...the homepage for the book is here: http://pythonforbiologists.com/index.php/introduction-to-python-for-biologists/ http://pythonforbiologists.com/index.php/introduction-to-pyt... Admittedly, it only has two reviews on Amazon, but they're both five stars, and they both seem to come from biologists who are apparently thrilled at being able to leverage code for their work...the funny thing is, the book itself is not "advanced" as far as what most professional programmers would consider "advanced"...the Beginners' book ends with "Files, programs, and user input" and the Advanced book ends with Comprehensions and Exceptions... I think we as programmers vastly understimate how useful even basic programming would be to virtually anyone today. I work at Stanford and it continually astounds me when I run into non-programmers who are otherwise doing data-intensive research, who fail to see how their incredibly repetitive task could be digitized and implemented as a for-loop. It's not that they are dumb, it's that they've never been exposed to the opportunity. And conversely, it's not because I'm smart, but I literally can't remember what it was like not to break things down into computable patterns. And I've been the better for it (especially because I'm generally able to recognize when things aren't easy patterns) Sometime ago, I believe it was Stephen Hawking who speculated that the realm of human knowledge was becoming so vast that genetic engineering of intelligence might be required to continue our progression...that may be so, but I wonder if we could achieve the same growth in capacity of intellect by teaching more computational thinking (and implementation), as we do with general literacy and math. As Garry Kasparov said, "We might not be able to change our hardware, but we can definitely upgrade our software." http://www.nybooks.com/articles/archives/2010/feb/11/the-chess-master-and-the-computer/ http://www.nybooks.com/articles/archives/2010/feb/11/the-che...

mariodiana 12y ago
> I think we as programmers vastly understimate how useful even basic programming would be to virtually anyone today. I used to work in second-level support. Out of a department of maybe 12, I think 2 of us knew how to program. I hate to even begin to describe some of the things I saw people trying to do by hand, and we were a software development company. I remember pleading with this one guy, "Please, before you ever try to do something like that again, come find me and ask if there is a way to automate the job." There are so many people that would stand to benefit if only they had so much exposure to programming that they could tell when it would be crazy to not grab the guy next to them and ask if a quick script might not turn a 2-day job into 1 hour's worth of work -- coding, testing, debugging, and execution all included. Sadly, even people (nominally) in technology jobs are ignorant of this.

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Fede_V 12y ago

As other people have mentioned - the problem is that academia rewards producing papers, not stable software libraries. For example - scikit-learn is an amazing project, lead mostly by a small group at INRIA with Gael at the helm - and in terms of academic prestige, scikit-learn is probably 'worth less' on your CV than a couple of Nature papers. This is of course ridiculous - scikit learn is used by a huge amount of people, it takes an insane amount of work to run the project, yet the incentives are what they are.

saraid216 12y ago
It's just a standard consequence of underfunding. No one gets paid to develop scikit-learn full time. An academic gets paid by getting grants. When you look at a lot of the best libraries out there, how were the top contributors employed at the time? Generally speaking, you'll find that most of them were employed by companies who were willing to let them burn money to develop the library. Universities don't have that luxury anymore.
- Fede_V 12y ago
  There actually are some full time developers for scikit-learn - and they are amazingly talented. There's lots of difficulties in securing that funding though. Getting funding for permanent staff is incredibly tough - usually you have 3 year contracts for 'postdocs' that pay around ~40% of what a similarly skilled software developer could earn. The only stable positions in academia are professorship - and even those are going away in favour of positions where people are expected to pay increasing portions of their salary from their grants. How can you credibly offer someone 40-50k dollars a year, with no job security, tied to some grants that might or might not be renewed and expect them to turn down offers from companies looking for data scientists?
  - zo1 12y ago
    "How can you credibly offer someone 40-50k dollars a year, with no job security, tied to some grants that might or might not be renewed and expect them to turn down offers from companies looking for data scientists?" The same way that restaurant owners ask their servers to work for sub-minimum wage and expect them to bring that up to minimum wage via "tips"(Law mandates they bump that up to minimum wage of 7+ if tips don't cover it, but that's separate). Not entirely the same, but it's an alternate form of payment for services rendered. If you ask me, I think it's perfectly reasonable as long as there are no laws meddling and causing an increase of such "odd" remuneration schemes.

roflmyeggo 12y ago

Computational and biological sciences will likely meet on a financial equivalent to commercial software applications at the intersection of epigenetics and pharmaceuticals in the new few decades. When scientists begin to discover feasible methods to cure or manage previously incurable diseases (a more recent example of this has been attempts to cure Cystic Fibrosis), or more specifically reversing some of the diseases that our older baby boomer populations are suffering from via epigenetic methods, you can bet your bottoms that there will be a huge influx of capital in the sector and a subsequent increase in demand for computational biologists. Of course we could end up in a sort of quasi-understanding parallel to that of quantum mechanics and end up in a epigenetic limbo, but the general feeling is that of high hopes.

sgt101 12y ago

A post about data science and scientific programming featuring a set of graphs with no y-axis scale and labels. At my gaff this kind of presentation of data leads to "scrap the whole analysis and start again".

aliostad 12y ago
dude, have you ever used Google Trends? It is a cut and paste from the source. The y axis is the popularity as the title says. It is so obvious even Google has omitted it.
- bbcbasic 12y ago
  should have labelled it "Magic Google Unitless Dimension"
  - sgt101 12y ago
    You can put all of the terms on the same graph; that would allow people to compare these on a like for like basis as opposed to "look at the shapes, here is an argument".
  - deleted 12y ago
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mollerhoj 12y ago

I believe the author is right - This is the reason why I'm spending my final ECTS points on statistics and machine learning.

iamwil 12y ago

What is deep learning currently applied to besides object recognition in images?

basaah 12y ago
https://en.wikipedia.org/wiki/Natural_language_processing https://en.wikipedia.org/wiki/Natural_language_processing
tangentspace 12y ago
https://www.quantamagazine.org/20141218-machine-intelligence-cracks-genetic-controls/ https://www.quantamagazine.org/20141218-machine-intelligence...

gpcz 12y ago

I'm having trouble understanding the definitions of these roles. I see the chart, but the terms are all vague to me. What does a data scientist do that a mathematician or scientist doesn't do, and what does a scientific programmer do that a data scientist doesn't do? My impression was that "data scientist" was a colloquialism for "statistician that knows how to program." Is a scientific programmer just a programmer that knows some statistics? Why is the direction important? The author says he/she feels that a programmer that knows statistics can make "more robust software" than the other way around, but what exactly does that mean? Do they mean "doesn't crash as much", or do they mean "gives the right answer more often?"

tarikjn 12y ago
for one thing statistics don't really encompass A.I., computer vision etc.
- Toenex 12y ago
  Sorry but this is wrong. Statistics and probability theory underpin most of AI and machine vision.
  - tarikjn 12y ago
    One thing can underpin another without encompassing it. Mathematics don't encompass physics and finance.
tjl 12y ago
Basically all of my Master's and PhD work involved scientific programming, but it was definitely not data scientist work. You're making a big assumption that science is statistics. There's scientific simulations (and deriving the models) and scientific visualization as well. Those are only two out of many possibilities.
collyw 12y ago
Robust software generally refers to it not crashing. "Correct software" would be how I described software that "gives the right answer more often".

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juretriglav 12y ago

Shameless plug: In January I'll be focusing on a project which deals specifically with scientific software: http://sciencetoolbox.org/ http://sciencetoolbox.org/ This current version is a product of a hackathon, but this month will be improving it and adding functionality which brings the scientific software developer and her efforts into focus. Scientific software is gaining importance, but the recognition its developers get is trailing behind - I want to raise the level of associated recognition/prestige (among other related things). Some other projects rely on data collected here, e.g. a recommendation engine for said software that enhances GitHub: http://juretriglav.si/discovery-of-scientific-software/ http://juretriglav.si/discovery-of-scientific-software/ Shameless plug continues: if you'd like to keep track of what I'm doing I suggest you either follow the project on GitHub (https://github.com/ScienceToolbox/sciencetoolbox https://github.com/ScienceToolbox/sciencetoolbox) or Twitter (https://twitter.com/sciencetoolbox https://twitter.com/sciencetoolbox).

toufka 12y ago
Units. I'd love so much for a standard fileformat/interpreter/concept that contained SI units as a requirement for most datatypes. Much to be learned from my TI-89.
- dalke 12y ago
  I don't know what you mean by "as a requirement for most datatypes". I work with molecules, where distances are typically measured in Angstroms and masses in amu. I don't want to have factors of 1E−10 and 1.660538922E−27 hanging around my code.
  - detaro 12y ago
    I assume that he means that the type system knows that these are in useful units, and tracks them, so you can have it check that your calculation actually results in a value of the unit you expected.
    - wtallis 12y ago

stared 12y ago

Plug: I made a list of software that is useful for scientists: https://gist.github.com/stared/9130888 https://gist.github.com/stared/9130888.

elliotec 12y ago

I have noticed a general trend upwards in the interest of scientific programming for a few months now, and the community (most specifically Hacker News) has driven my interest in that area as well. The idea of functional programming and thinking in mathematically sound ways really appeals to me, but my lack of math and comp sci background is holding me back from going full-speed learning and getting better at it. I feel many of us are lost swimming in a sea of opinions and juggling frameworks du jour, development methods, and business strategies, that it keeps us from focusing on improving our skills in areas that matter. This frustrates me and I've been looking for ways to get out of it. There is also this fear of another bubble mixed with trying to keep up with the trends and hipness of the industry, to remain gainfully employed. I realize I am sort of just reiterating the authors point, so I guess what I'm saying is I agree.

kirse 12y ago
HN tends to gives this perception of rapid change because you're always hearing about hot new tech and how great it is from the various tech evangelists. It's a side effect of their faith/vision in their own tech that you feel like you're missing the train. However, I've been following HN for 7.5+ years now, and change is hardly as fast as you think. There really is nothing new under the sun, and you see the same core principles (and human needs) being expressed as the underlying technology changes. The core principles of functional programming have been around since the 30s as lambda calculus, and since the 50s with their initial expression as LISP. It's better to learn FP to add a new way of thinking about problems to your toolbox, rather than treating it as a panacea of programming. If you're feeling lost in the sea, define your lighthouse. HN will most certainly have you paralyzed by the many waves of every new choice if you haven't defined a clear vision for how you want to harness your finite energy.
randlet 12y ago
"I feel many of us are lost swimming in a sea of opinions and juggling frameworks du jour, development methods, and business strategies, that it keeps us from focusing on improving our skills in areas that matter. " I sympathize with this a lot. It's extremely hard to prioritize learning new things and improving existing skills over just trying to plow through all the things on your plate. However, I keep trying to remind myself that the best way to get more stuff accomplished is to keep "sharpening the saw"[1]. Making time now for improving your skills will pay dividends in the future. [1] http://c2.com/cgi/wiki?SharpenTheSaw http://c2.com/cgi/wiki?SharpenTheSaw

tarikjn 12y ago

I think many in the comments are misunderstanding what the author mean to say in his post. He is not talking about working in academia or making scientific software. He talks about improving one's skills in basic science and such fields in computer science as A.I. which have historically been entrenched more in academia than industry.

sqrt17 12y ago
The author of the post started by getting into programming/CS by wanting to earn more/better money, by picking up an Access book and working it out from there, and now that he's more established, he looks down at the young hungry people who picked up an Access book in the hopes of more/better money, running through all the tropes that people who got where they are through knowledge will use. Yes, it's useful to understand things better, and to know math. And as always since the first CS degree started, CS people gripe that people should Know More Math. Sure it helps. Other, less prestigious, things also help but you don't hear people griping about it. TDD allows the idiots in. Yes, that's effectively why you want TDD, you want to get more mileage and solving more complex or more bug-sensitive problems using the same people. Building software is not about being smart (although that helps on occasions), it's about getting stuff done. Yes, machine learning and AI are the new kids on the block, and like Web programming, they will see a bloom of increased customer demand, and like Web programming, we'll get a progression from bespoke boutique software to frameworks that make people's lives easier to frameworks that allow any person with the intelligence of a pet rock to do simple stuff productively. Why is that? Because building frameworks is the only way that the smartest people can earn money faster than programming the (N+1)th variation on that theme everyone follows -- frameworks are what make people more productive, or allow you to use a workforce that's more accessible. As a Wizard With a Pointy Head (aka academic), I'd say that the need for Wizards With Pointy Heads in production work is often overestimated and/or idealized. There is a large number of PhD graduates, and the market happily gobbles them up (indeed, realizing that you can hire PhDs and have them do productive work is one of the things that made Google successful as a company back in the early 2000s).
dj-wonk 12y ago
I like the spirit of the article, but in my view, much of the author's writing falls prey to "either-or" thinking. See https://en.wikipedia.org/wiki/False_dilemma https://en.wikipedia.org/wiki/False_dilemma. > We can argue about how to version APIs and how a service is such RESTful and such not RESTful. We can mull over pettiest of things such as semicolon or the gender of a pronoun and let insanely clever people leave our community. We can exchange the worst of words over "females in the industry" while we more or less are saying the same thing, Too much drama. > But soon this will be no good. Not good enough. We got to grow up and go back to school, relearn all about Maths, statistics, and generally scientific reasoning. The programming community is large and diverse. We can do all of these things, including the rigorous (what the author seems to call "scientific" [1]) ones and the UI/marketing ones too. I think this diversity is a strength, as long as managers and entrepreneurs find and retain the skill-sets necessary for their domains. [1] I prefer to use "science" to mean falsifiability, preferably with strong experimental designs. Much of what the author talks about is mathematical rigor (from computer science), which is also important, but not experimental science. See also: https://en.wikipedia.org/wiki/Philosophy_of_science#Defining_science https://en.wikipedia.org/wiki/Philosophy_of_science#Defining...

crb002 12y ago

Pair programming keeps it human, and transfers knowledge very well. TDD is about reproducibility of results, which is very in line with the scientific method. Benchmark tests will show you when your solutions are getting out of hand on performance. The sunk cost fallacy is a big problem. Moving to a new platform like HTML5/iOS/Android gives a short reprieve, but soon those proprietary code bases will age. The other big problem is that usually a smaller portion jobs goes towards management in flatter organizations. Managers want lots of layers for job security. Eric Meijer is right that small teams which are given narrow mission objectives instead of detailed requirements, and measure their problem domain instead of guessing, will be effective. I'm curious if a Fat-Tree model of management will take hold, http://en.wikipedia.org/wiki/Fat_tree http://en.wikipedia.org/wiki/Fat_tree You get a flatness that improves communication latency, lots of bandwidth, and managers are happy because there are a lot of jobs at the top.

placebo 12y ago

Mostly agree with the article. Being myself fascinated with machine learning and in the process of refreshing mathematic knowledge I haven't used since university (too many years ago) in order to dive deeper into it, I can definitely relate. However, I think the main point is not that software developers should all hone their academic math skills (that would be probably be pointless for many if not most software developers), but rather that it would be best if software developers would strive to follow the scientific mindset when developing software - In my experience, occam's razor is just as important in software development (design, architecture, algorithms, testing, you name it) as it is in physics, chemistry or other sciences and it is this aspect (which I feel is the most basic and most important) that gets lost sometimes in the noise of software development trends and fashions.

samuell 12y ago

A look at the trends for "R programming", compared to "Python programming", is quite interesting too: http://www.google.com/trends/explore#q=R%20programming%2C%20python%20programming&cmpt=q http://www.google.com/trends/explore#q=R%20programming%2C%20... (Their curves are more or less parallel since 2011)

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michaelochurch 12y ago

First of all, I hate this "Agile" nonsense. I've seen it kill companies. It's truly awful, because it gives legitimacy to the anti-intellectualism that has infected this industry. It's that anti-intellectualism that, if you let it, will cause a rot of your mathematical and technical skills. Before you know it, you've spent five years reacting to Scrum tickets and haven't written any serious code, and your math has gone to the birds as well. It's insidious but dangerous, this culture of business-driven engineering mediocrity. I hope that it'll be the fakes and the brogrammers who get flushed out in the next crash. Who knows, though? Obviously I can't predict the future better than anyone else. To me, Python doesn't feel like a "scientific" language. Python's a great exploratory tool, and it's got some great libraries for feeling out a concept or exploring a type of model (e.g. off-the-shelf machine learning tools). That said, science values reproducibility and precision, which brings us around to functional programming and static typing... and suddenly we're at Haskell. (Of course, for a wide variety of purposes, Python is just fine, and may be a better choice because of its library ecosystem.) I do think that, as we use more machine learning, we're going to have a high demand for people who can apply rigor to the sorts of engineering that are currently done very quickly (resulting in "magic" algorithms that seem to work but that no one understands). I also agree that "deep learning" and machine learning in general are carrying some substance, even if 90% of what is being called "data science" is watered-down bullshit. I still don't feel like I know what a "scientific programmer" is, or should be. And I'd love to see the death of business-driven engineering and "Agile" and all the mediocrity of user stories and backlog grooming meetings, but there's nothing yet that has convinced me that it's imminent just yet. Sadly, I think it may be around for a while.

vskarine 12y ago
Curious, what are the software development processes that you do not consider nonsense?
- yuhong 12y ago
  I think it is the fake "Agile" processes that michaelochurch is referring to, not the real one.
- 12y ago

z3phyr 12y ago

So Machine Learning in general is an almost solved problem?

patkai 12y ago
I think the author claims more like "it works", not that "it is solved".

vonnik 12y ago

Deeplearning4j and ND4J contributor here: We've created a distributed framework for scientific computing on the JVM, ND4J, which is the linalg library behind Deeplearning4j, which includes ConvNets and other deep-learning algorithms. Contrary to the author, we believe Scala is the future of scientific computing. While Python is a wonderful language, the optimization and maintenance of scalable programs often happens in C++, which not a lot of people enjoy.

aliostad 12y ago
Thanks guys for your hard work.

briantakita 12y ago

What if we define intelligence, not from an anthropomorphic view, but from a systemic view; as all systems have intelligence. What is "Artificial Intelligence"? The opposite of "Natural Intelligence"?

basaah 12y ago
Saying that all systems have intelligence == anthropomorphizing all systems IF AND ONLY IF you consider intelligence to be a uniquely human trait. A more useful distinction for the realm of intelligence could be 'designed intelligence vs. grown/evolved intelligence' instead of 'artificial intelligence vs. natural intelligence'; however, stuff like reinforcement machine learning is then neither, or perhaps a hybrid form of intelligence. In the end the pragmatic value of the concept intelligence is low, both for systems as for humans.

patkai 12y ago

Great perspective on the future, we would need many more similar discussions. I love the main message of the post, i.e. "we need to grow up". I have the impression that while we are all optimistic that the future is owned by software developers we don't realise that not for all. There will certainly be more segmentation in our profession and there will be great demand for high-end developers. This requires a lot of learning, and I personally feel it's a tough challenge. The post also made me realise how much we still think in terms of disciplines. E.g. we think a developer should learn more mathematics. If we were thinking in terms of problem solving, or "modelling reality" (at least in part with software) we couldn't separate these so easily. E.g. if you are writing a software for vehicle condition monitoring you use a combination of engineering, physics, mathematics, computer science - the less you try to - or need to - separate them the better you do. I can't quite put it simple, but in my mind I can see the future "developer", how got a BSc in Physics, went on to work as a software developer for a couple of years and then continued to learn every day maths, physics, biochemistry, worked in various projects where she could use all these. She is neither a physicist, nor a software developer or mathematician.

lightcatcher 12y ago
> you use a combination of engineering, physics, mathematics, computer science - the less you try to - or need to - separate them the better you do. I disagree that mentally separating math, physics, and computer science is a good thing. I think a good scientific programmer should understand the science on its own, and then figure out how to model/approximate the science to do something useful on a computer. For instance, if you want to implement Reed-Solomon codes efficiently, you'll realize that understanding the algebra required to understand the code is more or less an orthogonal skill to designing an efficient encoders and decoders. As a personal anecdote, I had much better luck learning about waves and quantum mechanics after I knew about differential equations, orthogonal decompositions, and a fair amount of linear algebra than I did before I knew this math, even though the physics classes included all of the necessary math. I attribute this better understanding to have a cleaner mental map, because I knew which statements were true because of some sort of physical fact, and which statements were just mathematical results. In line with the generally smiled upon principle of decomposing problems, I think its particularly critical that the scientific programmer can decompose these interdisciplinary problems. A scientific programmer should understand the science (at least mostly), understand the hardware, and figure out how to efficiently and cleanly map the scientific problem to something that to be solved by a program.

thyrsus 12y ago

Here's my attempt at a TL;DR: * The '90s "Access in 24 hours" programmer has been replaced by the latest anecdote-based technique/toolset preacher; e.g., TDD. * Becase deep learning is better than humans at finding useful patterns in data (whether concerning biochemistry or web site interaction) it is the best technique. * Aesthetic (e.g., language) and social justice (e.g., feminisim) issues distract from utilitarian effectiveness. * Utility is only furthered by math and science (where for "science" read "patterns inferred from data"), and we should aspire to be "scientific programmers" who apply only math and science.

thyrsus 12y ago
My biases may be guessed at in my summarization, but let me make them more explicit. I think it odd that some of the techniques he rails against are inspired by mathematics: TDD tries to preserve invariants; RESTful design tries to impose the invariance of idempotency. If the question is whether those techniques make those who use computers more productive, then a scientific answer would involve a stunningly expensive human subjects experiment involving large numbers of people and complex problems. The likely result would be this: https://xkcd.com/1445/ https://xkcd.com/1445/ I'm a sysadmin doing my best to automate (e.g., puppet); I rarely have the luxury of collecting data sufficient to the immediate problem, so I rely on math and (unreliable) heuristics. I write perl/shell/puppet/ruby/anything in small fizz-buzz complexity chunks; an "artisan" if you will. I support CAE environments with low-latency and poor parallelism opportunities, and until that changes (e.g., becomes cloud compatible) I don't see my tactics changing significantly.
emmanueloga_ 12y ago
Your summary is indeed very biased. Maybe someone could use the first bullet point. Better to look at the last paragraph: We got to grow up and go back to school, relearn all about Maths, statistics, and generally scientific reasoning. We need to man up and re-learn that being a good coder has nothing to do with the number of stickers you have at the back of your Mac. It is all scientific - we come from a long line of scientists, we have got to live up to our heritage.