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Apple’s Secrecy Hurts Its AI Software Development

• bigethan 11y ago
  I'd say that it hurts _all_ of it's software development, though AI is probably the most obvious genre. If you can't discuss issues with outsiders how do you attract developers who are interested/skilled in those specific issues. I realize that Apple gets plenty of applicants, but when you're building something that requires some very specific sets of knowledge, it's harder to find them if you don't give them any way at all to find you. The developers who can make Pages/Numbers/Keynote/iCloud great are out there, but they certainly don't work at Apple.
  • amelius 11y ago
    Also, how are you going to attract scientists who want to make a career (which, perhaps unfortunately for them, involves publishing stuff). They should probably take an example from research.microsoft.com.
    • desilinguist 11y ago
      Some scientists just want to build cool things and not have to worry about publishing. Some of my colleagues over the years have moved to Apple and startups for this reason.
      • amelius 11y ago
        Perhaps so. Other scientists want to push the border of human knowledge.
        • eivarv 11y ago
          There is a difference between pushing the borders of human knowledge and disseminating said knowledge, though.
          • amelius 11y ago
            I guess that depends on your interpretation of "human knowledge" then :)
            • eivarv 11y ago
              By being a human, you would be able to expand human knowledge without letting other humans know about it. So yeah, it's a matter of definition;P
          • walterstucco 11y ago
            Exactly. The first, Without the second, is not called science.
            • eivarv 11y ago
              If the scientists in question are applying the scientific method as a means to "build cool things", can they not be said to be doing science?
              • cwyers 11y ago
                One of the key tenets of the scientific method is reproducibility. People can't reproduce your results if they don't know what your results are.
                • eivarv 11y ago
                  Reproducibility does not necessarily mean that other people should be able to reproduce the results in question (which would depend upon them knowing the relevant information in the first place) - but that an experiment can be replicated. I would agree that publishing results is better for several reasons (ethics, pragmatism, probably more thorough evaluation, etc.), but the definition doesn't really deal with the public at all. TLDR; Reproducibility != public reproducibility.
                • coldtea 11y ago
    • iQuercus 11y ago
      To my knowledge, Apple ran a program called "The Apple University Consortium," among others, where they would tap into work of higher education research. It's my understanding these efforts continue, you just don't hear a whole lot (if anything) about it publicly. Check it out, you'll see Apple quietly listed on several university websites, CMU for example: http://www.cmu.edu/corporate/partnerships/cic/tenants.html http://www.cmu.edu/corporate/partnerships/cic/tenants.html
    • username223 11y ago
      It has been awhile, but MSR used to be the place where people went and still published, while Google Labs was a black hole. I remember some Google guy giving a talk at NIPS, but it was just a presentation from a shared corporate slide deck, which did not fit in with the rest of the conference. Google attracted some really good machine learning folks (temporarily) by offering them boatloads of money and manpower to implement their ideas, not freedom to publish. Has that changed?
      • m0g 11y ago
        Yup, Google now has two teams publishing the very best ML papers alongside FAIR, Toronto, Montreal and some other places. The teams or Deep Mind and Google Brain.
      • dekhn 11y ago
        I don't know what you're talking about; Google researchers publish all the time and give presentations at machine learning conferences with plenty of interesting and unique detail. Google just didn't do much with machine learning in the old days- the pubs were mostly system infrastructure and data analysis. Here's an oldie but a goodie: http://people.mbi.ohio-state.edu/datta.53/philtalk.pdf http://people.mbi.ohio-state.edu/datta.53/philtalk.pdf note the talk refers readers to the patent. At the time, reading Google's patents was the best strategy to learn what it was doing.
• esolyt 11y ago
  I think this might matter most when it comes to self-driving cars. Google's cars have already been driving around for quite some time, in real traffic, against real drivers. It is impossible for Apple to keep their self-driving car project secret (if there is one) and get some real-world experience at the same time.
  • onion2k 11y ago
    It is impossible for Apple to keep their self-driving car project secret (if there is one) and get some real-world experience at the same time. How do you know they're not already testing a car in public and they can keep it a secret?
    • deleted 11y ago
      [deleted]
    • ucaetano 11y ago
      Because you need a permit for that, and Apple doesn't have one.
      • donkeyd 11y ago
        Unless they're doing it under the cover of a shell corporation.
        • ucaetano 11y ago
          Which would still be very easy to track, since the permits are public. Or do you think nobody would notice a permit issued to "Ladron de Manzanas Inc" or whatever name nobody ever heard of? Not to mention the accidents. How would Apple hide all those human-cause SDC accidents? Occam's Razor is telling me there are no Apple SDCs on the streets, but hey, maybe they invented an invisible, aethereal SDC registered under a shell corporation that nobody became curious about :)
          • 11y ago
• lordnacho 11y ago
  The quant hedge fund world is possibly the area where secrecy hurts the most. I worked for many years on a strategy that my colleagues were convinced was special. It did make a lot of money, but looking back it really suffered from not being able to take in any outside feedback. We had a meeting where I was the only dissenting voice in the question of whether to give any transparency into how the model worked. There's some really bad issues with trying to do anything like this in total secrecy: - We couldn't tell investors how the strategy worked. This is bad for building trust. When things go well, people will invest anyway. But they are quick to leave you when they don't. - We couldn't hire anyone without bolting down everything. I ended up having to do all the coding while making sure nothing went into the repo that was the secret sauce, having to send a new binary to a consultant (whom everyone knew) every time the sauce changed, and so forth. As a tiny team we had a lot of fiddling with the network to make sure nobody with the wrong credentials could get things they weren't supposed to. - It affected how we tried to hire people for other things, like new strategies. When you think everything you have is special, you tend to devalue what other people do. You also end up thinking you can take their secret sauce. There's at least one well known fund that thinks it can just interview people and get their ideas. It's pretty obvious when it's happening. - Because truly original ideas are hard to find, you stop looking and you think that's it. Or, as it happens, you read some paper, slightly modify it, and you announce your revolutionary new idea to the team. Unfortunately, people seemed to buy this. They couldn't just accept that a trading strategy is a whole bunch of ideas that come and go over time. And certainly not the idea that a tiny little tweak that makes everything work is probably a dangerous thing. - You end up not comparing your findings to the real world. Nobody was reading what was happening in the machine learning world (the guys are essentially using high school math to implement the model). Attempts at introducing things that were completely ordinary in software development were met with derision. Basically if something wasn't more secret sauce it wasn't interesting. - The crux of this thinking is you tend to think implementation is easy and idea generation is separate and difficult. Probably most people will find the opposite to be true. In truth, if you look at the financial code, it would fit on a screen. The other tens of kLoC was was implementation: back office checking, execution, writing things to database, connections to all the exchanges, and so on. If you look at the wider industry, you can talk to people at large funds where they don't actually care that lots of people are looking at the strategy. They know what the techniques are, that know how to build code collaboratively.
• jmcmahon443 11y ago
  I think this article is total shit... Apple is hurting software development? What utter bullshit. Any dev can tell you that they enjoy coding on a Mac more than a PC, but oh no - Apple needs to step up their game. Hey Bloomberg - how about you upgrade your legacy Fortran before you start pointing fingers at Apple?
  • SlashmanX 11y ago
    Did you read the article at all? It seems like you didn't even read the title of it correctly
    • jmcmahon443 11y ago
      Yes I did. It was focused on AI, which is a branch of software development. AI is the front of software that humanity is on right now. Therefore saying Apple isn't propagating AI = isn't propagating software = bullshit.
  • mizzao 11y ago
    Even the dig at Fortran is silly. Most numerical libraries and subroutines are still implemented in Fortran and you have to figure out how to call them from other languages (e.g. scipy, numpy).
    • jmcmahon443 11y ago
      Most? Do you have ANY proof to back this up?
    • jmcmahon443 11y ago
      Things get implemented because that's what people know, not because people want to build their ABC data-driven business on a new technology. You're kidding yourself if you think implementing Fortran is helping anyone but the two parties involved: the employer and the employee.
• tempodox 11y ago
  So this is the pit Apple will finally choke on. They will be able to change a lot of things and surprise in many ways but they will never ditch their extreme secrecy and become open and collaborative.
  • tempodox 11y ago
    What? I do have a crystal ball and that entitles me, as much as the next sage, to tell what's coming and share my wisdom. Downvotes here are nothing but a metric for denial in the under-informed masses.
• fitzwatermellow 11y ago
  Let's propose a hypothetical scenario. Imagine an investigator in a large research lab somewhere makes a revolutionary AI breakthrough. Not just an incremental step, but one that achieves 99.9999% success in image object recognition (current state-of-the-art using GPU-accelerated Sparse ConvNets puts Univ. of Warwick's Dr. Ben Graham's team in the lead at about 95%[1]). What would be the incentive to make the result public? Certainly if the discoverer were a grad student at Berkeley they may throw a brief summary onto Arxiv the next day to get some peer validation. But if it were the ML team at Uber's new secretive lab in Pittsburgh? Would they not have a compelling interest in keeping it under the kimono? There are several possible outcomes for this game to play out. But for right now let's bask in the warmth of our open source learning toolkit abundance and concentrate on the real question of the day: now that we can teach machines, are there better ways to use their time then ascertaining what makes a top selfie? [1] https://www.kaggle.com/c/cifar-10/leaderboard/public https://www.kaggle.com/c/cifar-10/leaderboard/public
  • segmondy 11y ago
    Never underestimate hubris and greed. If they made it know, they gain respect, they attract top notch researchers & thieves, the market rewards them with funding/buying more shares.
  • chimtim 11y ago
    This is not how research works or a new fundamental technique is discovered. Any result needs to stand the rigor of peer-review before the said breakthrough can be conclusively proven (more unbiased minds trying to find the limitations of the approach and applying the said approach to various problems). Also this process is slow, and requires many PhDs, students building upon one another's work. Also, going from 96-99 may not be really revolutionary, what really matters is how you got there e.g. was new research needed to detect new unicorn classes or scenes, or if it was simply solved by great engineering, more data, etc.
    • coldtea 11y ago
      >Any result needs to stand the rigor of peer-review before the said breakthrough can be conclusively proven Not if you can easily verify the claim is true with sample images (in the situation given). Then you don't need to care for any "peer review". In the same vein, if I come up with a data structure implementation that is 100x faster than common techniques, it just has to pass a suite of tests. No peer review needed. If some Intel engineer finds a revolutionary, 100% speed improving cache prediction method, ditto. And for lots of other cases. As soon as you check it works, you can start patenting and marketing pronto.
• deleted 11y ago
  [deleted]
• robg 11y ago
  It is amazing to me how little Siri has changed. Apple is the biggest company on the planet and with a huge cash hoard. And this is their AI? Either they are lacking in ideas or the execution is as bad as the article says.
  • chiph 11y ago
    I suspect Siri's lack of improvement is because Apple is a hardware company. Hardware companies tend to complete projects then say "Well, that's shipped and done. Lets go start something new now." and not invest in continuous improvement of the product like a software firm would.
    • robg 11y ago
      Except in their OSes. Bloated, but still the best.
      • Dolores12 11y ago
        My friend had windows 8 installed on his macbook pro. why?
  • coldtea 11y ago
    Compared to what? I haven't found Google's or MS's AI to be that much better. A little better yes, but not that much better. And perhaps they are "the biggest company on the planet" because they don't throw lots of their money on half-baked stuff like AI, but proceed conservatively with what they can reliably offer.
    • robg 11y ago
      Indeed both Google and MS have a better AI. All evidence suggests that next phase shift in AI will come from deep, recurrent networks. The article points our why that future is not looking good for Apple.
      • 11y ago
• snowwrestler 11y ago
  Is it true that "really strong people don't want to go into a closed environment?" By definition, people who prefer (or tolerate) a closed environment will be underrepresented in any public data set (e.g. lists of paper authors, conference presenters, etc.). All the quotes in the story are from professors, who are going to be more aware of those public folks than the ones that hire into more secretive companies. And, I would guess that professors are more likely to prefer (and therefore promote) a public approach to CS research. Certainly a closed environment will select for people who like that (or are OK with it), but that is not the same thing as selecting for talent, skill, or experience. It doesn't get more closed than the NSA, and they seem to do ok hiring top CS and math talent.
• j-pb 11y ago
  A couple days ago there was a guy looking for people on the clojure mailing list. He said he was hiring for a super secret ML project at a super secret company. His linked-in profile says he works as a headhunter for apple. Apples secrecy has become a joke, they're like that guy from sesame street selling you letters.
• ChuckMcM 11y ago
  I think the article misses the point, while its secrecy may hurt other people from benefiting from the investment they have made in AI, it doesn't hurt Apple. Its almost the perfect prisoner's dilemma right? Apple defected and everyone else co-operated so they got the most benefit? As a strategy I have observed that people with too much secrecy lose out on innovations that people outside the process could illuminate, but as a strategy it has a history of helping the company employing it stay ahead of the game. Look at Google's secrecy about its data centers, had it gone public with that stuff when it was done, perhaps 2 trillion kwH of power could have been used to do something else if other people had adopted those things. But instead it just helped Google grow faster and do more with their stuff. Is it bad for Google? No. Bad for the rest of us? Perhaps but we don't know one way or the other. So when Apple's efforts fail to produce a competitive product, then I think you can say its hurting itself, but until then I'm sure they see it as a competitive advantage.
  • schwabacher 11y ago
    I think apple has failed miserably to create a competitive product w/ siri, and I expect them to be far far behind google in other areas like cars.
    • ChuckMcM 11y ago
      If only they had a bespoke search engine backing her up :-)
      • danudey 11y ago
        And terabytes of users' data collected over years or decades…
  • monksy 11y ago
    This is pretty much Apple's MO since early on. They're a business heavy company that has a different twist on marketing. They have done some "somewhat" good things. (I.e. webkit) However, at the end they haven't followed through with it. (I.e. parts of Webkit were broken, they've refused to work with the community etc) At least with Google you're getting the benefits of the Google services and libraries that may not be their own products. (I.e. Guava, Dager, Guice, GWT, Angular, etc)
• shas3 11y ago
  I'm scratching my head here. Bloomberg casts Apple as the outlier for its secrecy. In reality, companies which publish their results openly are the outliers today. In the post-golden-era-of-Bell-Labs-and-industrial-research era, publishing is not much of a priority for most companies. Expediting research projects to commercialization leaves little time for going through peer review and publishing. A note on incentives: most companies these days reward R&D employees for commercialization and revenue resulting from their ideas and inventions. Even if they pretend to allow publishing, one can be quite sure that the publications are impacting the careers of authors within the companies in a minimal way, compared to deployments of products and services resulting from their inventions. Apple, Amazon, going one step further from all the old industrial and defense conglomerates, just seems to dispense with the charade about allowing publications. The Amazon example is particularly weird, because what they did (committee approving release, etc.) would be standard practice in most R&D labs, other than the usual exceptions of Microsoft Research, Mitsibushi ERL, etc.
  • mappingbabeljc 11y ago
    Hiya. Author here. I'd class some of Apple's main peers as: Google, Microsoft, Facebook, Samsung, Baidu, and, to a lesser extent, Amazon. These are all large consumer technology companies that are trying to develop quite intimate relationships with consumers, whether through phones or services. Among these companies, Apple's secrecy with regards to AI does make it an apparent outlier. All its peers are, to one extent or another, publishing much more aggressively than it in an apparent attempt to woo some of the most accomplished grad students in the academic community into considering going into industry. (Not mentioned in article, but relevant for this: Samsung has started publishing a few papers, and I'm seeing lots of collaboration between Samsung-affiliated researchers and SK academics pop up on Arxiv. Huawei is coming up as well, via its "Noah's Ark Lab" and some other R&D centers.) Tl,dr; most companies are v private and publishing so openly is the outlier, but among Apple's peers/significant competitors, there is a tendency towards open publication and interaction with the academic community.
• nickbauman 11y ago
  A friend was being heavily sought after by one of our nation's intelligence services. Excellent compensation package, lots of relevant work. But he'd be unable to have anything on his resume and would likely be unqualified for any work outside the organization if he wanted to leave. Sounds a little like Apple. He didn't take the job.
  • qyv 11y ago
    > But he'd be unable to have anything on his resume and would likely be unqualified for any work outside the organization if he wanted to leave That is an exceptional talent retention mechanism that I don't think enough people consider when they talk about how the three-letter-agencies maintain their large talent pools. Once you are in in is very difficult to get out.
    • Eridrus 11y ago
      Great for retention, pretty terrible for recruiting though. AFAIK most TLAs invest a lot in training since they're going to really struggle with recruiting people with the specialised knowledge they need; which is fine when that's a reasonable path for your org, but as a company, training AI experts seems really tough.
• mizzao 11y ago
  (Apple) Research? What research? We're a design company.