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Comment: We can thank corporate America (Score 5, Interesting) 281

by bangular (#47388763) Attached to: Ask Slashdot: How Often Should You Change Jobs?
Given a steady job with a pension that won't disappear, I think most people would rather stay at a company long term. Corporate America took this from us and now complains they can't keep people. They set the rules, we're just getting around to beating them at their own game.

Comment: Re:We need data, not algorithms (Score 1) 95

by bangular (#43245865) Attached to: DARPA Tackles Machine Learning
AI and ML are different. AI is this grandiose goal which we are many decades away from. ML is much more humble and doing very useful things today. I'm not really sure where the line for ML stops and AI begins. Maybe the name was just arbitrary so it wouldn't have 50 years of expectations on its back. But we're just leaving phase I in the history of ML (supervised learning) and getting into phase II (unsupervised). Next we'll have to figure out domain transfer knowledge. Who knows what's next after that.
You're showing disappointment in the industrial revolution because it hasn't invented the calculator yet.

Comment: Re:This headline pops up every few years (Score 2) 95

by bangular (#43245787) Attached to: DARPA Tackles Machine Learning
The same thing could have been said about computers. Machine learning algorithms can predict really useful things today way better than a human. Sure, they may not be able to understand the context of spoken language very well, but given sufficient training data we can already prescribe medical treatments from ML that surpasses a human doctor in effectiveness.

I do think understanding the human brain would be a big breakthrough, but I don't see them as sequential. ML will actually help us understand the brain better because it will allow us to process the big data of medical experiments in a meaningful way.

Comment: Re:Good luck with that (Score 2) 95

by bangular (#43245559) Attached to: DARPA Tackles Machine Learning
Creating new programming languages for domain specific problems has never worked. However, there really is a lack of developer friendly tools out there. On one end we have the researchers creating algorithms and (if we're lucky) implementing that algorithm as a stand alone script in Java. On the other end are developers. Most developers are fickle and if the tool requires knowledge of the internals, probably won't use it. That's where the Microsoft's and Oracle's and Google's are supposed to step in and make a crap-ton of money packaging these algorithms with a shiny API.

However, the current state is that no middle man has really stepped in.

Comment: Re:We need data, not algorithms (Score 1) 95

by bangular (#43245509) Attached to: DARPA Tackles Machine Learning
+1 to this. The algorithms are great and we are not using them anywhere near capacity. We lack standards for data formats and standards for interfacing. If I write a program using MySQL, it can reasonably be moved to another RDBMS with maybe 80-100% of the code saved. If I write a program using C4.5 in Matlab, good look porting it to Weka using Decision Stump and a meta learner. It boggles my mind that a company like Microsoft hasn't packaged it as Microsoft ML 2012 and have Visual Studio integration.

Comment: Quality of tools (Score 2) 95

by bangular (#43245361) Attached to: DARPA Tackles Machine Learning
I was just talking with someone about this the other day. Machine learning is going to be the SQL database of the next generation. In 15 years it will be hard to find basic apps that don't use it. The tools will reach a point that it's so easy to include them in your program, people will assume to include them even though they may not really be the most appropriate method to solve the problem. This is how SQL is today. Go to any SMB and try to find a non-trivial application that doesn't use a SQL database. It's difficult.

However, the state of current tools is not good. We currently have really good algorithms for machine learning. The gap is in actually getting a developer to use them. If it's not branded and blessed by Oracle or Microsoft, many businesses won't use it. If you search for implementations on the internet you can usually find an implementation of R or Matlab. However, people are weary of including R and Matlab in their programs to begin with. If it's not in .net or Java, they won't use it. Weka can be used for Java, but it's a difficult library for a machine learning novice to use. The developer has to know some internals of machine learning to know which algorithm to use and their pros and cons. Meta learners complicate the issue even more. Modern RDBMS have been sugar coated so much a developer can use a RAD IDE and not understand a single line of SQL. I'm not saying that's really a good thing, but it definitely has made SQL databases very common and improved the state of the industry for everyone.

Comment: Re:Purpose? (Score 1) 85

by bangular (#9851251) Attached to: ANSI C89 and POSIX portability?
I used to be on that boat, until I've had to work with systems that were implemented when I was in elementry school. When you have generation gaps of systems, it becomes EXTREMELY difficult to find people farmilar with it as the years go by. In this case, what if they don't update it and let it lapse another 10 years? How likely are they going to be to find someone that will be able to update it and add functionality then? They will probably pay a hell of a lot more to do it then than to do it now. It may seem silly and pointless to rewrite software every few years, but ultimatly you are doing yourself a HUGE favor. You think you are saving yourself time and energy now not rewriting and updating, but wait until you have to find and pay a consultant 100,000 dollars to add very simple functionality to your software.

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