It's not right to blame a tool, it's the way it's used. IMHO it's the way people embrace the abstraction in some 3GL and all 4GL languages, in general and model driven oriented design techniques that are to blame. These factors allow the rise of the Micro$oft/SUN/etc certified programmers who have no clue what happens in the real world. Yes, there are 6 more layers.. trust me.
I'm not saying abstraction is bad. It is a very powerfull tool that allows us to complete more in less. I'm not saying model driven development is bad (I think). It allows us to bridge between the worlds of normal people and geeks. But eventually you have to understand what happens at the lower/lowest levels to be able to get a decent result. Top down only delivers when you understand what's happening from the bottom up!
Don't know what you mean with abroad cause obviously rates depend highly on your geographic location. As mentioned in several other posts, on average the European techie gets about half as much paid as his/her American techie equivalent;(( but that is without taking holidays/social security/pension/etc into account! It's not that bad over here;)))
More important though I think, is that you shouldn't confuse employment with contracting cause the latter (even in Europe) hasn't got any of the benefits metioned above and then $50,- isn't all that much.
The bottomline in consultancy is that you are worth the money that they believe you are worth and this in turn depends on how well you can pretend, how stupid they are and how exclusive your knowledge on the specific area is. Unfortunatly I can't answer either of these questions for you;)
I think this guy should do a little math to make up his mind...
While browsing through this discussion I noticed that there are a lot of misconceptions in relation to what a neural network is. A lot of confusion emerges from the fact that the term itself is ill-defined and often misused in semi-popular publications, but there are a few things that I would like to point out...
First of all a lot of people seem to confuse the theoretical concept of neural nets with how they are implemented. These are two seperate issues! Someone wrote in a comment that back propagation networks can't learn because they use an algorithm to adjust their weights, but this is like saying that an object oriented design is bad because I implemented it without the use of classes (or structs if you like). In fact this person could (in his/her mind) have made an even stronger statement by saying that most neural nets are entirely implemented as c-code algorithmes, but this is obviously beside the point. The fact that this is the case is just because the computer as we know it is a great tool for exploring new theoretical frontiers because it's cheaper than building special purpose hardware, it's flexible and we humans like to use symbolic representations.
Second, there are several classes of neural networks. Back propagation is just one of them, but there are many more (e.g. interactive activation and constraint satisfaction). The only thing they have in common is the concept of distributed processing and representation. Some models use clusters, others layers and some no hierarchy at all. Some models use feed-forward activation, some bilaterale activation, some local inhibition and others combine several strategies.
Third, the potential of neural nets in general is often underestimated (at least I think so;)). Many posts in this discussion are aimed at what neural nets can not (yet) do. It's like reading a m$cr$s$ck-newsgroup on linux! While the fact is that a neural net can (in theory) do what any other computational model can do and much more! A neural net can 'learn' to perform tasks that are impossible to model in a 'traditional' computational model because the problem space is too complex for us humans to model. (note: in some cases we can retrieve the distinguising parameterset through hierarchicle clustering which then allows to write a conventional algorithm... thank you neural nets!!).
Finally, to address the question asked in the initial post, I think that using neural nets to regulate temperature and light in buildings is not a verry good idea. These are well defined problems which don't require extensive learning and are case based. They can be resolved (and are?) in a much easier and cheaper way..
First, it is a misconception to think that the back propagation-model is the only type of neural network, there are many more! There are also several types of models that do not need any 'external program' to modify there behaviour (for example Interactive activation).
Second, the fact that usually an algorithm is being used in backprop is not so much because the model can't do without, but because this is the easiest way (for now) to implement this subtask. The idea op backpropagation IS part of the model and may or may not be part of some implementation..
I'd say that the use neural in networks in such 'simple' tasks doesn't make much sense. Neural networks are powerfull in problem-spaces where the input parameter-set is large and/or hard to model and where the relative importance of the parameters isn't fixed and can only be measured through 'learning'. The examples you mention, in my view, do not constitute such problems.
In the past few years there have been lots of experiments involving adaptive cruise control, strict urban drive control and platooning. Results from these experiments have all sugested that these techniques in general will result in less accidents, improved trafic-flow and less harm done to our dear mother nature. Speeding to avoid accidents?? Speeding in case of an emergency?? I don't want to offend any individual... but are you serious? Again there may be freak cases where this might be the awnser, but in general this kind of behaviour costs more lives than it saves... This is the real issue.. would anyone care to oppose to sattelites preventing guns from shooting innocent people???? These things should matter to anybody and don't have anything to do with bb watching you. I don't care what you do in your own private life, but when you get on the road you are not alone! This is what government legislation should be about.. keeping the stupid people from doing stupid things.. OK.. so now we get to the issue of preventing bb to monitor our every step. This also is my deepest concern and the effort should be on making absolutely sure that abuse of this technology will be imposible!! back to you...
Slashdot Mirror
It's not right to blame a tool, it's the way it's used. IMHO it's the way people embrace the abstraction in some 3GL and all 4GL languages, in general and model driven oriented design techniques that are to blame. These factors allow the rise of the Micro$oft/SUN/etc certified programmers who have no clue what happens in the real world. Yes, there are 6 more layers.. trust me.
I'm not saying abstraction is bad. It is a very powerfull tool that allows us to complete more in less. I'm not saying model driven development is bad (I think). It allows us to bridge between the worlds of normal people and geeks. But eventually you have to understand what happens at the lower/lowest levels to be able to get a decent result. Top down only delivers when you understand what's happening from the bottom up!
More important though I think, is that you shouldn't confuse employment with contracting cause the latter (even in Europe) hasn't got any of the benefits metioned above and then $50,- isn't all that much.
The bottomline in consultancy is that you are worth the money that they believe you are worth and this in turn depends on how well you can pretend, how stupid they are and how exclusive your knowledge on the specific area is. Unfortunatly I can't answer either of these questions for you
I think this guy should do a little math to make up his mind...
financial benefits - financial loses - ticket - stay + adventure/fun = {yes,no}
cheers, DaBs
First of all a lot of people seem to confuse the theoretical concept of neural nets with how they are implemented. These are two seperate issues! Someone wrote in a comment that back propagation networks can't learn because they use an algorithm to adjust their weights, but this is like saying that an object oriented design is bad because I implemented it without the use of classes (or structs if you like). In fact this person could (in his/her mind) have made an even stronger statement by saying that most neural nets are entirely implemented as c-code algorithmes, but this is obviously beside the point. The fact that this is the case is just because the computer as we know it is a great tool for exploring new theoretical frontiers because it's cheaper than building special purpose hardware, it's flexible and we humans like to use symbolic representations.
Second, there are several classes of neural networks. Back propagation is just one of them, but there are many more (e.g. interactive activation and constraint satisfaction). The only thing they have in common is the concept of distributed processing and representation. Some models use clusters, others layers and some no hierarchy at all. Some models use feed-forward activation, some bilaterale activation, some local inhibition and others combine several strategies.
Third, the potential of neural nets in general is often underestimated (at least I think so ;)). Many posts in this discussion are aimed at what neural nets can not (yet) do. It's like reading a m$cr$s$ck-newsgroup on linux! While the fact is that a neural net can (in theory) do what any other computational model can do and much more! A neural net can 'learn' to perform tasks that are impossible to model in a 'traditional' computational model because the problem space is too complex for us humans to model. (note: in some cases we can retrieve the distinguising parameterset through hierarchicle clustering which then allows to write a conventional algorithm... thank you neural nets!!).
Finally, to address the question asked in the initial post, I think that using neural nets to regulate temperature and light in buildings is not a verry good idea. These are well defined problems which don't require extensive learning and are case based. They can be resolved (and are?) in a much easier and cheaper way..
cheers,
DaBs
Second, the fact that usually an algorithm is being used in backprop is not so much because the model can't do without, but because this is the easiest way (for now) to implement this subtask. The idea op backpropagation IS part of the model and may or may not be part of some implementation..
cheers,
DaBs
cheers, DaBs
In the past few years there have been lots of experiments involving adaptive cruise control, strict urban drive control and platooning. Results from these experiments have all sugested that these techniques in general will result in less accidents, improved trafic-flow and less harm done to our dear mother nature. Speeding to avoid accidents?? Speeding in case of an emergency?? I don't want to offend any individual... but are you serious? Again there may be freak cases where this might be the awnser, but in general this kind of behaviour costs more lives than it saves... ...
This is the real issue.. would anyone care to oppose to sattelites preventing guns from shooting innocent people???? These things should matter to anybody and don't have anything to do with bb watching you. I don't care what you do in your own private life, but when you get on the road you are not alone! This is what government legislation should be about.. keeping the stupid people from doing stupid things..
OK.. so now we get to the issue of preventing bb to monitor our every step. This also is my deepest concern and the effort should be on making absolutely sure that abuse of this technology will be imposible!! back to you