51–60 of 66 posts
If you have been following the recent advances in AI (and are not clueless) then it should be getting really REALLY obvious that most coding and design tasks will be automated within three to five years. AI will eat your career.
Not worried, I'm not a CRUD developer, so I don't see my job going away anytime soon.
The end of code seems like a title created by a sensationalist... I am excited for how these forms of pattern recognition and decision making can allow us to do more with software. There is still plenty of code to be written as we continue to apply software to every aspect of our world.
Amazing listening to all of the HN coder apologists here come up with excuses for why they aren't going to lose their jobs, too. If anything, abstraction, ever-tighter vendor lock-in, and improvements in static analysis will kill off many coder jobs. There aren't too many jobs out there for recreating basic UNIX tools like sed or grep -- soon, this will be true for higher level tools as well, until what you're doing…
Software just gets more complex and expands in scope when we can build on top of new abstractions. Each time this has happened we've ended up with more programming jobs not fewer.
If you have been following the recent advances in AI (and are not clueless) then it should be getting really REALLY obvious that most coding and design tasks will be automated within three to five years. AI will eat your career.
Can you articulate this in the form of a binary outcome? If so, I will happily make a $N00 sized bet (odds to be negotiated) on the false outcome with anyone who is interested.
By 2020 global job pool for software developers and user interface designers will start to contract due to competition from automated design/development AI.
If you have been following the recent advances in AI (and are not clueless) then it should be getting really REALLY obvious that most coding and design tasks will be automated within three to five years. AI will eat your career.
I must be clueless. Please cite an example where a software design task has been done by machine learning. Something where you start with a specification or description and the automation produces a usable design. Coding could possibly be replaced by something in a decade or so but I wouldn't hold my breath. We are just getting to compilers and JIT systems that do better than smart developers working at a lower level…
Dogs can be made to learn things - doesn't mean we allow them do surgery. Whoever wrote this article has negative knowledge about the state of AI. edit: Its also slightly insulting that this person thinks that the modern programmer spends all their time writing if/else/then statement.
I'm going to take the other side of this argument and suggest that we didn't create the dog and the limitations in the dogs abilities. I suspect that this will be difficult for the HN community to hear, but we must face the upcoming realities. Though I think the article may be assigning more abilities to AI than is actually feasible in our lifetimes, let us not forget that most back-end developers are doing basic I/O…
Automating 90% of the work will only make software cheaper, and more in demand, as we are very far from creating every possibly useful software. Developers pay might even increase because of it.
Automating 100% of it is a completely different thing. But well, that's hard AI definition. Software development is among the last kinds of work that will get automated.
Earlier quoted context omitted.
Current program synthesis isn't that advanced. At least not compared to state of the art AI. Recently AI has proved it can beat expert humans at all perfect information games. Go has a ridiculously huge search space, but by using neural networks AIs can learn to narrow it down to a manageable size. Program synthesis is just like a perfect information game. You have a search space, a state, a set of goals, etc. You co…
> You could theoretically make it into a board game But not actually. Program synthesis is hard even if you have perfect formal requirements. In practice, gathering formal requirements is by far the most difficult task. You're probably better off building a system using modern development practices than trying to collect formal requirements Let alone translating those requirements into code, which is easier than comi…
I mean yes good program synthesis wouldn't replace programmers on it's own. But it would radically change how programming is done and make our lives much easier.
I'm just saying that AI is a quickly advancing field and predictions like "not in 20 years" have been defied months later. Applying deep reinforcement learning to tasks like game playing is relatively novel, and has only really started to succeed in the last 2 years.
Earlier quoted context omitted.
Current program synthesis isn't that advanced. At least not compared to state of the art AI. Recently AI has proved it can beat expert humans at all perfect information games. Go has a ridiculously huge search space, but by using neural networks AIs can learn to narrow it down to a manageable size. Program synthesis is just like a perfect information game. You have a search space, a state, a set of goals, etc. You co…
I think that measuring state spaces is really misleading. (Although we totally did that to aggrandize how good our synthesis system was, at least to ourselves :P. The space of possible programs, even short ones in a small language, quickly gets hilariously large.) Past a certain point the structure of a space is what matters which dictates how well any given search strategy will perform, and that doesn't readily gene…
Earlier quoted context omitted.
I'm going to take the other side of this argument and suggest that we didn't create the dog and the limitations in the dogs abilities. I suspect that this will be difficult for the HN community to hear, but we must face the upcoming realities. Though I think the article may be assigning more abilities to AI than is actually feasible in our lifetimes, let us not forget that most back-end developers are doing basic I/O…
We have been there before, yet we are still here. The proportion of systems programmers that can writte assembly code consistently on par with the quality of that from a decent compiler (such as Gnu's GCC) is probably less than 1%, and they take orders of magnitude more time to do so. Yet, you do not observe system programmers dying out. They use C (and increasingly, C++, Dart, etc) to build bigger and more complex s…
At the same time, you say "We have been there before", but we haven't had the quality of AI that we are reaching today, so in many ways we have not 'been here before'. Let's not forget, there was a time when elevator operators thought they were irreplaceable by a machine. Today, you can't imagine having somebody operate an elevator.
A few years ago you had to be a 'web developer' to build a web-page. It was unimaginable that somebody who is barely computer literate would be able to put a website online. Today, you can run an online store without knowing anything about programming.
I suspect DBAs won't have an illustrious future as machines learn how to model data. Yes, they will need a person to train them, and tell them what to do, but I suspect an AI system can look at all the potential modeling options and give a layperson the option to pick one which best suits their needs, or recommend one.
As far as 'magnitude more time' for computers to do the work, I often find this argument interesting with respect to processing power in IoT. In life-threatening situations, performance is very important, but often as long as a persons time isn't being wasted, it doesn't matter if a machine takes twice as long.
Earlier quoted context omitted.
I'm going to take the other side of this argument and suggest that we didn't create the dog and the limitations in the dogs abilities. I suspect that this will be difficult for the HN community to hear, but we must face the upcoming realities. Though I think the article may be assigning more abilities to AI than is actually feasible in our lifetimes, let us not forget that most back-end developers are doing basic I/O…
How long is that though? Decades? More? How do you train a neural net to take a vague description of a desired outcome and produce results that fit the requirements? We are no where close to the general AI that would be required. I certainly believe that classes of automation problems will be done by machine learning but I have a very hard time believing all programming tasks are going to be replaced any time soon.
Build this -> builds -> fix this -> fixes -> change this -> changes.
Essentially, this is a learning model already.