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"We’re in an age where we’re being told AI is about to start writing its own software." The most pregnant sentence in the entire piece.

EliRivers 9y ago
All the problems in every serious piece of software I've ever worked on stem from the customer (be it external or in-house) not knowing what they want, the person in charge of capturing that doing a pretty bad job of it anyway, and the chain from there to the person writing the software taking that failure and making it worse. AI writing software will just get me another flavour of the wrong software. AI working out what people want the software to do would be useful.
- colemannugent 9y ago
  My thoughts exactly. Until the AI can hold useless meetings and design things by bureaucracy it will never be a serious threat to big businesses/government.
- kmicklas 9y ago
  You don't think good old fashioned bugs are a problem?
  - deleted 9y ago
    [deleted]
- letlambda 9y ago
  >AI writing software will just get me another flavour of the wrong software. AI working out what people want the software to do would be useful. Maybe we can deliver that wrong software in 100ms instead of 100 days.
sp332 9y ago
https://research.googleblog.com/2017/05/using-machine-learning-to-explore.html https://research.googleblog.com/2017/05/using-machine-learni... Right now they're pitching it as a way for non-experts to make better neural network architectures.
mormonchess 9y ago
Indeed.
kiernanmcgowan 9y ago
This is not new. We have been using machines, build by other machines, to make new machines for the last 200 years.
Animats 9y ago
"At last - the end of programming!" - 'The Last One'[1], from 1981, a BASIC code generator. [1] https://modeling-languages.com/last-one-code-generator-basic-1981/ https://modeling-languages.com/last-one-code-generator-basic...
- lostboys67 9y ago
  I remember that one
ansible 9y ago
I've been thinking about this more this year. Earlier this year, I finished watching "Person of Interest" (maybe not ironically) on Netflix. So far we've been applying ML/DL to problems like image recognition, which has produced quite useful products. But these things are still far away from Artificial General Intelligence. And I've been studying AI / AGI research for decades as well. In the early years, there was an emphasis on formal systems, that could logically infer the sidewalk was wet because it rained last night. More recently, we've seen research like Hierarchical Temporal Memory which attempt to address how the human brain processes information. Anyway... these approaches aren't the only ways forward. When you think about it, one thing we might want for an AGI is really just a software system that can improve itself (gaining new abilities), with increasingly less and less human supervision. I'm thinking of a giant codebase, not just a big library, but more on the order of the entirety of Python code in open-source projects. The entire ecosystem. And then adding methods for it to self-improve. For example, scanning through the codebase looking for bad style or other obvious bugs. And then automatically fixing them. The next step would be to give it the ability to automatically process certain kinds of information. It would scan an input document (file), and try to figure out what it is (HTML, CSV, whatever), and then try to match that to the kind of output which is required. And then try to find transforms in its own codebase which can accomplish this for the sample input / output. The next steps get more difficult where it is analyzing its own codebase not just for problems, but it understand the meaning. Maybe it can annotate it's own codebase with more meta-information. For example this function foobar() has a string argument, but it isn't just a string, it is supposed to be a street address. And at some point, getting the system to write code to solve new problems. I've looked at a bit of research in this particular direction, but didn't really find much.
- nradov 9y ago
  Static analysis tools can already find obvious bugs. Except they have a high false positive rate. And then fixing the bugs without breaking something else is still far beyond what we can automate. There is no universal fitness function to tell the difference between good versus bad software, thus no way to consistently automate improvements.
saurabh20n 9y ago
Actual work towards this: http://sparkz.org/ai/program-synthesis/2017/10/11/neural-programmer.html http://sparkz.org/ai/program-synthesis/2017/10/11/neural-pro...
goatlover 9y ago
I'll believe it when I see it. By that I mean, actually replacing programmers, not as an academic or highly specialized exercise. I think it will take AGI to automate programming on any large scale such as to eliminate a meaningful percentage of programmers. Otherwise, it just becomes another tool for the programmer, where they're being asked to build ever more complex software for ever more kinds of applications.
lostboys67 9y ago
I recall that being touted in the 70's there was a program marketed for PET's and Apples as "the last one" which would replace the need for almost all developers :-)

EliRivers 9y ago

All the problems in every serious piece of software I've ever worked on stem from the customer (be it external or in-house) not knowing what they want, the person in charge of capturing that doing a pretty bad job of it anyway, and the chain from there to the person writing the software taking that failure and making it worse. AI writing software will just get me another flavour of the wrong software. AI working out what people want the software to do would be useful.

colemannugent 9y ago
My thoughts exactly. Until the AI can hold useless meetings and design things by bureaucracy it will never be a serious threat to big businesses/government.
kmicklas 9y ago
You don't think good old fashioned bugs are a problem?
- deleted 9y ago
  [deleted]
letlambda 9y ago
>AI writing software will just get me another flavour of the wrong software. AI working out what people want the software to do would be useful. Maybe we can deliver that wrong software in 100ms instead of 100 days.

sp332 9y ago

https://research.googleblog.com/2017/05/using-machine-learning-to-explore.html https://research.googleblog.com/2017/05/using-machine-learni... Right now they're pitching it as a way for non-experts to make better neural network architectures.

mormonchess 9y ago

Indeed.

kiernanmcgowan 9y ago

This is not new. We have been using machines, build by other machines, to make new machines for the last 200 years.

Animats 9y ago

"At last - the end of programming!" - 'The Last One'[1], from 1981, a BASIC code generator. [1] https://modeling-languages.com/last-one-code-generator-basic-1981/ https://modeling-languages.com/last-one-code-generator-basic...

lostboys67 9y ago
I remember that one

ansible 9y ago

I've been thinking about this more this year. Earlier this year, I finished watching "Person of Interest" (maybe not ironically) on Netflix. So far we've been applying ML/DL to problems like image recognition, which has produced quite useful products. But these things are still far away from Artificial General Intelligence. And I've been studying AI / AGI research for decades as well. In the early years, there was an emphasis on formal systems, that could logically infer the sidewalk was wet because it rained last night. More recently, we've seen research like Hierarchical Temporal Memory which attempt to address how the human brain processes information. Anyway... these approaches aren't the only ways forward. When you think about it, one thing we might want for an AGI is really just a software system that can improve itself (gaining new abilities), with increasingly less and less human supervision. I'm thinking of a giant codebase, not just a big library, but more on the order of the entirety of Python code in open-source projects. The entire ecosystem. And then adding methods for it to self-improve. For example, scanning through the codebase looking for bad style or other obvious bugs. And then automatically fixing them. The next step would be to give it the ability to automatically process certain kinds of information. It would scan an input document (file), and try to figure out what it is (HTML, CSV, whatever), and then try to match that to the kind of output which is required. And then try to find transforms in its own codebase which can accomplish this for the sample input / output. The next steps get more difficult where it is analyzing its own codebase not just for problems, but it understand the meaning. Maybe it can annotate it's own codebase with more meta-information. For example this function foobar() has a string argument, but it isn't just a string, it is supposed to be a street address. And at some point, getting the system to write code to solve new problems. I've looked at a bit of research in this particular direction, but didn't really find much.

nradov 9y ago
Static analysis tools can already find obvious bugs. Except they have a high false positive rate. And then fixing the bugs without breaking something else is still far beyond what we can automate. There is no universal fitness function to tell the difference between good versus bad software, thus no way to consistently automate improvements.

saurabh20n 9y ago

Actual work towards this: http://sparkz.org/ai/program-synthesis/2017/10/11/neural-programmer.html http://sparkz.org/ai/program-synthesis/2017/10/11/neural-pro...

goatlover 9y ago

I'll believe it when I see it. By that I mean, actually replacing programmers, not as an academic or highly specialized exercise. I think it will take AGI to automate programming on any large scale such as to eliminate a meaningful percentage of programmers. Otherwise, it just becomes another tool for the programmer, where they're being asked to build ever more complex software for ever more kinds of applications.

lostboys67 9y ago

I recall that being touted in the 70's there was a program marketed for PET's and Apples as "the last one" which would replace the need for almost all developers :-)