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Machine Learning algorithms are only as good as the data they are trained on. For tasks like self-driving or spotting cancer in x-rays, they are producing nove

Machine Learning algorithms are only as good as the data they are trained on. For tasks like self-driving or spotting cancer in x-rays, they are producing novel result because these kinds of tasks are amenable to reinforcement. The algorithm crashed the car, or it didn't. The patient had cancer, or they didn't. For tasks like reproducing visual images or reproducing text, it _seems_ like these algorithms are starting to get "creative", but they are not. They are still just regurgitating versions of the data they've been fed. You will never see a truly new style or work of art from DALL-E, because DALL-E will never create something new. Only new flavors of something old or new flavors of the old relationships between old things. Assuming that it is even possible to describe novel software engineering problems in a way that a machine could understand (i.e. in some complex structured data format), software engineering is still mostly a creative field. So software engineering isn't going to performed by machine learning for the same reason that truly interesting novels or legitimately new musical styles won't be created by machine learning. Creating something new relies on genuine creativity and new ideas and these models can only make something "new" out of something old.

lazide 4y ago
Kinda - if you have a problem amenable to some kind of evolutionary training model/feedback, you could certainly improve it via ML. Anything from compression to data processing can and has been improved through evolutionary training processes. If you trained an ML model on recognizing patterns of older less efficient algorithms and swapping in new ones, that could be useful. It probably isn’t that much harder of a problem than knowledge graph building and objecg recognition. Also, having an ML model trained to recognize certain types of problem patterns in whatever source product/model/codebase or whatever and then spit out code to solve them is certainly not a theoretically impossible or low value task. That said, ML as an art or science is no where near being able to do that usefully near as I can tell. Hell even basic chatbots still suck. Hell, even getting Hibernate to usefully reverse engineer a pretty normal schema still sucks.
joe_the_user 4y ago
Machine Learning algorithms are only as good as the data they are trained on. Machine Learning algorithms are never better than the data they're trained-on. But they can easily be "worse". Specifically, an ML algorithm trained on one data set can have a hard time operating on a different data set with some similarities and some fundamental differences. This is related to algorithms generally not having an idea how accurate their predictions/guesses are. This in turn relates to current ML as being something like statistical prediction with worries about bias tossed-out (which isn't to dismiss it but to illuminate it's limitations). For tasks like self-driving or spotting cancer in x-rays, they are producing novel result because these kinds of tasks are amenable to reinforcement. The algorithm crashed the car, or it didn't. The patient had cancer, or they didn't. Ironically, both those applications have been failures so-far. Self-driving is far more complex than a binary crash-or-not scenario (usually the road is predictable but just about anything can wander into the roadway occasionally and you need to deal with this "long tail"). It also requires extreme safety to reach human levels. Diagnosis by image has problems of consistence, of doctors considering more than just an image and other things possibly not understood.
- oldjavacoder 4y ago
  Agreed. ML (a component of AI) faces the same issue today as it always has, outliers, i.e. the real world.
- ALittleLight 4y ago
  Alphago, and similar, are all examples of ML systems exceeding their training data and human abilities. It's clearly possible to beat human capability with ML.
  - trention 4y ago
    >Alphago, and similar, are all examples of ML systems exceeding their training data Alphago basically generated its own training data (hence, "exceeding" is not correct) because the domain was amenable to a specific form of reinforcement learning. Programming isn't such a domain.
falcor84 4y ago
I don't see any reason why an ML algorithm wouldn't be able to extrapolate to novel ideas. Once it has learned some high dimensional subspace based on the examples given to it, I suppose it could search for areas outside that boundary which are in some way likely to be interesting.
- cdrini 4y ago
  That's a super interesting observation; you've convinced me that ML algorithms could generate novel things.
kristianov 4y ago
This is why I'm gonna commit a few bugs to the main branch today.

lazide 4y ago

Kinda - if you have a problem amenable to some kind of evolutionary training model/feedback, you could certainly improve it via ML. Anything from compression to data processing can and has been improved through evolutionary training processes. If you trained an ML model on recognizing patterns of older less efficient algorithms and swapping in new ones, that could be useful. It probably isn’t that much harder of a problem than knowledge graph building and objecg recognition. Also, having an ML model trained to recognize certain types of problem patterns in whatever source product/model/codebase or whatever and then spit out code to solve them is certainly not a theoretically impossible or low value task. That said, ML as an art or science is no where near being able to do that usefully near as I can tell. Hell even basic chatbots still suck. Hell, even getting Hibernate to usefully reverse engineer a pretty normal schema still sucks.

joe_the_user 4y ago

Machine Learning algorithms are only as good as the data they are trained on. Machine Learning algorithms are never better than the data they're trained-on. But they can easily be "worse". Specifically, an ML algorithm trained on one data set can have a hard time operating on a different data set with some similarities and some fundamental differences. This is related to algorithms generally not having an idea how accurate their predictions/guesses are. This in turn relates to current ML as being something like statistical prediction with worries about bias tossed-out (which isn't to dismiss it but to illuminate it's limitations). For tasks like self-driving or spotting cancer in x-rays, they are producing novel result because these kinds of tasks are amenable to reinforcement. The algorithm crashed the car, or it didn't. The patient had cancer, or they didn't. Ironically, both those applications have been failures so-far. Self-driving is far more complex than a binary crash-or-not scenario (usually the road is predictable but just about anything can wander into the roadway occasionally and you need to deal with this "long tail"). It also requires extreme safety to reach human levels. Diagnosis by image has problems of consistence, of doctors considering more than just an image and other things possibly not understood.

oldjavacoder 4y ago
Agreed. ML (a component of AI) faces the same issue today as it always has, outliers, i.e. the real world.
ALittleLight 4y ago
Alphago, and similar, are all examples of ML systems exceeding their training data and human abilities. It's clearly possible to beat human capability with ML.
- trention 4y ago
  >Alphago, and similar, are all examples of ML systems exceeding their training data Alphago basically generated its own training data (hence, "exceeding" is not correct) because the domain was amenable to a specific form of reinforcement learning. Programming isn't such a domain.

falcor84 4y ago

I don't see any reason why an ML algorithm wouldn't be able to extrapolate to novel ideas. Once it has learned some high dimensional subspace based on the examples given to it, I suppose it could search for areas outside that boundary which are in some way likely to be interesting.

cdrini 4y ago
That's a super interesting observation; you've convinced me that ML algorithms could generate novel things.

kristianov 4y ago

This is why I'm gonna commit a few bugs to the main branch today.