The Limits of Machine Learning
nautil.us
The Limits of Machine Learning
1–10 of 48 posts
Re: The Limits of Machine Learning
#2I was expecting a discussion of the limits of statistical inference, but instead read a hand-wavy bit that only briefly mentioned a priori knowledge and without using that term.
Re: The Limits of Machine Learning
#3The author seems to say that the no free lunch theorem (NFL) indicates that creating a "Universal Learner" (or artificial general intelligence) is an impossible task.
I disagree based on the following:
1. I think NFL's definition of a universal learner is broader than the definition used by the average AGI researcher.
In practice, we are interested algorithms producing behavior at near-human levels of intelligence, not absolutely universal learning algorithms.
2. NFL does not seem to directly address the overall effectiveness of applying a combination of algorithms based on real-world experience to everyday problems.
Re: The Limits of Machine Learning
#4How is this article on nautil.us ? Did the author just read the wikipedia.com page on Machine Learning ?
There is an entire field in ML called unsupervised learning. Labelling data that do not have labels attached to them.
Its not a Fundamental (uggh) limit, I am not sure if the author even knows what Fundamental means, its not like the halting problem, or heat death of the universe.
ML is also a very young field with poor mathematical understanding, optimism is the best way forward. Christopher Columbus didn't discover an entire NEW WORLD because he had a pesimistic attitude towards his ideas.
Its going to take time, but historially, we are rapidly learning about how the human brain and intelligence works - similar to how we rapidly learnt a lot of physics in the 20th century.
Re: The Limits of Machine Learning
#5How is this article on nautil.us ? Did the author just read the wikipedia.com page on Machine Learning ? There is an entire field in ML called unsupervised learning. Labelling data that do not have labels attached to them. Its not a Fundamental (uggh) limit, I am not sure if the author even knows what Fundamental means, its not like the halting problem, or heat death of the universe. ML is also a very young field wit…
Actually it appears that they read that supervised machine learning has limitations according to the no free lunch theorem and applied it to all of machine learning. See http://www.no-free-lunch.org/
Re: The Limits of Machine Learning
#6The title of this article should really be "The fundamental limitation of 1950's Perceptron style ML".
These days ( 2016 ) there are 1,000's of algorithms, all tuned for a specific problem, ... image recognition, speech, music transcription from audio, text 'learning' say the bible to generate automatic text. Algo's have names like CNN, RNN, ... again 1,000's.
All ML is a 'hack', every algorithm has to be tuned and dialed in to get the coveted 99% 'confirmation'.
It would be easy to fine tune a machine for both answers to the problem described, likewise a human expert might as well see the two ( or maybe more ) correct answers. Then another algorithm could be trained to choose which 'correct' answer is best.
A universal machine, that requires an infinite number of hacked machines is just another 'tower of babel', not unlike the WWW ( port 80/HTML ) of today.
The real problem with ML is the holy-grail of 99%, which means that 1 of 100 innocent people go to prison, or 1 in 100 children die from robot-cars.
A society that allows the technical ( rich elite ) to govern a society that accepts 99% or even 99.9% to live and to hell with 1% or 0.01% this is the real problem with Judges, Executioners, and cops that make decisions fed by Google, Facebook, and all our other favorite fronts controlled by the CIA/NSA.
Re: The Limits of Machine Learning
#7How is this article on nautil.us ? Did the author just read the wikipedia.com page on Machine Learning ? There is an entire field in ML called unsupervised learning. Labelling data that do not have labels attached to them. Its not a Fundamental (uggh) limit, I am not sure if the author even knows what Fundamental means, its not like the halting problem, or heat death of the universe. ML is also a very young field wit…
Yeah, this article was not well-researched or useful at all. As mentioned upthread, it seems focused on a very specific type of supervised learning that has since gone through a major leap in usefulness in the last few years.
The problem posited at the beginning of the article is in fact one of the example applications in a couple of ML courses/textbooks.
Re: The Limits of Machine Learning
#8Note to casual commenters: the precise real-world implications of the NFL theorems proved by Wolpert and collaborators have been difficult to appreciate, even to people well-versed in the computational learning world.
Starting point: http://www.santafe.edu/media/workingpapers/12-10-017.pdf
where we read: "However, arguably, much of that research has missed the most important implications of the theorems."
Re: The Limits of Machine Learning
#9The title of this article should really be "The fundamental limitation of 1950's Perceptron style ML". These days ( 2016 ) there are 1,000's of algorithms, all tuned for a specific problem, ... image recognition, speech, music transcription from audio, text 'learning' say the bible to generate automatic text. Algo's have names like CNN, RNN, ... again 1,000's. All ML is a 'hack', every algorithm has to be tuned and d…
[deleted]
Re: The Limits of Machine Learning
#10The title of this article should really be "The fundamental limitation of 1950's Perceptron style ML". These days ( 2016 ) there are 1,000's of algorithms, all tuned for a specific problem, ... image recognition, speech, music transcription from audio, text 'learning' say the bible to generate automatic text. Algo's have names like CNN, RNN, ... again 1,000's. All ML is a 'hack', every algorithm has to be tuned and d…
[deleted]