"You might not need neural networks" would be a more appropriate title.
The non-nn example given here is one for epoch in epochs loop to take care of picking the best coefficients away from being a ML implementation.
You might not need machine learning
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Re: You might not need machine learning
#22I think links like this are really helpful. Too often I suffer from the (common on HN) mindset of, "I know nothing about this field, but I'm intelligent enough to know this is stupid". Posts like this are not only interesting, but give me a small dose of humility.
Where have I heard that before? https://news.ycombinator.com/item?id=25196754
Wow, how bizarre. Am I weird in jumping to the conspiracy theorist conclusion that this is some sort of experiment on us, like an A/B test to see what kinds of comment works on what kind of thread?
Re: You might not need machine learning
#23As a former data scientist I feel we try solve problems the hard way because one of two reasons (maybe both):
1. To feel smart 2. To justify our paycheck
Most of the time simple solutions like the one in the link will be more than enough but we just can't resist the urge to implement this new paper we just found. I remember thinking about using a NN for a problem we had, after looking it closely for two days all I needed was a simple linear regression and got the extra benefits of being able to explain what was going on.
Eventually I started looking for the "elegant" solution because now that was the thing that made me feel smart. Although some times using brute force is the best approach. There's a balance you find once you gain enough experience... I think.
Re: You might not need machine learning
#24Earlier quoted context omitted.
Where have I heard that before? https://news.ycombinator.com/item?id=25196754
Wow, how bizarre. Am I weird in jumping to the conspiracy theorist conclusion that this is some sort of experiment on us, like an A/B test to see what kinds of comment works on what kind of thread?
No, this is someone getting a bot some karma so it can eventually contribute to the manipulation of posts (i.e. getting things upvoted to the front page).
Re: You might not need machine learning
#25Earlier quoted context omitted.
Wow, how bizarre. Am I weird in jumping to the conspiracy theorist conclusion that this is some sort of experiment on us, like an A/B test to see what kinds of comment works on what kind of thread?
No, this is someone getting a bot some karma so it can eventually contribute to the manipulation of posts (i.e. getting things upvoted to the front page).
Does karma play a role in this on HN? I wasn't aware of that.
Re: You might not need machine learning
#26I love the article, but I don't agree with the premise that machine learning equals neural nets. In my understanding machine learning is a very broad term that just as well could be applied to the polynomial model if the constants were optimized algorithmically. I feel like the presented argument is more for transparent vs opaque models rather than machine learning vs something else. Also one could argue that the pol…
Well... I guess most people equal ML with AI and use these terms interchangeably.
If you just replace ML with AI everywhere in this article it is going to make sense.
The article has other problems, one being the main premise.
The problem isn't to drive a car around track (which is what the polynomials did) but rather write a program that can figure out how to drive a car without you knowing how to solve it.
Re: You might not need machine learning
#27I love the article, but I don't agree with the premise that machine learning equals neural nets. In my understanding machine learning is a very broad term that just as well could be applied to the polynomial model if the constants were optimized algorithmically. I feel like the presented argument is more for transparent vs opaque models rather than machine learning vs something else. Also one could argue that the pol…
The machine learning course at my university starts out with polynomial regression and estimators, statistics of classification, etc.. Neural networks are only one tool in a large toolbox.
But they are all the rage and it is no surprise that a lot of people want to play with them.
Cynically, neural networks are easier as you don't really have to think about your model. Give some examples with some classes and you're done. Or give examples of one class and let the neural net generate new ones. Doing away with the abstraction beforehand is an enticing prospect.
Re: You might not need machine learning
#28Yes ML is overkill in this case, now test it with sensor noise, track obstacles, and the simple polynomial version might break down
This is a pointless comment that misses the point of the post completely. It says "you _might_ not".
The given example in the opening paragraph describes a scenario where you don't need it.
Re: You might not need machine learning
#29I love the article, but I don't agree with the premise that machine learning equals neural nets. In my understanding machine learning is a very broad term that just as well could be applied to the polynomial model if the constants were optimized algorithmically. I feel like the presented argument is more for transparent vs opaque models rather than machine learning vs something else. Also one could argue that the pol…
Well... I guess most people equal ML with AI and use these terms interchangeably. If you just replace ML with AI everywhere in this article it is going to make sense. The article has other problems, one being the main premise. The problem isn't to drive a car around track (which is what the polynomials did) but rather write a program that can figure out how to drive a car without you knowing how to solve it.
That's not symbolic AI though. That's only statistical methods. The statistical methods are all the rage now, but explainable AI that can reason is an important area of computer science (and research) and uses formal methods.
Edit: yeah, you can downvote this, but current AI research splits right along this line, whether it's symbolic or statistical. Some AI courses will use NNs, others will use Prolog and ASP. You can't just dismiss a whole field of research by reducing AI to statistical methods.
Re: You might not need machine learning
#30That’s really not a good example. The polynomial-driven cars behave erratically and slow down for no reason - fixable, but the key is that improving that behaviour will take many man hours of trial-and-error work and math, where the ML version will just improve itself based on the goals.
The problems you mention are likely an artifact of him using a bad optimzation method, not inherently a limitation in the function approximator. Ironically, its most similar to genetic search, a method most commonly associated with machine learning.
If he used a standard optimzation method instead, convergence would be fast and the result much better. A similar problem, using splines to set force inputs for a robot that travels through a maze with barriers optimized using start and end position and force minimization, was a lab in a course I took last year.
The question becomes one of hyper parameter search, i.e. what kind of model/function approximator is sufficient. Here the problem is easy enough that its easy to find a sufficient simple model. The huge networks are nice for more general problems because they tend to work moderately well for everything... in the dataset.