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You might not need machine learning

nullprogram.com

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Re: You might not need machine learning

#121

Post by username90 »

disgruntledphd2 wrote: post #114
username90 wrote: post #97

Earlier quoted context omitted.

Machine learning is a set of techniques developed to attack modeling problems that traditional algorithms couldn't solve. So if the algorithm was developed and used before computers it definitely isn't machine learning. Everything done in this article was known and used before computers existed hence not machine learning. Doing automatically controlled systems was still possible before computers just that it required…

> Machine learning is a set of techniques developed to attack modeling problems that traditional algorithms couldn't solve. So if the algorithm was developed and used before computers it definitely isn't machine learning I think that it's important to note here that this was the set of problems that computer science researchers didn't know how to solve. In the early days, they mostly ended up re-inventing statistical…

> And to be fair, a neural net is just a bunch of linear models joined by a non-linearity.

Nobody did this before computers though.

> . In that case, it's essentially stacked logistic regression, which was invented before computers.

It isn't "basically logistic regression", it is just a technique which uses logistic regressions. The full technique is ML. If you remove the ML parts it is basically just logistic regression left though.

Re: You might not need machine learning

#123

Post by amatic »

I've read a wonderful paper on what information people use when driving, it is pretty similar to this driving simulation. The basic cue is "where you will be in T milliseconds", extrapolated using the current vehicle velocity, and trying to keep that point on the road by steering and throttling. Here is that paper, A unifying theory of driver perception and steering control (2019) https://www.researchgate.net/publication/337024514_A_Unifyin...

Re: You might not need machine learning

#124

Post by CraigJPerry »

tyingq wrote: post #100
m463 wrote: post #63

Earlier quoted context omitted.

in motorcycles (and rwd cars) everyone knows you steer with the rear anyway.

Somewhat related, it's interesting how non-intuitive steering is to people for motorcycles and bicycles. They do it correctly, but it's hard to reason about. That is, that pushing the left handgrip forward, at speed, turns left and not right. Yet, at very slow speeds, like walking it, it's the opposite.

>> pushing the left handgrip forward, at speed, turns left and not right

This is the most counter intuitive physics i’ve ever experienced.

The really weird thing is i’d been riding pushbikes all my life and had my motorbike license almost a year before i learned this. It wasn’t taught as part of the licence training.

Super handy to know as a tool, it’s kept me from going for a closer look at the scenery on a couple of occasions.

Re: You might not need machine learning

#125

Post by dmos62 »

mjburgess wrote: post #82
dmos62 wrote: post #34

Earlier quoted context omitted.

Well that depends on your definition of AI. Which isn't well defined. We call AI what we perceive as "magic". Black box algorithms have a higher chance of being perceived that way (e.g. neural nets). When you get some insight into how an algorithm works (easier for transparent box algos, but same holds for black box algorithms), you start to see it less and less as "magic", and, consequently, you're less likely to re…

I disagree. We don't find these systems intelligent because, on inspection, they arent. We are intelligent. Not "magically", but actually nevertheless. Our intelligence, and that of dogs (, mice, etc.) consists in the ability to operate on partial models of environments; dynamically responsive to them; and to skilfully respond to changes in them. This sort of intelligence requires the environment to physically recons…

You provided an illustration of "magic". It's important to realise that you don't need a complex algorithm to produce complex behaviour (see Stephen Wolfram and his work on cellular automata).

Re: You might not need machine learning

#126

Post by wokwokwok »

disgruntledphd2 wrote: post #112
wokwokwok wrote: post #42

I feel like there’s something more interesting going on here than the author is giving credit to. Most tools you can start by solving simple problems with, and gradually work up the complexity of the problem you’re addressing until it does “something useful”. This is a good way to learn what you can, can’t and should use a tool for. Deep learning is problematic though; solving trivial tasks is actually quite difficul…

> and we have like 20 years of proof that using hand crafted models has been proven not to scale effectively. What do you mean by this? In what context? Like, the twenty year old models are still running in credit risk and insurance, so I'm confused if you mean in ML/statistical modelling.

You know, driving cars, like the article was talking about?

...or, NLP, audio & image recognition, recommendations... come on. It’s not controversial.

Re: You might not need machine learning

#129

Post by fractionalhare »

dr_dshiv wrote: post #92
karolkozub wrote: post #20

I 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…

As soon as we recognize plain old regression as machine learning, then we start to see "averages" as models of systems and how practically useful could that be?

I think you're being facetious, but on the off-chance you're not, and for the benefit of others: averages are incredibly practically useful for modeling systems. Parameter estimation (which generalizes averages and applies to other distribution features like variance) is a foundational modeling methodology. It's useful for both understanding and forecasting data. Measures of central tendency are nearly always good (if obviously imperfect) models of systems.

Here is a trivial example: one of the best ways of modeling timeseries data, both in and out of sample, is to naively take the moving average. This is a rolling mean parameter estimate on n lagged values from the current timestep. Not only is this an excellent way of understanding the data (by decomposing it into seasonality, trend and residuals), it's a competitive benchmark for future values. The first step in timeseries analysis shouldn't be to reach for a neural network or even ARIMA. It should be to naively forecast forward using the mean.

You might be surprised at how difficult it is to beat that benchmark with cross-validation and no overfitting or look-ahead bias.

Re: You might not need machine learning

#130

Post by cptnapalm »

CraigJPerry wrote: post #124
tyingq wrote: post #100

Earlier quoted context omitted.

Somewhat related, it's interesting how non-intuitive steering is to people for motorcycles and bicycles. They do it correctly, but it's hard to reason about. That is, that pushing the left handgrip forward, at speed, turns left and not right. Yet, at very slow speeds, like walking it, it's the opposite.

>> pushing the left handgrip forward, at speed, turns left and not right This is the most counter intuitive physics i’ve ever experienced. The really weird thing is i’d been riding pushbikes all my life and had my motorbike license almost a year before i learned this. It wasn’t taught as part of the licence training. Super handy to know as a tool, it’s kept me from going for a closer look at the scenery on a couple o…

You should try a sidecar. Want to go left, turn left. Want to go right, turn right, but not too fast because the chair takes to the air and now motorcycle physics are involved.
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