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You might not need machine learning

nullprogram.com

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Re: You might not need machine learning

#131

Post by JackFr »

mjburgess wrote: post #79
maweki wrote: post #27

Earlier quoted context omitted.

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…

That's an excellent approach -- and how I try to introduce people to NNs. NNs are just polynomial regression with polynomial activations; and piece-wise linear regression with relu activations (etc.). A NN is just a highly parameterized regression model -- for better, or worse.

That was an eye-opener for me.

I had always thought of neural nets in terms of the massive connected graph, that in my head was somehow behaved like a machine.

When I realized in the end its just a representation of a massive function, f:Rm->Rn, which needs to fitted to match inputs and outputs.

I know this is not precisely correct and glosses over many, many details - but this change in viewpoint is what finally allowed me to increase the depth of my understanding.

Re: You might not need machine learning

#132

Post by thakoppno »

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.

that reminds me of a street hustle in london where a guy had reversed the steering mechanism and would bet you a pound you couldnt steer it straight 5 meters.

pretty interesting to me that we can reverse our intuition.

Re: You might not need machine learning

#134

Post by fractionalhare »

I agree with the premise. But to dig into the specifics here, because it's not clear in the article: is this model generalizable to arbitrary tracks, or will the author have to generate new coefficients for each track?

If you have to generate new coefficients for each track, your polynomial regression reduces to a polynomial interpolation of two-dimensional points which represent the track path on a plane. Which is fine and still accomplishes the specific goal, but doesn't solve what would generally be considered the actual research problem.

But then again I don't know if the neural network actually achieves this. It's a little unclear in the video: I don't know if the model is being able to learn from the human guiding the vehicle on n iterations or if the model is generated by the human guiding the vehicle on n iterations. Presumably the research goal is to develop a model which learns tracks (in this circumstance, that would be akin to the model choosing the coefficients rather than being the coefficients).

Re: You might not need machine learning

#135

Post by tgv »

disgruntledphd2 wrote: post #116
username90 wrote: post #106

Earlier quoted context omitted.

Saying that linear regression is machine learning is like saying that newtons laws is chemistry. There was no machine learning before computers, just regular old optimization algorithms.

Yes, there was. It was just called statistical modelling.

There were no machines in the sense of ML in 1740.

Re: You might not need machine learning

#136

Post by twelfthnight »

ccortes wrote: post #23

As 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 ne…

I've seen this pattern frequently on a few data science teams. However, I do think there is a lot of value to data science, but it more has to do with incorporating metrics and accountability rather than modeling.

For example, I've seen many mission-critical rules-based systems with no accuracy metrics. A data scientist is asked to beat this system using machine learning and fails miserably because the rules were developed by experts over years and the data scientist has a month and is only a few years out of grad school. However, in the process of building the model that data scientist built data pipelines for measuring the rules-based system's accuracy and fixed a small number of major data integrity issues. Unfortunately this isn't considered a win and the data scientist usually leaves the company soon after. :shrug:

Re: You might not need machine learning

#137

Post by sidlls »

mathattack wrote: post #120
blackbrokkoli wrote: post #80

Earlier quoted context omitted.

I see the article more as a broader metaphor for the AI hype. Take, I dunno, video recommendation. Sure, YouTube itself proably built insane stuff in their engine you could never replicate with classic methods (ignoring whether the YT algo is any good). However, if we are just talking about the Vlog of your real estate company, you should probably A/B-test whether your viewers prefer order by time or clicks and imple…

You may be overrating what they do. I suspect that 90% of the recommendation weight is based on what other people clicked after watching the same video.

I've seen so many really complex real-time recommendation pipelines that could be replaced by a simple weighted click-rate style algorithm.

The application of ML and data science in this industry is quite hilariously bad, really.

Re: You might not need machine learning

#138

Post by firethief »

xthestreams wrote: post #105

> Instead of doing anything fancy, my program generates the coefficients at random to explore the space. If I wanted to generate a good driver for a course, I’d run a few thousand of these and pick the coefficients that complete the course in the shortest time. It is worth pointing out that this strategy can likely overfit on the data that you have used for training: when you change the track, your car may not behave…

> It is worth pointing out that this strategy can likely overfit on the data that you have used for training: when you change the track, your car may not behave as good as before. In other words: the coefficients are only good for that specific track(s).

More generally, I think this approach is only suited to "static" courses, which only matter in contrived demos. Any real use of a steering algorithm requires reacting to conditions that can't be predicted a priori; e.g. if you have to avoid collisions with other cars, and one car is controlled by a human player, every run is effectively a different track and overfitting like this would not be an option.

Re: You might not need machine learning

#139

Post by galangalalgol »

finnthehuman wrote: post #119
mumblemumble wrote: post #108

Earlier quoted context omitted.

Video recommendation is the quintessential machine learning killjoy. YouTube and Netflix were a lot more interesting before they achieved algorithmic homogeneity.

They kill exposure to anything fresh, you teach it a couple things you like and then it keeps you swimming in the same pool. Rather than discovering something new, everyone just watches The Office and Parks and Rec., again and again. Now those theme songs make my skin fucking crawl.

On the other hand I taught pandora to only play songs by artists that had done heroin, thats kind of cool, it has tons of variety from ray charles to jhonny cash to alice in chains, and it finds artists I had no idea about like James Taylor. Also, don't try to code while listening to my horses channel... There may be variety but there is also a common quality of alert-sedation.

Re: You might not need machine learning

#140

Post by mattkrause »

wokwokwok wrote: post #126
disgruntledphd2 wrote: post #112

Earlier quoted context omitted.

> 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.

I do sorta wonder about that.

The deep learning "revolution" corresponded with an exponential growth in the time/effort/money being thrown at these problems and the amount of data available to do so.

In an alternate universe, could everyone be going crazy about kernel machines?

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