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You might not need machine learning

nullprogram.com

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Re: You might not need machine learning

#51

Post by spratzt »

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…

Genuinely curious as to what you moved into after working as a data scientist. I'm a data scientist and desperate to get out.

Re: You might not need machine learning

#52

Post by maweki »

laichzeit0 wrote: post #47
maweki wrote: post #29

Earlier quoted context omitted.

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 c…

When I see "symbolic AI" I immediately think of Gary Marcus and immediately feel disdain towards the topic because of his behaviour on Twitter and other places.

I don't know the dude. I "only" know that my field of research is deductive reasoning in interactive applications and that this area falls under "Logic Programming" and LP is an area of AI.

I know that AI researchers are usually a bit dismissive about the other area. I don't like statistics either. Reducing the whole of AI research to statistical approaches (and NNs are one of those) is disingenious and dismisses hundreds of researchers doing important work.

You may not want to have rule-based image recognition, but if your car decides to run over somebody, I feel we better have an explanation for this behaviour based on reasoning and logic.

Re: You might not need machine learning

#54

Post by Flashtoo »

spratzt wrote: post #51
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…

Genuinely curious as to what you moved into after working as a data scientist. I'm a data scientist and desperate to get out.

As a data scientist at the start of my career: why?

Re: You might not need machine learning

#55

Post by cupofcoffee »

I simply do not understand the ML hype. It's absolutely ridiculous. On top of that you have Elon Musk thinking we are a few months away from SkyNet even though we are decades and decades away from AGI.

Each and every time I see neural networks on some "techies" blog, I wanna vomit.

Re: You might not need machine learning

#56

Post by yes_man »

Generally it is easy to agree with the sentiment that machine learning is being used to solve problems where it isn't really needed. But on the other hand, the same argument could have been used when computers initially came along: "you don't need a supercomputer to calculate this function, I can do it with pen and paper faster". So the real value in projects like this is not that you can now drive 2d car game with a neural network, but that neural networks just took another tiny step forward and demonstrate that their capability to minimize error functions is in principle transferrable to real life problems no matter how stupid, and one day those small steps will have accumulated and neural nets will (and already have) supercede many limits that humans have.

Re: You might not need machine learning

#57

Post by mckirk »

cupofcoffee wrote: post #55

I simply do not understand the ML hype. It's absolutely ridiculous. On top of that you have Elon Musk thinking we are a few months away from SkyNet even though we are decades and decades away from AGI. Each and every time I see neural networks on some "techies" blog, I wanna vomit.

Decades and decades? I wouldn't be so sure in such a bold prediction. One breakthrough in meta-learning and we could be well on our way. (Which is not to say that it will happen next year -- just pointing out that a development like this is nigh impossible to predict with the certainty your comment expressed.)

Re: You might not need machine learning

#58

Post by Der_Einzige »

stfwn wrote: post #31

> A key feature of neural networks is that the outputs are a nonlinear function of the inputs. However, steering a 2D car is simple enough that a linear function is more than sufficient, and neural networks are unnecessary. This depends entirely on the definition of 'steering a 2D car'. In the model used, throttle is simply proportional to the distance to the nearest wall in front of the car. This means the agent wil…

Even more interesting is to show that you can also optimize the neural network weights using a GA or even the authors own basic method. Would be interesting to compare the results of the authors method with the neural network optimized in the same way.

Re: You might not need machine learning

#60

Post by Der_Einzige »

snemvalts wrote: post #5

The approach they show in the end - it's still machine learning though? Exploring a space and finding parameters to optimize for a loss function (speed around the track), just not deep learning with neural nets.

Correct. I was waiting for someone to point this out. He optimized the paramaters of a polynomial equation. The machine learned to effectively race around the track through this optimization. The machine learned
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