[stfwn]

> 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 will never accelerate coming out of a corner, because it can't know it has the headroom to steer away from the wall as it's coming out.

Similarly, the model for steering inherently steers the car towards the middle of the track. I would expect the car to wobble from left to right if the road's edges are ragged, make up its own corners if the track edges describe a 'fake' turn on a straight bit, and the car would likely crash if it were to encounter a Y junction or a pit stop. The neural network agents showed smarter behavior here because it is able to capture more complex cross-dependence between different inputs.

On the topic of junctions, if the track were to include them, perhaps it'd be nice if the car chose the quickest route to optimize for lap times. But maybe that stretches the problem statement too much.

> 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.

In theory this is more random and less efficient than an evolutionary algorithm, which searches the problem space in a structured way. If the author really wanted to hammer the point home, a least squares method to one-shot the coefficients would be more convincing.

All in all, the author doesn't make any hard claims that are false. But I would nuance the point of "neural networks are unnecessary" to "simpler models will do for simpler objectives".

[blackbrokkoli]

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 implement a decent title search bar. And kick the consultant hyping you up about ML out, now.

So my takeaway is that is not about whether AI is never useful or about 2D steering, but about using the right tool for the right job.

And building on that, I have to give the author probs to demonstrating an alternative solution to I problem wich I would have definitely solved via AI.

[mumblemumble]

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

[mathattack]

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.

[martamorena9182]

> Sure, YouTube itself proably built insane stuff in their engine you could never replicate with classic methods

Probably. But pretty much anything they recommend is junk, so... That's where the author may have a point. If you don't understand your AI algorithm anymore, it's hard to improve it or even realize how wrong it is. AI is generally good at steering the masses into a couple of "averaged" directions. At the individual level though, it's often crap, unless you are the perfect stereotype that the algorithms assumes you to be.

[noobermin]

That is the point, for some problems NN is unnecessary. That isn't an argument against the point, that is the point. Many problems in life allow simpler models and can save time and computational power if people used their brains first.

[Der_Einzige]

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.

[bluGill]

There is enough delay in the feedback loops that the simple steer to the middle works - despite the jagged edge, you still track a straight line to within a few cm.

[m463]

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

[tyingq]

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.