101–110 of 201 posts
Earlier quoted context omitted.
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.
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…
> 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…
Earlier quoted context omitted.
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.
In my understanding AI is an even broader term and means "any solution that imitates intelligent behavior". E.g. expert systems which are pretty much a bunch of if-then rules are also considered AI.
It's why I use "AI" and "ML" interchangeably although I know it's technically incorrect - the formal definition doesn't match what people are currently thinking.
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).
The author is still using Machine Learning, even if not with neural networks: the need for rigorous strategies for model selection doesn't disappear.
Earlier quoted context omitted.
It might not be a hard boundary, but I think the perception of ML vs. optimization is how much of a model you have. If all you have is a black box, then it's ML; if you know how the system you are studying works, it's (parameter) optimization.
That’s a very unfair distinction, almost like a No True Scotsmam fallacy to say machine learning is only bad and other stuff is only good (in terms of transparency). But machine learning has predated neural networks by hundreds of years. The core mathematical basis of all machine learning coursework linear regression and decision trees. Other models like SVMs, Bayesian models, nearest neighbor indexes, TFIDF text sea…
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?
Sure, they are dummy regressors [1], but they can be so useful for proving that your whatever ML model you choose is at least better than a dummy baseline. If your model can't beat it, then you need to develop a better one.
They can even be used as a place-holder model so you can develop your whole architecture surrounding it, while another teammate is iterating over more complex experiments.
You could also settle in for a moving average process as a first model in a time-series [2], because they are easy to implement and simple to reason about.
Never under-estimate the power of an "average".
[1] https://scikit-learn.org/stable/modules/generated/sklearn.du... [2] https://en.wikipedia.org/wiki/Moving-average_model
> 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…
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…
Manager - We need to do X, we need a ML Data Scientist.
Engineering Team - there's a simple solution we can use instead of ML.
Manager - No we need a ML Data Scientist, we need to do this right.
Time Passes, hiring a ML Data Scientist is hard, and the problem never gets solved.