I love that :)
You might not need machine learning
61–70 of 201 posts
Re: You might not need machine learning
#62Neural networks are just regressors. Yes you can learn the weights with genetic algorithms. Is this advised? Not so much : 99.99% of neural networks are trained with some variation of gradient descent on a specified loss function. I don't even know if I agree to the statement. Polynomial regression solves basically the same problem as neural networks but performs way, way, way worse on big datasets. But nonetheless I…
This post isn't about learning NN weights with GAs,
Also the reinforcement learning community does a significant amount of it's work with neural networks which are not trained using gradients. Gradient free training is a highly active research area. More like 90% of neural networks are trained with gradient descent
Re: You might not need machine learning
#63> 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…
in motorcycles (and rwd cars) everyone knows you steer with the rear anyway.
Re: You might not need machine learning
#64The 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.
I think Arthur Samuel would agree. This approach has a loss function, parameters, and inputs that feed in to a model to optimise the parameters. The big difference between this and the other approach mentioned in the article is the model is a simple one that's easy to understand instead of a many layered neural network which is rather opaque. I think the article may be better titled "You might not need neural network…
> I think the article may be better titled "You might not need neural networks."
Since a linear model is essentially a single layer neural network with linear activation, we can't even say that. The athor was using a neural network without realising it :)
Re: You might not need machine learning
#65Neural networks are just regressors. Yes you can learn the weights with genetic algorithms. Is this advised? Not so much : 99.99% of neural networks are trained with some variation of gradient descent on a specified loss function. I don't even know if I agree to the statement. Polynomial regression solves basically the same problem as neural networks but performs way, way, way worse on big datasets. But nonetheless I…
This post isn't about learning NN weights with GAs, Also the reinforcement learning community does a significant amount of it's work with neural networks which are not trained using gradients. Gradient free training is a highly active research area. More like 90% of neural networks are trained with gradient descent
It's a GA without crossover applied to a single layer neural network with a linear activation
Re: You might not need machine learning
#66I 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.
> 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.
The actual problem is that we don't know how long it'll be until we build an AGI. Experts put the range somewhere between 10 years and never.
Building an unconstrained AGI is an existential risk, so it's important to try and narrow the confidence bands on these questions. That's one of the reasons why Musk pledged $1 billion to OpenAI.
Re: You might not need machine learning
#67I 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.
Well neural networks have been used in lots of genuinely useful products. I use voice dictation on my phone all the time for example, and I don't think I could ever go back to manually cataloging all of my photos (I use Google Photos, but Microsoft Photos and Apple Photos also use ML).
Re: You might not need machine learning
#68Earlier quoted context omitted.
No, this is someone getting a bot some karma so it can eventually contribute to the manipulation of posts (i.e. getting things upvoted to the front page).
Then it is probably our duty to downvote.
Can probably just contact dang to ban him. But it's 4am and I'm too lazy
Re: You might not need machine learning
#69Earlier quoted context omitted.
No, this is someone getting a bot some karma so it can eventually contribute to the manipulation of posts (i.e. getting things upvoted to the front page).
Does karma play a role in this on HN? I wasn't aware of that.
Yes, either accounts need a certain amount of karma to make their votes matter or higher karma gives more weight to the vote. Then when you post links that promote your own content or business you can upvote them with your army of bots
Re: You might not need machine learning
#70s/machine learning/deep learning/
That's what the author means. There are a lot of "traditional" ML and AI techniques that can work very well for a lot of problems.