Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

You might not need machine learning

nullprogram.com

Post reply on HN
Print view View original

191–200 of 201 posts

Re: You might not need machine learning

#191

Post by vntok »

qayxc wrote: post #184
vntok wrote: post #181

Earlier quoted context omitted.

The datasheet of the A100 lists "remote intervention from an engineer's home or workstation" as a core feature of that box, using one of the dozens of hyperoptimized networking components, so... not sure what argument you are going for there. These hyperconnected boxes are definitely (hopefully?) not where an AGI will be built.

You are aware of what a symbol picture is? Also, yes, every server and super computer has network components, that still doesn't mean that the Lawrence Livermore National Laboratory's Sierra and its 4 GPUs per node are accessible from the internet, so your remark is kind of meaningless.

Incorrect. Out of the three Sierra hosted at LLNL that have the "4 GPUs per node" you're talking about, two of them are in fact accessible from the public Internet. One is in Collaboration Zone CZ (Sierra/lassen), another is in Restricted Zone RZ (Sierra/rzansel). Only one of the three is classified (Sierra/shark), and even then it's still sshable when one's logged into SecureNet.

Re: You might not need machine learning

#192

Post by disgruntledphd2 »

sdoering wrote: post #188
disgruntledphd2 wrote: post #113

Earlier quoted context omitted.

It depends. Rule based systems are great if you have people with deep domain understanding developing the rules. Unfortunately, those people are rare, so most rule-based approaches fail to perform well. However, most recommendation systems suck unless you get someone who knows what they are doing to build them. In terms of business value, I would be very hesitant to make strogn statements like the above (in both case…

Well - I said I have seen and tested. I would love for positive ML cases to arise. I really would. That would make it way easier to sell my Data Science colleagues to the respective clients on terms other than hype and buzzwords. I also believe that with a good situation in underlying data quality we could be talking about massively reduced costs in getting these systems up and running - and this would tip the scale…

Yeah, to make this stuff work well, you normally need lots of data, so consumer tech is mostly where you see successes.

If the data isn't being logged by automated systems daily, then you probably don't have enough to make these kinds of things work.

In smaller data environments, rules are going to perform much, much better (but still require the domain expertise, which isn't cheap).

Re: You might not need machine learning

#193

Post by mlthoughts2018 »

kgwgk wrote: post #84
mlthoughts2018 wrote: post #83

Earlier quoted context omitted.

Stochastic gradient descent and other things like genetic algorithms and simulated annealing are random search techniques specifically created and taught in the context of machine learning.

Simulated annealing goes back to the seventies and was definitely not "specifically created in the context of machine learning". Many (most?) optimization techniques have their origin in Operations Research.

Simulated annealing was developed for purposes of parameter fitting in physics modeling, based on Metropolis Hastings which was likewise developed in the context of parameter inference for model fitting. Simulated annealing for eg traveling salesman problem came later.

I do agree some optimization algorithms are rooted in other fields. I wasn’t trying to say that machine learning is the only historic field from which optimization methods were developed. I just wanted to point out it is a major historic field where some highly respected search and optimization procedures were first created, since people often overlook how old machine learning is and the vast set of modeling procedures apart from neural networks that make up the core of machine learning.

Re: You might not need machine learning

#194

Post by mlthoughts2018 »

cupofcoffee wrote: post #118
mlthoughts2018 wrote: post #83

Earlier quoted context omitted.

Stochastic gradient descent and other things like genetic algorithms and simulated annealing are random search techniques specifically created and taught in the context of machine learning.

Lol. Are all optimization problems specifically created and taught in the context of machine learning? Do you know any other context than ML?

Your comment is not coherent. You ask, “ Are all optimization problems specifically created and taught in the context of machine learning?” but this has no logical or semantic connection to my comment in any way. It fails to be a valid response or question.

Instead it seems you falsely believe you are writing with some sarcasm that endows rhetorical flair to undercut my comment. It’s very rude and juvenile in addition to being wholly ineffective.

Re: You might not need machine learning

#195

Post by mlthoughts2018 »

tgv wrote: post #135
disgruntledphd2 wrote: post #116

Earlier quoted context omitted.

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

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

This is very false. Least squares regression fitting, Chebychev polynomial approximation, and maximum likelihhod estimators all existed at the time and those are all classic examples of standard machine learning. The term “machine learning” essentially encompasses any type of algorithm that expresses inductive statistical reasoning. Even just elementary school descriptive statistics is machine learning. “Machine learning” is a super old subfield of applied mathematics. The fact that the terminology “machine learning” didn’t exist until things like perceptron and SVMs came along is utterly irrelevant semantic hairsplitting.

Re: You might not need machine learning

#196

Post by gbrls »

leobakerhytch wrote: post #182
thotsBgone wrote: post #165

Earlier quoted context omitted.

It's not a fault at all. If you're going to spend more time watching videos if you're recommended stuff Youtube knows you already like, that's what it's going to do. Youtube just wants you to watch more videos. They don't care whether you are exposed to a variety of content.

Except I think there’s convincing argument to make that engagement will go down over time, if the algorithm makes no attempt to prioritize or suggest novel content. The rare occasions I discover a new channel, it’s almost always from some source other than the algorithm: a referral from a friend, this site, another YouTuber, etc. My viewership of the same repetitive roster of videos absolutely tails off until I find…

I agree with you, I have the same feeling about Spofity, it's algorithm just doesn't work for me, I have to search somewhere else for recommendations.

Re: You might not need machine learning

#197

Post by laquition »

bayeslaw wrote: post #110

I thought hn has an intelligent and well informed crowd. ok I get it that engineers are not scientist but judging from the responses here it's quite staggering to me how clueless some ppl are here.. do yourself a favour and maybe learn a bit about ml before forming an opinion. massaging JavaScript for a living is cool I guess but maybe there's a reason faang is paying 5-10x your salaries to ml researchers. maybe just…

Basic programming is quickly becoming antiquated and articles like this, and many of the commenters are stuck in the past.

The efficiency of ML is in cost, not overall computation. Throwing machine resources at a problem is cheaper than hiring some guru with the necessary math and CS background to solve the problem.

We've seen the same thing with frameworks/libraries. Before it took specialized knowledge to do basic things like networking, media creation, etc. in code. Now there are existing tools that do everything for you.

The same has happened with optimization/algorithmic knowledge, and the genie is not going back in the bottle. There will definitely be specialized cases where that particular expertise is needed, but that is no longer the norm.

Re: You might not need machine learning

#198

Post by laquition »

astrea wrote: post #95

All he proved was you don't need a neural network for MicroMouse. We've known that for years. Some problems do require it. In my job experience, facial recognition can't be easily done with traditional signals processing. Stereo vision can be, but the algorithms are slow, expensive, and very error prone when compared to deep learning models.

The author is pretty much attacking a strawman he cherry picked. He took one toy example of someone learning ML and used it to attack the newest trend. Instead of taking the time to dive into the topic he's trying to discount it as less relevant. It's obvious he's stuck in the C/Unix era of programming, which has long since passed it's heyday.

Re: You might not need machine learning

#199

Post by laquition »

leobakerhytch wrote: post #182
thotsBgone wrote: post #165

Earlier quoted context omitted.

It's not a fault at all. If you're going to spend more time watching videos if you're recommended stuff Youtube knows you already like, that's what it's going to do. Youtube just wants you to watch more videos. They don't care whether you are exposed to a variety of content.

Except I think there’s convincing argument to make that engagement will go down over time, if the algorithm makes no attempt to prioritize or suggest novel content. The rare occasions I discover a new channel, it’s almost always from some source other than the algorithm: a referral from a friend, this site, another YouTuber, etc. My viewership of the same repetitive roster of videos absolutely tails off until I find…

Stagnation is a known problem in reinforcement learning and similar methods. It's very easy to get stuck at a local maximum. My favorite fun example is https://gym.openai.com/envs/BipedalWalkerHardcore-v2/ where a standard DDPG(https://arxiv.org/abs/1509.02971) will get stuck at pits in the environment. Although it could get a higher score if it learned to jump, there is a penalty with falling in that makes it stabilize on standing still and running out the timer. Video: https://www.youtube.com/watch?v=DEGwhjEUFoI

I suspect there is something similar going on with video/music recommendations. When a bad novel suggestion is made the penalty is likely too high to overcome (User immediately clicks off) with traditional reinforcement methods.

Post reply on HN