Machine learning is still too hard for software engineers • Hacker News Board

Re: Machine learning is still too hard for software engineers

#151

Post by neura » Thu, Feb 24, 2022, 8:00 AM UTC

zcw100 wrote: post #57

version_five wrote: post #4

I'd argue software engineering is still too hard for ML engineers. Most of ML (not research but commercial applications of what's already been demonstrated) is now well within the realm of engineering, but there are few standard practices, bodies of knowledge, or agreed on processes for doing anything. These are problems that engineering formalism solve, not another auto-ml tool. Maybe I'm saying the same thing as th…

I'd argue software engineering is still too hard for software engineers.

I'm sure there are plenty of quotes out there that are very similar to this, but kudos in stating what most of us (software engineers) already think, with such clarity. I laughed. I cried. I remembered that this is never going to change. ;)

Re: Machine learning is still too hard for software engineers

#152

Post by neura » Thu, Feb 24, 2022, 8:09 AM UTC

version_five wrote: post #146

disgruntledphd2 wrote: post #145

Earlier quoted context omitted.

Anyone's who hires an AI person unknowingly, probably doesn't know the difference. I mean, you can call it optimization with a specially chosen sparse function if you like.

From experience, there are lots of companies looking precisely for an "AI person" who is going to use a bunch of complicated sounding buzzwords and build some overly complex thing without evaluating simpler options. This kind of person gets way more attention than someone who suggests that what the company wants to do can probably be accomplished with a simple shell script, or someone that tells them AI is not magic…

Ran into this very thing writing software for customer support teams. There's a Data/ML team within the company and there was a lot of talk about getting them to help plan the software to add abilities to "learn" what customers wanted with their support requests. The reality of the situation is that you can just take a step back and look at the pain points for the CSRs, the highest cost calls and where the time is spent servicing the customer. In the end, it's pretty clear that we can just do a few queries up front to realize that "oh, this person ordered 2 items of the same size in their last order and haven't returned one yet, it's very likely that they want to return one" or any number of other common scenarios that were found and then prioritized based on a simple cost/benefit analysis.

Could the Data/ML team have done something cool and likely complex to show clearer patterns and maybe get something in place to continually refine that data? Probably. Would it have saved more money for the company eventually? Unknown. Would it have cost substantially more money to design and implement? Pretty sure.

Re: Machine learning is still too hard for software engineers

#153

Post by disgruntledphd2 » Thu, Feb 24, 2022, 1:30 PM UTC

neura wrote: post #152

version_five wrote: post #146

Earlier quoted context omitted.

From experience, there are lots of companies looking precisely for an "AI person" who is going to use a bunch of complicated sounding buzzwords and build some overly complex thing without evaluating simpler options. This kind of person gets way more attention than someone who suggests that what the company wants to do can probably be accomplished with a simple shell script, or someone that tells them AI is not magic…

Ran into this very thing writing software for customer support teams. There's a Data/ML team within the company and there was a lot of talk about getting them to help plan the software to add abilities to "learn" what customers wanted with their support requests. The reality of the situation is that you can just take a step back and look at the pain points for the CSRs, the highest cost calls and where the time is sp…

To be fair, any competent data team would have done the same analysis first, before attempting to build complicated models.

As I keep saying to people, sometimes the best model is a well chosen graph.

Re: Machine learning is still too hard for software engineers

#154

Post by neura » Sun, Feb 27, 2022, 7:47 PM UTC

disgruntledphd2 wrote: post #153

neura wrote: post #152

Earlier quoted context omitted.

Ran into this very thing writing software for customer support teams. There's a Data/ML team within the company and there was a lot of talk about getting them to help plan the software to add abilities to "learn" what customers wanted with their support requests. The reality of the situation is that you can just take a step back and look at the pain points for the CSRs, the highest cost calls and where the time is sp…

To be fair, any competent data team would have done the same analysis first, before attempting to build complicated models. As I keep saying to people, sometimes the best model is a well chosen graph.

I don't have a lot of experience working with data/ml teams, so this is good to know. Thanks. :)