5 ms·

Seems to me more of a case where companies no longer train grads, and instead tell them to work on a proof of concept on solving a hard problem without paying f

by ziont 8y ago

Seems to me more of a case where companies no longer train grads, and instead tell them to work on a proof of concept on solving a hard problem without paying for it, a sad state of affairs.

InterestBazinga 8y ago
Playing Devil's advocate: The other side is they hire a false positive, and suffer through the different sad state of affairs?
- ziont 8y ago
  Skunkworks: omg this kid has replicated our Auto-GAS system using only computer vision Lockheed Martin CEO: So what the fuck is this youtube video? https://www.youtube.com/watch?v=WkZGL7RQBVw https://www.youtube.com/watch?v=WkZGL7RQBVw
tensor 8y ago
The reality is that there are enough people with experience in machine learning that companies don't need to train people.
- soraki_soladead 8y ago
  This is not remotely true. It's true that there are lots of data scientists who can string together sklearn or R models. However, very few people know how these models work, when to apply them _and why_ or what concerns need to be addressed on data, metrics and deployment. Even fewer know how to improve these models once they catch all of the low-hanging fruit.
simonw 8y ago
I disagree. I think it is absolutely immoral (and dumb from an IP risk point of view) for companies to try to get interviewees to solve real problems for free. This is different though: this is about potential candidates finding creative ways to demonstrate their skills. The companies didn't even ask people to do this: these people chose to take on projects that would demonstrate their skills in a new space in which which they did no yet have commercial experience. I think that's commendable and a very smart strategy.
- munk-a 8y ago
  This isn't different though. This article was written as "How to get into ML" and is basically selling the idea that getting into ML requires (or is aided by) doing some independent open source work to bulk up a profile. A very competent developer who is interested in doing some ML work may have just recently read this article and gotten to work on an ML project because he feels that not doing so will hurt his chances of getting a job. As a note, I have no idea what the right solution to this problem is, it is good to confirm candidate's knowledge (I prefer creative ways similar to the parent) and part of that knowledge certainly can include open source work, but going down this road too much leads to people feeling obligated to make open source work to put on their resume and even to people faking open source work to try and land a job.
  - jimbokun 8y ago
    "but going down this road too much leads to people feeling obligated to make open source work to put on their resume" ...which makes the world a better place. Why do you object to people writing open source code?
- autokad 8y ago
  i agree, and I recommend doing it for those wishing to get into the field. i believe one of the things that helped me get where i am now is my performance in kaggle competitions. i do have one concern if it ever became an industry prereq, it becomes a filter for those who have more free time to work on side projects.
minimaxir 8y ago
The current problem with things like data science thought pieces and MOOCs is that they don't prepare for the realities of machine learning/data science in the real world. (longer blog post on the subject: https://minimaxir.com/2018/10/data-science-protips/ https://minimaxir.com/2018/10/data-science-protips/) Doing a unique project is much better for learning cost/benefits of implementing AI/ML, although this post may be overoptimistic on how that can lead to a job offer.
- technobabble 8y ago
  Thanks for posting the article. While I've read up on ML, I don't work in the industry. I'm curious to see what other people think of the article. Can other people weigh in?
  - solveit 8y ago
    I'm a junior SWE at a mid-sized AI startup. The article is pretty much spot on, but I'm not sure that the romantic view of data science the article argues against is actually that prevalent. It seems like you would need a very specific level of knowledge to romanticize data science in that way. Most people know too little (So you're basically trying to build skynet?), and most of the rest are either in the industry or know someone that is, and so have a more realistic view. I don't know, I did maths in undergrad, maybe some of the more clueless CS majors thought this way.
  - achompas 8y ago
    I agree with my sibling comment and would add: Max is correct to point out the irony in his anti-thoughtpiece thoughpiece as he falls into the same trap of vagueness as those other articles. Specifically, he rails against general “black box” approaches to modeling, then takes a general “black box” approach to the work of operationalizing a model (much harder than building the prototype to begin with!). The discussion of “pulling data” does not match the practical reality, since pulling via BI tools is not scalable and rarely automatable. SQL may cover this insofar as you dump data from SQL to...what, though? A Python session on your laptop? Automating this process allows a data scientist to scale their impact. For more specificity on engineering practices required for data science, I recommend Robert Chang’s series of posts: https://link.medium.com/CG7c7mQdyS https://link.medium.com/CG7c7mQdyS For details on how a data scientist can impact an organization, I recommend this from the FirstMark blog by Jeremy Stanley and Daniel Tunkelang: https://firstround.com/review/doing-data-science-right-your-most-common-questions-answered/ https://firstround.com/review/doing-data-science-right-your-...

by ziont 8y ago

Seems to me more of a case where companies no longer train grads, and instead tell them to work on a proof of concept on solving a hard problem without paying for it, a sad state of affairs.

InterestBazinga 8y ago

Playing Devil's advocate: The other side is they hire a false positive, and suffer through the different sad state of affairs?

ziont 8y ago
Skunkworks: omg this kid has replicated our Auto-GAS system using only computer vision Lockheed Martin CEO: So what the fuck is this youtube video? https://www.youtube.com/watch?v=WkZGL7RQBVw https://www.youtube.com/watch?v=WkZGL7RQBVw

tensor 8y ago

The reality is that there are enough people with experience in machine learning that companies don't need to train people.

soraki_soladead 8y ago
This is not remotely true. It's true that there are lots of data scientists who can string together sklearn or R models. However, very few people know how these models work, when to apply them _and why_ or what concerns need to be addressed on data, metrics and deployment. Even fewer know how to improve these models once they catch all of the low-hanging fruit.

simonw 8y ago

I disagree. I think it is absolutely immoral (and dumb from an IP risk point of view) for companies to try to get interviewees to solve real problems for free. This is different though: this is about potential candidates finding creative ways to demonstrate their skills. The companies didn't even ask people to do this: these people chose to take on projects that would demonstrate their skills in a new space in which which they did no yet have commercial experience. I think that's commendable and a very smart strategy.

munk-a 8y ago
This isn't different though. This article was written as "How to get into ML" and is basically selling the idea that getting into ML requires (or is aided by) doing some independent open source work to bulk up a profile. A very competent developer who is interested in doing some ML work may have just recently read this article and gotten to work on an ML project because he feels that not doing so will hurt his chances of getting a job. As a note, I have no idea what the right solution to this problem is, it is good to confirm candidate's knowledge (I prefer creative ways similar to the parent) and part of that knowledge certainly can include open source work, but going down this road too much leads to people feeling obligated to make open source work to put on their resume and even to people faking open source work to try and land a job.
- jimbokun 8y ago
  "but going down this road too much leads to people feeling obligated to make open source work to put on their resume" ...which makes the world a better place. Why do you object to people writing open source code?
autokad 8y ago
i agree, and I recommend doing it for those wishing to get into the field. i believe one of the things that helped me get where i am now is my performance in kaggle competitions. i do have one concern if it ever became an industry prereq, it becomes a filter for those who have more free time to work on side projects.

minimaxir 8y ago

The current problem with things like data science thought pieces and MOOCs is that they don't prepare for the realities of machine learning/data science in the real world. (longer blog post on the subject: https://minimaxir.com/2018/10/data-science-protips/ https://minimaxir.com/2018/10/data-science-protips/) Doing a unique project is much better for learning cost/benefits of implementing AI/ML, although this post may be overoptimistic on how that can lead to a job offer.

technobabble 8y ago
Thanks for posting the article. While I've read up on ML, I don't work in the industry. I'm curious to see what other people think of the article. Can other people weigh in?
- solveit 8y ago
  I'm a junior SWE at a mid-sized AI startup. The article is pretty much spot on, but I'm not sure that the romantic view of data science the article argues against is actually that prevalent. It seems like you would need a very specific level of knowledge to romanticize data science in that way. Most people know too little (So you're basically trying to build skynet?), and most of the rest are either in the industry or know someone that is, and so have a more realistic view. I don't know, I did maths in undergrad, maybe some of the more clueless CS majors thought this way.
- achompas 8y ago
  I agree with my sibling comment and would add: Max is correct to point out the irony in his anti-thoughtpiece thoughpiece as he falls into the same trap of vagueness as those other articles. Specifically, he rails against general “black box” approaches to modeling, then takes a general “black box” approach to the work of operationalizing a model (much harder than building the prototype to begin with!). The discussion of “pulling data” does not match the practical reality, since pulling via BI tools is not scalable and rarely automatable. SQL may cover this insofar as you dump data from SQL to...what, though? A Python session on your laptop? Automating this process allows a data scientist to scale their impact. For more specificity on engineering practices required for data science, I recommend Robert Chang’s series of posts: https://link.medium.com/CG7c7mQdyS https://link.medium.com/CG7c7mQdyS For details on how a data scientist can impact an organization, I recommend this from the FirstMark blog by Jeremy Stanley and Daniel Tunkelang: https://firstround.com/review/doing-data-science-right-your-most-common-questions-answered/ https://firstround.com/review/doing-data-science-right-your-...