Row 71924

Row ID: 71924 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 71924 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

This is a question with two very distinct answers:

* Do something very tangible. An opencv phone app which recognizes car license plates as they go by at speed (just as one example). This will prove you have the "chops" to take a problem and turn it into a viable solution which a fairly non ML person can understand. The key here is that you can make a program which uses ML, not just "do" ML

* Or get a PhD in math/ML and publish incomprehensible gibberish nonsense academic papers(5+) where you entirely made up your results and don't publish the data or your models. But, be prepared to be brutalized in interviews where you will be interrogated as to what you know about Hilbert Spaces. Don't bother with learning how to program as I've met very very very very very few ML PhDs who could program their way out of a wet paper bag. Since your published papers are made up derivative drivel, it's not important that you could make it work anyway. The interviews don't care if you can program much. By any capable programmer's standard, python 101 is sufficient.

There are three realities in ML:

* Some companies are doing cutting edge research such as deepmind, etc. I can count these with my fingers and toes. I mean actually doing cutting edge. Not merely claiming to be cutting edge, which is an unaccountably high number.

* There are a pile of large organizations where people who couldn't get a job with one of these cutting edge companies got a paper pushing nonsense job in their "Data Science" group. They do multi day multi hour interviews where they want you to prove you are an academic wannabe just like them. They all want to get a tenured professor position some day. These people get nothing done but what they claim to be working on is too hard for their bosses to understand so they don't get fired. A variation on these companies are consultancies where they are using jargon and ripping off their clients while mostly providing "awareness". These companies want PhDs so they can charge more. Some of these are heavily government funded to create AI centers of excellence. When they do the programming part of the interview; be careful not to show that you are any good. They will get suspicious and call you an "Engineer" which is their way to say you are human garbage because you don't belong in their rarefied academic circles.

* There are companies really doing things in ML. They know that most ML is just proper application of easily used libraries and common algorithms. Stupid simple things like XGBoost and random forest will solve a massive percentage of problems in the real world. Or worse, just basic stats can solve many "ML" problems. These are the guys who will want to see your OpenCV car app.

There's a third answer. Get a PhD and do some really cutting edge cool practical applications. This is how you get a job at deep mind. They don't just want someone with paper credentials like the large useless corporate "Data Science" departments, but someone who is extremely talented in both programming and ML.

FieldValue
text This is a question with two very distinct answers: * Do something very tangible. An opencv phone app which recognizes car license plates as they go by at speed (just as one example). This will prove you have the "chops" to take a problem and turn it into a viable solution which a fairly non ML person can understand. The key here is that you can make a program which uses ML, not just "do" ML * Or get a PhD in math/ML and publish incomprehensible gibberish nonsense academic papers(5+) where you …
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-24
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Raw Record

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  "text": "This is a question with two very distinct answers:\n\n* Do something very tangible. An opencv phone app which recognizes car license plates as they go by at speed (just as one example). This will prove you have the \"chops\" to take a problem and turn it into a viable solution which a fairly non ML person can understand. The key here is that you can make a program which uses ML, not just \"do\" ML\n\n* Or get a PhD in math/ML and publish incomprehensible gibberish nonsense academic papers(5+) where you entirely made up your results and don't publish the data or your models. But, be prepared to be brutalized in interviews where you will be interrogated as to what you know about Hilbert Spaces. Don't bother with learning how to program as I've met very very very very very few ML PhDs who could program their way out of a wet paper bag. Since your published papers are made up derivative drivel, it's not important that you could make it work anyway. The interviews don't care if you can program much. By any capable programmer's standard, python 101 is sufficient.\n\nThere are three realities in ML:\n\n* Some companies are doing cutting edge research such as deepmind, etc. I can count these with my fingers and toes. I mean actually doing cutting edge. Not merely claiming to be cutting edge, which is an unaccountably high number. \n\n* There are a pile of large organizations where people who couldn't get a job with one of these cutting edge companies got a paper pushing nonsense job in their \"Data Science\" group. They do multi day multi hour interviews where they want you to prove you are an academic wannabe just like them.  They all want to get a tenured professor position some day. These people get nothing done but what they claim to be working on is too hard for their bosses to understand so they don't get fired. A variation on these companies are consultancies where they are using jargon and ripping off their clients while mostly providing \"awareness\". These companies want PhDs so they can charge more. Some of these are heavily government funded to create AI centers of excellence. When they do the programming part of the interview; be careful not to show that you are any good. They will get suspicious and call you an \"Engineer\" which is their way to say you are human garbage because you don't belong in their rarefied academic circles.\n\n* There are companies really doing things in ML. They know that most ML is just proper application of easily used libraries and common algorithms. Stupid simple things like XGBoost and random forest will solve a massive percentage of problems in the real world. Or worse, just basic stats can solve many \"ML\" problems. These are the guys who will want to see your OpenCV car app.\n\n\nThere's a third answer. Get a PhD and do some really cutting edge cool practical applications. This is how you get a job at deep mind. They don't just want someone with paper credentials like the large useless corporate \"Data Science\" departments, but someone who is extremely talented in both programming and ML.",
  "label": "r/machinelearning",
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