In this original blog, with equally original title, Delip Rao poses twelve (+1) harsh truths about the real world practice of machine learning. I found it quite enlightning to read a non-hyped article about ML for once. Particularly because Delip’s experiences seem to overlap quite nicely with the principles of software design and Agile working.
Delip’s 12 truths I’ve copied in headers below. If they spark your interest, read more here:
- It has to work
- No matter how hard you push and no matter what the priority, you can’t increase the speed of light
- With sufficient thrust, pigs fly just fine. However, this is not necessarily a good idea
- Some things in life can never be fully appreciated nor understood unless experienced firsthand
- It is always possible to agglutinate multiple separate problems into a single complex interdependent solution. In most cases, this is a bad idea
- It is easier to ignore or move a problem around than it is to solve it
- You always have to tradeoff something
- Everything is more complicated than you think
- You will always under-provision resources
- One size never fits all. Your model will make embarrassing errors all the time despite your best intentions
- Every old idea will be proposed again with a different name and a different presentation, regardless of whether it works
- Perfection has been reached not when there is nothing left to add, but when there is nothing left to take away
Delip added in a +1, with his zero-indexed truth: You are Not a Scientist.
Yes, that’s all of you building stuff with machine learning with a “scientist” in the title, including all of you with PhDs, has-been-academics, and academics with one foot in the industry. Machine learning (and other AI application areas, like NLP, Vision, Speech, …) is an engineering research discipline (as opposed to science research).Delip Rao via deliprao.com/archives/227
Delip [bio] is the VP of Research at AI Foundation where he leads speech, language, and vision research efforts for generating and detecting artificial content. You can find his personal webblog here.
Cover image via the-vital-edge.com/lie-detector