Source: www.reddit.com
Writing a whole untested project from scratch to fulfill a specific use case and then not maintaining or scaling it.
Vs
Writing 10 LoC, spending 2 hours figuring out why it broke some tests, writing your own tests, realizing it doesn't behave as expected for some edge cases, fixing the edge cases, finding that fix breaks some different tests you'd assume to be unrelated, then realizing those tests were actually incorrect and testing incorrect behavior and you've uncovered a subtle existing bug, triaging the impact of that to see if you need to send up a flare, cutting a JIRA ticket for the new bug, rewriting the 10 LoC in a way that doesn't force the bug repro, then running integration tests against the other dozens of subsystems it interacts with for all builds currently in use, then documenting what you did, and it's somehow dark out even though you "started early today because you felt behind" and you're not sure if you actually drank any water today also your wife texted you 90 minutes ago asking if you were coming home soon.
But hey the pay's good.
- Myllokunmingia
Source: www.facebook.com
Someone coded stackoverflow without stackoverflow.
Source: www.reddit.com
The difference between knowing and not knowing in that case is just knowing what to look up
- corngolem
Source: www.reddit.com
I study a PhD in Security within Machine Learning and this is actually an extremely dangerous thing with nearly all DNN models due to how they 'see' data and is used within many ML attacks. DNN's don't see the world as we do (Obviously) but more importantly that means images or data can appear exactly the same to us, but to a DNN be completely different.
You can imagine a scenario where a DNN within a autonomous car can be easily tricked to misclassify road signs. To us, a readable STOP sign with always say STOP, even if it has scratches, and dirt on the sign, we can easily interpret what the sign should be telling us. However an attacker can use noise (Similar to the photo of another road sign) to alter the image in tiny ways to cause a DNN to think a STOP sign is actually just a speed limit sign, while to us it still looks exactly like a STOP sign. Deploy such an attack on a self driving car at a junction with a stop sign and you can imagine how the car would simply drive on rather than stopping. You'll be surprised how easy it is to trick AI, even big companies like YouTube's have issues with this within copyright music detection if you perform complex ML attacks upon the music.
Here's a paper similar to the scenario I described but by placing stickers in specific places to make an AI not see stop signs; https://arxiv.org/pdf/1707.08945.pdf
- _Waldy_
Load More