Not exactly on topic but after skimming the article and then the comments, there seems to be — albeit a very understandable — misunderstanding of what is being done in the field and how the public views and uses terminology.
People hear or read the words Machine Learning and assign a HAL like technology to it.
It’s definitely hard to explain an idea like SVM, it’s applications, and how it works/what it does without a background in some linear algebra.
And then to broach more complicated topics like temporal real time neural networks in computer vision for something like anomaly detection.
From my conversations with people because it’s something that isn’t understood there is almost a cognitive dissonance. One of which expects so much now, but at the same time denies the future application space when the conversation becomes more personal. “Why aren’t self driving cars a thing yet...” and then “A machine couldn’t do what I do.”
It’s worrying that there will definitely be regulation coming to the field, where the people writing the laws and the people the writers represent have a hard time understanding what they are trying to regulate. Add in the fact that the tech itself is somewhat cheap and will only get less expensive. You only have to look as far as the Deep Fakes fiasco on Reddit. The guy creating fakes of celebrities being superimposed on porn is a self proclaimed amateur and was able to accomplish a quite a bit at home with Keras.
Edit:
I do want to mention that a lot of the papers I’ve read recently on real time anomaly detection rely on automated labeling and not having human labelers.
It’s only important to have human readable labels if a human has to interpret directly.
People hear or read the words Machine Learning and assign a HAL like technology to it.
It’s definitely hard to explain an idea like SVM, it’s applications, and how it works/what it does without a background in some linear algebra.
And then to broach more complicated topics like temporal real time neural networks in computer vision for something like anomaly detection.
From my conversations with people because it’s something that isn’t understood there is almost a cognitive dissonance. One of which expects so much now, but at the same time denies the future application space when the conversation becomes more personal. “Why aren’t self driving cars a thing yet...” and then “A machine couldn’t do what I do.”
It’s worrying that there will definitely be regulation coming to the field, where the people writing the laws and the people the writers represent have a hard time understanding what they are trying to regulate. Add in the fact that the tech itself is somewhat cheap and will only get less expensive. You only have to look as far as the Deep Fakes fiasco on Reddit. The guy creating fakes of celebrities being superimposed on porn is a self proclaimed amateur and was able to accomplish a quite a bit at home with Keras.
Edit:
I do want to mention that a lot of the papers I’ve read recently on real time anomaly detection rely on automated labeling and not having human labelers.
It’s only important to have human readable labels if a human has to interpret directly.