is machine learning overhyped?
seems like everyone wants to go into Al and build hotdog not hotdog models. But i don’t understand the hype. ML seems to just be writing regressions and tuning your parameters. Any ML engineers out there wanna shed some light on this?
In my experience it rarely adds value. Usually a simple statistical model suffices. The few applications that are practical are speech recognition, image recognition, and self driving algorithms. For your run of the mill app/website you probably don't need it. Even for things like product recommendations.
Depends what you define ml. Those statistical models you mentioned are under ml umbrella.
True. In my definition applying a normal distribution or using collaborative filtering is just stats. While neural net is ML.
It's not hype. It's real.
ML is underrated.
Depends on which part of ML. Some methods are widely used in practice and their impact has been properly understood rather than exaggerated. I found Gartner has a hype curve for ML, which I think is very informative. Here is the link: https://www.gartner.com/smarterwithgartner/top-trends-on-the-gartner-hype-cycle-for-artificial-intelligence-2019
Disclaimer: I'm doing ml so I'm biased. I'd say it's real so far at least. There are so many new usecases that are possible now that weren't a few years ago.
Are you an MLE or DS or AS? What's was your interview process like at Zillow?
AS. Our team does an exercise where you need to solve a data driven problem and write a report. Then phone screen is needed. Then onsite.
It's real
POOP
It's a 60-year-old tech and they think that having two layer neural networks makes it brand-new yesterday. if you look at AI classes they spend most of their time taking credit for work in other fields outside of AI. This is a fad that we have to endure every 25 years and mark my words it will not last!
Didn't think Lyft would say this 🤔
It's old stats and old math that started to be monetized in the last 25 years. More so in the last 10 owing to compute and distributed systems advancements. But it's not hype. The potential is real. The concepts are age old
Every technology is overhyped when new. Look at Hadoop from 10 years ago, Openstack 7 years ago. It's Kubernetes and Machine Learning now .