This AI Boom Will Also Bust • Hacker News Board

Re: This AI Boom Will Also Bust

#21

Post by toomuchtodo » Sat, Dec 03, 2016, 5:05 PM UTC

whorleater wrote: post #8

adrenalinelol wrote: post #6

Earlier quoted context omitted.

No one can tell you for sure, if it's your passion do it, if you're hoping for a big payday, I'd reconsider.

I don't actually think that's true, if the "AI bubble" bursts at some point in the near future, the people who'll be in trouble with be those without formal education to back them up.

> the people who'll be in trouble with be those without formal education to back them up.

The people who can't hack it are those who'll be in trouble. Tech has never much been the place where credentials are necessary.

Don't specialize and saddle yourself with years of college debt if you're unsure of the field's long term prospects.

Re: This AI Boom Will Also Bust

#22

Post by cerrelio » Sat, Dec 03, 2016, 5:06 PM UTC

iwritestuff wrote: post #3

I plan to enter a PhD program in 1-2 years to specialize in ML/Deep Learning. Assuming it'll take 5-6 years to complete my degree how applicable should my skill sets be in industry at that point?

The important thing about the PhD is that you've become an expert in conducting experiments and research. It really doesn't matter what ML techniques you've done, as long as you know everything else associated with building those types of systems. Just hone your research and experimentation skills and you'll be fine.

I do have one suggestion: learn to handle dirty data.

I work with ML researchers and notice two things: they're pretty bad software engineers (no knowledge of software patterns, bugs galore), and they almost never know how to clean their data. The latter is because they do a lot of their research using pre-cleaned, standard data sets. You never get that in industry.

Re: This AI Boom Will Also Bust

#23

Post by rampage101 » Sat, Dec 03, 2016, 5:10 PM UTC

The more I get into machine learning and deep learning it seems like there is an incredible amount of configuration to get some decent results. Cleaning and storing the data takes a long time. And then you need to figure out exactly what you want to predict. If you predict some feature with any sort of error in your process the entire results will be flawed.

There are a few very nice applications of the AI techniques, however most data sets don't fit well with machine learning. What you see is that in tutorials use the Iris data set so much because it breaks into categories very easily. In the real world, most things are in a maybe state rather than yes/no.

Re: This AI Boom Will Also Bust

#24

Post by garysieling » Sat, Dec 03, 2016, 5:15 PM UTC

chime wrote: post #2

> Good CS expert says: Most firms that think they want advanced AI/ML really just need linear regression on cleaned-up data. Cleaning up data is very expensive. And without that, the analysis is good for nothing. AI helps provide good analysis without having to cleaning up data manually. I don't see how that is going away.

AI techniques can help with cleanup too. I've been using entity recognition to find speaker names to use in faceted search of lectures for https://www.findlectures.com. The NER library just finds low hanging fruit that I check manually.

Re: This AI Boom Will Also Bust

#25

Post by BatFastard » Sat, Dec 03, 2016, 5:17 PM UTC

chime wrote: post #2

> Good CS expert says: Most firms that think they want advanced AI/ML really just need linear regression on cleaned-up data. Cleaning up data is very expensive. And without that, the analysis is good for nothing. AI helps provide good analysis without having to cleaning up data manually. I don't see how that is going away.

Garbage in, garbage out.

I have always been a stickler for making sure the data going in is good. Takes a bit longer, but makes life much much easier in the long run.

Re: This AI Boom Will Also Bust

#26

Post by mcguire » Sat, Dec 03, 2016, 5:18 PM UTC

chime wrote: post #2

> Good CS expert says: Most firms that think they want advanced AI/ML really just need linear regression on cleaned-up data. Cleaning up data is very expensive. And without that, the analysis is good for nothing. AI helps provide good analysis without having to cleaning up data manually. I don't see how that is going away.

Garbage in, gospel out.

Re: This AI Boom Will Also Bust

#27

Post by yegle » Sat, Dec 03, 2016, 5:24 PM UTC

I started question the credibility of the article when the author mentioned "deep machine learning". Not an expert in ML, but it should be "deep learning" referring to a type of neural network based machine learning technique with deep hidden layers.

Re: This AI Boom Will Also Bust

#28

Post by h43k3r » Sat, Dec 03, 2016, 5:24 PM UTC

A little off topic but I think the VR boom will bust much more sooner than AI.

I can't think of normal people wearing those heavy gears in their normal life. There will be its use cases in specialized applications like education, industry, games but I don't think it will get popular like an iPhone.

AR is still OK since it augments real life but there is a long way before it will become mainstream.

Re: This AI Boom Will Also Bust

#29

Post by thesimpsons1022 » Sat, Dec 03, 2016, 5:27 PM UTC

h43k3r wrote: post #28

A little off topic but I think the VR boom will bust much more sooner than AI. I can't think of normal people wearing those heavy gears in their normal life. There will be its use cases in specialized applications like education, industry, games but I don't think it will get popular like an iPhone. AR is still OK since it augments real life but there is a long way before it will become mainstream.

have you tried it? i own an oculus and every family member i've seen has been shocked and loved it. Obviously the oculus is prohibitively expensive but with the release of Playstation VR i think the mainstream is poised to adopt it. I really believe the next game consoles that come out will simply be vr headsets

Re: This AI Boom Will Also Bust

#30

Post by kristjankalm » Sat, Dec 03, 2016, 5:28 PM UTC

IIIIIII wrote: post #13

jeyoor wrote: post #5

This article matches what I've been seeing anecdotally (especially at smaller tech firms and universities in the Midwest US). I've been hearing more folks in research and industry express the importance of applying simpler techniques (like linear regression and decision trees) before reaching for the latest state-of-the-art approach. See also this response to the author's tweet on the subject: https://twitter.com/and…

Saying that linear regression is easier to do properly than more complex methods like random forests, DL, boosting etc is like saying that people should code assembly instead of python

This is a false dichotomy. Both OLS regression and, say, random decision forest regression have the same objective (predict values) and achieve it with similar means (build a generative model / function). They solve the same problem. Contrastingly, assembler and python are broadly aimed at completely different use cases.

Broadly, whether you should move from OLS to random forest regression = SNR increase / increase in manhours and money spent.