developing an organisational capability to design, build, and deploy successful machine learned models in user-facing contexts is, in my opinion, as fundamental to an organisation’s competitiveness
You hear that, right? In 2019 already you have to have AI and do it well to be competitive. I just wanted to point out how cyberpunk that is.
> developing an organisational capability to design, build, and deploy successful machine learned models in user-facing contexts is, in my opinion, as fundamental to an organisation’s competitiveness
... I just wanted to point out how cyberpunk that is.
Nah, that is corporate flavor of the month/year/etc. It's not the 90s, so they're not "synergizing" any more but otherwise, whatever.
In #devops is turtle all way down but at bottom is perl script. - @devops_borat
Comedy: You, trying to launch a startup from scratch using Java. Tragedy: Me, trying to debug 27k lines of legacy Perl that brings $113MM/yr - @NeckbeardHacker
I read a lengthy blog post on how Booking.com basically has people code live in production (slight exaggeration) and they're fine with it, due to some monster of a monitoring test suite.
Booking has been around forever but I thought the later was a newish company? Did they consciously pick Perl in 2010 when they launched? I guess that makes sense if that’s the founders’ background and their business model is practically (crawling and) extracting and reporting job postings from all over the web.
Forecasting in 2019 is still using techniques from 1950. You can call that "machine learning", but only if you really want to make people confused by marketing speak.
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As an aside,
You hear that, right? In 2019 already you have to have AI and do it well to be competitive. I just wanted to point out how cyberpunk that is.
Nah, that is corporate flavor of the month/year/etc. It's not the 90s, so they're not "synergizing" any more but otherwise, whatever.
I believe booking.com ran on perl for a very long time. Maybe still does. ~relevant quotes from https://github.com/globalcitizen/taoup ...
In #devops is turtle all way down but at bottom is perl script. - @devops_borat
Comedy: You, trying to launch a startup from scratch using Java. Tragedy: Me, trying to debug 27k lines of legacy Perl that brings $113MM/yr - @NeckbeardHacker
I read a lengthy blog post on how Booking.com basically has people code live in production (slight exaggeration) and they're fine with it, due to some monster of a monitoring test suite.
It still does. Booking and ZipRecruiter are easily the two largest employers of Perl programmers.
Booking has been around forever but I thought the later was a newish company? Did they consciously pick Perl in 2010 when they launched? I guess that makes sense if that’s the founders’ background and their business model is practically (crawling and) extracting and reporting job postings from all over the web.
That gave me a flashback to the early 2000s AIML chatbot craze. "What, you have an online store and no customer chatbot? What are you doing?!"
Flipside is that this ML (whether you consider it AI or not) really is delivering huge value to the businesses that deploy them.
Not really.
Today's ML is really good at speech and image recogniziton, which makes for some very eye-popping layman demos.
Whereas what businesses really want is time series prediction, and modern ML really sucks balls at solving this problem.
Deep learning or machine learning?
I agree on the former, and quite strongly disagree on the latter, even if it means redefining ML to be dressed-up statistics.
Forecasting in 2019 is still using techniques from 1950. You can call that "machine learning", but only if you really want to make people confused by marketing speak.