Slightly OT: I hope that effort helps with their search results big time. Because it's a pain. I was just looking for an apartment for some vacation and cool that it showed 300+ results but a totally random ordering in my opinion.
I can't sort by price. The first couple of results weren't even close to the stuff I booked in the past. Plus even though I never booked anything but accommodation with them I still have to select manually that I want accommendation. Every single time, it's not even funny anymore. There are many many UX flaws when I book with them that this actually drove me to more traditional hotel comparison sites.
EDIT: removed some stronger language, but booking with Airbnb was great at one point and it degraded greatly IMHO. If anyone from Airbnb reads this, I am willing to get in contact directly. The feedback form is wasted time, I tried. ;-)
but booking with Airbnb was great at one point and it degraded greatly IMHO.
I couldn't agree more. I'm traveling pretty often but in the last year I have to say that I ended up hating Airbnb for their high fees, lack of customer support,and the plenty flaws in their system. Lately, I'm using booking.com just because are more professional and they even offer discounts after various bookings. A big minus with booking would be the small hotel rooms with no kitchen (not so cozy).
on mobile via brave browser, I could not find a way to display a map of results. Even checking "show desktop site" just showed a bigger but still mobile-optimized site.
the lack of sort by price, gets me every time im searching. I usually end up using a different site when it comes to booking
The article is about the progress of implementing deep learning, it mentiones a few pitfalls and points of interest and that's it, no conclusions, no interesting insides.
It's a really honest description of the attempts to move to neural networks.
At least the way it's written it feels like no 'data scientists' were involved, it was all done by data engineers (software developer rather than statistical/modelling knowledge)... Which is depressing, if even Airbnb are biased to hiring only good developers (rather than a mix)
Most companies don't need to hire data scientists.
They need to hire engineers who can take an algorithm implemented well by somebody else, and apply it to a business domain.
That's the future of deep learning, machine learning, and all other linear/logistic regression style technologies.
The mathematicians are going to have to wait for the age of quantum annealing to feel valuable again: reducing code into a function that works on a quantum setup actually needs those skills that developers struggle with.
Everything else though, outside of pure research and in the vast majority of companies, is already well on the road to commoditisation.
I've got bad news for you, but the current state of rabid hiring means that this was very likely written by people that airbnb calls "data scientists" or "machine learning engineers", I'm sure half of these people have a phd in some arbitrarily "quantitative" field.
The state of all large companies that I've seen that are not FAANG is that they are rushing to build teams of "data scientists" that slap together keras models and "ship" them, meaning the outputs are stuffed in a db only to be consumed by other keras models.
My favorite gem from the original paper, which shows the sad, sad state of deep learning in industry is this line:
>Out of sheer habit, we started our first model by
initializing all weights and embeddings to zero, only to discover that is the worst way to start training a neural network.
I can't imagine anyone who has even a mild understanding of how neural networks are implemented and trained making this mistake.
Airbnb search is terrible. I also can’t understand why they don’t let you save filters e.g. if I’m always searching for an entire place with 1 bedroom then let me save and apply that filter to every search.
[+] [-] cleansy|6 years ago|reply
I can't sort by price. The first couple of results weren't even close to the stuff I booked in the past. Plus even though I never booked anything but accommodation with them I still have to select manually that I want accommendation. Every single time, it's not even funny anymore. There are many many UX flaws when I book with them that this actually drove me to more traditional hotel comparison sites.
EDIT: removed some stronger language, but booking with Airbnb was great at one point and it degraded greatly IMHO. If anyone from Airbnb reads this, I am willing to get in contact directly. The feedback form is wasted time, I tried. ;-)
[+] [-] rakefire|6 years ago|reply
I couldn't agree more. I'm traveling pretty often but in the last year I have to say that I ended up hating Airbnb for their high fees, lack of customer support,and the plenty flaws in their system. Lately, I'm using booking.com just because are more professional and they even offer discounts after various bookings. A big minus with booking would be the small hotel rooms with no kitchen (not so cozy).
[+] [-] sharkmerry|6 years ago|reply
the lack of sort by price, gets me every time im searching. I usually end up using a different site when it comes to booking
[+] [-] zihotki|6 years ago|reply
[+] [-] IlegCowcat|6 years ago|reply
At least the way it's written it feels like no 'data scientists' were involved, it was all done by data engineers (software developer rather than statistical/modelling knowledge)... Which is depressing, if even Airbnb are biased to hiring only good developers (rather than a mix)
[+] [-] PaulRobinson|6 years ago|reply
They need to hire engineers who can take an algorithm implemented well by somebody else, and apply it to a business domain.
That's the future of deep learning, machine learning, and all other linear/logistic regression style technologies.
The mathematicians are going to have to wait for the age of quantum annealing to feel valuable again: reducing code into a function that works on a quantum setup actually needs those skills that developers struggle with.
Everything else though, outside of pure research and in the vast majority of companies, is already well on the road to commoditisation.
[+] [-] baron_harkonnen|6 years ago|reply
The state of all large companies that I've seen that are not FAANG is that they are rushing to build teams of "data scientists" that slap together keras models and "ship" them, meaning the outputs are stuffed in a db only to be consumed by other keras models.
My favorite gem from the original paper, which shows the sad, sad state of deep learning in industry is this line:
>Out of sheer habit, we started our first model by initializing all weights and embeddings to zero, only to discover that is the worst way to start training a neural network.
I can't imagine anyone who has even a mild understanding of how neural networks are implemented and trained making this mistake.
[+] [-] undefined3840|6 years ago|reply
[+] [-] lammalamma25|6 years ago|reply
[+] [-] mannanj|6 years ago|reply