In that incident, if "it's wrong" wasn't enough to dissuade them not to do that, then "because if it ever looks like we do this in the future it will be totally believable" should be enough. But I suppose some people will always want the marshmallow immediately.
And I suppose, today, Uber better be hoping none of its ex, current or future ex-employees ever get assaulted, baseball bat or not. For PR or legal defense, if not for actual human empathy.
Is anyone else getting tired of Uber as a whole? There's nothing amazing about the company itself, they've hardly been innovative beyond the idea of ride-sharing. Can't wait for Uber to crumble from the top down starting with their over-zealous, dark-triad CEO.
Business model is fine it's just that the problem that arises from the wanted business model is hard. Given their bad reputation, especially now, they just don't have quality engineers to solve the problem.
Ride sharing is computationally NP-hard (assuming vehicle capacity larger than 2), the whole fleet management itself is NP-hard and depends highly on the realtime routing capabilities and prediction.
To make a combinatorial optimization algorithm capable of incorporating ride-sharing constraints and realtime data is a serious feat which maybe they jumped in too early to solve (given that no current research published tries anything similar). For really groundbreaking work to happen one needs quality engineers and researchers, and realistically, who would want to work in a company that is openly criticized for having terrible work culture?
The only way they can lower the prices and then find the sweetspot prices after monopoly is by having superior machine learning and combinatorial optimization solution.
I don't think this problem is as hard as you think it is in practical terms. N is going to be relatively small given basic constraints (the number of drivers and requests that could feasibly be matched is geographically limited). The search space for a request is limited to a few hundred drivers at most, competing with what is likely an even smaller pool of requests, making even extremely inefficient brute force approaches feasible.
Yeah, problem is so simple that they have job listings for AI research engineers that should have a little bit of experience in TensorFlow, Theano, Caffe or Torch (obviously some lovely deep learning on who knows what). About a year ago they were searching for PhDs and asked for experience with combinatorial algorithms, especially travelling salesman and vehicle routing problems.
It's worth pointing out that Uber had their service working more than a year ago, so while the folks you mention might help take Uber to the N+1th order of optimization they are clearly not necessary to reach the Nth.
I suspect this is what the GP is getting at: reaching the levels of optimization on this problem which are required to launch the service is comparatively easy. Going from there to optimality is extremely hard, but may not be necessary from a business or end user point of view.
N that the GP is talking about is number of vehicles and number of requests.
Yes, service can obviously work with subpar algorithms but to really succeed at pricing it as cheaply as possible it requires practically an ability to successfully predict the whole day and then optimizing on the NP-hard problem of that whole day. Maybe sampling a million day variants while routing to make a single decision (which driver should pickup the next request).
Of course brute force greedy algorithms work but they can be, on a hundred vehicle scale, 30% away from the optimum cost.
I've been downvoted to oblivion so I cannot longer keep participating in this discussion (HN will shadowban me).
I've been experiencing something similar in my own life.
It comes down to basically believing what I immediately see and experience, rather than or regardless of: 1) What I am told; 2) What I might hope for -- either immediately or "at some point in the future".
There is a saying -- an aphorism? -- for this, that applies particularly -- but not only -- to initial impressions. (I won't say "first", in case of a literal bad day for someone.)
When someone shows you who [or what] they are, believe them.
This is also phrased as:
When someone tells you who [or what] they are, believe them.
When I reflect back on experiences with people who have ultimately disappointed me, I often find that indeed, they were telling me more or less directly that this would happen.
In descriptions of themselves. In descriptions of their other relationships. Sometimes in a particularly poignant word or sentence. It was there -- they were telling me so, right at the start.
I find that for institutions, too. And the personalities that comprise them and guide them.
Hell, in this case, even their name choice: U[e]ber. Really?
I'm getting tired of Uber. And AirBnB too for that matter. I don't know anything about the company, but the business model and rediculous valuation seems pretty much the same.
I'm confused, where is the evidence of a smear campaign? She was a big news story that's spawned a bunch of other stories in the past few days. It's expected there would be a bunch of journalists and bloggers sniffing around looking to do a story about her.
I'm not a fan of Uber either, but just based on that tweet it seems a little disengenuos to start another round of hammering the company.
Fowler's next tweet emphasizes that she doesn't know who is doing the digging. She doesn't accuse Uber, and it might well be someone else.
But given that Uber famously threatened to spend a million dollars digging up dirt on critics, and Fowler just wrote critically about her experiences at that company, its hardly surprising that people jump to the conclusion that Uber is doing what they once threatened to do.
If there's one thing I've learned about these types of stories in general, it's that there is no benefit to jumping to conclusions until all the information is in. I was also shocked by the sheer audacity of the behavior described in Susan Fowler's blog post, but the "good" news about her story is that pretty much all of the incidents can be backed up by either an extensive paper trail or numerous other witnesses or victims. All of that data should come out in short order.
I have personally witnessed (and have evidence to support) Uber being extremely heavy-handed in response to valid allegations about them. My experience was they absolutely have people standing-by to research and goto great lengths to undermine negative truths that arise about the company.
Can you provide your evidence? Who are the people that they have standing by?
I wonder if it would be possible to convince those people to support a better cause. There's a dire need for some good investigative journalism right about now.
I actually described the issue in question on HN in the past which promptly led to Uber threatening the employee who leaked the details to me. He was so scared that he asked me to reach out to YC and have the original post deleted.
I wouldn't hesitate to share the evidence with a journalist with a publication backing them though as that would be a more appropriate venue.
Have you reached out to anyone? https://www.nytimes.com/newsgraphics/2016/news-tips/ would be my first place to go. It even specifically calls out "Here is proof that this company is conducting itself unethically" as an example, and asks for evidence corroborating the story. So if you have it, that would seem to be the place.
Having said that, I was more wondering if the people doing the threatening could be turned to better use. They surely aren't in it for the warm fuzzies, and if they're good at digging perhaps they could be convinced to do it for the good guys.
look at her twitter before and after this scandal broke..now it's hugely popular...before it only had <5 hearts+retweets per tweet. now hundreds. Maybe this is part of a bigger self-promotion plan to sell amazon books and get affiliate revenue. She provided no documented proof..no screenshots. Maybe she determined there was no legal recourse, so she went to social media? The only info we have is that single blog post.
Sigh, that's swinging the pendulum too far the other way friend. Serious public accusations like that only 2 years into her career is a pretty silly way to get popular. If they turn out to be untrue she's unlikely to ever work in the industry again. It makes so little sense that it's easy to give her the benefit of doubt on her original accusations. It's also easy to understand that she may be paranoid of retribution and overreacting about a smear campaign. Or maybe it's true, we're talking about Uber after all.
I'm simply pointing out that her tweet is not enough evidence of fault to start another round of bashing the company as if it is true.
Uber has been the subject of a fair amount of bad press in the past few weeks. I wonder what it would take for a Zenefits-style meltdown to occur there.
http://www.theverge.com/2014/11/18/7240215/uber-exec-casuall...