A popular self-driving car dataset is missing labels for hundreds of pedestrians • Hacker News Board

Re: A popular self-driving car dataset is missing labels for hundreds of pedestrians

#161

Post by illinformed » Wed, Feb 12, 2020, 5:34 AM UTC

a_bonobo wrote: post #146

pavel_lishin wrote: post #142

Earlier quoted context omitted.

> I assume that special care will be taken and the cars themselves will be quite risk-averse. Bold assumption that's already got a counterpoint: https://en.wikipedia.org/wiki/Death_of_Elaine_Herzberg

Wikipedia already has a list: https://en.wikipedia.org/wiki/List_of_self-driving_car_fatal... Along with your pedestrian death there were five (!!!) driver deaths. There's a reason why places like Germany don't want Tesla to use the term 'autopilot' because it's reckless endangerment.

But no country bans women from driving during the 3rd and 4th trimester of pregnancy, as no medical expert has quantified the effects of choline deficiency on the brain and driving performance, despite insurance statistics showing pregnant woman have more road accidents in this time period than stoners, plus medical experts have quantified the 1st 1000 days of choline intake affects intelligence of the foetus/new born. Road Safety is beyond logic, but that is what you get when humans organise themselves.

Re: A popular self-driving car dataset is missing labels for hundreds of pedestrians

#162

Post by pcwelder » Wed, Feb 12, 2020, 5:40 AM UTC

ClumsyPilot wrote: post #127

paxys wrote: post #119

Earlier quoted context omitted.

IMO to be "scared" of self driving cars they need to be more dangerous than any other random car on the street today, and the bar for that is pretty low.

Nope, with other cars you know the factors that increase your risks (fog, drunk driving, distractions). You can make decisions, like not getting into a car with a drunk friend. With machine learning you never know when it might mistake the back of a semi for an overpass or soemthing.

Thank you for pointing this out. Every time they show road accident statistics as an argument for the safety of self-driving cars or airlines, I wonder how is the risk conditional on me in the vehicle. Surely the risk substantially decreases than the population statistics in case of road accidents, since I am much more prudent than the average. However, the risk remains the same in the case of airlines or self-driving cars since I am not in control.

Re: A popular self-driving car dataset is missing labels for hundreds of pedestrians

#163

Post by illinformed » Wed, Feb 12, 2020, 6:00 AM UTC

keenmaster wrote: post #70

mumblemumble wrote: post #31

Earlier quoted context omitted.

I'm saying that nuclear power could have been a safer energy option, but, in practice, the whole enterprise has been scuttled by a bunch of regrettably bad decisions that have pretty much destroyed everyone's trust. So now it doesn't really matter if it's safer, because it can no longer realistically be considered an option.

If everyone believes that everyone else will be irrational, then they themselves will not throw their support behind nuclear power, rendering their stance on nuclear de facto irrational. As Baby Boomers age, and Millennials/GenZ form a greater percentage of the voting population, we have an opportunity to press the reset button on nuclear. The younger generations don’t really have a solid opinion on the matter, and p…

>Scientists are in the best position to influence the public and push for change.

No they are not, as always the rich elite control the world, just take climate change, air temperatures normalised in less than 24hrs after 9/11 over North America because the artificial clouds in the upper atmosphere caused by aircraft contrails were removed with the flight ban. https://globalnews.ca/news/2934513/empty-skies-after-911-set... https://news.psu.edu/story/141202/2005/12/05/research/probin... https://web.archive.org/web/20160411094048/http://www.atmos.... https://www.livescience.com/14944-wwii-bombing-raids-contrai...

How can you stop business making and selling to the rich elite big flash properties around the world, big flash gas guzzlers, yachts, planes, jets and helicopters? If you remove the politics of envy, how else do you drive innovation?

Besides IF the eco friendly people wanted to be green why dont they use the most energy dense fuels on the planet? https://en.wikipedia.org/wiki/Energy_density#Tables_of_energ...

Energy density is a security risk, plain and simple, so just like your smart phone battery or laptop battery is a plastic explosive if heated above 60 degrees C, the use of nuclear fuel and engines are a security risk from natives going wild and foreign powers attacking with missiles.

Nuclear powered aircraft has been tried before. https://en.wikipedia.org/wiki/Aircraft_Nuclear_Propulsion

Unfortunately, the weight and size of the nuclear engine using 1940's/1950's technology made it unfeasible, but more recent developments and testing in submarines and more recently the US embassy in London as a building example, the potential for a slimmed down lighter nuclear engine for more common uses is becoming viable. However battery technology notably using graphene instead of lithium will be a seismic shift in energy storage by an order of magnitude, so I would expect to see electricity infrastructure upgrades first before the next gen graphene batterys hit the market with week long lifespans when a day used to be consider good.

Re: A popular self-driving car dataset is missing labels for hundreds of pedestrians

#164

Post by MrQuincle » Wed, Feb 12, 2020, 6:42 AM UTC

ummonk wrote: post #28

mumblemumble wrote: post #21

Earlier quoted context omitted.

It's scary when you consider two other factors: First, the AI hype train. People think that calling something "Artificial Intelligence" implies that it is artificial, yes, but also, critically, that it is intelligent. Many enthusiastic people, and also many policymakers, don't fully realize the extent to which machine learning is constrained by both the quality and nature of its training data, and the capabilities of…

Given that nuclear power is significantly safer than other forms of power, are you asserting that the risks of self-driving cars are more about PR and perception than actual risk?

Check this concept car from 1957: https://en.m.wikipedia.org/wiki/Ford_Nucleon

We could have had your safe nuclear power involved in every road accident. Yeah!

Re: A popular self-driving car dataset is missing labels for hundreds of pedestrians

#165

Post by TeMPOraL » Wed, Feb 12, 2020, 9:26 AM UTC

wpietri wrote: post #153

TeMPOraL wrote: post #88

Earlier quoted context omitted.

The thing with people is that we all share the same brain architecture - we mostly think alike. As a species, we have a hundred thousand plus years of experience dealing with each other; as a civilization, a couple thousand. We've explored most corner cases, designed our infrastructure around those, and built systems protecting us from outliers. For instance, people deemed too unpredictable will not be given driving…

I agree entirely with your point, and wanted to follow a small tangent: > they lack self-preservation instinct I suspect that a self-preservation instinct, even in relatively dumb animals, is enormously complex. It might seem simple to us because it's so evolved, so hardwired. If I had to bet, I'd bet that a mouse's self-preservation instinct is more complex than the first-to-market Level 5 self-driving car will be.…

That's a good point, and yes, I definitely don't want an automated vehicle to have self-preservation instinct. What we're all after is people-preservation instinct, which is not entirely unrelated to the former.

But the broader reason I brought up self-preservation is that in humans, it's not just about not getting killed in a crash. It's also about not having your life ruined by causing it, even if you walk away physically unscathed. A person has personal, deeply visceral stake in driving safely. A self-driving car AI currently doesn't, because it's not a mind; what it has is the reflection of priorities in the company that made it. And as it turns out, companies can kill people and get away with it, so I don't feel confident about how safety is prioritized (see also: this article).

Re: A popular self-driving car dataset is missing labels for hundreds of pedestrians

#166

Post by FAKEDETECTOR » Wed, Feb 12, 2020, 10:56 AM UTC

tomnipotent wrote: post #150

perl4ever wrote: post #148

Earlier quoted context omitted.

Deleting half a sentence and saying it doesn't make sense? The way "average" works is that you average over something - a population or set. It is very important to be clear about what that something is and whether it's appropriate. Why do you believe that Autopilot outperforms humans in comparable conditions? If this is based on Tesla marketing, I'm extremely prejudiced against them, and assume out of hand that they…

The average of something cannot be more than the average of... itself. Thus, "Humans are much safer than people on average" is nonsensical. > Why do you believe that Autopilot outperforms humans in comparable conditions? Because they have the data that proves it? > I'm extremely prejudiced against them And I've chosen to take them at face value with a grain of salt, and to believe that for the data they've collected…

"Our secret telemetry dataset gives us reason to believe that the cars produced by us are not safe."

Would a statement like this be a surprise for you?

Re: A popular self-driving car dataset is missing labels for hundreds of pedestrians

#167

Post by diffeomorphism » Wed, Feb 12, 2020, 11:41 AM UTC

jwandborg wrote: post #69

tomnipotent wrote: post #64

Earlier quoted context omitted.

> Is there any statistical/mathematical tool to completely eradicate or greatly diminish the effects of bad labeling Yes, it's called statistics and probability theory.

> Yes it's called statistics and probability theory. My understanding of statistics is: - I can halve the % insanity by adding another 100% of good labels. - If I want to reduce the insanity of labels to 1/33th of ~33% I need to add another 3200% of good labels. - If I want to reduce the insanity to 0% I need to balance the bad labels with an infinite amount of good labels. Is there anything I'm missing entirely exce…

Your understanding is wrong for anything nonlinear. The whole reason machine learning is useful is because it is nonlinear.

Re: A popular self-driving car dataset is missing labels for hundreds of pedestrians

#168

Post by diffeomorphism » Wed, Feb 12, 2020, 11:46 AM UTC

netsharc wrote: post #39

sgustard wrote: post #20

Earlier quoted context omitted.

People should learn not to go outside if they're not labelled.

What a funny future it'd be, if we have to wear something distinctive (giant QR codes?) so we don't get killed outside. As a side effect it would make tracking us much easier...

That is why you don't wear purely black clothing in winter and why visibility vest are a thing (think French yellow vests). Or why as a cyclist you want reflective stripes. Funny present.

Re: A popular self-driving car dataset is missing labels for hundreds of pedestrians

#169

Post by Karliss » Wed, Feb 12, 2020, 12:25 PM UTC

Isn't training against subset of data and then validating against rest a common practice? It wouldn't detect all the mislabeling but should detect some indicating that manual inspection is required, assuming error isn't very systematic.

Re: A popular self-driving car dataset is missing labels for hundreds of pedestrians

#170

Post by yeldarb » Wed, Feb 12, 2020, 1:19 PM UTC

jxramos wrote: post #71

yeldarb wrote: post #2

This is really scary. I discovered this because we're working on converting and re-hosting popular datasets in many popular formats for easy use across models... I first noticed that there were a bunch of completely unlabeled images. Upon digging in, I was appalled that fully 1/3 of the images contained errors or omissions! Some are small (eg a part of a car on the edge of the frame or a ways in the distance not bein…

Does this dataset have an auxiliary field for each data point to note if it's been human reviewed? We used to train models with only those datapoints that had n=3 concordance in manual reviewers.

It doesn't, but since the original was supposedly human generated and I've now checked them all (twice) it should be n=2. Would love for someone to triple check! If you find errors let me know and I'll be happy to correct them.