skysight-inc/HackerNews huggingface dataset:
Let's make the roads out of rubber and the wheels out of pavement. I feel like this is the backwards mentality with autonomous vehicles. I wrote a while ago about how they simply cannot solve the transportation problem, at least not in a way that's cheaper than just building mass transit:

https://penguindreams.org/blog/self-driving-cars-will-not-so...

I realize this particular case is different with us talking about assistance/safety features, but I still feel we're going the wrong direction here. These features reduce your awareness. They're like a teenage driver you constantly have to monitor, but worse.

They teach people to be comfortable and less alert with systems that have potentially fatal bugs that we can't yet identify.

We can build mass transit now, at a fraction of the cost of developing the tech for automated vehicles. There are so many corner cases, with highways, city driving conditions that simply cannot pan out for autonomous vehicles without a lot of testing; and a lot of people getting injured and dying.

I wish people would shelve this pipe dream for now. Maybe once we have really solid rail transport again in the US, like we use to decades ago, and rich and poor people can have a means to work even in smaller cities; then we can work on frivolous junk that will benefit the few that can afford it. Right now we just run the risk of killing more people who can't.
Every employee will be happy until they find something better, no? I mean I guess sure one could make a valid rebuttal that "overqualified" people are probably working on less borrowed time, if we take the metaphor all the way to the end of the line, right?

But I guess where I'm finding myself endlessly perplexed is the default assumption you're describing (and I'll gladly offer a concession to), versus the alternative of overqualified employees being seen as potential force multipliers for increasing productivity by allowing them to use their additional qualifications via some kind of tract towards management or specialized role? Even if it's on a small team?

Bob is overqualified for X, yet exceptional at Y. X+Y just happens to be a thing the team could possibly benefit from, helping the company as a whole. Maybe we should think about hiring Bob and grooming him to take ownership of X+Y and stewarding the rest of the team, helping them learn and become masters of X+Y in due time.

Does that make sense?

Hopefully the phrasing isn't too god awful there, coffee hasn't hit the lower brain yet :P
Tesla does not answer the most important questions about their own product.
At $5.25 mil, how far are you hoping they’ll depreciate?
Because Forbes actually has quality in depth reporting on a variety of topics.
It really bothers me manufacturers are pushing incremental updates to car safety systems. Does anyone besides me think this is a terrible idea?
No, but wait! We're just on the verge of replacing doctors! ;-)

There's still a lot of space for the improvement of "curve-fitting" AI in the workplace. The potential of existing tech is far from being thoroughly exploited right now.
I believe the next big improvements will come more from better integration in the workplace (or road system) than new scientific advances, so that might seem less sexy. But I also believe this will be a sufficient impetus to drive the field forward for the years to come.
Elon Musk has constantly underestimated the difficulty of autonomous driving.

This video (https://youtu.be/wsixsRI-Sz4?t=1h18m28s) shows Elon Musk, two years ago, saying the following:

"I basically consider autonomous driving to be a solved problem".

"A Model S and Model X can drive with greater safety than a person, already. Right now."

"We are less than two years away from complete autonomy".
Watch Argentina vs. Haiti 4-0 – All Goal and Highlights
A lot can happen in 2 seconds. I hear you and appreciate the detailed description, the best one I’ve read yet. But within it are hints that you are trusting the system more than the terms of service permit.

You feel comfortable looking away a full 400% more than you would under manual control. There is not supposed to be any delta. And for many people, it’s probably more than 2 seconds. Many of these accidents including the recent fatal one happened inside of a few seconds of distraction.
What's so hard to understand? Jewelry exists because it

(1) makes people look more attractive, and

(2) enables conspicuous consumption.

Humans aren't Vulcans who make decisions rationally. We're apes with a facility for reason tacked on as an afterthought.
Mail Rewards Club Login in – Mailonline.co.uk
Jeff Dean is head of AI at Google. He's one of the most prolific computer scientists there, and has worked on many big projects such as MapReduce, BigTable, Spanner, GoogleBrain and TensorFlow.

Yann LeCun is head of AI at Facebook and kinda knows as the father of convolutional networks.

There's a bunch of other ones from other big companies or big ivy league universities.
Seriously. Frankly, based on Uber's culture I would have been surprised if they didn't kill at least one person with their self-driving efforts. It's a total non-data point. The fact that Uber got as far as they did without killing anyone is strong evidence that the problem is tractable.

As for Tesla - Tesla isn't even trying to make proper self-driving cars. Tesla's goal has always been assisted driving. However you feel about that, it's really not relevant to the success or failure of self-driving cars.

OP can't possibly have been ignorant of the fact that Waymo is the clear leader here with a substantial head start, and a proven record (and an actual fleet of self driving cars now on the road), and yet he chose not to mention it. That really undermines his credibility for me - he seems clearly more interested in making his point than in accurately engaging with reality.
But diamonds aren’t a store of value at all; they have very little resale value.
Cool. As someone with a giant library of ripped cds organized in a nice file structure, this is definitely interesting. I hate going through the iTunes library.

Any chance you could let it access audio files stored in Dropbox?
And you just filter out most of of the world. Most in EU are against GMO, China is against imported GMO ( They only allow certain GMO options / products ) The only nation on earth that is a large proponent of GMO food is US. But even US consumers are divided on this issues

At one point in time I thought GMO was a necessary evil, in order to feed the world better. Simplot used to pride their GMO potato that has much better yields then anyone else on the planet. But climate changes proves no breed is best for all time. In the mean time, a few European companies ; failing to combat the GMO production yield and had comparatively worse climate to grown potatoes, manage to outrun any US companies potato yield with machine learning. This is of course an over simplify version of it, but the story proves we don't need GMO to feed the world.
Elon's argument is that it meets your hard boolean quality threshold and is as good as a human, and he's aiming for 10x lower accidents than humans. That may not be true but that's what he's going for.
Again, please provide any data that says otherwise.
At all.
Any!

So far the data in this conversation has been incredibly one sided, with me providing data, and you providing skepticism.

These systems exist in a lot of cars, yet nothing until Tesla suggests any issues anywhere (i'm ignoring things like news reports which are fairly biased)
Therefore, the extraordinary claim is not that Tesla is worse, it's that Tesla isn't worse.
“It is unclear how plant life could survive on Dakala. One scene, however, shows the surface covered in the dim light of a nearby star, perhaps supplying enough energy for some form of photosynthesis. Another theory, from the Star Trek Encyclopedia (4th ed., vol. 1, p. 177), is that the plant and animal life was supported by the surface heat from volcanic vents.“

From http://memory-alpha.wikia.com/wiki/Dakala
But that's not actual data on crashes. What op is suggesting is that crashes per mile driven is a more relevant statistic. Obviously self driving cars will be involved in accidents, the question is, is it happening more frequently than with human drivers? If yes, then there's a problem and an indication that the technology is not yet mature.
I hardly think that is the distinction. The word is literally accompanied by a definition after its first use.

"Your Tesla will match speed to traffic conditions, keep within a lane, automatically change lanes without requiring driver input, transition from one freeway to another, exit the freeway when your destination is near, self-park when near a parking spot and be summoned to and from your garage.

Tesla’s Enhanced Autopilot software has begun rolling out and features will continue to be introduced as validation is completed, subject to regulatory approval. Every driver is responsible for remaining alert and active when using Autopilot, and must be prepared to take action at any time."
Your advice would be a lot more convincing if you had a youtube video of a demo to come with it. Just saying. :P
> I'm not sure I've ever been to a hospital with anything non-hospital related within a 100 yard radius. And you can always adjust the center of your geotargeted area to make it more effective.

The large local hospital in Redmond WA (Evergreen) is right next to a strip mall and shares a parking lot with a movie theater.
To be fair, I believe this is being posted here again because the current top article is about De Beers, and there's reference to both this and the Atlantic piece throughout the comments of that.

I would hope that pricenomics at least verified the portions they quoted as still accurate, which would make it more immediately useful, as the original is now some thirty-plus years old and things might have changed in small or large ways.
Have anyone tried Intel NUC for home lab setup?
What are pros and cons?
The thing is, a tesla crashing while running on autopilot is more newsworthy than a tesla crashing while being driven. We should not let anecdotal evidence drive our fears.
I believe “Have you ever tried to sell a diamond” (1982) https://www.theatlantic.com/magazine/archive/1982/02/have-yo... is what you’re thinking of - it’s a great article and feel ahead of its time in style/composition.
DNSSEC does not in fact mitigate BGP attacks, because to the extent it works at all, DNSSEC protects only the mapping between IP addresses and names. A BGP attacker controls the semantics of the addresses themselves, and can simply leave DNS pointing where it's supposed to, but hijack the underlying address.

TLS, on the other hand, does address this attack, because controlling all the traffic to a TLS-protected site still doesn't give you a private key that produces a valid signature on a certificate for that site.
True, but nobody is going to not pay and sacrifice their life so that hypothetical future people won't get kidnapped.
You do realize that when driving, 90% of the things around you are stationary, right? Also, it may not apply to this specific case, but try comparing a plastic bag in the streets vs a big rock. Telling those two apart isn't trivial, but only one of those you can drive through.
"My hunch is that this kind of data driven insurance could help people avoid risky situations."
That's the ideal. The cheapest accident to pay for is the one that doesn't happen.

It's all about aligning incentives. The win-win dynamic is that the insurer can certify training courses, licensing, product design, etc. Customers can get discounts for making safer choices.

Selection effects can be more adversarial. e.g. You get dropped after the first incident. The people who would benefit the most from coverage can't get it. People with poor credit scores get discriminated against even in the absence of activity specific bad history.

Obviously as you mentioned perfect discrimination causes the system to break down. Risky customers can't get covered while safe customers don't buy because their risk is infinitesimal.
We've had mass transit for a century. I don't see much harm in trying something new that may save a million lives per year.
Wonder if we are repeating the sentiment from 1958: https://news.ycombinator.com/item?id=17184576
I've always understood the claim that deep learning scales to be a claim about deployment and use of trained models, not about training. The whole point is that you can invest (substantial) resources upfront to train a sufficiently good model, but then the results of that initial investment can be used with very small marginal costs.

OP's argument on this front seems disingenous to me.

His focus on Uber and Tesla (while not even mentioning Waymo) is also a truly strange omission. Uber's practices and culture have historically been so toxic that their failures here are truly irrelevant, and Tesla isn't even in the business of making actual self driving cars.

I'm the first to argue that right now AI is overhyped, but this is just sensationalist garbage from the other end of the spectrum.
So wait, rather than just pay attention to the road when driving my old beater, I could constantly be pointing at things and struggling with poor voice recognition? Somehow this sounds like more work than just driving the car myself.
Relevant Chappelle's Show clip?

https://youtu.be/HeOVbeh2yr0?t=318
On the flip side, I can't think of a single time an employer has questioned my complete lack of a public profile. For every discriminating company that thinks a saturated GitHub and routine Medium posts are meaningful heuristics, there might be at least 9 others that couldn't care less.
I'm just going to leave you to your non-data driven skepticism of "everything other than tesla", because this is clearly not a useful conversation.

That isn't what you originally asked (and as you see, i wasn't the only one who interpreted it this way), and imho it's not a useful comparison. I have no idea why one would ask "is autopilot worse" and then say "but only compared to humans, not taking into account the other systems that do the same thing and humans are also using on a regular basis"

It doesn't answer the question whether autopilot is really worse or not, because nobody cares if it's better or worse than a straw man human who has disabled the collision avoidance on their car.
Dennis Herrera is just trying to make a name for himself for when his re-election comes up on November 5th, 2019. Hating on tech is a re-election strategy.
Thanks for the details on how to use your benchmark script and for taking the time to investigate this. I hadn’t heard of your benchmark before and mc-crusher seems to work a bit differently than memtier_benchmark.

First a few significant differences:

1) Your value size is 10B which completely changes the results. Let’s keep the value size at 100B, which is more realistic.

2) The ratio of gets to sets significantly affects the requests per sec. We were assuming 1:1 ratio when we did our measurements. Increasing the percentage of gets really speeds up req/sec. We didn’t observe this effect on elasticache. Is this a recent improvement in the github version of memcached?

3) Your benchmark is using multiple keys in the same get command. What memtier does is pipeline multiple get commands each with one key. This seems more realistic.

4) We pipelined 16 get commands per packet while your configuration had 50 keys per get command.

I was able to reproduce the same setup as we had with ~1.2M req/sec with your mc-crusher benchmark using the following config. This has 1:1 get to set ratio with pipeline 16 and value size 100B.

send=ascii_set,recv=blind_read,conns=50,key_prefix=foobar,key_prealloc=0,pipelines=16,value_size=100
send=ascii_set,recv=blind_read,conns=50,key_prefix=foobar,key_prealloc=0,pipelines=16,value_size=100,thread=1
send=ascii_set,recv=blind_read,conns=50,key_prefix=foobar,key_prealloc=0,pipelines=16,value_size=100,thread=1
send=ascii_set,recv=blind_read,conns=50,key_prefix=foobar,key_prealloc=0,pipelines=16,value_size=100,thread=1
send=ascii_get,recv=blind_read,conns=50,pipelines=16,key_prefix=foobar,key_prealloc=1
send=ascii_get,recv=blind_read,conns=50,pipelines=16,key_prefix=foobar,key_prealloc=1,thread=1
send=ascii_get,recv=blind_read,conns=50,pipelines=16,key_prefix=foobar,key_prealloc=1,thread=1
send=ascii_get,recv=blind_read,conns=50,pipelines=16,key_prefix=foobar,key_prealloc=1,thread=1
send=ascii_get,recv=blind_read,conns=50,pipelines=16,key_prefix=foobar,key_prealloc=1,thread=1

I used the github memcached on an r4.4xlarge. I ran memcache-top on the server instance to measure the requests per second, showing about 750k gets/sec and 600k sets/sec.

With a ratio of 10:1 gets to sets I’m seeing about 3.5M req/sec which seems better than elasticache.
OK, the definition of scalable is crucial here and it causes lots of trouble (this is also response to several other posts so forgive me if I don't address your points exactly).

Let me try once again: an algorithm is scalable if it can process bigger instances by adding more compute power.

E.g. I take a small perceptron and train it on pentium 100, and then take a perceptron with 10x parameters on Core I7 and get better output by some monotonic function of increase in instance size (it is typically a sub linear function but it is OK as long as it is not logarithmic).

DL does not have that property. It requires modifying the algorithm, modifying the task at hand and so on. And it is not that it requires some tiny tweaking. It requires quite a bit of tweaking. I mean if you need a scientific paper to make a bigger instance of your algorithm this algorithm is not scalable.

What many people here are talking about is whether an instance of the algorithm can be created (by a great human effort) in a very specific domain to saturate a given large compute resource. And yes, in that sense deep learning can show some success in very limited domains. Domains where there happens to be a boatload of data, particularly labeled data.

But you see there is a subtle difference here, similar in some sense to difference between Amdahl's law and Gustafson's law (though not literal).

The way many people (including investors) understand deep learning is that: you build a model A, show it a bunch of pictures and it understands something out of them. Then you buy 10x more GPU's, build model B that is 10x bigger, show it those same pictures and it understands 10x more from them. Look I, and many people here understand this is totally naive. But believe me, I talked to many people with big $ that have exactly that level of understanding.
I'll (try to) stay out of political discussions, then. It's become a way too touchy subject lately.
Would targeting modern Js runtimes yield faster performance in browser as well?

I am guessing some loading time would be improved if code converted from ES2105 to ES5 is not required
That's the whole point. Conferences can provide a benefit worth paying for without putting the research itself behind paywalls like journals do.
My source for that assertion is from this different, April 2016 incident [1]. Relevant quote from the article:

"In contrast, Tesla says that the vehicle logs show that its adaptive cruise control system is not to blame. Data points to Simpson hitting the brake pedal and deactivating autopilot and traffic aware cruise control, returning the car to manual control instantly. (This has been industry-wide practice for cruise control systems for many years.) Simpson's use of the brake also apparently disengaged the automatic emergency braking system, something that's been standard across Tesla's range since it rolled out firmware version 6.2 last year."

[1] https://arstechnica.com/cars/2016/05/another-driver-says-tes...
Very often it's more that people struggle to find a way to start a story. In much the same way that students use cliche'd academic speak in the introductions to their essays. It's not a pose, it's a crutch.
One day man-made diamonds will be indistinguishable from natural diamonds.

When that happens I wonder what will become of the 'rule' that says a man should spend 3 months salary on a wedding ring. Perhaps a Rolex watch will substitute for a diamond ring in signalling to your friends that you have a rich husband?
A tiny bit of good news recently was North Korea getting rid of their fraction-of-an-hour different time zone and harmonizing with South Korea. Sadly, Australia and New Zealand are still some of the few holdouts with stupid pointless not-on-the-hour time zones :(
Here's another collection of set operations - I refer to this a couple of times a week at least. http://www.catonmat.net/download/setops.txt
I was sarcastic. There is just nothing in this that's interesting in any way for this site unless I didn't see what makes this jet in any way novel. Otherwise we'll have to start posting all press releases for the latest Porsche, Ferrari or Lamborghini.
Maybe whoever worked in the Commodore marketing department sold really expensive things in a past job then?

http://bash.org/?3763
I saw that, and it could be, but I have a (possibly false) memory of some orange-ish toned world map which showed the locations of major diamond exporters and the times they started exporting. Or maybe it was blue-ish? I think I've reached the point in trying to remember this where any additional details I might think I remember are more likely just cross-contamination of memories. :/
I'm sure the same could be said for early computer graphics before the GPU race. You don't need Moore's Law to make machine learning fast, you can also do it with hardware tailored to the task. Look at Google's TPUs for an example of this.

If you want an idea of where machine learning is in the scheme of things, the best thing to do is listen to the experts. _None_ of them have promised wild general intelligence any time soon. All of them have said "this is just the beginning, it's a long process." Science is incremental and machine learning is no different in that regard.

You'll continue to see incremental progress in the field, with occasional demonstrations and applications that make you go "wow". But most of the advances will be of interest to academics, not the general public. That in no way makes them less valuable.

The field of ML/AI produces useful technologies with many real applications. Funding for this basic science isn't going away. The media will eventually tire of the AI hype once the "wow" factor of these new technologies wears off. Maybe the goal posts will move again and suddenly all the current technology won't be called "AI" anymore, but it will still be funded and the science will still advance.

It's not the exciting prediction you were looking for I'm sure, but a boring realistic one.
How can something be biased when it's listing facts?

Those are actual features that are available today to anyone, that were made possible by AI. Do you think it would be possible to type "pictures of me at the beach with my dog" without AI in such as short time frame? Or to have cars that drive themselves without a driver? These are concrete benefits of machine learning, I don't understand how that's biased.
Go has always optimised for build speed. I guess they considered dependency resolution as part of the build process.

Which it technically is, I suppose, but when you're coding and iterating the code-build-run loop you generally don't need to add new dependencies each time. And that's when the build speed matters, of course.
I don't see how this comment could possibly be a reply to mine.
I don't disagree that levels 2/3 are a danger zone in self-driving technology, but I feel the need to point out that there are plenty of people who are disabled or suffer chronic injuries resulting from being rear-ended. Stop and go traffic can be plenty dangerous as well.
Is there a source for YouTube being profitable? I’ve only ever seen people say maybe it’s profitable and also assumptions or hand waving of how YouTube saves a lot of money by being able to use Google’s infrastructure. I assumed to this day YouTube isn’t actually profitable.