Homemade relay computer (6 Hz) - <AUTHOR> http://web.cecs.pdx.edu/~harry/Relay/

<comment><USER2> Wow. Not sure which is more hardcore -- this, or the guy who built a complete programmable replica of the Apollo guidance computer using only the original manuals: <http://klabs.org/history/build_agc/>

<comment><AUTHOR> I think he's using the computer to host his web site.

</comment></comment><comment><USER3> I read about this a month or two ago. Watched the video, read the whole paper, it's awesome. A great way to demonstrate that computers are really just machines and not magical at all.

</comment><comment><USER4> How does it make money?

<comment><USER5> You invite people over to use it, and then you make them watch an ad first.

</comment></comment><comment><USER6> Things like this make me wish (a little) that I had been an electrical engineer rather than a CS major.

<comment><USER7> You could do this with a CS background, I think. Relays are very simple to understand. If current is flowing from A to B, then current can flow from C to D. Otherwise, it can flow from C to E. They way they work (conceptually; I've never taken one apart) is that running current from A to B powers an electromagnet. There's a wire tied to C and the other end is pressed against E by a spring. The electromagnet pulls on the spring and moves the wire from E to D. They're just little mechanical if-then-else constructs.

If you run a wire from B to C and connect a battery to A and E, you'll get a nice buzzing sound as it oscillates between states.

</comment></comment><comment><USER8> Got to see a demo of this system in person, and it was truly spectacular. I learned more in 10 minutes watching this thing than I did reading multiple books. (I took 'Compilers' from this guy, HIGHLY recommended to anyone who happens to be at portland state.)

</comment><comment><USER9> Wow, just about perfect... sixth photo down, it seems to be in his bedroom. And there is a Home build CPU 'ring',yow!

</comment><comment><USER10> I could use one of those to illustrate basic internals to my father.</comment>


Ask HN: Timezone in web apps  - <AUTHOR> I'd like to share opinions on best practices for dealing with timezones in web apps, both at infrastructure level (how to set up servers) and at application level (server- and client-side), especially distinguishing between registered users and guests.
To make it short, my final solution is:
- store all times in the db in UTC
- display relative timings (e.g., 6m ago) in s/m/h when difference is less than 1 day
- display date (e.g., May 6) when difference is greater than 1 day, adding time only if the user saved his timezone (in particular no time for guests)
- possibly correct the time displayed via js if client-side detection works
At infrastructure level I believe that storing all times in UTC saves a lot of mental sanity. :) Modern dbms support automatic conversion from server local time to UTC (e.g., timestamp data type in mysql). Or one can configure all servers on UTC and be more flexible on data type (e.g., in mysql time/datetime would be ok).
Personally I prefer the second one. However, I've noticed that developing/debugging on my machine (which is not on UTC) may generate confusion.
Moving to application level, the most clever solution is probably to display relative timings (e.g., 6m ago) which is a good solution to avoid the problem :) However for some content, e.g. news/events, it may still be relevant to display the full time… so for the sake of completeness let's discuss it.
To the best of my knowledge from the http request there is no way to know users' timezone. So the only purely-server-side solution I see is to ask users for their timezone and the poor guests can't have timing information.
Adding client-side support, the best open source solution seems to be: https://bitbucket.org/pellepim/jstimezonedetect (there exist also jquery-plugins or other stuffs but all essentially based on this)
With such a tool one can update time information directly at client side.
Many solutions I've found on the web display an html tag with time in UTC and translate it in local time via js. This way to do is not perfect when js is not working. It's probably better to use a tag that displays a relative time information and has an attribute with UTC time for correcting the content to local time (this is implemented, e.g., by Facebook).
Finally, it would be useful/nice to use client-side detection to instruct the server. In principle, the detection could be run async immediately after the js code is loaded, and the page content could be already provided by the server with correct time information (imagine the fb page is downloaded with no timeline data, the timezone detected and sent to the server, then the timeline data loaded via ajax with time properly set). However I'm not aware of any implementations and I'm wondering if the result is worth the effort. <comment><USER2> At the infrastructure: yes, all servers should be UTC, (almost) no question.

As for guessing timezones - I use GeoIP to get the user's approximate location, then figure out the timezone based on that. It is not perfect, but I needed something that does not require JS.

<comment><USER3> If you don't need the user's timezone immediately for their first request (which I assume is why you can't use a javascript solution), using getTimezoneOffset() works pretty well.

Shameless plug: I wrote a gem to handle this in Rails ( <https://github.com/zmillman/browser-timezone> )

</comment></comment><comment><USER4> There's a potential monkey wrench with storing all times, in the database, as UTC: daylight saving time. If you have, say recurring events, or an event in the future, you need to take into account whether the DST state matches for today and said date. One could account for this before storing the time, but the rules might change on you, as happened a few years ago, here in the states.</comment>


OS X Lion: Adding Your Signature to PDFs Through Preview - <AUTHOR> http://www.macobserver.com/tmo/article/os_x_lion_adding_your_signature_to_pdfs_through_preview/ <comment><USER2> This is a nice to have feature.

Currently I do this with <http://hellofax.com>

They have a web based pdf editor / signature tool</comment>


Descent on the Net - <AUTHOR> http://www.myrkul.org/descent.html <comment><USER2> Date: 1997

It wouldn't be better on Medium, either.</comment>


Is this new technology? Hyperlink into content inside - <AUTHOR> https://medium.com/buk-io-the-c/deep-link-is-the-future-of-digital-content-e881cb3f062c <comment><USER2> The relevant Xanadu concept would be transclusions:

[https://en.wikipedia.org/wiki/Project_Xanadu](https://en.wikipedia.org/wiki/Project_Xanadu)

[https://en.wikipedia.org/wiki/Transclusion](https://en.wikipedia.org/wiki/Transclusion)

[http://www.jucs.org/jucs_11_6/fine_grained_transclusions_of/](http://www.jucs.org/jucs_11_6/fine_grained_transclusions_of/)

<comment><AUTHOR> Thanks for the information.</comment></comment>


Ask HN: Is GitHub Down? - <AUTHOR> <comment><USER2> No.

[https://www.isitdownrightnow.com/github.com.html](https://www.isitdownrightnow.com/github.com.html)

</comment><comment><USER3> It's down for me (from the UK).

Edit: It's back up again a min later.

</comment><comment><USER4> Seems like its up to me, I'm on Sonic in San Francisco.</comment>


Farewell to systemd - <AUTHOR> https://blog.firedrake.org/archive/2016/05/Farewell_to_systemd.html <comment><USER2> I've been using Redhat/CentOS as long as they have existed. I recently started converting all my VPS nodes to Alpine Linux for these reasons.

I don't imagine that happening at work any time soon. Our tooling is too heavily entrenched in CentOS. We've looked at CoreOS, but have a long way to go to containerize hundreds of chef roles. The containers are using CentOS; and in some cases, Alpine as the base.

</comment><comment><USER3> A report of the mentioned systemd bug, that locked a Debian 7 to Debian 8 upgrade, was actually filed before this article was written, a year and a half before in fact. It was reportedly fixed about four months after this article was written.

* [https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=774153](https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=774153)

* [https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=737825](https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=737825)</comment>


Tesla Live Stream – Autonomy Day [video] - <AUTHOR> https://livestream.tesla.com/ <comment><USER2> Karpathy's presentation is really good. Watch it later if you get some time. The key points are

\- Telsa is using fleet for learning (Fleet learning). This has 3 components, a. Trigger infrasture that collects the kind of data telsa is looking for training. The rational here is we don't need massive amounts of same kind of data but needs right kind of data for training neural networks correctly. b. Data Engine which learns from these examples. c. Shadow mode where they deploy the learned model and test how its doing in real world and iterate this process.

\- Drivers are themselves acting as labelers and tesla is in a unique position to take advantage of this.

\- People drive vision only. Visual recognition is essential for autonomy. Lidar has much less information than vision, for example to identify the thing on the road is a plastic bag, lidar provides few points. But vision gives more data for telling this.

<comment><USER3> Karpathy is great at presenting executive-101. However self-driving is much much more than image classification. You have a time component, you need to integrate information as it comes in to meaningful state (aka SLAM) and make sequential decisions that has potential to kill people. This is still _unsolved research problem_ unlike punny ImageNet classification. Karpathy could have easily spent entire day talking about current state of the art and how they have improved on it but instead we got Deep Learning 101. I hope he didn't intentionally used this tactic to avoid talking about the elephant in the room. It seems they are hell bent on vision-based end-to-end learning which vast majority of experts would agree that is far away in the future. So this whole thing is quite bold and if they really make this happen in 1yr3mo then I'd say you should dump your entire 401K in buying Tesla stock. Collecting data is actually more trivial part of this problem.

<comment><USER4> Good overview, I share your opinion.

Tesla is betting BIG on NNs.

In the presentation they say that we have NNs in our brain. Which is why we can look at one photo of a dog, and distill enough identifying information in order to spot the breed from then on. Training sample of 1.

Curiously though, they than say that NNs don't work like our brains do, and require A LOT of training data from all angles, etc. Training sample of N.

The presentation doesn't actually show, how they plan to train a single NN to encompass all the knowledge for self driving. In particular, the looooooong tail. But maybe they'll have independent models, say based on "observed" conditions, which swap in and out ... based on more NN logic.

But at the end of the day, there is no known (to me) examples, of NNs even approaching human ability. Tesla is bascially hoping we will believe, they will be the first to show this capability of NNs.

Let's wait and see, 2020 is not that far away.

<comment><USER5> > require A LOT of training data from all angles

Also, we learn things outside of our cars. I ride a bike, this gives me insight into what bike riders will do on the road.

I have kids, so I know a kid on the pavement will be more risky / unpredictable than an adult.

I drove down a single track road today and met another car coming towards me. We stopped, then he started to reverse, there was a passing place behind him. Another car pulled up behind him, so I then reversed back into a field to let the cars past. This sort of thing happened maybe six times today as I drove around. Not at all surprising, not even noteworthy enough for any of the drivers to acknowledge even a flick of the finger in thanks. Just part of driving here.

<comment> xkcd-sucks Hmm maybe the problem is actually interacting with humans naturally, on the road, so self-driving is really human-style general AI

<comment><USER6> This is actually the aspect that really doesn't get discussed enough. Imagine these scenarios:

a) Roadworks and a guy tells you to stop or waves you through. Or asks you to drive slowly. Or points to a direction that he wants you to go.

b) Woman on the side of the road is waving their arms about because her small child is crawling on the road trying to chase a toy.

If self driving cars can't look another person in the eye and identity their intent then there are going to be a lot of unforeseen problems.

<comment><USER7> What's the more interesting question is not whether there will be corner cases where self-driving fails, but when the average performance is so much better than humans that AI saves 10s if not 100s of lives every day, but that's still a reality where AI driving software could be actively out there involved in the deaths of a single digit number of _different_ people a day or week.

Do you deploy that build or not?

<comment><USER5> For sure deploy if the numbers are right. It's the edge cases that are interesting.

Cameras are fine in the day, but when I struggle to see at night, in the rain, with spray from cars, glare from on coming traffic... that's where I would LOVE to have a lidar assisted heads up display. How Tesla think they can do better with just cameras compared to another car with cameras AND lidar just baffles the mind.

<comment><USER8> Not better in absolute terms, but better in terms of the best car for a certain price. Sure a $200k car with redundant LIDAR and horribly inefficient aerodynamics because a giant spheres on top (like weymo) might be better than a $50k with just cameras.

But the hard part is the software, and it's unclear that the added complexity of lidar + camera will allow the software to be better than just cameras.

As an example a recent study at Cornell shows that a stereo pair of cameras mounted high up behind the windshield provided similar quality 3d data to LIDAR. Search for "Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving" or the more consumer friendly "Elon Musk Was Right: Cheap Cameras Could Replace Lidar on Self-Driving Cars, Researchers Find".

Seems much easier to work on a stereo video stream that includes 2D, color, and extracted 3D features than trying to achieve sensor fusion with lidar + video. Especially if you want to make full use of color for things like brake lights, traffic signals, color/markings of an ambulance, school bus, lane markings, and other important details that are invisible to LIDAR.

Especially consider that if weather is terrible and the camera vision is so bad that only 5mph is safe. If LIDAR can see significantly better, do you allow it to drive at 50 mph because it can see 200 feet? For just social reasons it seems like driving as much like a human as possible is going to be best until all cars are autonmous.

</comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER9> I'm sorry but I'm finding it really difficult to watch this and match up what the engineer is saying to what Elon Musk is saying.

For example, the engineer says the custom ASIC does 144 TOps for 2 chips vs the NVidia drive Xavier - does 21 TOps. Okay, well yeah I expect your custom ASIC does have a nice performance advantage over the equivalent GPU. at 3.5x advantage probably seems reasonable. Cue Elon Musk:

"At first seems improbable, how could it be that Tesla, who has never designed a chip before would design the best chip in the world but that is objectively what has occured. Not the best by a small margin, the best by a huge margin".

Mate, it's a dot product with some memory attached, and not a single detail your half hour deep dive has gone into suggests anything other than a bog standard ASIC.

"All the cars being produced right now have all the hardware necessary for full self-driving"

And this is where I'm totally lost. I want to believe! But he's lied so many times now. This man is sucking the credibility out of every engineer in the room. Don't repeat the same lie twice.

<comment><USER10> > For example, the engineer says the custom ASIC does 144 TOps for 2 chips vs > the NVidia drive Xavier - does 21 TOps. Okay, well yeah I expect your custom > ASIC does have a nice performance advantage over the equivalent GPU. at 3.5x > advantage probably seems reasonable. Cue Elon Musk:

Here's my issue with that. The on-chip SRAM is only 32MB, and the RAM is LPDDR4 rated at only 68GB/s.

Assuming a dot-product (multiply + add) over INT8 data, that's a limitation of 2-operations per 68GB/s (that the RAM moves at). Or 136 GIOPS (Giga-integer8 operations per second). You're limited by RAM, based on what I've seen in the presentation.

Unless their neural net is 32MB and fits entirely in on-chip SRAM. That seems unlikely to me...

<comment><USER11> Nvidia already responded:

""Tesla was inaccurate in comparing its Full Self Driving computer at 144 TOPS of processing with Nvidia Drive Xavier at 21 TOPS," a spokesman said in an email. "The correct comparison would have been against Nvidia's full self- driving computer, Nvidia Drive AGX Pegasus, which delivers 320 TOPS for AI perception, localization and path planning." The statement also contends that "while Xavier delivers 30 TOPS of processing, Tesla erroneously stated that it delivers 21 TOPS. "

<comment><USER12> From what I understand, Pegasus consumes about 500Watts, compared to under 100 Watts for Tesla's FSD computer. Elon in particular emphasized the performance per watt (as it's always possible to cram more chips to increase performance if you ignore cost and power consumption).

The comparison made in the video: 500Watts for an hour consumes about 2-3 miles of range. In a city in slow traffic, going 12mph, that's a significant range reduction. So you might have a 10% improvement in range for the Tesla ASIC in low speed conditions.

<comment><USER10> Its incomparable though. The Pegasus has far more compute power.

Just because the Pegasus has 500W worst-case TDP doesn't mean that its average case would be 500W constant. If you scale back your code and idle parts of the GPU, you can drop the energy cost arbitrarily.

At least, that's how GPUs on desktops work. They only use a ton of power if you give them a ton of work. Write your code in an energy-efficient manner, and the 2080 Ti will drop down to 20W, or scale all the way up to 300W.

[https://www.tomshardware.com/reviews/nvidia-geforce- rtx-2080...](https://www.tomshardware.com/reviews/nvidia-geforce-rtx-2080-ti- founders-edition,5805-10.html)

Modern chips idle very well. With the right code, the Pegasus could be tuned to only use 100W (assuming good enough programmers). But Tesla's chip will NEVER be able to scale above 100W.

<comment><USER12> Pegasus's chip is more general purpose and doubtless has far more general purpose compute power. But that's irrelevant. What's relevant is Tesla's chip is optimized specifically for their NN pipeline whereas Pegasus is based off of general purpose GPU architecture, thus Tesla's chip achieves a better TOPS per Watt than Pegasus. And it'd be strange if it didn't.

"Tesla's chip will NEVER be able to scale above 100W" okay, based on what? Tesla has a higher performance chip in the pipeline right now, and they could've used more silicon to achieve more TOPS if they needed it.

EDIT: Pegasus has 500W at 320 TOPS. Tesla's has 72 Watts at 144TOPS. Thus Tesla's chip, because it's focuses specifically on Tesla's NN pipeline, is about to get almost 4 times to performance per watt of the Pegasus and is much cheaper. Tesla's NN chip wouldn't help your video game, and Tesla isn't intending to compete in all the markets Nvidia operates in.

<comment><USER10> > Pegasus has 500W at 320 TOPS. Tesla's has 72 Watts at 144TOPS.

Theoretical TOPS which can only ever execute within the 32MB SRAM that Tesla has created. Otherwise, Tesla's compute chip is stuck at 68 GBps LPDDR4 RAM. Pretty slow.

Pegasus uses HBM2 chips at 500GBps. Pegasus will be able to efficiently compute neural networks that are larger than 32MB in size.

Tesla is making big bets about this tiny 32MB SRAM. Bits and pieces of the CNN can fit in there, but almost certainly not the entire neural network.

You're right that this is a specialized chip. But even for NN / Deep Learning inference, it seems a bit underpowered to me from a RAM perspective

<comment><USER12> Specmanship doesn't matter. What matters is how fast it's able to execute on the task at hand and for what cost in terms of purchase price and energy.

Nvidia's offering can be really good, and so can Tesla's.

</comment></comment></comment></comment></comment></comment></comment></comment><comment><USER13> I watch Tesla fairly closely from both the bull and bear sides. In short, I don't believe Tesla is anywhere close to Waymo. They won't achieve FSD in 2020.

It's important to see this event in the context of their significant demand and cash problems. Even enthusiastic Tesla investors like Galileo Russell suggested that Tesla is in a cash crunch and should raise money. Which hints at the main mystery about Tesla, why haven't they raised money yet?

Take a moment and think about _why_ are they doing this event now. Elon is setting a stage for a capital raise, he's pitching the autonomy narrative after the Model 3 cash cow narrative failed. They're trying to convince investors (and customers) to give them money because money-printing autonomous taxi service is coming next year.

Also, think about why basically everyone except Tesla uses Lidars. Is it because they're stupid or because Tesla _cannot_ use Lidars even if they wanted to?

P.S.: Nvidia issued a statement saying that Tesla's claims about their chip are incorrect: [https://www.marketwatch.com/story/nvidia-says-tesla- inaccura...](https://www.marketwatch.com/story/nvidia-says-tesla-inaccurate- in-self-driving-comparison-2019-04-22)

Edit: In 2012, Waymo reached the milestone of handling 8 100-mile routes, specifically chosen to capture the full complexity of driving. I doubt Tesla is currently at that level. Source: [https://events.technologyreview.com/video/watch/dmitri- dolgo...](https://events.technologyreview.com/video/watch/dmitri-dolgov-waymo- autonomous-cars/)

<comment><USER14> If I were a billionaire and I wanted to change the world, I would put pressure on existing companies by creating a competing company and pretending extraordinary achievements.

Truth or not, failure or not, Musk moves forward the hopes and expectations, and I'm thankful just for that.

I don't see how we can blame him for taking position

<comment><USER9> What you're describing is securities fraud.

<comment><USER12> Saying what your future goals are, while clearly and repeatedly reminding people of your past history of being late on those goals, is the opposite of fraud.

</comment></comment></comment></comment><comment><USER15> ...out of all the things I expected to see from a Tesla stream, I definitely did not expect a 25 minute discussion on the cost of 32bit additions, dot products, sram bandwidth, and chip design, at the level of a third year college hardware course.

<comment><USER16> I keep saying this he knows what he doing. You can tell he knows what's happening. Which is rare for a CEO to know this level of detail.

<comment><USER17> Which might explain why he's a crummy CEO. If your job is to strategize for the entire company and you're busy learning the minutiae that you spend a _lot_ of money for other people to worry about, maybe you need to consider using your time a little better. If you want to build neural nets, keep your stock, fire yourself and go build neural nets.

<comment><USER18> > If your job is to strategize for the entire company and you're busy learning > the minutiae that you spend a lot of money for other people to worry about, > maybe you need to consider using your time a little better

This is what everyone that is not Tesla or SpaceX are doing. And have been doing for a long time. If the CEO is not an engineer at heart, what are they?

I seriously doubt Elon Musk has more engineering knowledge than the people he hires on their specific fields. However, he can make pretty well informed strategic decisions if he knows WTF the engineers are talking about without taking their word – not even that, as explanations have to be dumbed down.

This is not a new thing. Bill Gates was like that (1). Steve Jobs was no dummy and had an engineering background, but not at the same level – he did parter with a genius engineer, however.

I think Musk is doing the right thing.

> Which might explain why he's a crummy CEO

That's quite debatable, I'd say. Isn't he getting results?

(1) [https://www.joelonsoftware.com/2006/06/16/my-first-billg- rev...](https://www.joelonsoftware.com/2006/06/16/my-first-billg-review/)

<comment><USER19> > This is what everyone that is not Tesla or SpaceX are doing.

If your goal is to make money, I'd follow anyone but Tesla or SpaceX's example.

<comment><USER20> How is SpaceX a bad example? They've managed to undercut everyone in the space launch sector while bringing in a larger profit per launch.

<comment><USER19> SpaceX isn't public or open about their financials, but there's a lot of evidence that they are simply losing money. Ex. they were raising a lot of money last year, profitable companies don't do that. Also read [https://www.theverge.com/2018/12/7/18129539/spacex- falcon-9-...](https://www.theverge.com/2018/12/7/18129539/spacex- falcon-9-rocket-water-landing-valuation-investors)

<comment><USER20> Yeah that article seems like a lot of tea leaf reading to me. All evidence points to SpaceX building orbital rockets cheaper than just about anyone else before reuse, but even if not reuse should drop their costs well below anyone else until blue origin comes online or until ULA decides to shake off the crust of old age. It's not at all surprising that investors are told not to expect to take a profit for 15 years because SpaceX is focused on growth and development. I wouldn't expect them to reinvest any less than 100% of their profits at this time. It makes just as much sense to assume they raised money to keep development moving at the pace they want which wasn't fast enough using profits alone.

Despite the fact that I own a Model 3 and own shares in Tesla I often find the critics make good points and just hope for the best anyways because I want electric cars to take over and I want self driving cars to develop sooner than later because driving is dangerous and tedious.

The SpaceX hate seems much less justified to me. SpaceX has been building launching, landing, and reusing rockets for years now. They've ushered in a new lower cost space age.

</comment></comment></comment></comment></comment></comment></comment></comment><comment><USER21> Holy cow Elon said _a lot_ of things that will _drastically_ change the world in the next 2-3-5 years. I think it's very, very clear they've been doing _a lot_ of stuff behind that scenes that nobody has given them credit for over these last 5 years. Everyone thought they were just throwing everything at the Model 3 ramp and doing nothing else, but it turns out the opposite is true.

At the end Elon said autonomy is basically their entire expense sheet!

Whether or not Tesla can pull it off is obviously going to be an enormous topic of debate with haters and lovers on each side.

This is super, super exciting. I'm going to grab the popcorn and enjoy watching Tesla try. Whether they succeed or fail I admire them aiming so high, and planning so far ahead.

<comment><USER22> And I think they are far ahead of the game compared to anyone else w.r.t autonomous driving, they have a fleet sending them data back real time.

<comment><USER23> This is definitely not true. The engineer explicitly stated that Tesla only gets data when trigger conditions are met (and that this was in fact always the case), so most of the miles that Tesla drivers have been driving was actually never even sent back to the mothership for use in ML training.

IOW, despite Elon claiming for years that they're ahead because of the "millions of miles" of free data collection by Tesla drivers, it turns out once the engineers on the ground actually speak...they're actually pretty far behind on the data front, and most of the data they are getting is the same safe routes over and over again since they're not trying to put Autopilot into novel situations to acquire new data or test the ML algorithm's capabilities to handle unique/novel situations (like trucks turning in the highway, or freeway dividers).

<comment><USER24> They cherry-pick rare cases and use their fleet to get more examples of these situations. This seems like the right approach given more miles following the same car in a straight line is pretty useless.

My takeaway from the presentation is that Tesla will perform better than other companies in this space (although I don't know enough about Waymo to comment) due to the following:

-You want a large dataset (Tesla and many companies have this and can simulate) -You want a varied/diverse dataset (Tesla and many companies have this and can simulate)--the point here is simulations for simple cases work (you can only simulate when you know), but for complex ones are close to the difficulty of actual FSD -You want a real dataset (Tesla is the only company who can say this and can say they have data on how X00Ks of drivers will handle these situations)

<comment><USER23> My point was that it appears that Tesla _doesn 't have a large and varied dataset._ It has a small and pre-selected data set, since the cars only transmit data when pre-determined triggers are fired. Thus, it doesn't matter how many 1000s of drivers Tesla "has" or how many "situations" they're in, since it's not actually collecting data from most of these situations.

And Autpilot's performance (including its numerous regressions) suggests very strongly either that it doesn't have a very large data set, or else that it has a large data set of everyone doing roughly the same thing almost all of the time. These are the two most logical explanations for Autopilot's tendency to veer toward freeway dividers even (especially?) after updates.

<comment><USER25> > My point was that it appears that Tesla doesn't have a large and varied > dataset. It has a small and pre-selected data set,

I don't thinks it is a fair characterization: first the notion of "large" is fairly subjective. But more importantly the fact that they collect data on pre-determined triggers is just a guarantee that the dataset is not over- fitted (let say to the 280 and 101 in the Bay Area and to Elon's commute in LA) and instead has good coverage of the world.

Their capabilities of triggering on situation allows them to grow the dataset quickly in a supervised way. It is all about the granularity of these triggers, imagine that you can express "collect situation in tunnels with jerk higher than X m/s3" or "collect all lane change abort in snow condition", ... In the next 24h you get data from all over the world and this data is automatically tagged and classified by the neural net.

</comment></comment></comment></comment></comment></comment><comment><USER26> So far, half an hour from the head of IC design. Nice special purpose IC and board. Dual everything for redundancy. Wide special purpose neural net evaluation. 100 watts for the compute system. Code signing. Shipping in new Model 3 cars since last 10 days.

"All we need to do is improve the software" \- Musk [12:07 PDT]

LIDAR "unnecessary" \- Musk [12:13 PDT]

Computer vision guy is now speaking.

Recognizes "driveable space", not just obstacles. Video shown, but just for a freeway. This is crucial to safety. Need to see this is a cluttered environment.

<comment><USER27> "LIDAR is a fool's errand. Everyone relying on LIDAR is doomed." edit: beat me to it

<comment><USER28> it snowed a few weekends ago in Chicago, my autopilot turned off because snow covered up the cameras. So I am not buying all this "self driving with no lidar" brouhaha

<comment><USER29> Doesn't LIDAR has similar problems with precipitation? Regardless of what solution proves to be the best, there is going to be a decent amount of time between when a self driving car can handle most scenarios and when it can handle all possible scenarios.

<comment> semi-extrinsic Here's a snow cover map of North America on a random day in January:

[https://www.natice.noaa.gov/pub/ims/ims_v3/ims_gif/ARCHIVE/U...](https://www.natice.noaa.gov/pub/ims/ims_v3/ims_gif/ARCHIVE/USA/2019/ims2019025_usa.gif)

Here is Europe and Asia on the same day:

[https://www.natice.noaa.gov/pub/ims/ims_v3/ims_gif/ARCHIVE/E...](https://www.natice.noaa.gov/pub/ims/ims_v3/ims_gif/ARCHIVE/EuAsia/2019/ims2019025_asiaeurope.gif)

For someone who lives in or close to that white part, "most scenarios" includes snow. Arguably for Level 4 autonomous cars in those areas, you either need to fully disable autonomy in September and enable it again in late April, or you'll need to handle snow.

<comment><USER29> You are comparing two different things. Old snow on the ground (your images) is not the same thing as active or recent snow accumulation on the vehicle (the original comment). It is completely reasonable for the car to disable autonomy due to snow accumulation until someone clears it off the vehicle. That says nothing about the autonomy of the vehicle with snow cover on the ground.

<comment> semi-extrinsic I'm assuming Level 4 is what Musk means by Full Self Driving. The bar to be passed is then

"No driver attention is ever required for safety (...) self-driving is only supported in limited circumstances (e.g. geofencing), and when these circumstances are no longer met the vehicle must be able to safely abort the trip, e.g. park the car, if the driver does not retake control."

How would that work if you're out driving on the highway, and it starts snowing hard so the car can't see anything? Just park on the highway?

If your car can't safely handle such a scenario, the automous feature would have to be "season-fenced" in addition to geofenced.

<comment><USER29> It isn't like it all cameras simultaneously go from 0% obstruction to 100% obstruction instantaneously. The car should pull off the highway and park or at least pull to the shoulder and park once it has identified decreased visibility to an extent that might impact safety.

<comment> semi-extrinsic It kinda is though. Snow deposition is a function of surface temperature, which is uniform across the sensor. Hit the wrong initial temp when you enter a blizzard, and deposition takes your vision in seconds. It can be hard enough to see out the windshield which has heating, wipers and anti-freeze wiper fluid fighting for it.

<comment><USER29> Do you realize how far you have moved the goalposts during this conversation? You started out with the suggestion that they "fully disable autonomy in September and enable it again in late April" and now you are talking about situations with "the wrong initial temp when you enter a blizzard".

I will simply refer back to my initial comment in this thread: "there is going to be a decent amount of time between when a self driving car can handle most scenarios and when it can handle all possible scenarios". Blizzards are not in the "most scenarios" group. Barring emergencies, no one should be driving in a blizzard let alone an autonomous car.

<comment><USER30> I don't think OP has moved the goalposts at all. LIDAR can work in snow but cameras get covered. And yes you are right, there will be a decent time between testing in some basic scenarios and testing in all scenarios. But I do not understand why Mr. Musk is trying to reinvent the wheel and rely only on vision when LIDAR has already shown that it works better.

[https://www.cambridge.org/core/journals/journal-of- glaciolog...](https://www.cambridge.org/core/journals/journal-of- glaciology/article/lidar-measurement-of-snow-depth-a- review/4419DF5C778946103080CB6187D434C0/core-reader)

<comment><USER31> > But I do not understand why Mr. Musk is trying to reinvent the wheel and > rely only on vision when LIDAR has already shown that it works better.

Because he's sold a bunch of cars (and a bunch of stock in the company selling the cars) without LIDAR with the explicit claim they are hardware-ready for full self-driving.

</comment></comment></comment></comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER32> The thing I don't get about Tesla is why they keep heaping more on their plate rather than _just_ (for large values of just) re-doing the motive power bit. That alone is a massive challenge and it is not something they've licked at economies of scale that allow a $10K vehicle to be brought to market, which is what it will take to really make this a success.

Every extra they tack on to the base product is one they will be expected to deliver on as well diverting attention from the main problem they are dealing with, which may in the longer term leave open enough room for the competition to wiggle through.

Sure, autonomy is a big deal, but it is _also_ something that will once cracked be an instant commodity and there is a lot of money and talent focused on that particular problem, which is surprisingly hard to do well.

If Tesla ends up going under because of one of the side shows (Solar City, autonomy, Power Wall etc) that would be a serious loss.

<comment><USER21> I think it's related to who Musk is, and the kinds of things he wants to achieve.

SpaceX isn't trying to just do what has always been done and just make it 5 or 10% cheaper. That's boring. SpaceX is completely turning the launch industry on it's head.

Tesla is the same. Elon isn't interested in making "just" an electric car. He wants to change the world, and electric cars that drive themselves will do that.

Can he do it soon? I'm not an expert, I don't know. But damn it's exciting to watch.

<comment><USER33> The purpose of Tesla is to save the world from global warming, though. I'm not sure self-driving is that relevant compared to the electric propulsion part.

<comment><USER34> If anything it'd be detrimental since it would allow more people to drive longer and more often.

</comment></comment></comment></comment><comment><USER35> Interesting pieces:

\- First principles hardware design of focused self driving computer (many times better than any competing existing hardware). Already shipping in all newly produced cars. Currently working on next gen that will be 3x better to ship in a couple years.

\- Lidar is an unnecessary mistake that competitors are making that won't succeed (too expensive, need too many, unnecessary).

\- Real world fleet testing is critical to success, simulations are not good enough since there are too many unknown unknowns in the real world. Tesla uses simulations too, but nobody else comes close on real world fleet testing.

<comment><USER3> > Lidar is an unnecessary mistake that competitors are making

This is very controversial and rest of the industry thinks exact opposite, many even claiming that Tesla is being irresponsible and even delusional in trying to do autonomy without lidar. The main points I have heard in favor of lidar are that computer vision is very flaky not only in suboptimal weather but even in good weather. Cameras are simply no where close to in performance in dynamic range, rapid adaptation, focusing etc as human eye. Imagine car going under the shaded road with rapid changes between bright light and shade. The likelihood that your depth estimation will get messed up is very high. Of course, night driving becomes highly questionable as well. In additional the long range depth estimation is very flaky with stereo vision right now and a topic of research for mono-vision. If you want to retreat to level-4 only and that too with conservative speed, weather etc then may be vision+radar more doable?

<comment><USER35> They pair it with a forward facing radar which is inexpensive and good at depth perception.

Elon predicts all competitors will eventually drop lidar. He mentions it's expensive, but also not as good in a lot of cases (and all roads/signs are designed for vision).

He argues that getting vision to work is a prerequisite for getting self- driving to work and once you have it working, lidar is worthless (and unnecessary).

<comment><USER23> Elon Musk is noted for his premature optimization.

Case in point: both the Gigafactory (vastly overbuilt for the quantity of batteries actually produced) and the Alien Dreadnought (vastly overbuilt for the number of cars Tesla current produces...assuming that Tesla is ever able to get the fancy automation working). Boring Co digging a two-mile tunnel in West LA without bothering to learn how to pour concrete smoothly, or to make the "rails" the proper width, or learning about ventilation, or access points....

<comment><USER36> It's easier to optimize a working system than it is to get an optimized system working.

<comment><USER37> So when are they going to get them working?

<comment><USER36> Beats me. It could be a while before we see anything that can compete with conventional rideshare at scale.

</comment></comment></comment></comment></comment></comment></comment><comment><USER38> Tesla: "We have a global network of cameras that can be queried to find anything on or near roads and send back photos of it."

Every law enforcement entity on the planet: _drool_

<comment><USER39> It was pointed out that All data is anonymized.

<comment><USER38> That's fine. I don't care who's driving the car carrying the camera. I care about what the camera can see. If you've ever been sent an Amber alert for a local child abduction it often comes with a description of the car that the suspect is driving. If I'm in law enforcement I'd much rather get an emergency warrant to ask Tesla to query the network to find that kind of car in a given geographic area.

<comment> grey-area Too late.

The future is here, and computers will see _everything_ in excruciating detail via multiple planes of information. The power you consume, the data you send, the money you spend, the media you watch, the words you write, where you go.

It's all out there in the world. There's no taking it back, though we may be able to legislate about its use.

</comment></comment></comment></comment><comment><USER40> At the tail end of Karpathy's presentation, he said something that reminded me of why Peter Norvig decided to join Google: because that's where the data is. In Tesla's case, they have a unique and likely accelerating advantage in having the best source of data up on which to train their models. I think this forms the basis of an enduring moat relative to their other competitors, none of which are collecting data at the same scale.

<comment><USER41> Is Tesla collecting that data? I've looked at their earnings and didn't see any big expenses related to datacenters. Amount of data they would need to store and process is massive, that would for sure show up in earnings, as one of the biggest costs.

</comment></comment><comment><USER42> While you're waiting for the main event to start, here are some recent interviews with Elon about self-driving cars. He's _very_ confident.

"To me right now, this seems 'game, set, and match,'" Musk said. "I could be wrong, but it appears to be the case that Tesla is vastly ahead of everyone."

I am eager to see what they unveil today.

[https://www.youtube.com/watch?v=dEv99vxKjVI](https://www.youtube.com/watch?v=dEv99vxKjVI)

[https://ark-invest.com/research/podcast/elon-musk-podcast](https://ark- invest.com/research/podcast/elon-musk-podcast)

<comment><USER43> Hmmm so then why is Tesla ranked last for autonomous driving by third party researchers?

[https://www.google.com/url?sa=i&source=web&cd=&ved=2ahUKEwiR...](https://www.google.com/url?sa=i&source=web&cd=&ved=2ahUKEwiR7KHlq- ThAhUPHzQIHYU2DqEQzPwBegQIARAC&url=https%3A%2F%2Fwww.ibtimes.com%2Felon-musks- tesla-surprisingly-fails-lead-autonomous-driving- technology-2786913&psig=AOvVaw1gWIsj1jFDhjHpGWy2DVii&ust=1556044547357367)

And Elon has a long history of making false claims about Tesla's progress. For example in 2015 and 2016 he claimed that Teslas would be fully self-driving by 2018.

So why shouldn't we be skeptical?

[https://arstechnica.com/cars/2019/03/teslas-self-driving- str...](https://arstechnica.com/cars/2019/03/teslas-self-driving-strategy-is- outdated-and-possibly-dangerous/)

<comment><USER16> But Navigant curriculum was very unscientific. There no actual quantitive reason Tesla is worse. Is was based mainly on business factors like go-to market strategy and vision.

<comment><USER44> As opposed to the "scientific", "quantitative" reasoning behind Tesla being the leaders in FSD?

<comment><USER16> Yes absolutely saying Tesla who gets camera data from it's a half million car doesn't give it an advantage is crazy. That not even including the fact it's the only company who can do its strategy. Google would need to get constant data and GM and legacy automakers would need sensor suites on all it's cars yesterday.

No one knows if Tesla strategy will work because they don't have the data collection in place.

<comment><USER43> Neither does Tesla which makes it a moot point.

They have no way to store or transmit the massive data you are describing off the platform do they?

My understanding is that they have very limited storage and transmit onboard.

<comment><USER16> Based on their talk today and Andrew previous talk where he shows explicitly tools that do just that download data constantly is exactly what they do. [https://vimeo.com/274274744](https://vimeo.com/274274744)

I mean saying a phone can upload videos to youtube but a can can't to tesla is a weird ledge to stand on. Even their windshield wipers work based on sending video data to tesla to be learned on.

</comment></comment></comment></comment></comment></comment></comment><comment><USER38> I really really don't want Tesla to die. I think it's an important company for a sustainable future. Failures in autonomous driving could easily turn into the straw that breaks the camels back.

If you call something "autopilot" and promote it as if it will drive for you and then it ends up killing dozens of people ... that's where successful class actions come from.

(I obviously don't want people to die either.)

<comment><USER45> And I don't want them to succeed because they are actively hostile towards 3rd party repairs and car modifications, plus their current model 3 microtransaction bullshit scares me as a customer, I'm dreading the moment other companies catch on with that. I'm specifically talking about the fact that the base model 3 ships with heated seats out of the factory but you can pay to have them unlocked with a software update. I suspect that having them enabled with a simple software mod would get you accused of piracy since you're using something you haven't paid a licence for(even though you've obviously paid for the hardware)

<comment><USER46> Personally speaking I would not want to share a road with an autonomous car that had been tinkered on by some random Joe. Autonomous farm vehicles or something, sure, but public roads are way too risky. This is not the desktop environment of your Linux laptop, it's monstrously complex software controlling thousands of pounds of metal and batteries.

<comment><USER45> See, I don't agree at all. Just like all cars have to pass a pretty strict annual inspection to be on the road, I imagine autonomous cars will have to go through regular assessment too.

More specifically, I'm bothered by the fact that if you get into a crash in a Tesla, Tesla can disable your car and stop it from activating unless you do repairs at their approved dealership. You can't just buy parts from a scrapyard and get it running again, Tesla is the gatekeeper and they don't let anyone else hold the keys(they don't even release service manuals unless absolutely required by law). In contrast I could buy a brand new Mercedes CLS with its very respectable smart cruise control and Mercedes can't do anything to stop me from replacing the engine, the head unit, or fixing the whole thing if it's totalled - they just can't. It's not a freedom I'm willing to lose by buying a Tesla.

<comment><USER47> We don't have open source implantable medical devices either.

</comment></comment></comment></comment></comment><comment><USER48> This focus on the hardware is silly. Assume for a second that their new hardware is 50x faster than their last hardware.

That does not mean that their cars can self drive today.

That does not mean that their cars can self drive three years from now.

It's 100% not proven or obvious how car self driving skill and car self driving error rates scale with compute -- but it's surely not linear.

<comment><USER16> What if they proved it by improving hardware from self-driving hardware 1 to 2. If they proved that and proved it made their NN better then it makes sense. Of course, it wouldn't to you cause you have no inside knowledge but it may make perfect sense to them based on their data.

</comment></comment><comment><USER49> Musk: "You're only going real fast in the forward direction."

Dozens of times in my life, I have been driving down the freeway at 70 MPH in a 60 MPH zone, and I've noticed someone weaving through traffic behind me, going closer to 140 MPH.

I need to know whether to change lanes, stay in my lane, stop changing lanes, pump my breaks to indicate there is slowdown ahead of me that car might not see, etc.

Just food for thought.

EDIT: I'd also like my vehicle to be good at avoiding a car that's about to T-Bone me, at night, with no headlights on. I may not be very good at avoiding that kind of accident today, but if LIDAR is necessary to protect me from that kind of accident, then I might think it's a wonderful idea.

<comment><USER50> You're not responsible for dodging the person going 140 MPH behind you _to the same extent_ as you are for dodging the tire doing 0mph in front of you. Humans generally don't because they aren't capable of paying enough attention to what is behind and in front of them simultaneously. The car - with 360 degree vision - is more capable in fact.

<comment><USER49> Even if I'm not responsible, it's Defensive Driving. It can save my life to be aware of the dangerous situations that other drivers are causing.

</comment></comment></comment><comment><USER51> Those are some very bold claims. Level 5 by the end of this year? I find the software approach intriguing and Karpathy's segment was enlightening and did a lot to convince me of Tesla's advantages.

On the other hand, I've been sensing that Tesla is finding it harder and harder to raise cash and has been getting increasingly desperate. Are we on the cusp of a new transformative technology or the peak of the mother of all bubbles? Time will tell.

<comment><USER52> Option 1: They have solved a problem so hard that no one else, even Waymo, is claiming to be close even with $100k+ worth of sensors and a lot more compute and they have also quietly solved multiple hard unsolved research problems.

Option 2: He's lying to get money.

</comment></comment><comment><USER26> Here's the new self-driving demo video from today.[1] From Tesla's HQ in Palo Alto, out to I-280, down one exit, use interchange at Sand Hill to turn around, come back. No visible conflicting traffic on non-freeway streets.

Compare the 2016 demo video.[2] That's a tougher route. That's the one where we now know it took a lot of tries to get a clean video.

Waymo and Cruise have put up videos of their cars in city traffic. They get criticized for things like getting stuck behind double-parked cars, and being a bit shy of parked cars that project into a traffic lane. But they get where they are going. Tesla is not showing anything near that level.

Supposedly the analysts at the meeting got to ride in a self-driving car. Anyone seen reports from them?

[1] [https://www.youtube.com/watch?v=tlThdr3O5Qo](https://www.youtube.com/watch?v=tlThdr3O5Qo) [2] [https://www.youtube.com/watch?v=eAal0juXXzU](https://www.youtube.com/watch?v=eAal0juXXzU)

<comment><USER53> [https://twitter.com/hamids/status/1120466861970399232](https://twitter.com/hamids/status/1120466861970399232)

These guys also went on the ride-along: [https://youtu.be/2BZHXh1nbWc?t=360](https://youtu.be/2BZHXh1nbWc?t=360)

<comment><USER26> That's helpful.

Second video says the driver "touched the steering wheel once" in 15 minutes of driving. That's a disconnect. So, one disconnect in, what, 10 miles of driving? Waymo reported one disconnect per 1,392 miles to CA DMV last year.

Amusingly, the riders were not allowed to take video.

<comment><USER7> Actually it's one per 11,017 miles! But be careful quoting the number. It is not _all_ disengagements or all times the autopilot was turned off after being turned on.

It is "deactivation of the autonomous mode when a failure of the autonomous technology is detected or when the safe operation of the vehicle requires that the autonomous vehicle test driver disengage the autonomous mode and take immediate manual control of the vehicle."

</comment></comment></comment></comment><comment><USER54> Feels like they're just throwing a bunch of hardware specs at investors to distract from any discussions about the current state of the software beyond "just need to improve it now".

</comment><comment><USER22> You cannot access the stream from Tesla anymore, I found it here - [https://www.youtube.com/watch?v=tbgtGQIygZQ](https://www.youtube.com/watch?v=tbgtGQIygZQ) Karpathy did a great job explaining this, I took Neural Networks a long time back and his back to basics approach refreshed a lot of concepts I'd forgotten!

<comment><USER55> It's no longer in that link. I found it here: [https://www.youtube.com/watch?v=tbgtGQIygZQ](https://www.youtube.com/watch?v=tbgtGQIygZQ)

<comment><USER56> Is that the full stream though, it seems very perfectly set at 2 hours, like it's the last 2 hours... idk, seems to jump in half way.

</comment></comment></comment><comment><USER57> Currently just showing some generic car footage. Apparently they will demo their self-drivin features: [https://www.teslarati.com/tesla-self-driving- autonomy-day-wh...](https://www.teslarati.com/tesla-self-driving-autonomy-day- what-to-expect/)

<comment><USER58> Yes, I was feeling dumb watching the same thing in a loop, hopefully they'll start soon with the real meat...

</comment></comment><comment> grey-area "Anyone using Lidar is doomed"

Strong words from Musk about sticking with video only.

Later on in the software talk:

"Lidar is really a shortcut which sidesteps the fundamental problems...and gives us a false sense of progress"

<comment><USER59> I wonder if he's going to explain this position at all? Or if it's just posturing against Waymo et al. Seems like more data is always better than less data for this application?

<comment><USER16> Because lidar is expensive, error-prone, and can't be created without flaws. So if you make a car LIdar will fail way before camera. The only problem is if camera only can work

<comment><USER60> Why not both? Infact, Waymo has Lidar, radar and cameras (Tesla have just the last 2 and are working overtime to demonize the first). More data (and more redundancy) is better - assuming you have the computing power to process all the sensor input. As far as I can tell, the rubbishing of Lidar is FUD.

<comment><USER16> Because if you're using lidar for depth and you try to mass produce the there a high chance the lidar will be broken and because it accurate people will assume it more correctly. Eyes and camera may sometime make mistake with distance but usually it will be software not hardware. Lidar opens the issue for both. So it's easier to use the camera and just try to make a camera as good as lidar.

</comment></comment></comment></comment></comment><comment><USER61> Seems to me that relying too much on Machine Learning for this is actually the real risk, not LIDAR. Tesla's competitors are using ML, but combining it with sensor fused data from LIDAR, Radar, accurate maps, and visual data. In other words, they're starting from a view of the world with super-human sensing.

Starting from a purely visual domain likely dooms your system to making the same types of mistakes that humans make visually in judgement. At least with accurate maps and LIDAR as backup, you can sanity check output of your visual processing against a map and LIDAR. If your claim is that the map might be out of date, or the LIDAR too low rez, it still helps to err on the size of caution.

In the worst case, the map tells you you can't go somewhere that you're allowed to go. In the case where it says you are allowed to go somewhere were you shouldn't, well your visual system should be telling you not to go there. If it isn't, you're in a lot worse trouble than having a map with flaws.

Likewise, the claims that "LIDAR is expensive" is like claiming EVs are expensive because "batteries are expensive!" If AVs become common, then they'll be a huge demand for LIDAR and the costs will decline. LIDAR costs are already declining. Maybe if Elon took some of Tesla's miracle engineers and had them make a LIDAR, they could not only defeat Nvidia at chip making, but defeat all LIDAR makers as well. (sarcasm) My guess is the real problem with LIDAR is drag and vehicle trim/styling.

At this point, Tesla is basically stuck. They bet big early on a shitty sensor suite before AV technology had been worked out, and wave their hands about how using consumers as guinea pigs to feed them fleet data will magically fix deficiencies in sensors. Well, what if this is a false hope and it doesn't? It would mean they'd get sued by everyone who bought the AV suite as an option and have to issue refunds or recalls to upgrade.

I've said it before and I'll say it again, you don't ship AV as an MVP on a $100k vehicle and promise magic upgrades and fixes later before the technology is even close. It's risking people's lives and it's already killed people.

<comment><USER62> Indeed, Tesla is on the wrong side of history in sticking to their guns on pure vision autonomy.

And they're going to kill people.

It's hard enough already to communicate to the public in any meaningful sense ML results without #SKYNET brigading, Tesla is going to make it 100x harder dragging us all through their mud.

I think at this point (of having been a matrix boi long enough) I am allowed to publicly state my belief that we are very very very far away from human- level visual perception. Just staggeringly, incomprehensibly far. We've just barely started to be able to do the most basic things, sometimes.

Is it an incredible amount of collective dunning-kruger that is blinding tesla? Or perhaps willful ignorance?

</comment></comment><comment><USER49> Q: "What's the primary design objective of the next-gen chip?"

Mumbled Answer: "Safety."

...doesn't that mean the current-gen chip... isn't as safe as you want?

<comment><USER63> I don't see this line of reasoning. To me "safer than humans" or "in the 90th percentile among drivers" is a fair go to market line.

Optimally safe should still be the goal. That is, no human could have provided alternative input to the computer that would have created a safer outcome.

<comment><USER49> I think you just agreed with me. Please reconsider what I've said and what you've said.

</comment></comment></comment><comment><USER64> Man -- his engineers must cringe quite a bit when he talks about a future self-driving architecture built on DOJO with 'video-in, actuator controls out' ... just a tiny bit of speculation goes into saying something like that ... To my mind, it undermines the presentation quite a bit which otherwise appears to mainly argue that they will reach full self-driving by iterating on their neural network sensor perception model.

<comment><USER65> Andrej Karpathy (who was one of the presenters today) actually made similar statements last year, about how the part of the software stack that is not neural networks is gradually shrinking: [https://youtu.be/y57wwucbXR8?t=352](https://youtu.be/y57wwucbXR8?t=352)

</comment></comment><comment><USER66> Some video of demo rides from someone who was at the day:

Lane change on crowded freeway: [https://twitter.com/hamids/status/1120472369762590722](https://twitter.com/hamids/status/1120472369762590722) \- It manages it if not quite as smooth as a human.

Summon in parking lot: [https://twitter.com/hamids/status/1120446405410205701](https://twitter.com/hamids/status/1120446405410205701) likewise.

There's also an official Tesla sped up demo vid [https://www.youtube.com/watch?v=tlThdr3O5Qo](https://www.youtube.com/watch?v=tlThdr3O5Qo) \- hard to tell really.

Same video slowed with the display magnified and some Reddit discussion [https://www.reddit.com/r/teslamotors/comments/bgb9or/fsd_dem...](https://www.reddit.com/r/teslamotors/comments/bgb9or/fsd_demo_slowed_down_with_blownup_augmented/)

</comment><comment><USER67> I think it's a safe assumption that driver alertness positively correlates with lives saved. But it's hard to market this product with the sort of rhetoric that would optimize for that, so they've found a few ways to console themselves as they name the feature "autopilot" and endeavor to make the tech appear magical.

If they think they've cleared the bar just by beating the crash statistics of all cars, then they are still in the business of trading lives.

</comment><comment><USER68> Here is the link to the recording: [https://www.youtube.com/watch?v=Ucp0TTmvqOE](https://www.youtube.com/watch?v=Ucp0TTmvqOE)

<comment><USER69> That video link shows as unavailable.

<comment><USER68> Yeah, I saw that but somehow I'm still watching it in another tab.

</comment></comment></comment><comment><USER70> What kind of event is this and who is it targeted to? Right now there's a comparison of energy consumption for different instructions on processors, I'm very confused.

<comment><USER71> Surprisingly technical versus what I'd expected, but it is targeted specifically at investors.

<comment><USER72> The technical angle seems like a very smart one for this talk. You can just smell the cowering insecurity of the analysts and their deference to Musk when the technical descriptions whoosh past them. They may be (rightly) skeptical, but they don't want to look stupid while being so.

</comment></comment></comment><comment><USER73> To summarize, it appears that their new chips can do 96x96 dot product in a single cycle (multiplying neural network weights by values), and have hardware for activations (ReLU, softmax, tanh) and pooling (as in convolutional neural networks). This results in a crazy 144 trillions ops/second.

How does this compare to TPUs and the Neural Engine in iPhone CPUs?

<comment><USER74> something I don't understand, is this chip brand new? is it essential to the autonomous drive?

<comment><USER16> Chip is new and is needed for autonomous driving

</comment></comment></comment><comment><USER75> Is there a link to a replay of the live stream? The link on the Tesla IR site just takes me to their main page.

<comment><USER76> It was on Tesla's youtube channel up to about 2:30pm, then it just went 'unavailable' as I was watching it, and is now unavailable.

</comment></comment><comment><USER77> It's interesting to contrast Musk's stated aversion to LIDAR with his previous statements about new technologies always starting at the high end and working their way down [1]. Tesla appears to be trying to start at the low end (HW is included with the purchase of a car even if you decide not to opt for the software to make it function. You might imagine that you would start with all the bells and whistles and pare them down as the technology matures and you invent ways to do without.

[1][https://twitter.com/elonmusk/status/1075126514851602432](https://twitter.com/elonmusk/status/1075126514851602432)

</comment><comment><USER78> How can Musk make the prediction of a million autonomous taxis in less than two years? That's just fantasy. There are just too many unknowns to solve. One of the big ones is permission from the city governments. Once a car hits a pedestrian or multiple taxies start having accidents, governments will bring the project to a halt. Tesla might have a small test fleet somewhere but not 1 million taxis.

My guess is that Tesla will need another round of financing along the way and they have to make sure that the stock's price stays up. Musk is a great promoter but at some point he needs to come back to reality.

</comment><comment><USER79> I wonder if they are going to address potential limitations to their systems (bad weather, snow etc). Their tech seems absolutely impressive but I suppose there will always be failure. Does the system fail "gracefully" ?

</comment><comment><USER66> New link: [https://www.youtube.com/watch?v=tbgtGQIygZQ](https://www.youtube.com/watch?v=tbgtGQIygZQ)

(replay after the livestream ended)

</comment><comment><USER80> It seems like Tesla is at a pivotal moment.

On one hand there are hyper-bulls who claim Tesla is a $4000 stock and the future of transportation. On the other, hyper-bears claim the equity should trade around $0-$10. There seems to be no middle ground.

It seems like they are almost betting the company on FSD. I don't think FSD is really even close to a possibility over the next 5-10yrs. I hope I'm wrong, but if I'm right, I don't see how Tesla keeps going on like this.

<comment><USER48> > It seems like they are almost betting the company on FSD.

IMO they don't have any choice. The more and longer they operate like a car company shipping bigger and bigger volumes, the more their financials will be undeniably trend towards those of the existing car companies and the more their existing market valuation will be hard to justify.

They need something like this to not get traded at traditional car industry multiples.

<comment><USER81> If you just measure based on Tesla's last two quarters, their P/E is just 28. That's high for a car company but pretty low for tech. Self-driving is not necessary to maintain that valuation, only continued growth of the electric car market in general.

<comment><USER48> > That's high for a car company but pretty low for tech.

Yes! Totally agree.

> Self-driving is not necessary to maintain that valuation, only continued > growth of the electric car market in general.

Disagree. The key properties of the car business are high capital costs and high variable costs and not huge margins. The key property of the tech business is low variable cost and often low capital costs (but not always) and high margins.

There's nothing "tech" about building electric cars vs. normal cars and 10 years from now the margins and capital expenses of electric car business will be like the existing ICE car business.

So this is Tesla saying: "Yeah, our financials are starting to look like a normal car company, but we've got this thing that you should keep value us even more like a tech company than you do today."

<comment><USER81> The market values Tesla like a car business that is growing ~50% per annum.

> 10 years from now the margins and capital expenses of electric car business > will be like the existing ICE car business.

Tesla already has margins similar to existing car companies.

> So this is Tesla saying: "Yeah, our financials are starting to look like a > normal car company, but we've got this thing that you should keep value us > even more like a tech company than you do today."

Yes, they're saying that, but the market is obviously not buying it.

</comment></comment></comment></comment></comment><comment><USER82> I have several questions about Karpathy's presentation: 1\. How to label automatically using fleet learning? An example he gave is detecting cut-ins. However, I am wondering if it could be applied to more general and basic cases, for example, labeling lane marking/vehicles. 2\. How do you know if someone is a good driver (which is essential for imitation learning)?

</comment><comment><USER83> I read through all comments. Very informative.

How many people and companies are going to freak out if Elon and his team deliver FSD without LIDAR? Say in 2022, not even 2020. My layman logic is that AI is improving exponentially, and we do not know what artificial vision can do in 2 years.

</comment><comment><USER84> This is the most exciting technology (software & hardware) and business event in a long time.

</comment><comment><USER85> They use the batch size as 1. That is surprising to me as all the online course i see the batch size b/w 32 and 256.

</comment><comment><USER86> Elon just promised Level 5 FSD this year, by the end of it.

<comment><USER87> If by this year he means 2050, then I'd still be kinda surprised.

I just spent the weekend racking up several hundred miles in a brand new Model P3D. Phenomenal performance, I am completely sold on the future of EV. I was underwhelmed by autopilot however. Drives like a drunk. The adaptive cruise control part actually made me motion sick. I had high hopes going in, so it was a pretty big let down.

I suspect Tesla is going to find themselves on the other end of a class action lawsuit over all the FSD upgrades they've been selling.

</comment></comment><comment><USER88> I wish someone will ask what gaps remain for full self driving? It seems like Elon is saying that it just a data collection issue at this point.

</comment><comment> mellow-lake-day Absolutely great talk! Especially impressive how big the fleet is and how well they can use the data they have.

</comment><comment><USER89> 15 minutes in and it is still just showing stock footage. This thing was supposed to start at 11am PST, right?

<comment><USER90> 11am Pacific Standard Tesla Time, the conversion to PST is iffy

<comment><USER89> Looks like it is ~+40 mins

</comment></comment></comment><comment><USER91> is anyone knowledgeable with how this compares to Waymo's solutions? I'm curious.

<comment><USER92> They are fundamentally different: to summarize, Waymo follows the HD maps approach when you have a very precise map of a given city, with both metric (actual 3D shape of the environment) and semantic (lanes, sidewalks, signs) information, in which you localize yourself with centimeter-level accuracy (with GPS, SLAM etc). When you're localized you can combine information that you see now such as cars around you and high quality information coming from the map.

Tesla, on the other hand, thinks this is too fragile to changing environments and works with regular (think google maps level of detail, probably a little better) maps, combined with local semantic information such as signs and lane markings. This also means that they put less pressure on localization, because they don't use the map to detect i.e. speed limit (I'm pretty sure Waymo can detect those signs, it's just that the existence and position of the sign is already known in a global world frame).

</comment></comment><comment><USER93> link to the livestream video on youtube: [https://www.youtube.com/watch?v=Ucp0TTmvqOE&t=4150s](https://www.youtube.com/watch?v=Ucp0TTmvqOE&t=4150s)

</comment><comment><USER94> I wonder how commaai is taking the news. Fleet + imitation was their angle too.

</comment><comment><USER95> Uber and Lyft are fucked.

<comment><USER96> How so?

<comment><USER50> If Tesla succeeds at FSD - Tesla is competing against them with hardware that operates at (using the numbers from the presentation, if I recall them correctly) ~1/10th the price.

They have contracts in place with the owners of cars they have sold preventing them from letting Uber and Lyft use the self driving capability to compete.

<comment><USER16> Not to mention they have the apple vs android advantage where since they build their own hardware optimization is far easier than google partnering solution.

</comment></comment></comment></comment><comment><USER97> Ok, I am late to the show. Where can I watch the recording?

<comment><USER66> [https://www.youtube.com/watch?v=tbgtGQIygZQ](https://www.youtube.com/watch?v=tbgtGQIygZQ)

</comment></comment><comment> sahin-boydas It is pretty techincal. Wow. Great. Thank you Tesla.

</comment><comment><USER98> How much b-roll do they have?

<comment> sea-shore Maybe it is a test to see when the AI generating the video breaks

</comment></comment><comment><USER99> Did that roadster just pass two model 3s on a double yellow blind turn? Smh.... but that was awesome

</comment><comment><USER100> Stream just went offline. Anyone have a link?

<comment><USER100> Here it is: [https://www.youtube.com/watch?v=Ucp0TTmvqOE](https://www.youtube.com/watch?v=Ucp0TTmvqOE)

</comment></comment><comment><USER101> doesn't portend well the way it is going, looks like they don't have anything to show here.

</comment><comment><USER102> Musk: "Lidar is a fools errand"

Let's hope he is right.

<comment> swagasaurus-rex "It's like having two appendices. One appendix is bad"

<comment><USER103> I don't know the context for this, but one appendix isn't bad... appendicitis is bad.

<comment><USER104> He means the appendices in a book.

<comment><USER103> I just watched the 5minute distillation and I'm pretty sure he was talking about the organ.</comment></comment></comment></comment></comment>


Show HN: We Built A Website Builder With Ruby On Rails - <AUTHOR> https://www.nikktto.com/ We're in very early stages & currently doing user testing. If anyone is interested to be a tester please sign up. Any feedback is good. :) <comment><AUTHOR> We're currently looking for user testers. If anyone is interested please sign up. Any feedback is good. :)</comment>


The Proof of Fermat's Last Theorem by R.Taylor and A.Wiles (1995) [pdf] - <AUTHOR> http://www.ams.org/notices/199507/faltings.pdf <comment><USER2> I don't get the point of the title. 4 pages + a PhD in math is needed.. lol.

Here's even a shorter summary:

Wiles proved a special case of the Taniyama-Shimura conjecture, ie establishing that for every rational elliptic curve there is a modular form with the same Dirichlet l-series. Faltings previously proved that if there was a counterexample to Fermat's Last Theorem, there'd be a certain kind of elliptic curve that is NOT modular - thus Wiles, by proving the modularity theorem for this class, proved FLT. But his proof had an essential gap, so he had to invent a certain kind of Iwasawa theory as an alternative method, to complete the proof.

In otherwords, "summarized" really is an important word.

[edit: fixed spelling]

</comment><comment><USER3> I was doing a Master degree in Algebraic Geometry, though I was working on projective geometry rather than number theory. Because of this, I can follow this quite easily right up until it goes into intricacies of modular forms, after that there are some familiar concepts like deformations or Gorenstein modules, but the whole picture is completely blurry.

What I find interesting though is that behind _every single sentence_ I could I understand from the first half of this article, there were literally _hours_ of time spent on learning the concepts. For instance:

>With this addition, the solution set has the structure of an abelian group, with ∞ as the neutral element. The inverse of (x, y) is (x, −y), and the sum of three points vanishes if they lie on a line.

This just reminds the hours I spent learning about group of Weil linear divisors, how they correspond to Cartier divisors on nonsingular varieties, why O(a) != O(b) for a != b if a, b are codimension 1 subvarieties (i.e. points) of eliptic curves, and so on, and so on. Literally every single sentence brings back memories of hours of study.

Now, reading sentences from the half I don't understand makes it completely obvious to me that just like the hours I spent to understand the first half, I need more hours to understand the second half. I feel sad I don't do math anymore.

</comment><comment><USER4> I was obsessed with trying to understand it ever since I read the book. How many years of math studies one need to make sense of it? (I'm doing a CS master degree but I'm not even close to feeling that I know anything about math, imposter syndrome or more likely not enough math skills...) Any chances? Or should I probably give it up?

<comment><USER5> It took Wiles seven years to prove the thing (and the proof needed to be patched!), so I wouldn't be discouraged if it takes a while to understand. A nice thing about mathematics is, it waits for you.

If you're interested in the mathematics behind this, I'm not sure a direct attack on the FLT proof is the best route to take. The paper that proves a famous conjecture is normally sitting on a mountain of prior work, which means the final paper (1) assumes familiarity with that mountain and (2) is highly technical because all of the understandable things have already been tried.

So instead, why not start learning about the mountain?

The truth of FLT follows from the two claims:

(1) Taniyama--Shimura--Weil conjecture: "Every elliptic curve is modular."

(2) Ribet's Theorem: "If FLT has a counterexample, then such and such an elliptic curve is not modular."

As it happens, TSW was originally believed to be too difficult to prove, but I suppose the connection with FLT motivated people. Taylor and Wiles proved the absolute minimum of TSW that they could get by with and still get a contradiction from Ribet's theorem. (My understanding is that TSW is now fully proved -- the "modularity theorem".)

If you're wanting to "get" FLT, I'd encourage you to look into elliptic curves, modular forms, and their relationship. I wonder if working on the mathematics of elliptic curve cryptography might be a good way to get a feel for elliptic curves.

However, if you do want to take the direct route, I believe that Faltings's highly compressed article provides a syllabus. Once you can read and completely understand every sentence of that article, it is highly likely that the Wiles and Taylor and Wiles papers will make sense. I... would really not recommend this route.

</comment></comment><comment><USER6> If you haven't read it, I highly recommend:

Fermat's Enigma: The Epic Quest to Solve the World's Greatest Mathematical Problem by Simon Singh

The proof of Fermat's last theorem has a long and intriguing history, and Singh's writing is accessible and entertaining for anyone with an interest in math and science, regardless of education level.

</comment><comment><USER7> I understood the abstract...

I am not only not one of the 'specialists', but am not one of the 'wider mathematical audience', it appears.

<comment><USER8> Modern algebraic number theory is pretty hard even for non-specialist mathematical audiences so if even if you're steeped in mathematical lore and culture it still can be hard to grasp.

<comment><USER9> And algebraic topology, and algebraic geometry, (..and I'm sure analysis and basically every other subset of mathematics). [http://matt.might.net/articles/phd-school-in- pictures/](http://matt.might.net/articles/phd-school-in-pictures/) You get far enough into any field (and it doesn't even have to be in the hard sciences,) be it law, medicine, linguistics, or something as seemingly trivial as making industrial bearings, and you'll need not only a graduate level education but an additional 5 or 10 years to get up to par with the rest of the field. The world is so catastrophically complicated at this point, it often overwhelms me.

</comment></comment></comment><comment><USER10> Does anyone have a mirror of this? I can't find this except for AMS's notices board, which is what this links to.

<comment><USER11> [http://vtf-math.narod.ru/pdf/faltings.pdf](http://vtf- math.narod.ru/pdf/faltings.pdf), several more at [https://scholar.google.com/scholar?cluster=17113326573299277...](https://scholar.google.com/scholar?cluster=17113326573299277476&hl=en&as_sdt=0,33&sciodt=0,33)</comment></comment>


Gig: A new method for explaining complex ensemble ML models - <AUTHOR> https://www.zest.ai/blog/introducing-generalized-integrated-gradients-gig-a-practical-method-for-explaining-diverse-ensemble-machine-learning-models <comment><USER2> I think that assigning credit to individual features doesn't work if there is a strong interaction among features. I would like to see a method of constructing a map that describes sets of features with strong interaction, that is new features that are not reducible to individual features in order to explain how the model work.

Edited, (1) is a good first step at shapely value theory and applications:

(1) [https://medium.com/datalab-log/understanding-the-impact- of-f...](https://medium.com/datalab-log/understanding-the-impact-of-features- and-data-through-shapley-values-f235489b0b3e)

</comment><comment><USER3> Not a big fan of the idea of assigning credit to individual features, but as far as this idea goes, this seems like a very useful improvement.

The blog mentions an evaluation on some loan related data, and this data does not appear in the paper. I wonder wether it is available somewhere.

</comment><comment><USER4> Interesting. Has anyone played with this yet?</comment>


Ask HN: What are the most annoying things about tech recruiters? - <AUTHOR> <comment><USER2> 1\. Sometimes they pretend to have an open position "X". They do them to increase their database of candidates, just in case a customer will require that kind of profile in future. You know what's happening because, even if you apply for that position 1 ms after the publication of the ad, they respond that the selections are closed but they have "similar positions";

2\. Sometimes they pretend to be an "official" and "exclusive" recruiter for the company "X" but after a brief search you can find out plenty of ad for the same position around Internet;

3\. Related to the point 1 and 2, sometimes, when you require information about the position or the company, no information is provided because the position doesn't exist, the think you can contact directly the company that's looking for employees or both these reasons;

4\. Sometimes recruiters don't know what are speaking about/ what they are looking . I can testify hundreds of example related to IT.

5\. Sometimes they aggressively try to contact you by phone even when you, after receiving necessary clarifications,you tell them you are not interested to the position;

6\. Sometimes they are spammers, mostly spammers by mail and spammers by Linkedin. About Linkedin, using it as a type of FB surrogate, sending tons of links with private preferences, kitties, funny viedo on Youtube, photos of celebrations in their offices, advertisement ot their customers, etc. Why they don't send the only thing you want from them ? I'm speaking about _job specs_ of course.

</comment><comment><USER3> In my experience tech recruiters do not know anything about IT and seem to have no ability to reason logically. So they call and email about jobs that absolutely don't match my profile nor CV. On the rare occasion that I call them about an advertised job they seem to ask masses of irrelevant questions.

It is extremely rare to come across a tech recruiter who has actually worked in IT and has a minimal level of understanding.

</comment><comment><USER4> When they email you a second and third time about the position that you deliberately ignored because it was such an obvious poor fit that you'd have reported it to LinkedIn as spam if there were a way to do so.</comment>


No CEO is Worth Their Multi Million Dollar Paycheck. Except... - <AUTHOR> http://blog.foundrs.com/2011/08/27/no-ceo-is-worth-their-multi-million-dollar-paycheck-except/

<comment><USER2> CEOs matter because, as Warren Buffett said in one of his annual letters: If a company generates, say, 10% free cash flow per year and that money is retained, after only a few years the CEO will have been responsible for the allocation of the equivalent of the company's entire equity. Doing a good job there makes all the difference in the world, and sadly, good capital allocators are rare.

Most CEOs are probably worth a lot less than what they are paid, but some are definitely worth paying up for.

<comment><USER3> Assuming _the CEO_ is the one responsible for that. I mean, that's just this huge assumption.

<comment><USER2> Even if he hires other people to do it, he is ultimately responsible for it and must know what he's doing, if only to hire the right people and fire them if they don't do the job. It's not something you can truly delegate and wash your hands off from.. Just like how the CEO is ultimately the real 'chief risk officer' in a company and can't delegate managing risk to someone else.

<comment><USER3> I don't find this to be a meaningful reply. Hiring people is important, sure. So is maintaining cash flow. So is accounting. So is marketing. Etc.

But all these things are only important _because_. The CEO is the head of _because_ ; if that's not what he's actually head of, he's doing it wrong.

<comment><USER2> I disagree. The whole point of a business is to use assets to get the best possible returns for shareholders. The CEO is the guy in charge of those assets, and another name for that is capital allocation. It's the top job on the list, basically. It's not just one more thing he does...

<comment><USER3> I've already addressed this: <http://news.ycombinator.com/item?id=2937832>

</comment></comment></comment></comment></comment></comment><comment><USER4> No other CEO? What about John Chambers who took Cisco from $1.6bn in revenue to $40bn?

I'm sure you could easily find a lot of CEOs who are not worth their paychecks, but it probably isn't hard to find the opposite too if you look.

<comment><AUTHOR> Yes, John Chambers looks like a good candidate to be in the exclusive club of "worthy" CEOs. Bill Gates too.

For smaller companies, there are plenty of CEOs who _are_ their company, and deserve all the credit (or the blame).

There just happens to be so many public companies that are run by MBA-type consultants, managing for wall street, with no product or industry vision. And they tend to make as much or more money than the other CEOs.

</comment></comment><comment><USER5> Again I'll tout Alan Mulally. Ford was headed for bankruptcy until he came in and he's done a magnificent job. Ford avoided the bailout and is now actually turning a profit. Without him, the company would be in the same spot (likely worse) as GM right now. I think he makes $10M a year, and he deserves every penny of it.

<comment><USER6> Another vote for Mullaly. The UAW likes to demonise his salary and claim he's overpaid compared to the factory workers. Yes, workers actually make the cars, but workers aren't worth shit if their leader leads them off a cliff (GM and Chrysler). If his job is so easy, then why did the CEOs of GM and Chrysler drive their companies to the ground and force a bankruptcy? Not just one CEO, but a long line of really bad CEOs at GM and Chrysler. Mullaly is worth every penny and more.

<comment><AUTHOR> 100% in agreement, and notice how your argument relies on _comparing_ with his peers. If you had just stated that Mullay is a great CEO, it would just be your opinion. But by using compare and contrast, you build a valid argument.

</comment></comment></comment><comment><USER7> Jobs makes consumer electronics, so it makes sense that his work is the most visible to us. How can we even begin to judge CEOs working in e.g. Agriculture, Pharma, etc etc? Clearly Jobs is a god among CEOs, but without really doing quite a bit of research it seems like poor form to say that no one else is worthwhile.

</comment><comment><USER8> Must say I disagree here. The CEO job is a market just like any other market. If you have ever served on a board of directors you will get a much better appreciation for exactly what the CEO adds or doesn't add to the company's value.

There is a fairly aggressive "CEOs are paid too much" meme about and while it sounds great to someone who can't get a job, if you look at it objectively there are few contraints on the board with respect to CEO hiring. And contrary to what folks might say, CEO compensation is a mix of salary and stock generally so if they suck at the job part of their pay auto-magically evaporates.

alain94040 uses the disparaging "so many public companies that are run by MBA- type consultants" which is false. There may be such types running the company but if they are it is the desire of the board that they be run that way. People who take the job generally try to do it to the best of their ability I've found, but my experience is limited to a few dozen CEOs I've actually gotten to know over my career.

Getting an MBA costs maybe $150K at a top executive-MBA type program, if you could pop one of those off and get a million dollar salary do you really think folks wouldn't be lining up? If all it took was the MBA then the MBA market would show huge inflation as the demand would far outstrip the capacity of the system to provide diplomas.

If you don't understand why someone is compensated the way they are that is one thing, to assert they are not worth their compensation when you don't understand why they are compensated in that way, that's just a lack of understanding showing through.

<comment><USER9> _If you have ever served on a board of directors you will get a much better appreciation for exactly what the CEO adds or doesn't add to the company's value._

I suspect that a CEO has a lot more impact on the board than she has on the company as a whole, and it's this impact that boards are really reacting to when they judge a CEO's performance. Unless someone has been in the position for a very long time, it's difficult to attribute a company's long term success with the actions of its CEO. There are just too many variables. Boards want to hire someone with a resume similar to their own (ie, experienced and wealthy), and by definition that is going to be an expensive pool of prospects regardless of their individual management qualities. In reality there are probably a lot more people with the inherent skills to be successful CEOs than exist in that exclusive pool, but since the boards don't really know what makes a great CEO (or even whether there is any such thing) they fall back on the basic instinct to stick with their own kind. This is one of the contributing factors in the increasing concentration of wealth that we observe in our economy.

</comment></comment><comment><USER10> > Can any one person really be worth 10 million dollars, or 100 million > dollars?

In sports and entertainment it's not even that hard to measure. Put Matt Damon in your movie and a lot of people are going to go see it, just because he's in it.

<comment><USER11> And it doesn't even have to be that you like the actor.

If I see Brendan Frasier in a movie, I think "that dude seems to ONLY pick terrible movies."

Nic Cage: "His appearance in a movie often has nothing to do with it's quality, although as time goes on he's gotten pretty bad at picking movies"

Leonardo DiCaprio: "Never picks a bad movie. Regardless of opinion on his skill, he has never made a Ghost Rider or a Mummy Returns."

I'd say Joseph Gordon-Levitt also seems to have great taste in scripts, as long as you realize how hard it is to turn down being "the GI Joe bad guy alongside Doctor Who."

YMMV

*Directors, sadly, seem to be much less consistent. Besides Nolan.

</comment></comment><comment><USER3> This discussion is dumb. To begin with, CEOs don't all do the same thing. To end with, it's only a certain sort of CEO people don't like.

In reality, the CEO is just the top level person in an organization. The most important task the company faces should be expertise and focus of the CEO. If your company is product driven (Apple), it should be your chief product dude. If your company is advertising driven (Coca-Cola), it should be your chief marketeer. Etc. This ensures (we hope) that the focus of the company is on its most important dimension because the CEO can quash all opposition to its goals (on paper).

The CEOs who are truly reviled are those which are "standard management hacks". These are placeholder CEOs. If your company has one of these, it's in trouble.

</comment><comment><USER11> You can just as easily say "only founding CEOs are worth the money" using this sort of analysis.

It seems to me that the _right_ CEO is worth way more than the 2nd best. It's just that you might do a poor job of determining who is right.

Kind of reminds me of the old joke "what do you call the guy who graduated last from med school? 'Doctor'!"

</comment><comment><USER12> > Here's one wild idea: the CEO stock options should be compared to a > reference index built with its main competitors.

It's not clear that US tax law makes that reasonably possible. If it doesn't, it's a safe bet that the relevant provision was promoted as "protecting investors".</comment>