The first person to hack the iPhone is building a self-driving car
bloomberg.com
bloomberg.com
What he did is impressive. But the results are not that outlandish for a talented person.
1) Hook up a computer to the CAN-Bus network of the car [1] and attach a bunch of sensor peripherals.
2) Drive around for some time and record everything to disk.
3) Implement some of the recent ideas from deep reinforcement learing [2,3]. For training, feed the system with the oberservations from test drives and reward actions that mimick the reactions of actual drivers.
In 2k lines of code he probably does not have a car model that can be used for path planning [4] (with tire slippage, etc.). So his system will make errors in emergency situations. Especially since the neural net has never experienced most emergencies and could not learn the appropriate reactions.
And guess what, emergency situations are the hard part. Driving on a freeway with visible lane markings is easy. German research projects autonomously drove on the Autobahn since the 80s [5]. Neural networks were used for the task since about the same time [6].
[1] http://www.instructables.com/id/Hack-your-vehicle-CAN-BUS-wi...
[2] http://arxiv.org/abs/1509.02971
[3] http://arxiv.org/abs/1504.00702
[4] http://www.rem2030.de/rem2030-wAssets/docs/downloads/07_Konf...
[5] https://en.wikipedia.org/wiki/Eureka_Prometheus_Project
[6] http://repository.cmu.edu/cgi/viewcontent.cgi?article=2874&c...
The parent's checklist misses a bunch of things. For instance "1) Hookup a computer to the CAN-Bus network of the car". That alone is not trivial. It is trivial if you want to read the car's odometer, but good luck doing more than that. For instance, people are still trying to make sense of the reported battery cell voltages in the Nissan Leaf. All interesting features are not documented and require serious reverse-engineering. "Hooking up to the CAN-Bus" can easily become a task for a whole month, full-time. Not to mention that the most useful features for the self-driving part are probably not accessible by the CAN-Bus - people are still trying to unlock the doors of the aforementioned Nissan Leaf. Steering, acceleration and braking are unlikely to be on the CAN-Bus "2) Attach a bunch of peripherals" is also hand wavy, and same goes for the rest of the post.
It would be like dismissing SpaceX accomplishments by saying: "1) Build rocket frame. 2) build engine 3) Program flight software 4) Fill up the tanks with fuel 5) Push a big red button". The devil is in the details.
With that out of the way: if the events happened as described, this guy should be convicted of reckless "driving". Taking a prototype that had only started working a few hours prior to an actual test run in a freeway with other cars is insane. What about some simpler, more useful and less dangerous goal? Such as a lane-departure warning add-on for cars which lack that capability?
The article title is the worst part though. It's not "clever dude created a self-driving car prototype by himself". It is "Dude is taking on Tesla by himself". Which is bullshit.
EDIT: Fix typo.
I'm painfully aware of this. Ten years ago I ran one of the 2005 DARPA Grand Challenge teams. That's about what we produced with less than three full time equivalent people. We didn't have to handle other vehicles, but we did have to handle off-road conditions. Ours didn't make many mistakes, but it was very conservative and kept stopping to rescan its environment with a line-scanning LIDAR on a tilt head.
I'm scared of happy-case automatic driving implementations. Tesla went down that road and had to back up, removing some features. Cruise's PR indicates they were going that way, but they now realize that won't work.
> Not that outlandish for a talented person
What planet are you living on? I don't know what you did today, but I played with some jquery animations. This guy drove around in a self driving car that he built himself. It doesn't solve for edge cases? Neither do 90% of CRUD apps. Holy shit.
Give some credit where credit is due. This is not an ordinary or average outcome.
Nevermind the ridiculous amount of engineering that was required to build all the tools he's using and the order of magnitude more engineering required to make this a safe, mass producible product.
But nah, let's just praise the founder, allow him to get rich while we all do the dirty work.
This article is a hero worship piece about a guy rather than a story about the technology. It's like how you can't find an article about Theranos that isn't actually just a photo-shoot/celebrity worship article about its founder.
In the video, he claims to want to achieve level 3 driving. Let's see how he can do the following under non-perfect conditions:
- switching lanes
- stopping at lights
- turning cornerns
- turning left corners in traffic
Then we can move on to the more difficult situations.
The real issue here is that self-driving cars are probably the wrong place for that to happen in AI. At best, a solo project creates a crappy prototype where there was no product before (again, see Woz/Apple). The expectation for driverless cars is too high – they need to be 100% good, because your life is on the line, not 80% good.
What's the AI project that would blow people away, even if it was a shadow of a working prototype? I think that's the real question.
Finding a safe place to test an autonomous vehicle on a budget is hard, but not impossible. Our initial testing in 2004 was in a large unused Sun parking lot in Fremont.[1] (Sun got carried away with expansion plans, and started building a big facility there. They paved the parking lots and poured the building foundations, then stopped construction.) Later off-road testing was at the Woodside Horse Park. We also looked into testing at the Hollister off-road vehicle park, and discovered we could book a sizable area on a weekday for our exclusive use. We never used that, though. We'd also looked into using the old FMC tank test track in San Jose, but never found a good contact there.
In other words, I don't care if this guy is painted as a genius or script kiddie. He's not relevant in my life, and I will forget about him a week later. However, the lessons about machine learning and engineering that I can find in thsi article is the reasons I subscribe to HN (yes, I don't really know shit about these topics, and don't have enough time to fill gaps with real sources), and this comment is the most informative, just because he tries to cover what the article didn't.
This is just the start, not the end.
General cynicism isn't really adding much to the conversation in my opinion, since almost everyone here probably knows this already, and too much cynicism can put people off starting projects and people starting projects is something we should cherish.
I do think your point about emergency situations is substantive though. Perhaps he is only planning for self-driving while supervised by humans, but his idea for training as described (become an uber driver) would not at all produce the kind of dataset that would assure me that I would be safe. I think a lot of training with advanced drivers in simulators where you can have crazy life threatening situations would be the absolute minimum. I'd be worried that bad habits picked up on the thousands of uber rides would kick in during an emergency rather than the couple of situations that would be feasible to train on in real life.
What's better about an AI powered by neural nets is that you could train an AI to go offroading.
Get enough data and you've got a model for dealing with a given situation. Google's biggest strides with OCR, Voice recognition, Spam filters and other AI tech early on came from its ability to gather a huge corpus of data.
The real challenge is two fold. Gathering data, and feeding the AI with inputs with data actually matters. This is the secret sauce that Hotz refers to in the article as the information he's not willing to disclose. That information will become commoditized in due time (like low-latency optimization for HFT), but it will take plenty of institutional money & experience (Google, Apple, Tesla, Ford, etc) to get it there.
It's fairly easy to train, and verify a system for driving in well-behaved traffic. Unfortunately, the problem space of not-well-behaved traffic is far wider - and is very hard to gather enough data to train a system well.
What you're going to get is self-driving cars which handle 99% of driving just fine - and when they end up in emergencies, find the human 'driver' to have dozed off at the wheel. (All-in-all, their safety record might end up better then the status quo - but that's not a certainty.)
I have no inside knowledge, but I would be very surprised if Google's self-driving cars use neural nets as a significant component right now (which isn't to say there aren't people exploring its use).
I could probably do this using ROS, opencv, and pcl. At least on a level where the car could recognize the road well enough to drive on it, but I imagine both my car and his car are nothing that any sane human would want to sit in. That last 20% focusing on safety and edge cases is going to be 100x the work/innovation/testing/staff/code/talent/smarts here.
As a side note, I am intrigued by the idea of a FOSS self-driving car. Its a little worrysome we'll never see the code Tesla, Mercedes, etc are using.
So yes, it's an amazing project for a single person, but it's not really a self-driving car.
Could you explain on what basis you claim this? Do you have intimate knowledge of his prototype, the amount of work he put in, or the novel ideas that he brought to the project in addition to integrating pre-existing tech?
From what I can understand, your argument seems to be "lets see if I can guess what he did". If you're an authority in this field, then your guess could be very accurate I suppose.
I would be very surprised if you got deep reinforcement learning to perform well on a self-driving task, even on a highway. If you did, well, your faculty position at Stanford is waiting for you.
more states? add neurons! more search space? add layers!
except they have unpredictable resonances, especially multi-layer networks:
http://www.i-programmer.info/news/105-artificial-intelligenc...
they're just starting to understand this, but I believe the myth of the 'do it all dnn' is gonna die. it's time to start thinking about cluster of independent neural network, supervising each an independent aspect of the search space and/or each other.
Small personal example: my family lives out in the suburbs. My dad works in a neighboring city. His commute is about a half hour, 20 minutes of which is a straight shot on a major highway. I'm sure he'd be willing to pay a few hundred to reclaim that 20 minutes each way to read a book/the newspaper, check his email, browse the internet, etc.
It'd also be good for road trips.
I am totally for a computer telling me when I'm driving dangerously because I'm distracted or tired.
Take the case of the Wright Brothers who faced two well funded adversaries. Samuel Pierpont Langley had a chair at Harvard, worked at the Smithsonian and had among others funding him $50K from the US War Department. Alexander Graham Bell, the inventor of the telephone, was an avid aviation enthusiast and an already wealthy man. One of Bell's assistants was Glenn Curtiss who went on to found his own plane company.
Who would bet on two bicycle mechanics from Dayton, Ohio? No one, yet they were the first to fly.
The first popular microcomputer would surely come from IBM or HP yet it didn't. Two guys in a Cupertino garage built it and neither of them was a college graduate.
This guy may fail but I am not going to bet against him. In fact I hope they televise the race between the Comma and the Tesla. I'll bring the popcorn.
I.e. they did much more than simply throw some ideas and parts together and see what stuck, like every other contemporary experimenter.
Learning for the AI does not have to be from real world experiences only. Simulated/controlled emergency situations would help as well! Further even if the 2K lines of code stretches a bit more to deal with unknown situations that isn't so bad either
Here has been dedicated on making maps at a quality level that is needed for automous cars. A few issues with the data that is available currently is that the data isn't very detailed, and you're at the mercy of volunteers (tiger(old), openmaps data), or for a company that's main focus isn't maps. (Google)
What if you could simulate these conditions in a safe/controlled environment, and remove the driver from harm via remote control? Maybe build a virtual world that simulates the inputs as best as possible. That would be the cheap way, although you may lose fidelity.
If you had enough money you could build a simulated town/city, similar to a movie set, that throws all possible dangerous scenarios at you and operate the car remotely through these scenarios.
http://velodynelidar.com/lidar/hdlpressroom/pdf/Articles/The...
I think it's likely that much more of self-driving car development is smoke and mirrors than people realize. Best case scenarios are promoted as examples of how innovative a company is. Great PR, not necessarily a practical result.
This is a sensor limitation. They have fully admitted this several times (heavy rain too). Equating the fact that this guy can't handle any emergency situations with a sensor limitation all of the lidar systems suffer from is stupid.
Google has shown many times that they have logic to handle routes around obstructions, construction, etc as well as cars running red lights, pedestrians walking into the street, etc. At least read up on something before you call it smoke and mirrors.
The biggest thing here IMO is this is self-funded. Any startup trying to do what he is doing in this environment would have raised $50 Million, hired 100's of engineers from top notch schools, become accepted in YC, and have Marc Andreessen, Paul Graham, Sam Altman and all singing their praises.
Kudos to him for being self-funded.
Seems they recently raised $15m http://techcrunch.com/2015/09/18/cruise-2/
Wonder how they compare tech wise to Geohot's thing
I'd prefer my autonomous cars to have gone through insane amounts of testing, regulation, etc. This is just too new of a field, and the amount of edge cases you have to handle is practically infinite.
Investors know that their returns are generated by a handful of super-successful companies. And so they have a natural pressure to "swing for the fences".
Founders have a tremendous amount tied up in THIS company, and are naturally risk-adverse.
So you get conflicts like the following. There is an initiative which has 20% chance of losing everything, but could double how much you make. Investors will always want to go for it. Founders reasonably may not.
A million times this. I never really understood how hard it was to explain a (in my mind) simple new technology to the lay person until I had to do it. This is even after spending years as a technical briefer for high power executives.
As for money, yes, it can accelerate growth in its first-order effect; but it also induces stress and so threatens early exhaustion of your other precious resource: personal motivation.
So, as a crack-shot programmer, if you know with 90% certainty you can crank out a self-driving car in 6 months by yourself or fail, but only 20% certainty you can arrange a cohesive team with someone else's money to crank out a car in 1 month or fail (and alienate your team, and ruin your credit)... I would advise taking the 6 months route. Patience is a virtue and sometimes it's better not buying into every pot of snake-oil the SV hype machine wants to sell us.
It's a nice dream, but the idea of AI and robots doing dishes, picking strawberries, washing cars, cooking meals will never happen.
The best AI cannot beat a population of Mexicans who are basically the glue that holds out modern society together.
If you wanted to see how the U.S. Will completely come to a screeching halt, it would be if the rapture took place and only claimed all Mexicans.
Our entire way of life depends on them. AI will never replace them.
"Jobs" are not an end in themselves, and are decreasingly relevant in the information age.
Like Palmer Luckey of Oculus VR, I hope G Hotz has a similar story to tell at the end of it all.
Yep, he's still in his twenties.
Naivety is a very good thing at times.
I've seen average people achieve incredible things, and not because what they did was incredible... but just because they started work on things that no-one else thought they could complete. Some way into it, when enough progress has been made, people have rushed to give support because "halfway there but badly done" is a hell of a lot better than "not even started yet".
But also now that I am in my 30s, and they are as well, we frequently look back at that time and laugh about being that young. "Man, you were fun to work with, but also what were we thinking"
So I definitely wish Hotz all the luck. If nothing else, the more smart people working on the problem of self driving cars, the better.
My comment mostly stemmed from amusement of his quotes.
Part of this was hubris. The thought of someone I considered less capable than myself accomplishing something I felt I could not damaged my ego. This was humbling.
Part of this was experience. The experience to know that attempting the hard or impossible is sometimes worth the effort, whether you succeed or not. This was educational.
Part of this was ambition. Ambition to do something new, to ignore the naysayers and noways when needed, and forge your own path, which I've always felt short on, but have steadily worked on over time. This is ongoing.
"There's nothing like succeeding at something you weren't even qualified to attempt."
Thanks for sharing.
Thats what I think 'foolish' means in "Stay hungry, stay foolish."
He might not be naive.
In my opinion, we must never underestimate people.
>>but just because they started work on things that no-one else thought they could complete.
Nothing fails like smartness. The reason why a few people achieve the impossible while far more intelligent and smart people don't is because of the curse of intelligence makes them believe certain things are impossible.
The fool didn't know it was impossible, so he did it.
I also realize this kid probably won't end up making a huge dent in the universe.... but.... statistically speaking, there should be several "Leonardo da Vinci"- level humans alive right now. Why not this kid?
Impressive as they are, his chops still don't support his claim to "know everything there is to know". The Dunning-Kruger effect is in full swing.
I kind of took that to be like how Musk talks about needing to know first principles. In the article you can see that he was humble about what he thought he knew, took jobs here and there and eventually confirmed that he was at the cutting edge, that he knew 'everything there is to know' about this special area.
That's when he realized that he was qualified to try this. IMO, anyway ;)
I assume you're talking about using backpropagation with gradient descent. Backpropagation itself isn't all that interesting. The interesting part is that it works for practical problems and doesn't get stuck in shallow local minima.
Someone who has digested enough of the AI literature to think about the methods in aggregate is very likely to be in a position to see any particular method as a "simple" implementation of some more general set of principles.
But the particular quote is referring to learning rates in autonomous robotics, especially visual classification in complex real-world scenes.
I have worked and published in ML since the early 1990s, was a program chair for the learning track at NIPS one year, participated in the same DARPA learning-to-drive program that Yann LeCun did, and don't consider the math behind "state-of-the-art papers" to be simple.
Just taking deep learning: there are a lot of tricks and recipes (e.g., rectified-linear activations, number of layers, staged training) that are not mathematically understood. It's exciting, but mathematically still a jungle. Just because a neophyte can code and optimize a network does not mean that the math that explains why it actually works is simple. As engineers, we need to understand why it works before using it in a safety-critical situation.
Now, to understand WHY the algorithms work, and gives you the result it claims to calculate, is quite hard, but that understanding is not required to implement those algorithms.
If only he knew about the Dunning Kruger effect...
I think the point you are making is generally valid... But he is a savant. I don't think it is wise to apply generalities to him.
Yes, age will change some of his sharper edges, but he is already pretty unusual.
I don't think this has anything to do with saying he knows everything he needs to know in the world.
Anyways, wouldn't you agree that it is better to be empathetic rather than thinking you're an idiot?
Self-driving cars (in some form or the other, under some loose definition of "self" and "driving") have been around since the 20s. But it still remains a vexing problem.
It is quite easy to program a car to stay between 2 cars and follow the car in front. It is quite another to have the same car drive on (a) a road without lane markings; (b) in adverse weather conditions (snow, anybody? Hotz should take the car to Tahoe); (c) in traffic anomalies (ambulance/cop approaching from behind; accident/debris in front; etc. etc.); and so on.
No offense to GeoHot, but I'd love to see his system work in rush-hour 101 traffic; or cross the Bay Bridge, where (coming to SF) the lanes merge arbitrarily.
The key challenges are not only to drive when there's traffic; but to also drive when there's NO traffic, because lane markings, etc. are practically nonexistent in many places.
Having said all that, I still admire his enthusiasm and drive(no pun intended). Tinker on!
Mobileye is doing something interesting by curating the reliable parts of the dataset (e.g. they have curated databases of traffic signs for each region) -- again not something you could do own your own, and seemingly archaic (hence GeoHot's criticism), but if you can afford it can speed up the training significantly.
Tesla is a massive resource here because they already have a huge fleet of internet connected cars proving enough data to fill the aforementioned training set in a matter of days or months: let's estimate their fleet at 40,000 cars -- then they could fill that minimum dataset in less than a day, and in a month they might have a 100x safety margin. Of course, there's a big technical problem of relaying all that video (maybe they just relay prediction failures), but the data is there.
Another fundamental problem with exclusively hands-off training (and little optimal control theory, etc) is picking up bad habits from drivers -- even the best algorithms will have a hard time and be only about as good as a good driver in each scenario, in the best case -- since the training data is acting as a ground truth.
The problem is: there are new edgecases born every day.
Consider, for example, an accident where the cops have set up flares. How often do you come across one of those? Very rarely, I imagine. And even if you did come across it in your training set: how does the ML know that you are following the cops' signals, and not just randomly switching lanes? That the flares are a critical signal?
Ultimately as long as the cars driving autonomously is small enough and procedures change slowly enough you should be able to continously update the driving system.
But let me reinforce that a pure learning approach even with very large datasets may not be efficient as one would like -- the curation of signs is a good idea, and manually reviewing accidents and near misses (a highly human-intensive task) and perhaps flagging bad driving behavior (probably after some outlier screening, which can be good or bad) will be important to get it really good with the training-intensive approach (and not the top down optimal path planning and control approach).
EDIT: Mobileye CEO discusses some interesting design issues and manual validation (and shows they have lots of data, good sign) https://www.youtube.com/watch?v=kp3ik5f3-2c&feature=youtu.be...
It depends on what sensors are in use and how the environment affects them. I can't get into much detail unfortunately, but I have seen radar systems that use naive Bayes classifiers for target detection and classification. Those systems required large numbers of examples across a large, multi-dimensional space to work effectively. Target detection and identification is a trivial task compared to what the control system of an autonomous vehicle needs to handle.
who validates all this data?
attaching a dnn to a driver as a training set is a pipe dream, for now. maybe after we understand how our brain perceives time and build models of future outcomes, we could apply it to build better nn. For now, nn are just best used as classifiers in a controlled environment, not from an environment with unpredictable states.
and especially not in an environment with adversaries http://spectrum.ieee.org/cars-that-think/transportation/self...
Sensor failure or well characterized adversarial inputs are actually really easy to deal with -- they are very easy to simulate with a given dataset and self-validate using traditional techniques -- simply make one or more cameras fail (or receive spurious sigs) and verify the output.
It's a good point that probably all autonomous cars will need a contingency plan (probably human intervention and/or blind emergency stops) with non-zero probability -- even if you have a redundant network of cameras around your vehicle a critical number can and will occasionally fail (when you look at the fleet sizes that will be dealt with).
He starts the video by giving credit to other people involved in the project.
The testing of a hacked-together system on the public road is not. He probably won't kill anyone, but if he were to I suspect he'd get the book thrown at him in the way that everyday death-by-DUI drivers don't.
Actually I'll go futher with this criticism: we've just seen drones being FAA regulated because users were unable to refrain from doing dangerous or nuisance things with them, such as flying near airports. DIY self-driving car research is similarly likely to damage the concept if it goes wrong.
There's a good reason began on closed courses, imagine a kernel panic locking the car on a busy freeway...
The article does a good job of explaining the inherent danger someone (and those around him) faces when attempting something like this. The point is that he's attempting to push the boundaries and not asking for permission -- generally the conditions required to innovate. Maybe that should be celebrated before you bust out the "b-b-but it's dangerous!"
His name was JB Straubel, and nowadays he's Tesla's CTO.
Best of luck to Hotz!
Geohotz makes a decent point. The way the industrial revolution reduced manual labour, and made thinkers and tinkerers much more valuable, the advent of AI (true AI, mind you, not the tiny stuff that we currently assume) might actually make us obsolete. It is a peaceful and yet terrifying thought.
Didn't the opposite happen? Suddenly artisans were out of business as nobody wanted beautiful hand-made luggage, or whatever, that would last decades, but instead we'd get some engineers and maybe consults an old-school luggage guy, get some specs, and toss that data into cheap mass production and be done with it. Sure they're uglier and last only a few years, but they're cheap and plentiful. Now those artisans had to close their shops and either go work in the factory or retrain for something else.
The I.R. was great for making things fast and cheap, but it was pretty much a full frontal attack on "maker" culture that has only rebounded in certain areas fairly recently, namely computers, because you can own the "means of production" trivially, namely a computer with a compiler.
I see AI going the same way. It'll be shoddy, cost jobs, and work "just good enough." The question is is "just good enough" appropriate for 5,000lbs death machines speeding at 75mph? Think of all very serious development protocols and standards aerospace and NASA use. And that's for machines that will never have to worry about poorly painted/paved roads, kids running into the street, snow covering everything, etc. I think the AI-guided future will be far off and hit a lot of practical limits quickly and the bullish attitude behind AI is more than a little unjustifiable.
There weren't a lot of people employed making movies or TV shows before the industrial revolution; now those industries provide lucrative employment to millions. Maker culture shifted with the technology.
That said, I agree with you that AI is being pretty dramatically oversold (again).
1) Historically technology has created more jobs than it has destroyed. http://www.theguardian.com/business/2015/aug/17/technology-c... One BIG difference, technology till now has been designed to aid humans, not replace them. But I think that technology will shift a lot of jobs in another sector but will not bring the sum of human brains needed to run it to 0.
2) Society will not be so nice to organisations taking away ALL its jobs. There will be a huge friction from society when they loose out ALL their jobs at this scale, it could vary from small riots to some new age guerrilla warfare. It will be interesting to see how politicians will react when they loose their jobs to computers though.
Entertainment, health care, and communications are industries that have exploded, in terms of economic value and employment, since the industrial revolution. One thing these industries have in common is that the value they provide is dependent on humans. People want to talk to people, see stories about people, and be healthy themselves. Technology enables these industries but technology does not replace the heart of the value.
Maybe someday if AI evolves into artifical persons, then AI could take part in conversations, be entertained, and wish to prevent it's own death. But that is probably a long way off, and might never come. It's not like people are working on artificial personalities now. No one is working on car AI that will tell its owner to fuck off, it's watching Mad Men and doesn't feel like driving.
The industrial approach to AI is to build better tools. Not to build new independent beings. Maybe it's possible that a tool can become a being, but we have no evidence at all that such a thing can happen.
So the future of employment will probably not be unemployment, it will be different employment. Truck driver jobs will get replaced by truck-driving AI, but the children of truck drivers won't miss it. They'll grow up in that world, and work in some other new industry, like virtual world designer or personal medical consultant or artist or athlete.
We will become the children of our new AI parents. We are building our future caretakers.
>“Dude,” he says, “the first time it worked was this morning.”
I can't tell if this is a joke or unbridled hubris. Either way, self driving cars seem like a new hacker space.
I'm sure he has a good handle on the tech running the car, he's at least looked at licensing it to test on the freeway (learning from the backlash on the Jeep Hack), and he had tested the car extensively before inviting a reporter with children to test it at 70mph on the freeway.
If you include these details and less of the sassy dialogue and flourishes of panache [1], you get a less interesting article.
Bloomberg goes for sexy. Take their reporting with a grain of salt. It's more art than facts-based.
[1] At one point, the virtual-reality company Oculus Rift failed to man its booth at a job fair, and Hotz took it over, posing as a recruiter and collecting résumés from his fellow students. None of this was enough to keep him interested. “I did two semesters [at CMU] and got a 4.0 in their hardest classes,” he says. “I met master’s students who were miserable and grinding away so that they might one day earn a bit more at Google. I was shocked at what I saw and what colleges have become. The smartest people I knew were in high school, and I was so let down by the people in college.”
I think it is more about the reporter's writing style. The reporter -- Ashlee Vance -- is also the author of a recent book on Elon Musk.
Good luck to him understanding how to fix corner cases: he's built a black box.
> “I appreciate the offer,” Hotz replied, “but like I’ve said, I’m not looking for a job. I’ll ping you when I crush Mobileye.”
> Musk simply answered, “OK.”
I have to agree with Elon here, Hotz is such a good fit there. But Hotz knows best, if he thinks he can take down Mobileye then he did the right decision, sucks that Tesla wouldn't back it. I'm sure other car companies would buy Hotz's software
(*cue the preemptive disclaimer about MBTI being for entertainment)
Also pretty cool he's working in his garage :P.
He seems like a good person to get into business with. He's so non-judgmental. Reminds me of myself and all the stupid things I said to VCs in my 20s.
It is my belief that this self-driving car project is really a kind of first for Hotz. He's famous for finding and exploiting the error-modes in what other people built; now he's actually building something. Methinks he's about to learn a lot about himself and the nature of the world. He's clearly extremely smart, so I'm actually rather excited for him!
The former is a result of experience and social intelligence; the latter is mix of bad attitude and superiority complex.
What to communicate? I'm not sure, to be honest. Road conditions or notifications of the position of obstacles is one obvious thing. Advertising the current version of the software and pushing signed OS upgrade binaries is another. Voice/Video chat with other vehicles in range would be cool, as is media syncing and discovery.
Building in some kind of Bitcoin based payment protocol would be fun too. You could load your cars Bitcoin wallet with some funds and tip cars around you all over the LiFi.
I'm not saying you need to build all that stuff, just put in a good hackable messaging protocol into the system before wide release :-)
Great work man. Good to see people with a good hacker ethos accomplish really cool things.
I'm not sure those two are equally horrible though - fixing complex bugs requires using lot of skills and the high you get when you finally nail it is nothing to miss.
Getting people to click on ads though - that's genuinely depressing.
Usually before you are allowed to use something like this on a public road your stuff has to be tested and approved by the state. At least this is how it is in Europe, does this not matter in the states?
[1] https://en.wikipedia.org/wiki/Autonomous_car#Legislation
And I think you missed the part where he wants to drive for Uber, so he's also endangering the passengers in his car. (!)
>Sitting cross-legged on a dirty, formerly cream-colored couch in his garage, Hotz philosophizes about AI and the advancement of humanity. “Slavery did not end because everyone became moral,” he says. “The reason slavery ended is because we had an industrial revolution that made man’s muscles obsolete. For the last 150 years, the economy has been based on man’s mind. Capitalism, it turns out, works better when people are chasing a carrot rather than being hit with a stick. We’re on the brink of another industrial revolution now. The entire Internet at the moment has about 10 brains’ worth of computing power, but that won’t always be the case.
The other side of the coin of his "technology outgrew slavery, and thus we got rid of it", is that if tomorrow's technology demands some moral monstrosity, to "work better", we can't but bend over and accept it.
Neither human struggles (from the civil war to MLK and Rosa Parks), nor desires and leadership come into any of this, neither is "working better" (towards what? for what purpose? etc) defined.
I think that's basically wrong also. It fits better with the advent of the printing press than the steam engine.
When did he do that?
George Hotz working his magic on the computer is the most fucking legit thing I have seen in my life.
Quality.
He thinks machines will take care of much of the work tied to producing food and other necessities. Humans will then be free to plug into their computers and get lost in virtual reality.
Well, that's an astronomically depressing future.Are you in for living in the woods and preying for food when humanity becomes a society of degenerate VR junkies?
[1] http://www.acura.com/Features.aspx?model=MDX&modelYear=2016&...
Like the article said it sure beats writing code to make people click ads or fixing some obscure deadbeat bug in some useless software which nobody uses.
This has echoes of J.R.R. Tolkien:
Anyway all this stuff is mainly concerned with Fall, Mortality, and the Machine. By the last I intend all use of external plans or devices (apparatus) instead of development of the inherent inner powers or talents -- or even the use of these talents with the corrupted motive of dominating: bulldozing the real world, or coercing other wills. The Machine is our more obvious modern form though more closely related to Magic than is usually recognised. . . . The Enemy in successive forms is always 'naturally' concerned with sheer Domination, and so the Lord of magic and machines.
Also, a book with the same name
That stunt is also what lead to a coordinated attack against PSN that took the service down for more than a month.
If we could move the liability to the car itself, then maybe we could just add the car to its own insurance policy, you know, as if it were a dependent, like a teenage driver.
Clearly, you can't meaningfully punish computers. Hah, or banks, for that matter.
I'd not be surprised to see some interest and support from nvidia on this (if not, then they should REALLY look into it).
[0] http://www.bloomberg.com/features/2015-george-hotz-self-driv...
Except the law when it comes to exceptions for being in control of your vehicle at all times. Somebody take this guys license before he kills someone due to a divide-by-zero. Testing this in an abandoned parking lot would be ok with me (probably still against the law but fine). In traffic is a definite no.
Really? I did not expected this from him. Why don't he put his sensors\cameras\kit on few other hundred\thousand cars and pay them some money or get some early adopters.
Why am I seeing Ubuntu on Screens of developers, experts, et cetera in Cover Stories such as these, most of the time with the 100% plain Ubuntu Desktop with all the craziness that comes with it? It feels like this is the case 90% of the time. Two more (recent) examples I can remember:
1) Fyodor (Guy behind nmap) running plain Ubuntu on a Notebook while giving a speech at a conference
2) Developers at Honda (Video was an Asimo promotional video) running plain Ubuntu
Since in my personal opinion Ubuntu is not the technically superior choice in these cases (though that can be debated), it can not simply be explained with it being backed by a company, there being support you can buy for the system if you need it.
What motivates technically extremely skilled people to use "Plain Ubuntu" instead of one of the many alternatives?
I really don't understand, please enlighten me!
(I actually think it's worth "spending" some Karma on this if I for once get a satisfying answer)
When you spend all your time at a prompt you don't really care about unity vs cinnamon vs anything else.
Debian is stable and has fantastic package management, and Ubuntu builds on that with a vibrant community and wide support. For these users, Ubuntu 'just works' and lets them get on with what they want to do. It's about as simple as that.
If I am honest, I spend 90% of my time in a terminal, browser or editor (which is also in a terminal), so give me a system that allows me to easily install my most beloved programs via command line and handy binary packages, a system that is reasonably stable, runs on most hardware, and that has a big community and a lot of documentation, sounds like a good deal actually.
I imagine there will still have to be some hard rules in case the AI encounters edge cases.
Autopilot is understood to mean a limited computer assist that can help with routine tasks, but not expected to allow a plane to fly without a pilot.