jayjay71

You're missing the point. Your argument is it's impossible for someone to make great strides without a traditional education, and I already provided you with two specific counter-examples. Unless I'm lying about the personal anecdote and the Wright brothers didn't invent the airplane, then your argument doesn't hold water. If you want to argue that most people benefit from having traditional backgrounds, then what you're saying makes sense. But I've already disproved your original statement and you've done nothing to address my evidence.

If you ever become a manager, please don't disregard what people say just because they don't have the same background as you. I've worked with people like that in the past, and they are terrible managers/coworkers. They are needlessly pedantic and often feel the need to do things the way they learned in school (or read about some complicated technique in a paper they have to try), when there is obviously a simpler and better solution. The sad thing is, they think they're better than everyone else and that nobody appreciates their "superior" point of view - if you feel that way, you should reconsider how you approach problem solving.

I haven't met you so I can't comment on you specifically, just speaking as to my general experience. Whatever the case, I wish you luck in your career.

You seem to have a lot of experience and a way of doing things which works for you, and that's fine. Your experience seems to have made you conservative, for better and for worse. Of course people with traditional experience will have learned a lot, and that's useful. Even the people that are looking to disrupt the status quo can benefit from this as it helps them to understand how things are already done so they don't reinvent the wheel. But it's not a prerequisite. The biggest disruptions often come from out of left field. The Wright brothers worked on bicycles before inventing the airplane, and they had a lot of better funded, better educated competition. Is that an outlier? Absolutely. But outliers are the people that make huge strides suddenly, and you can't discount them just because they don't have a traditional background. There are many examples of people making enormous contributions to fields outside of their own, engineering not excluded.

It only takes a single person to come out of nowhere and have a truly novel idea because his/her field of view wasn't constrained by previous experience. You really think there's nobody alive on the planet that fits that description for mechanical/electrical/aerospace/robotics engineering? There was a problem my engineers were struggling with at my company for months. The guy who finally figured it out was a chef, and he concocted his own mechatronic solution which he then machined himself. He has no engineering education, he just had a unique idea that all the engineers overlooked.

The notion that a piece of paper makes you elite is archaic.

I'm not sure why you hold such a high regard for degrees. I also got my EE degree from UIUC, and while I think it is/was a great university most of what I learned was self-taught. I'm also a PhD dropout, worked at prestigious companies, yatta yatta ya. Then I started a robotics company, and I've looked at thousands of resumes and interviewed at least a hundred people. One thing I've learned is that degrees, grades, etc are completely worthless. They tell you absolutely nothing about whether or not someone can actually build something. The only thing I care about is what people have built, and I don't care if it was at a company or in their spare time. It's much more impressive if they did it by themselves though as opposed to being supported by all of the resources of a giant company, as being resourceful is a great trait for a talented engineer (and especially important at a startup).

Long story short - it only matters what people have built. It doesn't matter what their pedigree is.

Yep. Supposedly he came from a tiny town where people only hard first names, and when he moved to the US he just became Herman Herman. When I was interviewing there, the recruiter referred to him as Herman squared.

While technically correct, calling NREC a CMU robotics lab is really a stretch. They are surprisingly independent entities - I know this as a former student of Carnegie Mellon and employee of NREC. Most people at CMU, including some of the robotics students, had never heard of NREC (although I imagine that's no longer true after Uber).

Anyway, onto the subject at hand. Yep, Herman is right. Everybody thinks it's just around the corner because they see things move so fast in Silicon Valley. But most of the companies that grow exponentially are based on a sound business and relatively (compared to robotics, nuclear fusion, new silicon chip manufacturing) simple technologies. I love robots and I think they will have a profound impact on the world moving forward, but holy fuck it is hard to make a good robot. Self-driving cars are a long way from happening. I think the gains are so great, and the technology so difficult, that they will augment the problem by changing public infrastructure. Making smart traffic lights that can talk to cars wirelessly, and cars that can communicate with each other, and simple road markers specifically for autonomous vehicles, will drastically reduce the complexity of the problem and allow for self-driving cars to happen far sooner (and safer).

"A robot is a device that doesn't work (yet); as soon as it works, it is renamed"

I love this definition.

TechShop is great for hobbyists with some money to spare (there are a lot of hidden costs, both time and money, that they don't tell you about), but terrible for professionals. Their machines are regularly not working for days at a time, and I've had experience with multiple locations so this is indicative of the company as a whole.

If you want to manufacture with plastic, I'd be surprised if there was any reason to do it outside of China. If you're working with metal, it's a much trickier question.

Along with EMachineShop, there's also Plethora. I've never used Plethora and it seems incredibly limited, but they've been around a few years and have a ton of people on staff (which can't be cheap) so they must be getting decent business.

At a very high level, instead of comparing everything against a map, we try to figure out what it is. For example, the way Google detects traffic lights is using geometry to infer where a traffic light will be. Their maps are so good that they know where the car is, and where the traffic light is, and just use math to figure out where in the image there should be a traffic light. Then they just look at the pixels and figure out which color is lit - red, yellow, green. The way we did it is constantly scan for traffic lights (you can do lots of sanity checks, for instance using GPS are we near an intersection?), which means figuring out whether or not a traffic light is in the image, and then once we detect one figure out what color is the light.

Google's approach is simpler but is highly dependent on the maps. The other approach is easier to scale, but is obviously harder and likely takes more work to get the same results. Something I think a lot of people gloss over is that these maps take up huge amounts of data - not something you can stream over a cell network and probably not even high speed WiFi (maybe you'd download a map for your trip the night before). And also, as I understand it, the maps require regular maintenance.

The real question is how will this scale? And honestly I don't know. My theory is the tech is so hard and the rewards are so great that infrastructure will change to make it easier. By installing smart traffic lights, having cars talk to each other, and adding various road marks specifically for self-driving cars you can drastically reduce the complexity of the problem.

Agreed 100%. I'd be amazed if a stage 4 autonomous vehicle existed in less than 5 years, honestly I'd guess closer to 10. I do think highway driving can be fully automated within 2-3 years, so Otto makes a ton of sense.

I actually think because the rewards are so great, and the technology is so difficult, that infrastructure will change before the tech. There are things you can do to drastically reduce the complexity, like install smart traffic lights and have cars talk to each other, in addition to special road markings designed for self-driving cars.

CMU has been working on self-driving cars since 1984, people seem to think nobody was doing anything before Google (and they also think that Google's cars are far more capable than they are). Yes, money makes a difference, but sometimes reality just disagrees with profit. And if there's something startups are good at, it's hyping themselves up to jack up valuations.

I used to work on this - the approach is very different. That's a pretty hollow statement on the internet though, so take it as you will.

An interesting aside - it seems like most of Google's team left this year. Chris Urmson left, Anthony Levandowski left, and many of their engineers are gone as well (many went to Otto). It'll be interesting to see what happens now that they've lost all of the original leadership (Sebastian Thrun left long ago).

The maps Google uses for their self-driving cars, as I understand it, require constant maintenance. Also just the amount of data is huge. If this weren't the case, surely they would have driven in many other places by now?

I would actually love for someone who works at Google to chime in here. It'd be great to find out their system is more robust than I think it is.

How do you know that? There hasn't been a competition since 2007, and that was before Google was involved.

I think it actually doesn't make that much difference. The problem, I believe, is that people are used to rapid advances in software. Lots of small iterations can take you a long way, but sometimes you just reach a hurdle which requires a breakthrough. A fully self-driving car would need a huge breakthrough in perception (which most people categorize under AI) in order to work.

Computer vision actually started out in the 60's because how hard could it be to classify objects in images? Well, decades later and it's still a hugely unsolved problem.

Robotics can be broken into 3 main categories: perception, planning, and controls. Perception is always the weak point. Robots just cannot see and understand their environments like people can. When you put a robot in a dynamic environment, they fail spectacularly. To give some idea of this, look at videos of robots falling down at the DARPA Robotics Challenge - and keep in mind those were made by teams of top roboticists with millions of dollars in a highly constrained environment (it's hard to say that obstacle course was "real").

I really, really doubt it's going to happen in a few years. I would love to be wrong though.

Everybody wants to jump straight to 4, because 3 is actually dangerous and likely to cause deaths as people are lulled into a false sense of safety. The question is how long will it take? Some people say 2 years, some people say 20.

This is the way Google does it as well, and in my opinion is a fundamental architectural failure. There's a reason after several years Google still has tested in so few locations.

Delphi has a more robust system (in theory), but the accuracy isn't quite as good. To the best of my knowledge Ford is also taking a different approach which doesn't depend on super-detailed maps to operate. The technology to compare the surroundings against a map is far easier than figuring out the surroundings and then navigating within that (not to trivialize this - both are extraordinarily difficult and nobody has made a fully self-driving car yet).

But hey, like they always say in YC - do things that don't scale. Uber is clearly pushing ahead faster than anyone else, and they have a hell of a team behind this project.

Regarding bridges - to oversimplify the simple answer is they're far noisier for the sensors (all of them, including cameras) and it just wreaks havoc on the sensor fusion system.

I'd love to sit in one of those cars and see how well the cars can drive themselves, especially during the winter. It's just a really awesome experience being in a self-driving car - it'd be amazing if you could just hail one with an app.

The acquisition of Otto is interesting. I think Anthony Levandowski is incredible and the trucking industry is perfect for automation given the state of the technology and for logistical reasons. But it seems odd that Uber wants to branch out at this point.

While I don't disagree with you, I think the real credit goes to Dr. Tony Tether who was the director of DARPA for the duration of the self-driving car competitions. He set aside money for the races and helped design them. After the last race in 2007 Google acquired most of their talent from various teams.

The tricky part is understanding consumer behavior. McDonald's has been rolling out touchscreens in San Francisco where they are still operating, but they also tried this in Colorado and in under a month they removed most because they were losing customers. Operational efficiencies are always wonderful on the backend, but when you put them in front of your customers it's important to understand their reactions as they may have negative reactions. There are lots of people who are not happy about robots taking jobs, and if they can see these robots it will influence their loyalty to that store.

The inventory-checking robot is an interesting pivot for Bossa Nova, a company that is 11 years old and originally focused on robots that could balance on a single ball (it spun out of Carnegie Mellon where the original Ballbot was created). They struggled to monetize it and made toys, which they then sold off and tried to figure out what to do. They ultimately moved away from their special technology entirely to work on inventory. It seems like a huge market, although I wonder how well it works since its vision is largely limited to the shelf item at the front and it likely cannot tell how many there are most of the time. A much simpler solution I've seen at many stores is to have a simple electronic system for each item and it keeps count of how many there are, I imagine each of these costs around a dollar (although you need hundreds or thousands). It's also simple and reliable, of course the robot scales easier for mega-stores assuming it can handle larger inventories easily.

I think Mashgin has a really fascinating idea which is to automate the checkout counter at grocery stores, although I know nothing about how their algorithms work.

I actually made a mistake. Stanley was the winning entry in the 2005 DARPA Grand Challenge. Junior was the 2nd place winner in the 2007 Urban Challenge.

Don't forget GM also funded the winning team of the DARPA Urban Challenge in 2007, and then let the majority of talent go to Google. Stanford's entry Stanley came in 2nd and is at the Smithsonian National Air and Space Museum. CMU's entry Boss came in 1st and was left in a garage/workspace and (partially) stripped for parts.

I am still amazed that GM managed to lose that IP to Delphi. It was amazing tech (I worked on it).

Sure, the cost will come down. But comparing the cheaper $7k sensor to the original $90k sensor is disingenuous, as the original is still several times better than the cheaper one in terms of FOV.

I was referring to IBEO sensors which are already used in automotive manufacturing. When these cars are actually commercialized, the sensor suite is still going to cost tens of thousands of dollars.

Anyway, I'm sick of coming across as so negative. I think self-driving cars are awesome (it's pretty surreal when you actually sit in one driving around a city) and I look forward to them becoming more common. I just think it will take longer, and cost more, than most people like to admit.

The real issue with Tesla is that they marketed it as a self-driving feature when really they had developed cruise control plus. Lots of manufacturers have had cruise control and lane following with automatic braking for some time, but they didn't hype it the same way Tesla did. I don't think Tesla built a bad system, they just made it out to be more than it was, and given the circumstances I agree it was arguably reckless (it's hard to say how much they directly marketed it and how much was just hype from the public in general, as they obviously can't control users uploading videos of hands free driving on YouTube).

I do think the way they've handled the crashes lately though has been pretty poor - especially spinning bad statistics to justify what happened rather than admitting fault.

Agreed, but that's because they're actually selling cars. Most people don't realize a single sensor can cost $20,000 - now imagine you have 10 of them. That's an order of magnitude more expensive than the car.

I'm not trying to play devil's advocate, just pointing out that it's a fiendishly difficult problem. I like what Tesla did, I just dislike how they handled the PR.

Likewise I like what Volvo is doing, and I'm curious how they're going to price it.

I agree that Volvo has a better approach and from what I can tell the necessary hardware to have a much more robust system. What I'm arguing is that there are too many problems that still need to be solved for them to have a stage 4 autonomous vehicle.

I used to work on self-driving cars, specifically this one - https://www.washingtonpost.com/local/trafficandcommuting/dri... - and while that was a few years ago I know just how many things the technology is fundamentally not ready for, such as adverse weather.

I agree that Tesla took the wrong approach, I was just commenting that I would be amazed if Volvo was that many years ahead of everybody else.

I love the work that Volvo is doing, but I am highly skeptical they will release a stage 4 autonomous vehicle in 1 year's time. You might be buying into their PR the same way everybody else bought into Tesla's PR.

They don't in Chicago.

I notice you comment on self-driving cars a lot and seem knowledgeable on the subject. Is this just something you follow closely or do you have some background in this area? I ask solely out of curiosity.