"In fact, the only machine learning program in the world, according to Thrun, is at Carnegie Mellon, which still isn’t churning out talent fast enough to meet the industry’s demand"
While there is a separate department for ML at CMU, many schools have machine learning and/or robotics departments. Suggesting it's the only place to become educated in this stuff is simply false.
It's probably not the only place but they may be way ahead of the others. I recollect attend a robotics workshop in 1998 where Dr. Takeo Kanade (http://www.ri.cmu.edu/person.html?person_id=136) from CMU gave a presentation of their work on self driving cars and recollect being amazed by their work and their demo video of US coast to coast driving. They have definitely been working on this for a long time and hence may be way ahead of the others.
I graduated from CMU with a CS degree and a specialization in robotics but never seemed to be able to catch the eye of any companies. My female classmates did great, though.
The two statements here may be factually true. 1) You had a hard time. 2) Your female classmates, all else being equal, did better. But there's something unsettling about the implication. Namely, by putting these two assertions together, you suggest that they have something to do with each other, which, given the appallingly low representation of women in all areas of computer science, is obviously not true.
All things weren't equal. The women weren't as qualified so we had to help them catch up. Not sure what kind of help we were supposed to expect from them though. Plenty of white knights to knock us down, though. I haven't worked in any areas of computer science with appalling low representations of women, sorry. Where have you been seeing it?
Every tech company wants equal gender ratios and female CMU grads are usually top tier. Not that the guys are bad either but when you churn out a couple hundred per year it's not as special.
One of my friends got a job I interviewed for without ever interviewing because I told her she knew the interviewer. They were surrounded by people willing to help them find a job and for some reason aren't able to return the favor.
Due to the current climate of race-diversity and gender-equality, there is an incredibly high demand for female employees in fields and jobs that females generally don't go into.
This creates a demand for ANY-level talent of that variety - no matter the qualifications or abilities (companies will even start a bidding war over the person).
There are even examples of companies openly bragging about not hiring new employees (or at least making it very difficult to do so) that are male and/or white (e.g., GitHub); or not using suppliers that are male and/or white (e.g., Sam's Club CEO).
My university tells me to just keep looking for a job. They don't offer any other advice. Of course I know I'm supposed to be looking for a job. All I get is suicidal. I thought there would be some sort of help... somewhere. I got a computer science degree from Carnegie Mellon University and it was completely worthless and everyone just laughs at me. I worked very hard for years of my life and have absolutely nothing to show for it. All I get is hate and ridicule. Why? Because I want a job? Why is that such a contemptible thing?
Short version of the article: A few startups in the self-driving space were recently acquired [by Uber,GM,Google,...] for $10M per head.
It's common to see successful tech startups selling for $1M per head. The extremes go for a lot more money. Nothing new. Way too much clickbait in the title.
It's simple economics. Once the problem is solved, or at a certain threshold, the value for other acquisitions begins to plummet. The others begin to realize far less diminishing returns relative to their investment.
Edit: This also brings the other aspect of Western educational system in regards to monopolization resource, where they strictly control the education of AI engrs to control the price per head and market.
The other problem is also the US social environment, social conflicts and 'wars', where it is far from conducive to support the level of education necessary for highly skilled STEM grads at the cutting edge. The environment itself is conducive to a restricted output of qualified engineers, researchers, scientists, etc needed for the pace and expansion. Mind you, a significant amount of these grads are competing for a negligible amount of resource to succeed,and this is the system and environment the US prefers to culture their grads.
It is cheaper in the short term just to steal the skill and discoveries from other countries with promises of wealth, or other questionable means.
10m per head is far, far cheaper, even if CM is the only output 'available immediately'.
Hi all! I work at Udacity on the Nanodegree program mentioned in this article. Feel free to ask any questions and I'll do my best to answer them. You can also email me at dhruv@udacity.com or join our slack channel for interested applicants at nd013.udacity.com
So when can I finally pay you and take the nanodegree? ;-) Why do you limit the number of seats for the program?
When Sebastian sent me the new nanodegree heads up I was mildly excited and now as you filled in the actual content I can't wait. With MIT's Underactuated Robotics at edX (which was fun) this is probably the most exciting set of courses I can see on the Internet these days!
Great questions. We limit the first batch for two reasons:
1. This is the first batch of the program ever and we want to make sure it's great before opening it up. We want to use this batch to learn and improve and then focus on larger class sizes.
2. One unique thing about Udacity is we provide real human services throughout the program such as a mentor, a code reviewer, and a career support rep. Many of these individuals are actually current or former students of our program. Since this program has never been done before, we are limited in how many such people we can find. Once we get more students in the program, that pool will naturally expand.
It's a difficult, 9 month program where we cover the following (some of which you mentioned):
- Computer Vision and OpenCV
- Deep Learning
- Sensor Fusion (Radar, Lidar)
- State Tracking with Filters (Kalman, Particle) and Localization
- Controllers
- Vehicle Dynamics
We have come up with this curriculum after talking to the heads of Engineering at Mercedes Benz, Otto, and NVidia at length. In addition, we have an open ended section where students can dive deeper into an area of their choosing.
Like you mentioned, a lot of this is Math heavy, which is why we have applications to enroll. Hope that answered your question!
Cool, here in the Netherlands I have all the time trouble finding master students that have a thorough machine learning background (https://dobots.nl/hall-of-fame/). There is quite a need for this, also in general autonomous robotics! And there is lower hanging fruit than self-driving cars...
(1) If you need some help, you can find me here. I'm currently playing with MCMCs in nonparametric Bayesian methods that adapt to the structure in the real world. It is a waste to sample everything. For examples aisles in a supermarket have structure to them. In the "visual grammar" of the supermarket, they are aligned with each other. MCMC that can encapsulate this type of grammar will mix much faster.
(2) If you find a pupil interested in the combination of transfer learning and deep learning, feel free to refer to me. I'm not interested from the viewpoint of domain adaptation, but from the viewpoint of robotic communication.
Yes, I found out that the topics are typically hard enough that around 9-10 months are spent on them, typical graduation time in Europe. Of course, it also gives me the opportunity to see how someone works, rather than relying on a short application process. :-)
Does Udacity offer programs for those lacking _(partially or entirely)_ in the math(s) required for these types of courses? Ie, to bring them up to speed?
I'm not sure if Udacity is even interested in teaching these types of courses, fwiw... though, i know Khan has lots of math courses.
TO be fair, I think the type of folks that these company needs aren't necessarily the ones doing original research / math heavy work. They need people who can translate what the PHDs do into practical solutions.
I'm trying to get into the program, working my way through your pre-req courses. I do a lot of self studying on the side, but don't have anything to show for it on Udacity. I'm shooting for 33% completion on the 3 Ai-related courses before the application date. Do you think this looks better, or would you rather see I go for a 100% completion on one course?
If I didn't complete related courses on Udacity but relevant advanced courses on edX/Coursera, like MIT's Underactuated Robotics, would that be OK as well?
What about folks who have taken no online courses, but instead have MS/PhD degrees with a specialization in related areas (Machine Learning) and want to use this course to get into the self driving space?
Also, what will the workload be like? Can this be done on evenings/weekends while holding a full time job?
I went to MIT for my masters and undergrad in CS and took many of these types of classes. However, I've found that these courses were generally more theoretical and not applied enough to be immediately relevant to industry. And this makes sense to me because my Professors were researchers, not applied Self-Driving Car engineers. Some things I was lacking include:
- practical knowledge of libraries (such as OpenCV), and best practices for implementing a robust Computer Vision system.
- awareness of vehicle dynamics and the engineering behind cars.
- sensor fusion and the engineering behind collecting and processing the data a car needs.
- practical knowledge on how to implement a controller and all the required software on a car.
The general point is that these courses give the theoretical background you need but you still need the real practical skills that come with actually implementing these ideas on a real car. I think that's what makes the folks at CMU's robotics lab and Otto so valuable.
Full disclosure, I work at Udacity on the Self-Driving Car Nanodegree program and my knowledge around what skills are needed to be a self-driving car engineer come from talking to Sebastian and the heads of engineering at Otto, Mercedes Benz, and NVidia.
CS courses provide background and some training, but one must still put in the 10,000 hours of practice to really become "skilled" at something. Applied is best learnt applying!
$10M for a truly skilled self driving car engineer doesn't seem that weird in context, since training (well, more like nurturing) one up takes much more time than money.
It depends the discipline, I guess.. I turned to MIT courses from time to time when the ones (EE, Control Systems) at my University were too theoretical and I needed something more accessible (I wasn't the brightest).
Sure, we'd design RST controllers, on pages of paper, but I wanted to actually apply that knowledge.
I remember discovering OCW and the first image they showed was magnetic levitation and they had a lab where they had fun. In our lab, we'd crunch the numbers with pen and paper, then see how good our kung-fu was looking at how the system behaved on MATLAB.
Damn you, people with gear in their labs touching things and having fun!
Fun fact: our programming exams were with pen and paper where you'd write programs (x86-PIC ASM, C, Pascal) (you'd better debug it on another sheet before you turned it in).
>Doesn't every CS program everywhere have courses like that? My brother is finishing up a CS at an Ivy
LOLno, not unless you think all school are Ivies. I go to NC State, all of our CV classes (believe me, I've tried to get into some..) are limited to our ECE department, and even then they are on the graduate level. We do have some AI and data mining classes available to undergrads but none that would be sufficient for the sorts of techniques modern autonomous vehicles use. No robotics/mechatronics available for CS as well.
It's honestly kind of disheartening if you're interested in these sorts of things.
Maybe it is now but 10 years ago I don't think it was. My guess it it is very hard to find people with experience and probably easier to find new grads.
There are a lot of recent grads out there with CV and machine learning experience. Maybe not 5000 but there are more than the article makes it sound like.
When we had a CV internship listed we got multiple applications per day with minimal promotion. A reasonable percentage of them were decent (experience, working towards post grad, etc). CV is cool shit so tons of people want to do it.
Oddly, this sounds about right considering the weight you would have to bear forever if you ever hear of some fatal incident involving some technology you built.
No question that thought is comforting, but that is the example of the benefit which never appears before your eyes (i.e. it is not tangible, unlike the converse). I am not saying people should stop working on autonomous cars, it was just a comment on how fraught with moral weight the occupation is, particularly for the pioneers.
But commercial aviation is extremely safe. I think the point is where it will be, not where it is.
This being said, having to balance business issues and safety of a system with as long a lever-arm as this seems blindingly difficult. I wonder hos much of the real work here is ultimately more like insurance than engineering ( as if there were any real difference to start with ).
I think you overdramatize. If lives saved is greater than lives lost, your doing great!
It's not like all military contractor engineers live under a constant cloud of self hatred and suicidal thoughts, either, and they build things that are about trading "many" "enemy" casualties for "few" "friendly" ones.
By contrast, self driving is doing almost entirely good along any moral spectrum.
Just trying to construct the cost model for that is pretty deep. The high-altitude view is that there will be a process that slowly eliminates safety hazards.
An analogous process is lineman safety in the electric grid; at one point the probability of fatal accident for electric linemen was quite high, and it declined to next to nothing as safety procedures improved.
I don't think it'll really be safe until autonomous cars have transponders and can negotiate space in real time, but then you still have all the legacy vehicles out there that don't have transponders.
In the United States, human error accounts for 95% of car accidents, with 33000 fatalities and 2.35m injured each year.
Behind the house, the second most expensive purchase in a typical person's life is their car, and yet in a car's lifetime, only 2.56% is spent driving on roads with the remainder spent parked or in traffic.
Under this model, drone operators should get paid $100M/year. I sincerely doubt the satisfaction from all the disability adjusted life years saved from facilitating the creation of autonomous cars would be overshadowed by the occasion life lost. It is a similar level of power as a commanding officer in a conflict situation.
The how much do you wish to pay the hundreds of thousands of people working for example, in aeronautics or railway, just to stay in the domain of transports? 200 times more?
Why do you say the space was dead in 2006? That was just before the Urban Challenge in 2007. 2008 was the dead year before Google started working on it.
So how unreasonably would it be to ask for a 1 million(+) cash signing bonus from Google as a fresh CMU graduate (if they want to I'd agree and make it repayable proportionally if I leave after X month)?
At the very least I'm guessing that fresh employees have more leverage than they think. If you think 1kk is too much maybe 100k? Or maybe ask for them to pay your rent while you work there?
I have a mixed hands-on practical history, and an eclectic self-taught background in AI, computational intelligence, and machine learning before it was called that. From experience, can say you need the guy who can put it all together in a real working prototype. There are tons of academics smarter than me, and some of my past collegues, but it only counts where the rubber meets the road, aptly punned.
I have been self-teaching myself neural networks, genetic programming and algorithms and AI since the 80s. I remember the 'Decade of the Brain' the 90s and reading Patricia Churchland and Terence Sejnowski's book 'The Computational Brain'. I was also a welder building motorized and pneumatic and hydraulic animiatronics in the 90s. I started to go deep on the engineering, and it helped a lot, but there was a guy I worked with who commanded the time and space and materials in front of him, and had a gut feeling on how to put it all together.
Systems integrations is important, but interative and incremental design, also familiar as a design methodology in coding is the way to achieve results. This is because the individual engineering of subsystems, and the subsequent computer modeling fall short of the emergent behaviors of a real physical prototype.
If I were hiring, I would not be scouring Udacity or the Unis, but lone wolf garage engineers and tinkerers with the math aptitude too. Two of the successful companies I worked at started in somebody's garage, and both were not college educated. Find people who have managed to somehow put together 60% of what you're looking for and then fund them and set them loose.
Too many of the engineers I've worked with were great with churning out the stuff they were taught, but in the one-offs, or bespoke, which seems to be the fashionable word nowadays, they failed miserably with 'paralysis through analysis' too much analysis.
This is why when I had my own business in the early 2000s, I lamented the death of the trade school in the U.S. It was very difficult finding young people who could actually build stuff. The maker movement is welcoming, but a lot of it tends to be mechatronic, and high tech. You don't see too many 'makers' nowadays that are capable of fabricating without a 3D printer, or making heavy-duty iron mechanical monstrosities like some 'Junkyard Wars' aficionados were building for a while.
This is also why it is hard to find people to work on the BIG projects like tunnel-boring machines (well this has picked up somewhat), or other big equipment. Now imagine finding someone who can also design compliant controls and mechanics for all of this. Certainly worth $10M per person!
I wish more people built stuff as well, but I think you're confusing passion and profit. There are fewer people with those skills because the markets are changing - there's just less demand than there used to be.
I don't think it is just markets, or any confusion with passion and profit. Having grown up in Brooklyn in the late 60s/early 70s and then some, I have seen regulation quell a lot of hacking or making in the US. It struck home when I tried to buy my kids chemistry sets in the 2000s. Baking soda and vinegar basically, that was it, due to all of the safety and litigation concerns.
I am not calling for a wild west mentality, but the opposite of trying to legislate intelligence is a sure way to stagnation and oppression of creativity, passion and possible profit.
I was making blackpowder from my hobby store-bought kit in the 70s, and playing with all sorts of chemicals. My mom & dad did not discourage me. I had to setup my darkroom after they went to bed, since we lived in a railroad apartment. If my Dad got up to go the shared hallway bathroom, there went my negatives or prints!
I went on to melt and cast aluminum, solar ovens, tesla coils, lab mice, the whole shebang.
Anyway, I am optimistic by some of the things I have seen kids do on YouTube in the US, but some of them seem like they have been funded by a huge corporation (VIDEO: https://www.youtube.com/watch?v=92M5qcjDkaU). I give my kids as much support as I can, but even they will not accumulate the materials present in that video even with a singular-purposed feat such as that.
My son had told he might not have been able to bring his science project to school, a home-made cathode ray tube, for safety reasons even though the teacher knew it didn't emit harmful Xrays at the power and configuration of the setup. He finally proved it, and was allowed, but there were other 'mysterious' concerns about such devices and the local police.
Now I have lived in SE Asia for almost 8 years. Chinese farmers/tinkerers are building home made submarines to harvest ocean bottom sea life that are so much a part of Chinese cuisine. That and trike planes, basically tricycles with a hang glider and a huge propeller on back - sort of a real hacked ultralight you would see in the US. The scene is surprisingly large in the light of such a heavy-handed government.
Even now where I am in East Java, people hack together some weird stuff, and the police don't stop them or pull them over - imagine a train of dollies being pulled by a small 125cc scooter on a secondary road with passengers! Scary, but I am glad to be around creativity and people using their wits to solve problems with constrained resources.
I doubt the average engineer at one of those companies got compensation of $10M. Most of the money/shares/options/compensation likely went to management and investors. It would not be surprising if sama made more on a deal than an acquired engineer.
If there were 10 engineers, no one else, and no investors, and they each had 10% of the company, and that company sold for $100M, then they would have each gotten 10% of the company.
But that's not likely.
From the point of view of a buyer who spent $100M and got 10 engineers out of the deal, the cost was $10M/engineer, whether the engineers each got $10M injected into their pockets or not.
> From the point of view of a buyer who spent $100M and got 10 engineers out of the deal, the cost was $10M/engineer
That's not even so: he didn't buy the engineers, they are not his property and they are free to walk away the day after, so he hardly got them. For a day, OK :-)
While there is a separate department for ML at CMU, many schools have machine learning and/or robotics departments. Suggesting it's the only place to become educated in this stuff is simply false.