Next Wednesday, March 7, Iâll be holding a workshop on Deep Learning for Autonomous Vehicles as part of the Automotive Tech.AD conference in Berlin. My colleague Aaron Brown and I will walk participants through how to build and train basic convolutional neural networks for traffic sign recognition.
If you work in the automotive industry and have read a lot about deep neural networks, but have never built them yourself, this is the workshop for you. Youâll get hands-on experience setting up and training your own classification networks.
Update: There is a Udacity student discount! Email me directly (david.silver@udacity.com) for the discount code.
Sign up now! Intersect, Udacityâs annual conference on lifelong learning, will be on Tuesday, March 27, at the Computer History Museum, in Mountain View, California.
Early bird tickets expire tomorrow!
I am so excited about this conference. Do recognize these four technology power players?
Seriously, if you donât recognize any of these speakers, stop reading this post and visit the Intersect Speakers page to learn about how they are changing the world.
Did I mention Udacity always keeps a few big announcements up its sleeve for Intersect?
Plus I will be there! Iâll be talking about self-driving cars, and giving rides in Carla, Udacityâs own self-driving car. Have you ridden in self-driving car recently? Come ride in ours.
On Monday I will be in Stuttgart. Join my Udacity colleagues and me at the Connected Autonomous Vehicle Meetup on March 5, organized by Udacity student Rainer BareiĂ. RSVP here.
Berlin
On Wednesday I will be in Berlin. Join us on March 7 at Udacityâs Berlin office. Free pizza and drinks! Iâll show off some self-driving car videos and share the latest news about what Udacity is building for students. 7â9pm. RSVP here.
The story mainly hooks on Avis and the special cleaning techniques they have contracted to perform for Waymoâs self-driving cars. They have to be really careful!
âFor example, soap residue or water spots could effectively âblindâ an autonomous car. A traditionalcar washâs heavy brushes could jar the vehicleâs sensors, disrupting their calibration and accuracy. Even worse, sensors, which can cost over $100,000, could be broken.
A self-driving vehicleâs exterior needs to be cleaned even more frequently than a typical car because the sensors must remain free of obstructions. Dirt, dead bugs, bird droppings or water spots can impact the vehicleâs ability to drive safely.â
Washing Carla, Udacityâs self-driving car, is less of a challenge, because we can pretty easily dismount the roof-based lidar and store it in the trunk. And our cameras are inside the vehicle.
Still, we take Carla to our local brushless carwash every month, and each time I get a little terrified.
Intermediate programming ability in C++ or Python (the languages of the autonomous vehicle industry)
Basic linear algebra
Basic calculus
Basic statistics
Basic physics
As a student in the Intro to Self-Driving Cars Nanodegree program, youâll build your skills up over the course of a four-month curriculum path that tackles each of these areas at a pace that is both manageable and rewarding. Best of all, youâll practice putting these skills to work on the types of projects that real self-driving car engineers work on every day.
If you love self-driving cars, but thought youâd never get the chance to work on them, then this is the program for you.
How many people in the West even know what Geely is?
Perhaps in Europe, where the Chinese auto manufacturer also owns Volvo, a majority share of Lotus, and now a 10% share of Daimler (itself the parent of Mercedes-Benz).
ââNo current car industry player is likely to win this battle against the invaders from outside without friends. To achieve and assert technological leadership, one has to adapt a new way of thinking in terms of sharing and combining strength. My investment in Daimler reflects this vision,â Li said.â
In case youâre not clear what that means:
âOnly two or three manufactures will likely survive in the auto industry going forward, a source familiar with Liâs thinking told Reuters, prompting Geely to seek access to carmakers with a technological edge.â
The line between Li Shufu and Geely is a little fuzzy here, as CNBC reports that Li made the share purchases, but Geely now has access to Daimlerâs technology.
It looks like there are a few things happening:
Geely wants access to Daimlerâs battery technology, in advance of upcoming Chinese quotas for electric vehicles.
Geelyâs ownership of Volvo has seemed to work out well, so it makes sense that they might continue their European expansion.
Perhaps Geely is hedging its bets by owning several different automotive manufacturers, on the theory that at least one will survive the transition to autonomous vehicles.
One of the big worries about self-driving cars is the extent to which they will cause unemployment. Approximately 5 million Americans drive for a living, mainly as long-haul truckers, although also as taxi drivers, local delivery drivers, and other driving occupations.
Will self-driving cars drive unemployment for these workers?
One thing to notice is that âunemploymentâ is subtly different than âjob lossâ. If somebody loses a job (say, as a long-haul trucker) but gets a new job (say, as a local delivery driver), then that person has lost a job but is not unemployed. They might be worse off in their new job (or maybe better off!), which is also important track, but they wonât show up in the unemployment statistics.
This is all a long lead in to Scott Alexanderâs very, very long blog post: âTechnological Unemployment: Much More Than You Wanted To Knowâ. This isnât a post on self-driving cars specifically, and itâs not even a post about the future. Itâs more about technological unemployment in the present and recent past. But the present and recent past are probably our best guides to the future, so itâs relevant.
The whole post is definitely worth reading. Itâs almost unfair to excerpt it, because you canât appreciate the full power of the meta-research without the run-up, but this the summary:
âHere are some tentative conclusions:
1. Technological unemployment is not happening right now, at least not more so than previous eras. The official statistics are confusing, but they show no signs of increases in this phenomenon. (70% confidence)
2. On the other hand, there are signs of technological underemploymentââârobots taking middle-skill jobs and then pushing people into other jobs. Although some people will be âpushedâ into higher-skill jobs, many will be pushed into lower-skill jobs. This seems to be what happened to the manufacturing industry recently. (70% confidence)
3. This sort of thing has been happening for centuries and in theory everyone should eventually adjust, but there are some signs that they arenât. This may have as much to do with changes to the educational, political, and economic system as with the nature of robots per se. (60% confidence)
4. Economists are genuinely divided on how this is going to end up, and whether this will just be a temporary blip while people develop new skills, or the new normal. (~100% confidence)
5. Technology seems poised to disrupt lots of new industries very soon, and could replace humans entirely sometime within the next hundred years. (???)
This is a very depressing conclusion. If technology didnât cause problems, that would be great. If technology made lots of people unemployed, that would be hard to miss, and the government might eventually be willing to subsidize something like a universal basic income. But we wonât get that. Weâll just get people being pushed into worse and worse jobs, in a way that does not inspire widespread sympathy or collective action. The prospect of educational, social, or political intervention remains murky.â
Iâm more optimistic about the future than Scott Alexander seems to be, although I am humbled by the research he has done. I am a little tentative disagreeing with him based on what mostly amounts to my own intuition.
It seems to me that technological progress over the long-term has made jobs much better. And these âbetterâ jobs have funded a safety net for people who cannot work, although we can debate the appropriate strength of that safety net.
My hope is that autonomous vehicles provide whole new classes of better jobs for future workers, and progress marches forward.
âPeak oilâ was a popular theory about a decade ago, based on the idea that the usage of oil would fall as the world ran out of supply. The theory ran out of gas (sorry), due to a combination of fracking (increased supply) and the Great Recession (reduced demand).
Interestingly, however, BP now predicts the world will hit peak oil (they donât actually use that term, I donât think) around 2040, this time due to demand-side pressures.
Their projection is that a combination of solar energy, wind energy, and electric vehicles will put global demand for oil on a downward trajectory, starting in 20 years or so.
Big change from ten years ago, when some experts thought the world might run out of oil. Now at least some experts think we wonât need it (or at least as much of it) anymore.
Listening to Tim Harford on a back episode of EconTalk, I was struck by their extended discussion of the elevator as one of the original autonomous vehicles.
The discussion seems both distant from self-driving cars, but also strangely relevant. Harford and EconTalk host Russ Roberts touch on safety, technological unemployment, and traffic optimization.