If you are looking for information about my upcoming Big Data course, which starts on April 2, 2018, it is in a different blog. Please go here to learn about the textbooks, and to see how the course worked last year.
By now, Tesla’s manufacturing problems are completely predictable. See my explanation, after the break. At least Wall St. is starting to catch on.
Also in this article: Tesla’s gigafactory for batteries has very similar problems. That surprises me; I thought they had competent allies helping with batteries.
But one engineer who works there cautioned that the automated lines still can’t run at full capacity. “There’s no redundancy, so when one thing goes wrong, everything shuts down. And what’s really concerning are the quality issues.”
My friend Don Norman wrote an op-ed this weekend calling for an FDA-like testing program before autonomous cars are put on the roads in the US. Clearly, some level of government approval is important. But I see lots of problems with using drug testing (FDA = Food and Drug Administration) as a model.
Here is an excerpt from a recent article about testing problems with Uber cars, which were the ones in the recent fatal accident. After the break, my assessment of how to test such cars before they are allowed on American roads.
Waymo, formerly the self-driving car project of Google, said that in tests on roads in California last year, its cars went an average of nearly 5,600 miles before the driver had to take control from the computer to steer out of trouble. As of March, Uber was struggling to meet its target of 13 miles per “intervention” in Arizona, according to 100 pages of company documents obtained by The New York Times and two people familiar with the company’s operations in the Phoenix area but not permitted to speak publicly about it.Yet Uber’s test drivers were being asked to do more — going on solo runs when they had worked in pairs.And there also was pressure to live up to a goal to offer a driverless car service by the end of the year and to impress top executives.
So Uber car performance was more than 100 times worse than Waymo cars?!
Once again, Tesla demonstrates no understanding of volume manufacturing! Newspaper: “Tesla reworks 40% of its parts.” Tesla response: “But we inspect every car carefully before shipping it!”
Tesla fires back against a CNBC report that cited unnamed employees’ complaints about the electric carmaker cranking out a high number of parts that need to be repaired or replaced. Tesla say…
But as Deming and others pointed out decades ago, you cannot achieve good final quality by doing lots of inspection. There are many reasons for this, including that inspection/testing is not 100% accurate. The whole field of statistical process control, which eventually morphed into today’s “Six Sigma,” was invented as an alternative to massive inspection.
So if Tesla claims that it can make parts so poorly that 40% need rework, but still have defect-free cars, it is an admission of ignorance. This continues Tesla/Musk’s consistent pattern of not understanding that high volume manufacturing is not just “low volume manufacturing repeated many times.” (See my October post about Tesla’s attempts to ramp up Model 3 production.)
My suggestion for people with a Model 3 on order: don’t expect it to be on time, or have good build quality. Sorry.
I will be co-teaching, with Parand Darugar and Paul Kedrosky, a course on starting a company. This 10 week course requires each team to develop a real project, to the level that they can test it with potential customers. (Beta test, approximately.)
Doing this in 10 weeks can be daunting, but it can be done. I used to teach a course on hardware product development, which required working physical prototypes and 2 long reports in 10 weeks. This year we expect little or no hardware. Instead we are looking for web-enabled, mobile-enabled, or other service ideas with little or no associated hardware. What you will sell is some form of a system that fulfills a need.
Part of entrepreneurship is looking for, and “sensing,” unmet needs. Do this in your everyday life, in your courses, as you watch people on screens. Here are a few ideas to suggest how little is needed. Please, add your own. It’s ok to list an unmet need without saying how to solve it.
Classroom feedback using sheets of paper
A visual barcode based version of the teaching “clicker” that many classrooms use to gather real-time student feedback. Students hold up a piece of cardboard, which the faculty member scans with a phone. https://www.plickers.com Why didn’t I think of that? Get rid of the student-purchased hardware, and probably increase reliability at the same time. (Although I’m sure it sacrifices some capabilities of the electronic clickers.)
Search my many sources of free books
My wife and I read a lot of books, still. In the last 5 years a number of access methods for e-books have come along. I can buy ebooks from Apple, Amazon, or others. I can also borrow them free from my local library, Amazon Prime, scribd, my employer (UCSD’s library) and probably some other services. Each service has its own catalog and its own ways of searching. Checking each of the free services is time consuming, and half the time none of them has what I want. Solve this pain! There are many ways to solve the pain, and the concept could be integrated with existing book services/systems in a variety of ways.
Academia has a problem: the value, necessity, and practices of collaboration are increasing, but the system of giving credit is inadequate. In most fields, there are only 4 levels of credit:
- None at all
- “Our thanks to Jill for sharing her data.” (a note of thanks)
- First Authorship (This is ambiguous: it may be alphabetical.)
- Listed as another author
In contrast to this paucity, modern empirical paper writing has many roles. Here are a dozen roles. Not all of them are important on a single paper, but each of them is important in some papers.
- Intellectual leadership.
- Source of the original idea
- Doing the writing
- Writing various parts, e.g. literature review
- Doing the grunt work on the stat analysis. (Writing and running the R code)
- Doing the grunt work of finalizing for publication. (Much easier than it used to be!)
- Dealing with revisions, exchanges with editors, etc.
- Source of the data.
- Funder of the data
- Raised the funding;
- Runs the lab where the authors are employed
- Source of the money: usually an agency or foundation, but sometimes the contracting author is listed as a coauthor.
A contributor to Dave Farber’s IP (“Important People” list) recently stated that 1 Megabit per second (Mbps) is adequate bandwidth for consumers. This compares to “high speed Internet” which in the US is 20 Mbps or higher, and Korea where speeds over 50 Mbps are common.
My response: 1 Mbps is woefully low for any estimate of “useful bandwidth” to an individual, much less to a home. It’s risky to give regulators an any excuse to further ignore consumer desires for faster connections. 1 Mbps is too low by at least one order of magnitude, quite likely by three orders of magnitude, and conceivably by even more. I have written this note in an effort to squash the 1Mbps idea in case it gets “out into the world.”
The claim that 1 Megabit per second is adequate:
>From: Brett Glass <email@example.com>
>Date: Sun, Dec 31, 2017 at 2:14 PM
> The fact is that, according to neurophysiologists, the entire bandwidth of
> all of the human senses combined is about 1 Mbps. (Some place it slightly
> higher, at 1.25 Mbps.) Thus, to completely saturate all inputs to the human
> nervous system, one does not even need a T1 line – much less tens of megabits.
> And therefore, a typical household needs nowhere near 25 Mbps – even if they
> were all simultaneously immersed in high quality virtual reality. Even the
First, I don’t know where the 1Mbps number comes from, but a common number is the bandwidth of the optic nerve, which is generally assessed at around 10Mbps. See references.
Second, a considerable amount of pre-processing occurs in the retina and the layer under the retina, before reaching the optic nerve. These serve as the first layers of a neural network, and handle issues like edge detection.