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.
- 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.
It has always been clear that Musk does not understand high-volume manufacturing. Building rockets is very hard, but building 100,000 cars is very hard for a different reason! His predicted ramp rate was absurd. In the last 6 months, I think he has started to realize this.
Tesla has little chance of hitting its 5,000 weekly output during the fourth quarter. The chief reason: Its current production line can’t build vehicles at that rate unless it runs two 10-hour shifts seven days a week, which is
Source: Tesla | Hiccups Threaten to Slow Model 3 Launch | Industry content from WardsAuto
According to the article, Tesla has also deliberately ignored much of the accumulated wisdom about how to ramp in auto production. That might be OK for his second high-volume vehicle.
More details on Tesla’s ramp plans:
Tesla now has 2 choices, both bad:
- Go ahead and start building and shipping as fast as possible. The result will be multiple problems that require expensive hardware recalls.
- Add another 6? months to the schedule to run the as a pilot line for learning, rather than for volume. Expect zero salable output during that period. (As one of the comments said, they can give/sell those cars to employees.)
Added December 27: Tesla “still in manufacturing hell.”
Latest of many articles about Tesla manufacturing problems.
Here is a comment on that article: Musk needs to face up to having made a MAJOR mistake when he skipped some steps in the original manufacturing ramp-up.
He is probably also making another major mistake at present: adding new machines to the manufacturing process, before he has the existing machines working perfectly. This seems logical to people with no manufacturing experience, but it does not work. For one thing, it diverts his key resource, which right now is manufacturing engineers.
According to the author, naval ship handling still relies heavily on craft expertise. His article writes down some formulas and procedures to reduce collision risk. Source: When Am I Committed to Collision? | U.S. Naval Institute My own reaction is in a brief comment at the end of the article.
Here is another article in the same issue of US Naval Institute Proceedings that does a great job of explaining how collisions can happen, and why the captain of a USN ship is always responsible, and never completely safe.
This is the burden of command. A captain puts the lives of several hundred sailors into the hands of a young officer, typically 25 years old and typically green. So what does a captain count on to prevent disaster? The captain has “standing orders.” These are the rules in his or her ship that everyone (especially the OOD) lives by. …”
Elon Musk clearly has a blind spot about manufacturing. Building a giant factory for the first use of a new process does not work, and theoretically it cannot work. Even if it did work, it would be non-competitive. Once a factory is built and machines installed, subsequent new discoveries/knowledge cannot be incorporated, except at the margins.
To reach the 100-megawatt goal, sources indicate that the pilot production line in Fremont would eventually need to yield between 800 to 1,000 high-efficiency Whitney panels per day. But the team was not able to automate the process consistently enough to produce more than dozens of Whitney panels per day, according to people familiar with the matter. Most of the production resulted in “scrap,” they say. “The big problem was simply that they couldn’t scale up the technology to the point where you could run it in a factory,” a source familiar with the development explains.
Source: Can Elon Musk Get SolarCity’s Gigafactory Back On Track?
One of my students reported that he was having trouble finding my lecture notes from this course, so I am putting them in one place. I will update this for the last few classes.
Some of the aviation discussions are not yet here.
Sniffing disease markers is a fundamentally promising concept. We know that dogs have very good smell, so that is an existence proof that something interesting can be detected in the air. (In my family’s experience, human smell can also become amazingly good, at least for pregnant women!) In fact, if B.F. Skinner were still alive, I wonder if he would be training pigeons to sniff out disease?
But although air is feasible, it does seem like blood is a better choice because it is likely to have stronger signals and lower noise. Air-based sensors would be non-invasive, so perhaps that is why some groups are pursuing air.
…a team of researchers from the ..Monell Chemical Senses Center and the University of Pennsylvania [are working] on a prototype odor sensor that detects ovarian cancer in samples of blood plasma.
The team chose plasma because it is somewhat less likely than breath or urine to be corrupted by confounding factors like diet or environmental chemicals, including cleaning products or pollution. Instead of ligands, their sensors rely on snippets of single-strand DNA to do the work of latching onto odor particles.
“We are trying to make the device work the way we understand mammalian olfaction works,” … “DNA gives unique characteristics for this process.”
Judging by research at UCSD and elsewhere, I envision tests like this eventually be run as add-on modules to smartphones. Buy a module for $100 (single molecule, home use) up to $5000 (multiple molecules, ambulance use), and plug it into your phone. Above $5000, you will probably use a dedicated electronics package. But that package might be based on Android OS.
This is also another example of Big Data science. It could be done before, but it will be a lot easier now. Blood collected for other purposes from “known sick” patients could be used to create a 50,000 person training set. (The biggest problem might be getting informed consent.)
AI, machine learning, etc only appear to be objective. In reality, they reflect the world view and prejudices of their developers.
Algorithms have been empowered to make decisions and take actions for the sake of efficiency and speed…. the aura of objectivity and infallibility cultures tend to ascribe to them. . the shortcomings of algorithmic decisionmaking, identifies key themes around the problem of algorithmic errors and bias, and examines some approaches for combating these problems. This report highlights the added risks and complexities inherent in the use of algorithmic … decisionmaking in public policy. The report ends with a survey of approaches for combating these problems.
Source: An Intelligence in Our Image: The Risks of Bias and Errors in Artificial Intelligence | RAND