When Am I Committed to Collision? A case of art going toward science, but only very slowly.

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. …”

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Can Elon Musk Get SolarCity’s Gigafactory Back On Track?

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?

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Automation and the Future of Work – Lecture Notes 2017

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.

 Topic  Date of class  File name+link
Final projects;
Diffusion of innovation;
financial evaluation;
technology life cycles
 May 15, 17 A+W 2017 May 17 Bohn adoption
models

3 cases of service automation  May 8   Internet of things
Human expertise
& AI in medicine
April 17   Q+W week 3 medicine
 Trends in employment  April 4  A+W17 Bohn April 4

Some of the aviation discussions are not yet here.

One Day, a Machine Will Smell Whether You’re Sick – The New York Times

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.)

 

Big data and AI are not “objective”

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

The 50 Greatest Breakthroughs Since the Wheel – The Atlantic

Why did it take so long to invent the wheelbarrow? Have we hit peak innovation? What our list reveals about imagination, optimism, and the nature of progress.

Source: The 50 Greatest Breakthroughs Since the Wheel – The Atlantic

A few years old, but still interesting. For example:

By expanding the pool of potentially literate people, the adoption of corrective lenses may have amounted to the largest onetime IQ boost in history.