# 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. …”

# Showing linear regression coefficients

I have just finished my Big Data course for 2017, and noted some concepts that I want to teach better next year. One of them is how to interpret and use the coefficient estimates from linear regression. All economists are familiar with dense tables of coefficients and standard errors, but they require experience to read, and are not at all intuitive. Here is a more intuitive and useful way to display the same information. The blue dots show the coefficient estimates, while the lines show +/- 2 standard errors on the coefficients. It’s easy to see that the first two coefficients are “statistically significant at the 5% level”, the third one is not, and so on. More important, the figure gives a clear view

of the relative importance of different variables in determining the final outcomes.

The heavy lifting for this plot is done by the function sjp.lm from the sjPlot library. The main argument linreg is the standard results of a linear regression model, which is a complex list with all kinds of information buried in it.  Continue reading

# 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.

# Very good news: “Exhaustion doctrine” strongly supported by Supreme Court

SC overturns Lexmark’s patent win on used printer cartridges. Since the 17th century, restricting resale has been “against Trade and Traffique.”

Summary: once a product is sold, the original patent holder can’t control how it is subsequently used.

Not the only seller.

Today’s ruling is a win for many tech companies, with companies like Vizio, Dell, Intel, LG Electronics, HTC, and Western Digital all taking the side of Impression Products. [the winner] …The companies on Lexmark’s side, no surprise, were heavy licensers of patents, including tech giants like Qualcomm, IBM, Nokia, and Dolby. Biotechnology and pharmaceutical groups also supported Lexmark. Those lineups largely mirror industry divisions over Congressional debates around reforming patent laws, with the pro-Impression companies favoring user-friendly changes to patent laws, and the pro-Lexmark companies wanting more changes that favor patent owners.

I often gripe about the Supreme Court’s seeming “go with the big \$” jurisprudence. But in this case, there was plenty of corporate power on both sides. And the 7-1 verdict means it was not a close call.

# 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.

# Recent stories on AI, automation, and the future of work

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# Melinda Gates and Fei-Fei Li Want to Liberate AI from “Guys With Hoodies”

Who designs software makes a big difference. And Silicon Valley employees are not a cross-section of anything, except each other. Nor need they be; but some balance is needed to make sure products are designed to help diverse people.

As a technologist, I see how AI and the fourth industrial revolution will impact every aspect of people’s lives. If you look at what AI is doing at amazing tech companies like Microsoft, Google, and other companies, it’s increasingly exciting.

But in the meantime, as an educator, as a woman, as a woman of color, as a mother, I’m increasingly worried. AI is about to make the biggest changes to humanity and we’re missing a whole generation of diverse technologists and leaders.  Source.

For one reason this problem is growing right now, see the next story: oligopoly control of AI applications in our lives.

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Another case of the “Big 5” grabbing new AI-related technology before it becomes public.

Apple acquires AI company Lattice Data, a specialist in unstructured ‘dark data’, for \$200M

The strength of this pattern, where the Big 5 (Apple, Amazon, Google, Microsoft, Facebook) buy out each novel tech idea and hide it in-house,  as anti-competitive and bad for society as a whole. Apple, because of its level of secrecy, may be worse than some of the others. In a competitive world such purchases would not be a big problem – let the market figure it out. But with the huge cash levels of these companies, which itself indicates monopoly power, they can effectively stifle new ideas that might threaten them in the long run.

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Amazon’s new age grocery likely wasn’t technically possible even five years ago.

How Amazon Go (probably) makes “just walk out” groceries a reality | Ars Technica

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# Theranos as innovation+disaster case study

I just taught the Theranos case in my course on “Innovation and Industry Development,” co-taught with Prof. Elizabeth Lyons. The first half is about positioning a startup: powerful new technology, established incumbents, how should we enter to disrupt the industry and make the world a better place? Any moderate set of numbers makes Theranos’ reputed  \$9,000,000,000 valuation look reasonable.

The “case” presently consists of four articles. I put together a set of overhead slides to generate and lead the discussion. The first half ends with some general lessons about disruptive innovation and whether to follow an open or closed IP strategy. The second half starts in December 2015 and discusses the crash. I also compare Theranos with the Google contact lens (another technically impossible pseudo-invention).

“That’s a type of Silicon Valley arrogance,” he said. “That isn’t how science works.” (re Google, not Theranos)