Photovoltaics in Mission Bay neighborhood = 30% wasted

TL;DR In Southern California should put PV on houses and buildings that are far from the coast, because coastal areas are cloudy much of the summer. But the actual pattern is the opposite. I estimate a 30% magnitude of loss. Even my employer, UCSD, has engaged in this foolishness in order to appear trendy.

The bumpiness of this graph shows the effects of coastal weather in August.

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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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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.”

Source: Supreme Court overturns Lexmark’s patent win on used printer cartridges | Ars Technica

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.

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.

Der Untergang der Titanic

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)

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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