IP (Intellectual quasi-property)

Memorial Sloan Kettering’s Season of Turmoil – The New York Times

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America’s health care research system has many problems. The overall result is poor return on the money spent. The lure of big $ is a factor in many of them. Two specific problems:

  • What gets research $ (including from Federal $) is heavily driven by profit potential, not medical potential. Ideas that can’t be patented get little research.
  • Academic career incentives distort both topics of research (what will corporate sponsors pay for?) and publication. The “replicability crisis” is not just in social sciences.

This NYT article illustrates one way that drug companies indirectly manipulate research agendas: huge payments to influential researchers. In this article, Board of Directors fees. Large speaking fees for nominal work are another common mechanism. Here are some others:

Flacking for Big Pharma

Drugmakers don’t just compromise doctors; they also undermine top medical journals and skew medical research. By Harriet A. Washington | June 3, 2011

I could go on and on about this problem, partly because I live in a biotech town and work at a biotech university. I have posted about this elsewhere in this blog. But since it’s not an area where I am doing research, I will restrain myself.

does the Harvard Business School, Michael Porter, teach the essence of business strategy is the elimination of competition, by regulation if possible. Is this legal? Is this basically socialism or communism? – Quora

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Original question on Quora: Why does the Harvard Business School, Michael Porter, teach the essence of business strategy is the elimination of competition, by regulation if possible. Is this legal? Is this basically socialism or communism?

My response: Trying to pin this on Michael Porter is ridiculous. He says no such thing. Based on the way the question is phrased, I wonder if there is an ideological purpose in asking it.

But in any case, there is a serious issue behind the question, namely an increasing level of oligopoly (decreasing levels of competition) among companies in many US industries. See, for example, “Big Companies Are Getting a Chokehold on the Economy Even Goldman Sachs is worried that they’re stifling competition, holding down wages and weighing on growth.”  or.

“America Has a Monopoly Problem—and It’s Huge”.

One theory about this trend is that it is partly due to growing power of corporations in Washington. That, in turn, may be traced partly to the increasing role of money in elections, largely as a result of the infamous Supreme Court “Citizens United” decision. For example, the way Trump’s massive tax cuts were put together without any hearings and in a VERY short period of time, and the amount of “goodies” for many industries in the resulting package, would never have happened with previous massive changes in taxes.

An effective strategy in some highly concentrated industries is to persuade the government to selectively regulate your industry, in ways that favor large and established companies. That is, all companies may experience higher costs because of a regulation, but if your company can respond more cheaply than anyone else, it is still a net win for you. An example is pharmaceuticals. For example pharma companies increasingly use the legal system, regulations, and side deals to keep generic drugs off the market for years after drug patents expire. The industry has also been very effective at keeping foreign competitors out – e.g. blocking imports by individual citizens from Canada.

(I buy one medication at $1 per pill from abroad, when it costs $30/pill at the local Rite-Aid. But it takes a lot of research and effort.)

Source: (32) Why does the Harvard Business School, Michael Porter, teach the essence of business strategy is the elimination of competition, by regulation if possible. Is this legal? Is this basically socialism or communism? – Quora

Would ‘explainable AI’ force companies to give away too much? Not really.

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Here is an argument for allowing companies to maintain a lot of secrecy about how their data mining (AI) models work. The claime is that revealing information will put  companies at a competitive disadvantage. Sorry, that is not enough of a reason. And it’s not actually true, as far as I can tell.

The first consideration when discussing transparency in AI should be data, the fuel that powers the algorithms. Because data is the foundation for all AI, it is valid to want to know where the data…

Source: The problem with ‘explainable AI’ | TechCrunch

Here is my response.

Your questions are good ones. But you seem to think that explainability cannot be achieved except by giving away all the work that led to the AI system. That is a straw man. Take deep systems, for example. The IP includes:
1) The training set of data
2) The core architecture of the network (number of layers etc)
3) The training procedures over time, including all the testing and tuning that went on.
4) The resulting system (weights, filters, transforms, etc).
5) HIgher-level “explanations,” whatever those may be. (For me, these might be a reduced-form model that is approximately linear, and can be interpreted.)

Revealing even #4 would be somewhat useful to competitors, but not decisive. The original developers will be able to update and refine their model, while people with only #4 will not. The same for any of the other elements.

I suspect the main fear about revealing this, at least among for-profit companies, is that it opens them up to second-guessing . For example, what do you want to bet that the systems now being used to suggest recidivism have bugs? Someone with enough expertise and $ might be able to make intelligent guesses about bugs, although I don’t see how they could prove them.
Sure, such criticism would make companies more cautious, and cost them money. And big companies might be better able to hide behind layers of lawyers and obfuscation. But those hypothetical problems are quite a distance in the future. Society deserves to, and should, do more to figure out where these systems have problems. Let’s allow some experiments, and even some different laws in different jurisdictions, to go forward for a few years. To prevent this is just trusting the self-appointed experts to do what is in everyone else’s best interests. We know that works poorly!

Hollywood as a model for academic research

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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.

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

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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.

Theranos as innovation+disaster case study

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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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Each new generation needs to learn the lesson of Theranos: appearance ≠ reality

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Every 10 years or so, a conspicuous bubble bursts, and in doing so it resets the expectations of the next generation of young adults.

  • Enron
  • 2008 financial collapse
  • Now Theranos

Reading this article, I’m astonished at how little substance the adulation of Elizabeth Holmes was based on. And how much secrecy her investors allowed her. Given that she was claiming that her system would be ~100x better than established technologies, why didn’t they demand evidence? Why was it left to a reporter to figure out that the emperor had no clothes? And, was she nothing more than a successful con-artist with no genuine scientific expertise?

“In a searing investigation into the once lauded biotech start-up Theranos, Nick Bilton discovers that its precocious founder defied medical experts—even her own chief scientist—about the veracity of its now discredited blood-testing technology.”

Source: Exclusive: How Elizabeth Holmes’s House of Cards Came Tumbling Down | Vanity Fair