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.
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)
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
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.
Its economists used to champion big firms, but the mood has shifted
Source: Schumpeter: The University of Chicago worries about a lack of competition | The Economist
There is an emerging consensus among economists that competition in the economy has weakened significantly. That is bad news: it means that incumbent firms may not need to innovate as much, and that inequality may increase if companies can hoard profits and spend less on investment and wages.
Yes, I certainly see this in tech fields.The double consequences are scary.
Thanks to colleague Prof. Liz Lyons for suggesting this.
Selling “light,” not light bulbs, is one way that companies providing long-lasting bulbs hope to stay in business, even after “socket saturation” sets in.
Source: Trying to Solve the L.E.D. Quandary – The New Yorker