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.

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.)

 

Lots of technology policy stories this weekend

There are lots of technology-policy-related stories this weekend.  The first three concern about excess market power in tech markets, and its effects. The remaining three are miscellaneous subjects at the intersection of technology, policy, and politics.

Suggestion: If a newspaper is refusing to let you read an article, you can often get it by searching for it (on Google – irony alert, see one of the stories below), and visiting from the search result.

And a humble brag: Only the last of these stories directly concerns He Who Must Not Be Named. Nor did I mention Juicero, whose idiocy I tweeted about when it first came to market.

Is It Time to Break Up Google?

In just 10 years, the world’s five largest companies by market capitalization have all changed, save for one: Microsoft. Exxon Mobil, General Electric, Citigroup and Shell Oil are out and Apple, Alphabet (the parent company of Google), Amazon and Facebook have taken their place.

They’re all tech companies, and each dominates its corner of the industry: Google has an 88 percent market share in search advertising, Facebook (and its subsidiaries Instagram, WhatsApp and Messenger) owns 77 percent of mobile social traffic and Amazon has a 74 percent share in the e-book market. In classic economic terms, all three are monopolies.

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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.