Singapore’s nuTonomy, founded by two researchers from the Massachusetts Institute of Technology, said Thursday it began testing a free taxi-hailing service in a small business district in Singapore called one-north, a campus-like space dominated by tech firms and biotechnology companies. Other tech companies including Chinese internet giant Baidu Inc. have been testing self-driving cars on the roads for years, but this is the first time the vehicles have been open to public use.

…Mr. Parker said the Singapore government had laid out a series of milestones for nuTonomy to achieve before it is allowed to extend its trials to other areas of the city. He declined to provide details on those milestones, but said the next stage would be to expand the service to a neighborhood adjacent to one-north.

Here is the WSJ piece, here are other articles.  I recall predicting about a year and a half ago that Singapore would be the first to do this.  A Singaporean countered me, and interjected they were very worried that their plans were falling behind.  I said: “That is exactly my point.  You are worried that you are falling behind.  Congratulations.”

Worry.  Singapore.  Think about it.

A few of you have asked, I considered that question in 2012, here is a significantly revised update:

1. Now I know how to text, sort of, though I hardly ever do it.  It strikes me as the worst and most inefficient technology of communication ever invented (seriously).  It’s not that fast, and it’s broken up into tiny bits of back and forth.  I don’t see how it makes sense beyond the “What should I get at the supermarket? — Blueberries” level.  There is intertemporal substitution, so just, at some other point in time, spend more time talking, writing longer letters, making love, whatever.  Not texting.  It is never the best thing to be doing, except to answer some very well-defined question.

2. I now carry only one iPad around, as I donated my spare iPad to a poor Mexican family.  I use it very often for directions, book and restaurant reviews, and general life advice.  Plus email and keeping current on my Twitter feed.  I simply don’t want a screen any smaller than that.  My iPad now also has a rather pronounced crack on the front glass, but that adds to its artistic value.  I dare not drop it again.

3. I have an iPhone, which I hardly ever use for anything.  Occasionally someone calls me on it, or I use it to check email in situations when it might be rude to pull out the iPad.  Other times I am rude, but it’s actually a form of flattery if I am willing to check my iPad in front of you.  You may not feel flattered, however.

3b. Except for the occasional Uber ride, I don”t use apps and hate reading news sites through the apps, I won’t do it.  I’m used to the web, not your app, and I hope I can get away with being a stubborn grouch on this forever.

4. I now have a Bloomberg terminal, which is very cool.  It is amazing that a product designed in the “before the internet as we know it” era still is the clear market leader and the best option.  Bloomberg is a great company with a great product(s).  Right now I can do about 5 of the 25,000 separate commands, but the fault is mine not theirs.  In the meantime, send me email at my gmu address, not what is listed on the Bloomberg column.

5. I use my Kindle less over time.  It remains in that nebulous “fine” category, but I prefer “real books.”  Kindle is best for works of fiction when I know in advance I wish to read every page in the proper order.  I am continuing with my long-range plan to read Calvin’s Institutes on my Kindle, bit by bit, in between other works.  This will take me ten years, but a) he is a brilliant mind, and b) in the meantime I won’t lose sight of the plot line.

6. I have a new Lenovo laptop, sleek and fast, plus some computers at work.  I don’t even know what they are, but probably they are quite subpar.

Way more iPad and way less texting are I suppose the main ways in which I deviate from the dominant status quo.  Come join me in this and we shall conquer the world.

Uber passengers in Pittsburgh will be able to summon rides in self-driving cars with the touch of a smartphone button in the next several weeks. Uber also announced that it is acquiring a self-driving startup called Otto, co-founded by Israeli Lior Ron, that has developed technology allowing big rigs to drive themselves.

Via Mark Thorson, here is more.  And in Finland:

Residents of Helsinki, Finland will soon be used to the sight of buses with no drivers roaming the city streets. One of the world’s first autonomous bus pilot programs has begun in the Hernesaari district, and will run through mid-September.

Finnish law does not require vehicles on the road to have a driver, making it the perfect place to get permission to test the Easymile EZ-10 electric mini-buses.

So perhaps Finland can become a market leader in this area.

I am all in favor of San Francisco’s $13 per hour minimum wage (which rises to $15 by 2018), plus mandatory paid sick leave, parental leave and employer health care contributions. But labor costs at restaurants are inching past 50 percent of total expenditures, an indicator of poor fiscal health. Commercial rents have also gone bananas. Add the ever-rising cost of frisée and pastured quail eggs and it’s no wonder that many restaurants are experimenting with that unique form of sadism known as “small plate sharing,” which amounts to offering a big group of hungry people something tiny to divvy up. Even nontrendy joints now ask $30 for a proper entree — a price point, according to Mr. Patterson, that encourages even affluent customers to discover the joys of home cooking.

That is by Daniel Duane for the NYT, on how Silicon Valley shapes the northern California dining scene and it is of interest more generally.

I have been pushing this line for a while, now I am pleased to see coverage from The New York Times:

Industry leaders point to a number of areas where China jumped first. Before the online dating app Tinder, people in China used an app called Momo to flirt with nearby singles. Before the Amazon chief executive Jeff Bezos discussed using drones to deliver products, Chinese media reported that a local delivery company, S.F. Express, was experimenting with the idea. WeChat offered speedier in-app news articles long before Facebook, developed a walkie-talkie function before WhatsApp, and made major use of QR codes well before Snapchat.

Before Venmo became the app for millennials to transfer money in the United States, both young and old in China were investing, reimbursing each other, paying bills,and buying products from stores with smartphone-based digital wallets.

The Paul Mozur article is interesting throughout.

Delphi Automotive Plc, the vehicle-electronics supplier that last year conducted the first coast-to-coast U.S. demonstration of a self-driving car, will begin testing autonomous autos in Singapore this year that may lead to robot taxis by the end of the decade.

The test will involve six autonomous autos, starting with the modified Audi Q5 the supplier used last year to travel from San Francisco to New York in self-driving mode. In Singapore, the cars initially will follow three predetermined routes and by 2019 will range freely based on customer requests, without a driver or a human minder, according to Glen DeVos, a Delphi senior vice president.

“We actually will have point-to-point automated mobility on demand with no driver in the car,” he said at a briefing with reporters at Delphi’s Troy, Michigan, operations base. “It’s one of the first, if not the very first, pilot programs where we’ll demonstrate mobility-on-demand systems.”

Here is more from Keith Naughton.

That’s why they have Cowen’s First Law!  Here is new research by Mitch Downey at UCSD (pdf):

Recent research emphasizes the pressure technological change exerts on middle-wage occupations by automating routine tasks. I argue that technology only partially automates these tasks, which often still require labor. Rather, technology reduces task complexity enabling a less skilled worker to do the same job. The costs of automation, then, are not only the costs of the technology itself but also of low-wage workers to use it. By raising the cost of low-wage labor, the minimum wage reduces the profitability of adopting automating technologies. I test this prediction with state variation in the minimum wage and industry variation in complementarity between low-wage workers and technology. I show that accounting for state price differences induces new and useful minimum wage variation, derive new measures of complementarity from the Dictionary of Occupational Titles and the CPS Computer Use Supplement, and build a measure of technology based on IT employment, the largest component of IT spending. My results imply a $1 decrease in the minimum wage raises the average industry’s technology use by 30% and decreases the routine share of the wage bill by 1 percentage point (3.3%), both relative to a counterfactual without complementarity. Routine-intensive industries often exhibit high complementarity, making the minimum wage an important policy lever to influence the pace of routine-biased technical change.

I owe this link to someone other than myself, but can no longer remember who that is…sorry!

This possibly gated but excellent nonetheless piece is from the FT, here is one excerpt:

A few weeks ago was typical. After some time off, my feed aggregator displayed 794 blog posts, 56 of them foolishly filed into the “must read” folder. Here lay a polemic blasting the FT for worrying about China’s debts; there a graph strewn post about US inflation expectations. Virtuoso “infovore” Tyler Cowen had dug up a fascinating passage on how China runs monetary policy. Another polymath, Brad DeLong (former Clinton staffer and tireless scourge of rightwing bunkum), had spent some minutes producing a few hundred words on “the intellectual role of the economist in public life”, throwing out references to pre-Christian philosopher Hermippos of Smyrna as a warm-up. Another writer, an anonymous retired trader with a bad back, explained how quantitative easing exposes central bankers as a bunch of bungling frauds. It felt like his fifth such post in a week.

And so on…

And yet in 10 years of trying to make sense of the economic world around me, I have found nothing as reliably good as the blogosphere.

And so on!  How can you not love an article that refers to an “omni-reading angel in the celestial library”?

There is a hat tip to Scott Sumner and a nice appreciation of Steve Randy Waldman as well.

Households making $25,000-$35,000 a year spend ninety-two more minutes a week online than households making $100,000 or more a year in income, and differences vary monotonically over intermediate income levels.

That is from a new NBER paper by Boik, Greenstein, and Prince.  Do note that the authors adjust for age and other demographic variables.

The upshot is that the real “undervalued” services from the internet come from its risk-sharing properties, not from the supposed lack of pricing of internet services.  If something bad happens to you, well…there is always the internet to fall back upon, at least provided you still can afford the connection.  This also means that business cycles are not quite as painful as before, but also that labor markets will be slower to adjust.

Some also may find in this fact an optimistic statement that “real life” (ha ha) has more to offer than the internet, with the caveat that real life is expensive.

The data in this very interesting paper also indicate that Chat has largely collapsed since 2008 as a way of spending time on the internet, internet time devoted to news sites has fallen from 10% to 5%, and social media and video are on the rise.

Here is my previous post “Let them eat ideology!”

Here is Erik Hurst, from an excellent piece profiling Erik Hurst:

Right now, I’m gathering facts about the possible mechanisms at play, beginning with a hard look at time-use by young men with less than a four-year degree. In the 2000s, employment rates for this group dropped sharply – more than in any other group. We have determined that, in general, they are not going back to school or switching careers, so what are they doing with their time? The hours that they are not working have been replaced almost one for one with leisure time. Seventy-five percent of this new leisure time falls into one category: video games. The average low-skilled, unemployed man in this group plays video games an average of 12, and sometimes upwards of 30 hours per week. This change marks a relatively major shift that makes me question its effect on their attachment to the labor market.

To answer that question, I researched what fraction of these unemployed gamers from 2000 were also idle the previous year. A staggering 22% – almost one quarter – of unemployed young men did not work the previous year either. These individuals are living with parents or relatives, and happiness surveys actually indicate that they quite content compared to their peers, making it hard to argue that some sort of constraint, like they are miserable because they can’t find a job, is causing them to play video games.

This problem, if that is the right word for it, will not be easily solved.

From Istanbul, follow him here.  Here is my 2010 post “Why Timur Kuran is one of our most important thinkers.”  Timur’s work has held up very well since then, to say the least.

Addendum: Here are remarks from Turkish economist Dani Rodrik.

For some time now I have had mixed feelings about the move to electronic medical records, here is another reason why:

On the dark web, medical records draw a far higher price than credit cards. Hackers are well aware that it’s simple enough to cancel a credit card, but to change a social security number is no easy feat. Banks have taken some major steps to crack down on identity theft. But hospitals, which have only transitioned en masse from paper-based to digital systems in the past decade, have far fewer security protections in place.

…These records can sell for as much as (the bitcoin equivalent) of $60 apiece, whereas social security numbers are a mere $15. Stolen credit cards sell for just $1 to $3. During the tour, we spotted one hacker who claimed to have a treasure trove of just shy of 1 million full health records up for grabs.

As IBM’s Kuhn explained in a follow-up interview, these medical records can be leveraged for a wide variety of nefarious purposes. In some cases, it’s about stealing a person’s identity and billing them for a surgery or a prescription, and in others it’s about opening a new line of credit. Security researcher Avi Rubin told Fast Company in an recent interview that he suspects hacked medical records are often routinely used for blackmail and extortion.

Such hacking is indeed a trend:

More than 113 million medical records were hacked in 2015 alone, according to data compiled by the Health and Human Services. A newly released report from the Institute for Critical Infrastructure Technology, a cybersecurity think tank, found that some 47% of Americans have had their medical record hacked in the past 12 months.

That is from Christina Farr.

“The definition of an anchor has changed,” said Stephen Lebovitz, the chief executive of mall owner CBL & Associates Properties Inc. “Cheesecake Factory does as much business as Sears used to do.”

That is from Suzanne Kapner at the WSJ, on the decline of traditional anchor stores.  Yet not all of the new service sector jobs will be there forever:

“Right now we’re doing a couple hundred videos a day,” he said. “We think we need to be doing 2,000 videos a day.”

Mr. Ferro’s comments added to mounting confusion over his embattled company’s sudden rebranding. How could a newspaper publisher create nearly three-quarters of a million videos a year?

But as jarring as Tronc’s goals may sound, the company’s plan is far from novel. In pursuit of more lucrative video advertising and success on dominant social platforms like Facebook, a growing number of publishers have turned to technology that promises to streamline video production, sometimes to the point of near-full automation.

That is John Herrman from the NYT.  File under Marginal Revolution Robot University.  And if you are wondering how it works, here is a snippet:

The two services’ automation features work in similar ways. They analyze, and may summarize, text, be it a script or a traditional news article, and then automatically find photographs and video clips to go with it. The services typically get the videos and images from sources like The Associated Press and Getty Images.

Additionally, the tools offer the option to quickly put large animated captions over the videos, in a format that has become popular on Facebook, where videos begin playing automatically and are often watched with the sound off. Each can also supply, through a third party, on-demand human narration; Wibbitz offers computerized voice-overs as well.

What does this say about the media sector more generally?

A neural network can be designed to provide a measure of its own confidence in a categorization, but the complexity of the mathematical calculations involved means it’s not straightforward to take the network apart to understand how it makes its decisions. This can make unintended behavior hard to predict; and if failure does occur, it can be difficult to explain why. If a system misrecognizes an object in a photo, for instance, it may be hard (though not impossible) to know what feature of the image led to the error. Similar challenges exist with other machine learning techniques.

That is from Will Knight.  This reminds me of computer chess, especially in its earlier days but still today as well.  The evaluation functions are not transparent, to say the least, and they were not designed by the conscious planning of humans.  (In the case of chess, it was a common tactic to let varied program options play millions of games against each other and simply see which evaluation functions won the most.)  So when people debate “Will you buy the Peter Singer utilitarian driverless car?” or “Will you buy the Kant categorical imperative driverless car?”, and the like, they are not paying sufficient heed to this point.  A lot of the real “action” with driverless cars will be determined by the non-transparent features of their programs.

How will regulatory systems — which typically look for some measure of verifiable ex ante safety — handle this reality?  Or might this non-transparency be precisely what enables the vehicles to be put on the road, because it will be harder to object to them?  What will happen when there is a call to “fix the software so this doesn’t happen any more”?  To be sure, adjustments will be made.

More and more of our world is becoming this way, albeit slowly.

For the pointer I thank Michelle Dawson.

Popular Science: A pilot A.I. developed by a doctoral graduate from the University of Cincinnati has shown that it can not only beat other A.I.s, but also a professional fighter pilot with decades of experience. In a series of flight combat simulations, the A.I. successfully evaded retired U.S. Air Force Colonel Gene “Geno” Lee, and shot him down every time. In a statement, Lee called it “the most aggressive, responsive, dynamic and credible A.I. I’ve seen to date.”

What’s the most important part of this paragraph? The fact that an AI downed a professional fighter pilot? Or the fact that the AI was developed by a graduate student?

In the research paper the article is based on the authors note:

…given an average human visual reaction time of 0.15 to 0.30 seconds, and an even longer time to think of optimal plans and coordinate them with friendly forces, there is a huge window of improvement that an Artificial Intelligence (AI) can capitalize upon.

The AI was running on a $35 Raspberry Pi.

AI pilots can plan and react far quicker than human pilots but that is only half the story. Once we have AI pilots, the entire plane can be redesigned. We can build planes today that are much faster and more powerful than anything that exists now but the pilots can’t take the G-forces even with g-suits, AIs can. Moreover, AI driven planes don’t need ejector seats, life-support, canopies or as much space as humans.

The military won’t hesitate to deploy these systems for battlefield dominance so now seems like a good time to recommend Concrete Problems in AI Safety, a very important paper written by some of the world’s leading researchers in artificial intelligence. The paper examines practical ways to design AI systems so they don’t run off the rails. In the Terminator movie, for example, Skynet goes wrong because it concludes that the best way to fulfill its function to safeguard the world is to eliminate all humans–this is an extreme example of one type of problem, reward hacking.

Imagine that an agent discovers a buffer overflow in its reward function: it may then use this to get extremely high reward in an unintended way. From the agent’s point of view, this is not a bug, but simply how the environment works, and is thus a valid strategy like any other for achieving reward. For example, if our cleaning robot is set up to earn reward for not seeing any messes, it might simply close its eyes rather than ever cleaning anything up. Or if the robot is rewarded for cleaning messes, it may intentionally create work so it can earn more reward. More broadly, formal rewards or objective functions are an attempt to capture the designer’s informal intent, and sometimes these objective functions, or their implementation, can be “gamed” by solutions that are valid in some literal sense but don’t meet the designer’s intent. Pursuit of these “reward hacks” can lead to coherent but unanticipated behavior, and has the potential for harmful impacts in real-world systems. For example, it has been shown that genetic algorithms can often output unexpected but formally correct solutions to problems [155, 22], such as a circuit tasked to keep time which instead developed into a radio that picked up the regular RF emissions of a nearby PC.

Concrete Problems in AI Safety asks what kind of general solutions might exist to prevent or ameliorate reward hacking when we can never know all the variables that might be hacked? (The paper looks at many other issues as well.)

Competitive pressures on the battlefield and in the market mean that AI adoption will be rapid and AIs will be placed in greater and greater positions of responsibility. Firms and governments, however, have an incentive to write piecemeal solutions to AI control for each new domain but that is unlikely to be optimal. We need general solutions so that every AI benefits from the best thinking across a wide range of domains. Incentive design is hard enough when applied to humans. It will take a significant research effort combining ideas from computer science, mathematics and economics to design the right kind of incentive and learning structures for super-human AIs.