Web/Tech

Language barriers while traveling the world may become a problem of the past with the advent of new technology. The latest craze in the tech world was recently unveiled at the 2016 Electronics Show: a wearable translator. The Japanese startup Logbar plans on releasing the portable translator called the “ili” this summer. The actual device looks like an Apple TV controller and is hung around your neck.

With the press of a button, the device is allegedly capable of simultaneous translation.

There is more here by David Grasso, including a video, from bold.global.

Defer to the Algorithm

by on February 6, 2016 at 1:39 pm in Current Affairs, Economics, Web/Tech | Permalink

A BuzzFeed article predicts that Twitter will soon move from a time-ordered feed to an algorithmic feed, one that shows you tweets that it predicts you will like before it show you lesser-ranked tweets. Naturally, twitter exploded with outrage that this is the end of twitter.

My own tweet expresses my view ala Marc Andreessen style:

It is peculiar that people are more willing trust their physical lives to an algorithm than their twitter feed. Is the outrage real, however, or will people soon take the algorithm for granted? How many people complaining about algorithmic twitter don’t use junk-email filters? I want ALL my emails! Only I can decide what is junk! Did junk email filters ruin email or make it better?

Facebook moved to an algorithm years ago. At the time, the move caused complaints but I think algorithmic feed has made Facebook more relevant, especially in recent years when the algorithm has gotten quite good. The profits agree with my assessment. Many people don’t understand that there is no serious alternative to an algorithmic feed because most people’s uncurated feeds contain well over a thousand posts every day. It’s curate or throw material out at random.

Think of the algorithm as an administrative assistant that sorts your letters, sending bills to your accountant, throwing out junk mail, and keeping personal letters for your perusal. The assistant also reads half a dozen newspapers before you wake to find the articles he thinks that you will most want to read that morning. Who wouldn’t want such an assistant? Moreover, Facebook has billions of dollars riding on the quality of its assistant algorithms and it invests commensurate resources in making its algorithm more and more attuned to our wants and needs.

It’s not simply that the algorithms are good and getting better it’s that the highest productivity people will use their human intelligence to complement machine intelligence. That means trusting the machine to curate millions of items, bringing only the most important to your attention, and then using human intelligence to take action on the most important items. By trusting the machine intelligence to filter, you can open yourself up to a much wider space of information. I have many more friends on Facebook than I have IRL because I trust the algorithm to bring me only the best of my friends on any given day. A twitter algorithm will mean that I can follow more people without being overwhelmed. Even when the filter is imperfect, you are more likely to discover something of importance from 100,000 items imperfectly filtered to 100 than from 1000 items perfectly filtered to 100.

As Tyler argued in Average is Over, the future belongs to people who can defer to the algorithm.

Hanging up on annoying telemarketers is the easiest way to deal with them, but that just sends their autodialers onto the next unfortunate victim. Roger Anderson decided that telemarketers deserved a crueler fate, so he programmed an artificially intelligent bot that keeps them on the line for as long as possible.

Anderson, who works in the telecom industry and has a better understanding of how telemarketing call-in techniques work than most, first created a call-answering robot that tricked autodialers into thinking there was an actual person answering the phone. So instead of the machine automatically hanging up after ten seconds, a simple pre-recorded “hello?, hello?” message would have the call sent to a telemarketer who would waste a few precious moments until they realized there really wasn’t anyone there.

But Anderson then wondered just how long his robot could keep a telemarketer on the line for. It turns out, for surprisingly long.

…Here’s the best part: anyone can connect telemarketers calling and harassing them to Anderson’s auto-responding robot using the simple instructions he’s posted to his site

There is more here, via HarpersNotes.

I found this David Segal NYT article difficult to summarize, and surely it was difficult to title, but I found it one of the most interesting pieces I have read in weeks.  Allow me to start in the middle:

To fight lead gens, Google deploys a little-known army of volunteers, called Mappers, many of whom are engaged in a contest that takes wit and stamina. These are people around the world who propose and approve edits to Google Maps, with an assist from Google employees, all in the interest of refining the product and fighting spam — a term that in this context means anything fake and misleading.

It may seem bizarre that people would work gratis for one of the world’s richest companies. But many Mappers turn the job into a calling. For Dan Austin, who lives in Olympia, Wash., it was more like an addiction.

A former truck driver for DHL, he became a Mapper after he was laid off from his job and started fixing mistakes he had noticed on Maps while on the road. By the fall of 2011, Mr. Austin had discovered locksmith spam and was soon spending 10 hours a day, seven days a week, deleting it from Maps.

The premise is this:

The goal of lead gens is to wrest as much money as possible from every customer, according to lawsuits. The typical approach is for a phone representative to offer an estimate [for locksmith work] in the range of $35 to $90. On site, the subcontractor demands three or four times that sum, often claiming that the work was more complicated than expected. Most consumers simply blanch and pay up, in part because they are eager to get into their homes or cars.

Here is another bit from the middle:

Mr. Alverado said those fake buildings were necessary because getting to the first page in Google results now took ingenuity and cunning.

“You have no idea,” he said, sounding a little weary when asked about competition. Israelis were his toughest rivals, he said, and they had instilled a kind of awe in him. “I can tell you point-blank, they are freaking smart,” he said. “I really admire them.”

Every single paragraph is interesting in a different and substantive way, an almost impossible achievement for a piece.

For the pointer I thank the excellent Samir Varma.

As tech firms and law enforcement experiment with radio jammers and net-wielding interceptor drones to take down rogue quadcopters, police in the Netherlands are trialling a simpler solution: eagles. The country’s law enforcement has teamed up with a raptor training company named Guard From Above to see if birds of prey can be used to safely intercept quadcopters.

In the video demonstration above, an eagle is seen easily plucking what looks like a DJI Phantom out of the air. However, it’s not clear how dangerous this is for the bird.

File under “But at a price.”

The article is here, with an excellent video, and for the pointer I thank @alsoyourbrother.

Although Stockfish and Komodo have differences in their evaluation scales—happily less pronounced than they were 1 and 2 years ago—they agree that the world’s elite made six times more large errors when on the lower side of equality.

We don’t know how general this phenomenon is, but interestingly it seems to hold much more strongly for top players than for weak players.  That is from chess of course.

Here is much more detail from Ken Regan, along with some suggested hypotheses and resolutions.

The excellent Susan Athey addresses that question on Quora, here is one excerpt:

Machine learning is a broad term; I’m going to use it fairly narrowly here.  Within machine learning, there are two branches, supervised and unsupervised machine learning.  Supervised machine learning typically entails using a set of “features” or “covariates” (x’s) to predict an outcome (y).  There are a variety of ML methods, such as LASSO (see Victor Chernozhukov (MIT) and coauthors who have brought this into economics), random forest, regression trees, support vector machines, etc.  One common feature of many ML methods is that they use cross-validation to select model complexity; that is, they repeatedly estimate a model on part of the data and then test it on another part, and they find the “complexity penalty term” that fits the data best in terms of mean-squared error of the prediction (the squared difference between the model prediction and the actual outcome).  In much of cross-sectional econometrics, the tradition has been that the researcher specifies one model and then checks “robustness” by looking at 2 or 3 alternatives.  I believe that regularization and systematic model selection will become a standard part of empirical practice in economics as we more frequently encounter datasets with many covariates, and also as we see the advantages of being systematic about model selection.

…in general ML prediction models are built on a premise that is fundamentally at odds with a lot of social science work on causal inference. The foundation of supervised ML methods is that model selection (cross-validation) is carried out to optimize goodness of fit on a test sample. A model is good if and only if it predicts well. Yet, a cornerstone of introductory econometrics is that prediction is not causal inference, and indeed a classic economic example is that in many economic datasets, price and quantity are positively correlated.  Firms set prices higher in high-income cities where consumers buy more; they raise prices in anticipation of times of peak demand. A large body of econometric research seeks to REDUCE the goodness of fit of a model in order to estimate the causal effect of, say, changing prices. If prices and quantities are positively correlated in the data, any model that estimates the true causal effect (quantity goes down if you change price) will not do as good a job fitting the data. The place where the econometric model with a causal estimate would do better is at fitting what happens if the firm actually changes prices at a given point in time—at doing counterfactual predictions when the world changes. Techniques like instrumental variables seek to use only some of the information that is in the data – the “clean” or “exogenous” or “experiment-like” variation in price—sacrificing predictive accuracy in the current environment to learn about a more fundamental relationship that will help make decisions about changing price. This type of model has not received almost any attention in ML.

The answer is interesting, though difficult, throughout.  Here are various Susan Athey writings, on machine learning.  Here are other Susan Athey answers on Quora, recommended.  Here is her answer on whether machine learning is “just prediction.”

Bloomberg: Apple Inc. said it acquired education-technology startup LearnSprout, which creates software for schools and teachers to track students’ performance.

Apple is working on education tools for the iPad, which will allow students to see interactive lessons, track their progress, and share tablet computers with peers….More than 2,500 school districts in 42 U.S. states use LearnSprout’s software, according to the company’s website.

As I said in my post, Apple Should Buy a University:

Apple University would be a proving ground for educational technologies that would be sold to every other university in the world. New textbooks built for the iPad and its successors would greatly increase the demand for iPads. Apple-designed courses built using online technologies, a.i. tutors, and virtual reality experimental worlds could become the leading form of education worldwide. Big data analytics from Apple University textbooks and courses would lead to new and better ways of teaching. As a new university, Apple could experiment with new ways of organizing degrees and departments and certifying knowledge.

That is the title of the new and forthcoming Robin Hanson book, due out in May.  I was asked to supply a blurb, and offered two possibilities.  One was:

“Robin Hanson is one of our most original and important thinkers.  This is his book.”

The ostensible premise of the book is that people have become computer uploads, and we have an entirely new society to think about: how it works, what problems it has, and how it evolves.  One key point about this new world is individuals can be copied.  But this is more than just a nerdy tech book, it is also:

  1. Straussian commentary on the world we actually live in.  We are already something-or-other, uploaded into humans,and very often Robin is describing our world in cloaked fashion, albeit with some slight tweaks to parameters for the purpose of moral illumination.
  2. A reminder of how strange everything is, and how we use self-deception to come to terms with that strangeness.  It’s a mock of all those who believe in individual free will.
  3. An attempt to construct a fully rational theology, proving by various deductions that God is not fully benevolent in the traditional sense.
  4. An extended essay on the impossibility of avoiding theology, given the imposition of competitive constraints on a world where production and copying are possible.  And ultimately it is a theodicy, though it will not feel that way to Westerners, Jews, Christians, or Muslims.  It hearkens back to medieval theology, Descartes, and the idea of living in God’s possibly terrifying simulation.
  5. A satire on the rest of social science, and how we try to explain and predict the future.
  6. A meta-level growth model in which energy alone matters and the “fixed factor” assumptions of other models are relativized.  Copying is taken seriously, besides how special are you anyway?  In the meantime, we learn just how much of the world we know depends upon the presence of various fixed factors.  But surely that is temporary!
  7. A challenge to our notions of wherein the true value of a life resides.

I hope enough readers pick up on some of this.  And yes, there is a chapter on sex, love, and affairs.

It is hard to excerpt from this book, but here is one short bit:

Compared with humans, ems fear much less the death of the particular copy that they now are.  Ems instead fear “mind theft,” that is, the theft of a copy of their mental state. Such a theft is both a threat to the economic order, and a plausible route to personal destitution or torture.  While a few ems offer themselves as open source and free to copy, most ems work hard to prevent mind theft.  Most long-distance physical travel is “beam me up” electronic travel, but done carefully to prevent mind theft.

I am wildly enthusiastic about everything the Robin upload does, and some of his copies are better yet.  Here is the book’s home page.

Em

Addendum: Here is Robin Hanson’s response.

New start-up flips homes and cars

by on January 23, 2016 at 6:13 am in Economics, Web/Tech | Permalink

When Luke Dalien and his family needed to quickly sell their Phoenix-area house, they didn’t turn to a real-estate broker. Instead, they sold the home within two weeks to an Internet startup eager to pay cash.

The startup, OpenDoor Labs Inc., resold the house a month later, pocketing an estimated $20,000. The San Francisco company has repeated this profitable flip scores of times in the past year, catching the eye of Silicon Valley insiders.

OpenDoor is among a small group of startups armed with data scientists and software that believe they can identify the right prices on homes and cars and simplify the sales process online. Shift Technologies Inc., for example, guarantees it can sell people’s cars for a certain price within 60 days, and pays the difference if they sell for less.

But OpenDoor is unique in that it owns all the homes it lists for sale, countering the prevailing trend in Silicon Valley of solely running a marketplace that matches buyers and sellers. As a result, OpenDoor’s strategy is steeped with risk, potentially backfiring if the economy tailspins.

“We’re introducing liquidity to a marketplace that doesn’t have any,” said the company’s co-founder, Keith Rabois, a venture capitalist who was an early executive at payments firms Square Inc. and PayPal Holdings Inc.

That is sort of good news, and you can think of that as a partial response to the housing finance overregulation in some parts of Dodd-Frank.  A second and less obvious lesson is that an economy with higher income inequality is more dependent on leverage in some of its corners, but also can resort to corners and pockets which don’t require much leverage at all.  That creates new potential solutions to liquidity problems, requiring only a minimum of coordination.  That all said, this is also a potential longer-term source of reemergent systemic risk.  No matter how sound this start-up may be, you can see the potential for less well-capitalized and less well-run successor firms.

The full story is here, via the excellent Samir Varma.

We discussed this at lunch yesterday, here are my predictions:

1. Singapore will have driverless or near driverless neighborhoods in less than five years.  But it will look more like mass transit than many aficionados are expecting.

2. The American courts and regulators will not pin too much liability on the car companies or software architects.  That said, the regulators will move slowly, and for some time will require a human driver stay at the wheel, even though this seems to be more dangerous.

3. Mapping the territory, reliably, will remain the key problem.  Until that is solved, driverless cars will be a form of mass transit — except without the mass — along predesignated routes.

4. A Chinese city will do it before America does, but Singapore first of all.

5. In less than two or three years, you will see some American car dealership advertising “driverless cars,” but in a gimmicky way.  You’ll still have to sit at the wheel and…drive them.  But they’ll park themselves and have super-duper cruise control and the like.

6. The big gains come from everyone having driverless cars and that is more than twenty years away, but well under fifty years away.

Here is a related NYT article.  I thank Megan McArdle, Robin Hanson, Alex, and others for their contributions to this conversation.

Addendum: We also talked about whether “Virtual Reality” will be a revolutionary technology.  It will have its fans, but I don’t see it as a major breakthrough.  It makes too many people dizzy, and doesn’t really have a killer app; perhaps it will change sex however.

Three Words – Any Place

by on January 14, 2016 at 7:30 am in Data Source, Travel, Web/Tech | Permalink

Here’s an amazing new tool. what3words has identified every one of the 57 trillion 3mx3m squares on the entire planet with just three, easy to remember, words. My office, for example, not my building but my office, is token.oyster.whispering. Tyler’s office just down the hall is barons.huts.sneaky. (Especially easy to remember if you recall this is Tyrone’s office as well.)

Every location on the earth now has a fixed, easily-accessible and memorable address. Unpopulated places have addresses for the first time ever, of course, but now so do heavily populated places like favelas in Brazil where there are no roads or numbered houses. In principle, addressing could be done with latitude and longitude but that’s like trying to direct people to web sites with IP addresses–not good for humans.

Algorithms have assigned words to avoid homophones (sale & sail) and to place similar combos far from one another to aid in error detection. Simpler, more common words are used to address more populated areas and longer words are used in unpopulated areas.

Moreover the three word addresses are available not just in English but in French, Spanish, Portuguese, Swahili, Russian, German, Turkish and Swedish with more languages on the way. The addresses in other languages are not translations but unique 3 word addresses in those languages.

All of this is available in a small app so that it can be used even offline on a simple smartphone. Find your address here.

Hat tip: The Browser.

I don’t think climate change is the right framing for this effect, nonetheless this is an interesting result, with the subtitle “Evidence from a billion tweets.”  Here is the abstract:

What is the welfare cost of environmental stress? The change in amenity values resulting from temperature increases may be a substantial unaccounted-for cost of climate change. Because there is no explicit market for climate, prior work has relied on cross-sectional variation or survey data to identify this cost. This paper presents an alternative method of estimating preferences over nonmarket goods which accounts for unobserved cross-sectional and temporal variation and allows for precise estimates of nonlinear effects. Specifically, I create a rich dataset on hedonic state: a geographically and temporally dense collection of updates from the social media platform Twitter, scored using a set of both human- and machine-trained sentiment analysis algorithms. Using this dataset, I find limited evidence of temperature effects on hedonic state in low temperatures and strong evidence of a sharp decline in hedonic state above 70◦F. This finding is robust across all measures of hedonic state and to a variety of specifications.

That is the job market paper (pdf) by Patrick Baylis, a job candidate from UC Berkeley.

And here is a new result that Canadians are more polite on Twitter, I wonder what happens if you control for temperature…

For the pointer I thank Samir Varma.

Why are Amazon ebook reviews from US readers more important than reviews from international readers?

Have you noticed that reviews from Amazon.com are aggregated across all other international Amazon sites, but that the reverse is not true? If someone kindly posts a review of a book on Amazon.co.uk, it is stuck there, and not aggregated to Amazon.com. Why? Is a UK review less valuable than a US review? Are reviews from Canadians, Australians or India inferior to US reviews?

Source here, via Sofia Tania.

Reddit is collecting some of its best Ask Me Anything interviews — with Bill Gates, Waffle House workers, Spike Lee, nuclear missile operators — in a “beautiful coffee table book.” It’s 400 pages. It’s $35. (Yes, there’s an e-book version, too.)

That is from Michael Rosenwald.